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• Published: 30 October 2018

A 3D geological model of a structurally complex Alpine region as a basis for interdisciplinary research

• James M. Thornton   ORCID: orcid.org/0000-0002-1447-1554 1 ,
• Gregoire Mariethoz 2 &
• Philip Brunner 1  

Scientific Data volume  5 , Article number:  180238 ( 2018 ) Cite this article

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• Hydrogeology

A Publisher Correction to this article was published on 13 December 2019

Certain applications, such as understanding the influence of bedrock geology on hydrology in complex mountainous settings, demand 3D geological models that are detailed, high-resolution, accurate, and spatially-extensive. However, developing models with these characteristics remains challenging. Here, we present a dataset corresponding to a renowned tectonic entity in the Swiss Alps - the Nappe de Morcles - that does achieve these criteria. Locations of lithological interfaces and formation orientations were first extracted from existing sources. Then, using state-of-the-art algorithms, the interfaces were interpolated. Finally, an iterative process of evaluation and re-interpolation was undertaken. The geology was satisfactorily reproduced; modelled interfaces correspond well with the input data, and the estimated volumes seem plausible. Overall, 18 formations, including their associated secondary folds and selected faults, are represented at 10 m resolution. Numerous environmental investigations in the study area could benefit from the dataset; indeed, it is already informing integrated hydrological (snow/surface-water/groundwater) simulations. Our work demonstrates the potential that now exists to develop complex, high-quality geological models in support of contemporary Alpine research, augmenting traditional geological information in the process.

Machine-accessible metadata file describing the reported data (ISA-Tab format)

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Background & summary.

Three-dimensional (3D) geological models are digital representations of subsurface formations and their associated features. Recently, the appreciation of their utility to several disciplines has grown, and software tools enabling their construction have proliferated 1 . In earth sciences and engineering, they have, inter alia , contributed to the development of improved earthquake location catalogues 2 and informed excavation and tunnelling projects 3 , 4 . They are also supporting ongoing radioactive waste storage site assessments 5 . In hydrogeology, meanwhile, they have facilitated groundwater resource estimates 6 , enabled the characterisation of karst aquifer geometries, flow pathways, and catchment areas 7 – 10 , and provided a basis for numerical modelling related to geothermal energy prospection 11 .

In tectonically and topographically complex sedimentary settings like the European Alps, 3D models must be generally detailed, high-resolution, and accurate in order to be suitable for their intended application(s). The term detailed refers to the representation of certain characteristic features which, here, would include folds, faults, and spatially-variable formation thicknesses. Developing a model with high spatial resolution, meanwhile, might involve employing a fine (e.g. cell size ≤ 10 m) Digital Terrain Model (DTM) to define the topographic surface, and/or using a sufficient density of georeferenced points to closely replicate the shapes of observed geological features. When exporting a model onto a grid or mesh, care must be exercised to ensure that the resolution is commensurate with the modelled features, especially thin layers or complex shapes. Finally, a model can be considered accurate if the estimated formations and associated features are close to their true positions (although the true positions may be impossible to establish perfectly).

Approximately 30% of the Alps are composed of carbonate rocks, the majority of which are karstified (i.e. discrete conduit networks have developed via dissolution) 12 . However, these rocks are not uniform in their chemico-mineralogical composition, and hence their degree of karstification. Moreover, they are commonly interspersed with lower permeability layers, such as marls and shales. The entire sequences have been folded, fractured, and faulted into complex geometrical arrangements by tectonic forces. Since well-karstified limestones are several orders of magnitude more permeable than marls and shales, the contrasts in hydraulic conductivity within these sequences can be considerable. Where so, the stratigraphic geometry exerts a profound influence on groundwater flow patterns 7 . For example, in a karstified limestone aquifer overlying a marly aquiclude, flow would typically be concentrated just above the interface, its direction corresponding to that of the maximum dip, i.e. flow would be broadly parallel to the strata (and so highly anisotropic). It follows that in folded settings, anticlines – assuming normal orientation – typically act as regional groundwater divides, with synclines conversely representing locations of accumulation 13 . Faults can also have a notable influence 14 ; on one hand they may act as preferential pathway permitting flow across the strata, including enabling formations that would otherwise be considered aquicludes to be bypassed, but on the other, their offsets can disconnect aquifers.

As such, considering 3D geology is crucial when conceptualising and seeking to simulate groundwater flow in these environments. 3D geological models are considerably more powerful with respect to the development, visualisation, and communication of geological understanding than traditional 2D maps and cross-sections. They also provide a direct foundation for subsequent (3D) numerical flow modelling. However, for applications in topographically complex and potentially karstified limestone terrain, geological models must meet several criteria. Firstly, subsurface features that can affect flow must be accurately characterised. Secondly, to provide a realistic overall depiction, the topographic surface must be represented at high resolution. Finally, models must be spatially extensive enough to capture any proven or hypothesised subsurface connections; such connections can function over distances of up to several kilometres, and are capable of importing or exporting water across topographical boundaries.

Despite the improving capabilities of 3D modelling software and a large body of existing geological data pertaining to the Alps, these combined requirements (for geological models to be detailed, high-resolution, accurate, and spatially-extensive) continue to represent substantial technical and computational challenges to model development. It is therefore unsurprising that, irrespective of intended application, few such geological models exist in the Alps; those that do are generally very large in scale, and therefore limited in detail 15 – 17 , although there are exceptions in this regard 18 . Furthermore, the resultant datasets themselves are rarely made available as to the broad, interdisciplinary community who could potentially benefit from them.

Focussing on hydrogeology specifically, it may be noted that flow modelling tools which are capable of incorporating 3D geological information are increasingly widespread. Consequently, research into the interactions between geology and hydrology in Alpine regions is arguably now more limited by a lack of appropriate, accessible geological models than by flow simulation code capabilities. For instance, predicting the impacts of anthropogenic climate change on mountain streamflow regimes is an important and frequently undertaken task in hydrology 19 – 23 . However, (at least partly) due to a lack of explicit 3D geological information, such studies typically employ conceptual, box-type hydrological models like HBV 24 and PREVAH 25 . These models have highly simplified structures and lack physically meaningful parameters, leaving them heavily reliant on calibration to reproduce historical observations. Indeed, they are commonly calibrated solely against stream discharge at the catchment outlet, despite multi-objective calibration and evaluation approaches – particularly those which consider spatially-distributed information – often being advocated 26 – 29 . Consequently, although it may be possible for such models to satisfactorily reproduce historical observations, they may be doing so for the “wrong reasons” 30 . Such a situation would compromise the robustness of any subsequent predictions, especially should forcing conditions exceed the range of the calibration dataset.

The representation of groundwater processes in box-type models is particularly concerning. Essentially, they contain at best only implicit information on the spatial distribution of subsurface properties. In many conceptualisations, fluxes between a soil-water reservoir and a groundwater reservoir are simply estimated as a calibrated function of the amount of water in the soil reservoir 31 . Even in spatially-distributed models, the subsurface is rarely discretised vertically, and lateral groundwater movement is generally unaccounted for. Consequently, any process understanding and predictions derived are in danger of overlooking site-specific geological influences. Even the user guide of the more physically-based and otherwise comprehensive model WaSiM recommends that coupling with an external groundwater model be undertaken wherever groundwater is expected to play an important role 32 .

Several reasons exist as to why groundwater can indeed be expected to play an important role in mountainous environments. Firstly, and most obviously, large hydraulic gradients exist. Secondly, as a result of the very tectonic processes that led to mountain formation, pronounced topography and geological complexity go hand in hand. Thirdly, it is known that this complexity in bedrock geology can strongly influence groundwater processes (and by extension overall catchment function) not only in calcareous settings, as discussed above, but elsewhere too 33 – 36 . Taken together, and alongside the fact that temperate mountain regions presently hold great hydrological importance for adjacent populations (primarily as a result of the orographic enhancement of precipitation and the storage and delayed release of water stored as snow and ice on seasonal and longer timescales) 37 , these points cause one to question whether the routinely made simplifications are appropriate.

As a result of ongoing anthropogenic climate change, mountain hydrology research is becoming increasingly pressing. Two key components of such systems, the snowpack and glaciers, are already demonstrating pronounced sensitivity 38 – 40 . Accordingly, concerns about future water resources, especially during dry summer and autumn periods, are increasing 41 , 42 . Groundwater, vegetation, and permafrost will also respond to climate change, but may do so in a more subtle fashion, involving various interactions and feedbacks with other system components 43 . Predicting the overall changes in the quantity and timing of downstream discharge thus requires more advanced hydrological models. Even prior to that, however, our fundamental knowledge of how and to what extent high-elevation aquifers are recharged, as well as how they transport and discharge water to maintain stream baseflows and spring discharges, must urgently be improved 44 , 45 . Integrated hydrological models like ParFlow-CLM 46 , HydroGeoSphere (HGS) 47 , MIKE SHE 48 , or CATHY 49 may be useful in both regards. Such models generally solve equations for 2D surface and 3D subsurface (both saturated and vadose zones) simultaneously and, crucially, can explicitly represent 3D variability in hydraulic properties defined, for instance, according to a 3D geological model. They also represent most other pertinent elements of the water cycle (including snow accumulation and melt), and simulate the interactions between them, in a coherent, spatially explicit, and transient fashion. In HGS, for instance, several options also exist with respect to karstified formations; the subsurface can either be treated as an equivalent porous media at the elemental scale, as having dual permeability or porosity, and/or as being discretely fractured. All this is possible whilst simultaneously simulating; i) surface flows (important for flood risk and sediment transport), ii) interactions between soil moisture/groundwater, vegetation characteristics, and evapotranspiration, and iii) snow processes. Theoretically, these capabilities leave integrated models uniquely placed to quantify the physical relationships between climate, geology, hydrology, vegetation, and snow in mountainous environments. However, they have found few applications in steep, geologically complex terrain to date. The limited availability of data with which they might be parameterised, calibrated, and evaluated – including 3D geological information – may be posited as an important contributory factor.

In this context, and as part of an interdisciplinary project seeking to improve predictions of Alpine water availability and vegetation species distributions ( http://wp.unil.ch/integralp ), a novel dataset characterising a section of a well-studied nappe fold in the Swiss Alps is presented. No 3D model of this complex region previously existed, and it was unclear at the outset whether developing an appropriate model was even feasible. Alongside various other datasets, the resultant dataset is currently informing catchment scale, integrated hydrological modelling efforts (to be presented in subsequent publications). Various other ongoing or future interdisciplinary environmental investigations could also benefit from the development.

Geological context

The Nappe de Morcles (western Swiss Alps) is a world-renowned example of a tectonic nappe fold, having an amplitude exceeding 10 km and a prominent inverse limb whose stratigraphy is completely reversed. It is the lowest of several such tectonic entities that strike to the SW-NE and together comprise the Helvetic Nappes ( Fig. 1 ). The nappe sits above a region of autochthonous and parautochthonous material which, in turn, overlies the crystalline Aiguilles Rouges massif. It is composed primarily of calcareous shelf sediments (limestones, marls, shales, and sandstones) of Jurrassic to Paleogene age.

The Nappe de Morcles, which is the focus of this work, is the lowermost of the three nappes (Source: modified after Renard et al. 82 © Dunod, 2015).

The structure of the nappe has been established and refined by a series of notable works 50 – 58 . To summarise, the main fold axis plunges on average ~27° towards the NE59, and several spectacular secondary folds are superimposed upon the main structure forming a characteristic “oak leaf” pattern. Faults of potential hydrological relevance in the focus region are generally oriented E-W, although their extents are limited. No relevant thrust surfaces are present in this zone. The Nappe de Morcles remains an important focus for contemporary nappe fold formation studies 60 , 61 .

General characteristics of the focus area

At the outset of the wider project, two adjoining valleys in – the Vallon de Nant and the Vallon de la Vare – were identified as the focus for subsequent hydrological model development. The geological model domain was therefore centred on this area. These valleys lie within the north-western section of the Nappe de Morcles ( Fig. 2 ). More specifically, the Vallon de Nant has been eroded from the inverse zone of the nappe, whilst the Vallon de La Vare lies in the frontal zone. For reasons that shall be explained shortly, the focus area was extended to the southeast to include La Sarvaz spring.

( a ) Tectonic sketch indicating the general geological situation (reproduced from Badoux 53 , non-digital version, © Source: Swiss Federal Office of Topography, 1971). ( b ) Illustrative cross-section through the Nappe de Morcles showing its pre-erosion structure, including secondary folds, and the present topographic arrangement (reproduced from Badoux et al. 53 , © Source: Swiss Federal Office of Topography, 1971), and c) The “original” and “final” geological model domains, major springs locations and the area of hydrogeological interest (the “focus area”) (original figure). In ( c ), selected peaks are marked (brown triangles); starting from the north and proceeding clockwise, these are: Tête à Pierre Grept (2,904 m), Grand Muveran (3,051 m), Petit Muveran (2,810 m), Grand Chavalard (2,899 m), Dent Favre (2,917 m), Grand Dent de Morcles (2,969 m), Petit Dent de Morcles (2, 929 m) and Point des Savolaires (2, 294 m). The village of Les Plans-sur-Bex (hollow black circle) is also marked.

The elevation range within the focus area is considerable (~2,500 m), and precipitation abundant (annual average ~1600 mm·yr -1 in the lowest reaches, increasing with elevation). Low winter temperatures result in a significant proportion of the annual precipitation falling as snow 62 , and small glaciers are able to persist at relatively low elevations in the uppermost parts. Considerable diversity is encountered with respect to vegetation, geomorphology, and hydrology. The area has remained practically untouched by anthropogenic activity; indeed, the Vallon de Nant has been a designated natural reserve since 1969. Overall, the area represents an ideal “natural laboratory” for research across the environmental sciences 63 .

The first stage of the geological model development involved establishing the spatial domains (see Fig. 3 ). Formations belonging to the other Helvetic Nappes or the Ultrahelvetic zone were excluded from the model to keep the degree of complexity manageable.

Steps 7 and 8 involved an iterative process of: i) comparing the model to the input data on both the surface and vertical sections, ii) adjusting the interpolation parameters and/or the input data density, iii) re-computing the model, and iv) re-comparing with the input data.

Stratigraphy and initial hydrogeological inferences

A sketch of the stratigraphy in the area of the Vallon de Nant, modified after Badoux 59 ( Supplementary Figure 1 ), provides a useful introduction to the regional geology. (Note that this diagram does not include all the formations that were eventually modelled; for that, consult Table 2). Having said that, the 1:25,000 scale geological maps of “Morcles” 53 and “Les Diablerets” 56 represented the primary sources of detailed geological information. The lithological descriptions provided in the accompanying explanatory notes enabled the likely hydrogeological importance of certain formations to be promptly identified. Reviewing previous studies conducted on neighbouring or nearby tectonic units where equivalent formations are encountered 12 , 64 further elucidated the probable hydrostratigraphy.

In this way, it was possible to establish, for instance, that the massive Urgonian limestone is likely to represent an important karst aquifer(s); this formation is renowned for its purity at other sites, and it hosts a major aquifer in the overlying Nappe de Diableters 64 . The Malm and Valanginian limestones are also likely aquifers 14 . The more siliceous Hauterivian limestone should still be permeable, but probably offers more resistance to flow 65 . In contrast, the Berremian marl is likely to act as a regional aquiclude, as has been reported elsewhere in the Hevetic zone 12 . However, the thinness of this unit in our study region (only ~ 30 m in the region of the Vallon de Nant, according to Supplementary Figure 1 ), and its inability to prevent a hydrological connection with La Chambrettes 65 , mean that its effectiveness as an aquiclude at our site is not guaranteed. The “top” of nappe (i.e. the “bottom” in areas with inverted stratigraphy) is comprised of a thick, clayey Oligocence flysch that is expected to have very low permeability on a regional scale. This brief overview merely seeks to highlight some potentially important formations, and should not be considered exhaustive.

Few hydrological or hydrogeological investigations have previously been conducted in the study area. The karstic source of La Chambrette, which emerges above the northern bank of L’Avançon de Nant near the village of Les Plans-sur-Bex, was, however, the subject of an early artificial tracer test 65 . This experiment demonstrated the existence of a hydrological connection between the closed basin of “La Varre” and the spring. Since these two locations are separated by a formation (the Lower Barremian, as mentioned above) that should be rather impermeable according to its lithology, it was proposed that a fault of some description must enable flow to traverse it. Today, the spring is exploited by Romande Energie SA. Another spring, Le Rippaz, is no longer karstic by the time it emerges rather diffusely from Quaternary moraines, a little downstream from La Chambrette on the opposite (southern) bank of L’Avançon. Currently, it is being developed to augment the water supply of a neighbouring commune 66 , 67 . An associated tracer test confirmed that the formations of the Lower Cretaceous near Pointe des Savolaires constitute the main karstic aquifer. This system could thus be thought of as a chain of aquifers – first karstic and then unconsolidated gravel – which together dampen the variability of spring discharge to snowmelt and rainfall inputs. Finally, as mentioned earlier, the focus area was extended several kilometres to the southeast to include La Sarvaz; another, higher discharge karstic spring that emerges near the village of Saillon, Valais. This decision was taken because, according to our initial understanding of the geological structure and likely favourable hydraulic properties – specifically of the Malm – some the precipitation falling on the southern and eastern ridges of the Vallon de Nant may ultimately drain to this location, rather than via L’Avançon de Nant. Additionally, the discharge of La Sarvaz has been monitored for several years, providing observations that could prove useful during hydrological model development.

One aspect of the regional geology that remains somewhat unclear is the extent to which the various theoretically karstifiable formations are actually karstified at this site. The only known speleological exploration in the region returned an inventory of only six small caves 68 , although this exploration was neither systematic nor exhaustive. The walls of the Urgonian limestone near Pointe des Savolaires were reported to be the only limestones pure enough to contain cavities of speleological interest 70 . However, while the presence of caves is an unambiguous indicator of karstification, the inverse is not true; an absence (or non-discovery) of explorable caves certainly does not necessarily indicate an absence of hydrologically meaningful karstification, since conduits much smaller than humanly accessible can still transport significant volumes of water extremely rapidly. Nevertheless, it should be remembered that flowing water is required for conduit development; if a theoretically karstifiable limestone has been disconnected from recharge or circulation by low permeability layers, it may remain little karstified.

Although the conclusions of this speleological prospection broadly concord with our hydrogeological expectations drawn from the lithological descriptions, they do highlight the issue of chemio-mineralogical purity, and more specifically the possibility that the degree of karstification in our study area may not be as high as elsewhere in the Helevetic zone. In any case, a benefit of our approach – of only estimating formation geometries initially – is that in contrast to the methodology followed by other authors 8 , 10 , there is no need to definitively categorise each formation as being either an aquifer or aquiclude initially; reality is certainly not this binary. Rather, it is our intention to vary and hopefully constrain parameters representing the hydraulic properties of the various formations during subsequent numerical model calibration.

Finally, aquifers are of course not confined to bedrock formations. Glacio-fluvial and other sediments (e.g. talus cones) fill the valley floors. It is expected that the recharge of these units (where permeable) is dominated by spring snowmelt, with drainage subsequently taking place over the course of the late spring, summer, and autumn via a number of springs and ephemeral “seeps”. The geological model presented herein does not include these unconsolidated formations, focussing instead on the more voluminous bedrock. That said, geoelectrical surveys will be undertaken shortly with a view to representing the geometry and heterogeneity of unconsolidated material properties in the integrated hydrological model.

Surface hydrology and its association with bedrock geology

The surface hydrology of the Vallon de Nant is characterised by the eponymous Nants; the torrents that course down its steep slopes. They are located principally to the south and east, and have a highly variable flow that responds rapidly to rainfall and snow and ice melt. The most important are the Nant des Têtes and the Torrent des Martinets, which together constitute the majority of the discharge of L’Avançon. The torrents are also responsible for a great deal of sediment transport; these deposits accumulate to form a large alluvial fan in the middle section of the valley.

The annual hydrological regime of L’Avançon de Nant may be classified as nivo-glacial 69 ; that is to say, it has a mixed snow and ice-dominated response with a discharge peak in early summer corresponding to melt of the snowpack, variable discharges from one year to another, diurnal cycles superimposed on the hydrograph due to ice melt, and groundwater contributions maintaining baseflow.

Surface water is noticeably scarcer in the upper part of the Vallon de La Vare than the Vallon de Nant, although a small stream – Le Richard – does develop and joins L’Avançon downstream of Pont de Nant. This contrast between the two valleys can probably be explained by differences in their position in the nappe. Specifically, the Vallon de Nant is underlain by low-permeability flysch, which limits groundwater exportation and permits the existence of a relatively long section of permanent stream (it is only ephemeral in the upper section in dry, cold autumn and winter periods, when the groundwater level in this upper section has fallen such that it can no longer contribute to streamflow). A clayey layer of glacial till at some intermediate depth between the surface and bedrock interface may also contribute to this behaviour, although this hypothesis remains to be tested by geophysics. In contrast, the Vallon de La Vare lies in the inverse zone, and thus the impermeable flysch is oriented approximately vertically away to the north, near L’Argentine. Hence, the bedrock beneath the valley floor is composed of the more permeable limestones of the Lower Cretaceous. One aim of the 3D model is to help visualise and better understood such influences. Additionally, having two somewhat geologically contrasting catchments adjacent to one another provides the opportunity to explore the specific influences of geology on hydrology whilst other factors, e.g. climate, remain fairly constant.

A wealth of geological information pertaining to the European Alps exists, having been developed over decades of dedicated study by committed regional experts. Presently, this information exists primarily in the form of two-dimensional (2D) maps and cross-sectional diagrams. Despite observational advances in most other fields of the geosciences, the field mapping techniques and concepts underpinning the production of such structural geology datasets have changed little in over a century 70 . As such, their quality remains similar to more contemporary outputs. Moreover, the prevalence of observable features such as stratigraphic interfaces, faults, and folds mean that such datasets are typically more accurate in mountainous regions than in settings where the bedrock is more obscured by unconsolidated deposits 18 . Indeed, the deep incision made by the Vallon de Nant into the inverse limb of the nappe can be thought of as a kind of “window” into its interior − a visible cross-section. Certainly, the availability of appropriate geological data should rarely be a limiting factor for the development of 3D models in the Alps, although the arguable under-exploitation of the existing body of information means it might have been hitherto.

As already mentioned, thanks to its reputation as a classic example of a first-order nappe and intriguing attendant complexity, the structural geology of our study region has a long history of being studied and mapped. At this juncture, it is worth briefly highlighting the effort that the production of the maps, cross-sections, and associated explanatory notes that comprise the current Geological Atlas of Switzerland in this region entailed. In the introductory remarks to his illustrated text Tectonics of the Morcles Nappe between Rhone and Lizerne , Badoux states that the work undertaken prior to the publication of the second edition maps took 8 years! 54 By this time, under the tutelage of Lugeon, he had already developed a great passion for, and expertise on, the geology of the Vaud Alps. These maps remain the highest resolution, most current geological dataset of the Swiss Confederation in this region.

As Table 1 indicates, input data for our model were derived from three primary sources: i) a Digital Terrain Model (DTM), ii) surface geological maps, and iii) vertical geological cross-sectional diagrams.

Having gained an appreciation of the study area and identified and sourced appropriate input data pertaining to it, several sequential steps were followed in order to develop the geological model ( Fig. 3 ). In summary, data extracted from existing geological maps and vertical cross-sections were compiled along with a digital terrain model (DTM) in the GeoModeller software environment 71 . GeoModeller is a commercial platform developed by the BRGM (French Geological Survey) and Intrepid Geophysics; for further information, see Calcagno et al. 72 and Guillen et al. 73 . It facilitates the estimation of continuous geological models that respect all available data indicating the locations of interfaces between different lithological formations, the spatial orientations of these formations, and any faults present in the domain. Certain geological rules are also taken into account. The following sections describe each phase in more detail.

Defining the model domain and resolution

Studying the regional geology and hydrogeology enabled the initial and final domains for the geological model development to be established ( Fig. 2 ). The initial model domain was slightly larger than the final one to ensure that all data that could potentially inform the geological model in the smaller focus area were included in the estimation. In light of the complex topography and our prior knowledge of the presence of geometrically complex and thin units, it was decided that the model development should proceed at a resolution of 10 m.

Preparing and importing the Digital Terrain Model (DTM)

The swissALTI 3D digital terrain model (DTM) is a raster dataset with a horizontal cell resolution of 2 m. It represents the land surface without vegetation or buildings. For further information, see: https://shop.swisstopo.admin.ch/en/products/height_models/alti3D .

The vertical uncertainties associated with the product, quoted in Table 1 , are more than low enough for the application at hand.

For the purpose of model development, the dataset was resampled to 10 m. This resampling served to smooth out high-frequency noise (i.e. small-scale topographic features) and reduce the computational burden. The resultant dataset was imported into GeoModeller ( Supplementary Figure 2 ). It forms the upper surface of the geological model.

Defining the geological pile to be modelled

The term geological pile refers to the sequence of lithological formations to be modelled. Since the domain extends over two separate geological map sheets, each having a slightly different formation classification scheme, some reconciliation was required to arrive at a single sequence. In the end, the sequence modelled was extremely similar to that of the Morcles map sheet. Given the potential multi-disciplinary applications of the dataset, maintaining a classification that closely resembled that of the map legends was considered preferable to grouping any formations a priori . Of course, should further simplification be required for a particular end use, the formations represented in the output dataset can be grouped later.

Preparing and importing surface formation interface data, orientation data, and faults

Two types of surficial geological data from the relevant map sheets 53 , 56 were obtained in digital format ( Fig. 4 ). These were: i) polylines formed by joined points indicating the locations of the interfaces between the formations of the geological pile which outcrop at the surface (also known as “contacts”), and ii) point features describing the orientation of these formations at certain locations on the surface (also known as “structural data”).

Georeferenced point features (formation interface locations and marked structural data) extracted from the surface map (left, Badoux et al. 53 , Badoux and Gabus 56 , Source: © Swiss Federal Office of Topography, 1971, 1991) and imported into the GeoModeller interface (right). The legend refers to the coding of the formations of the stratigraphic pile. Circles indicate interface points, and arrows indicate orientations. Note that since the DTM had already been imported at this juncture, these points were in fact located in 3D and not only 2D space (i.e. they could be associated with a z-value).

Where necessary, interface polylines were reattributed to match the formations defined in the geological pile. Whilst taking care to maintain their shapes, they were also simplified to reduce the number of vertices. This spatial data processing was conducted using the open source software QGIS. The processed polylines were then imported into GeoModeller. Given the complexity and number of interface shapes in the study region, this approach was more efficient than the more common practice of importing a pre-georeferenced image of a geological map into GeoModeller and then manually digitising the surface formation interfaces in that software environment. In locations where it was clear that a given boundary was continuous beneath the Quaternary cover (and hence was not actually defined) but the location of the boundary could be easily estimated, additional points were inserted. In certain other locations, bedrock interfaces completely obscured by the surficial cover could not be estimated. The consequent data gaps represent a source of uncertainty in the final model. (The model proposes continuous boundaries as a result of the interpolation).

The orientation points were attributed to formations of the geological pile by means of spatial intersection. Some surface orientation data points that fell between vertical cross-sections in the heavily folded frontal zone had to be discarded because at these locations, the polarities/younging directions (i.e. whether normal or overturned) could not be determined with confidence.

Faults that have offsets that were believed large enough to potentially disconnect aquifers, numbering 10 in total, were explicitly represented. Firstly, their surface extents were digitised and imported into GeoModeller. The trace of only one of these faults appeared on a vertical cross-section; the shape of this one was therefore be digitised in the vertical plane also. The remainder were assumed to be vertical in the absence of any other orientation information. Finally, since all faults are shown by the existing geological maps to be finite, which is to say that they do not extend across the entire domain, it was necessary to estimate an “ellipsoid of influence” for each.

Faults with more limited offsets, which are not represented, could still be responsible for preferential hydrological flow pathways; an effect that could be represented in any subsequent hydrogeological modelling by prescribing discrete fractures, or by treating the corresponding volumes as having dual permeability.

Georeferencing vertical cross-sections, digitising subsurface interfaces and orientations

The “stacked” vertical cross-sections 53 , 56 ( Fig. 5a ) provide interpretations of the subsurface structures. Including such information in the estimation of model was crucial because the non-stationarity of the domain, which arises due to the pronounced folding and faulting, precludes the straightforward extrapolation of surface observations into the subsurface.

( a ) Incoming vertical cross-sections. Note that only C-I (i.e. those from the Morcles map sheet) are shown in this panel (Badoux et al. 53 , Source: © Swiss Federal Office of Topography, 1971), although Sections 2-12 (from the Diablerets map sheet; Badoux and Gabus 56 , Source: © Swiss Federal Office of Topography, 1991) were also used in the model development, ( b ) All 11 cross sections, having been georeferenced, visualised in the 3D viewer of GeoModeller, and ( c ) An example of a georeferenced cross section (GG’) visualised in the 2D viewer of GeoModeller, with subsurface interface and orientation data points digitised according to the locations and dips of the various interfaces shown (coloured lines and arrows, respectively). Once these sections were viewed, it was also ensured that the topographic surface illustrated on the diagram closely matched that derived from the DTM along the same profile (red line); a close match, as seen in this example, indicates sound georeferencing. Furthermore, since the DTM has been developed using modern technology (LiDAR and photogrammetry) and is therefore of high quality, a close match also indicates an accurate representation of the topographic surface in the original figure.

Prior to being imported into GeoModeller, the diagrams were cropped and the cross-section start and end locations georeferenced ( Fig. 5b ). For each cross-section, the correspondence between the topographic surface profiles in the GeoModeller environment (i.e. the profile of the DTM between the georeferenced cross-section start and end points) and the representation of the topography on the georeferenced diagram was assessed (see the red line in Fig. 5c ). The close matches observed gave us confidence that both the georeferencing of the cross-section diagrams and the original representation of the topography along the sections were satisfactory. Fig. 6

To avoid cluttering, only interface data points are shown, although orientations were also visualised and checked in the same fashion. Here, the view is towards the north-east.

The lengthy task of manually digitising the subsurface formation interfaces illustrated on the cross-sections, and their associated orientations, was then undertaken. In this process, the interface surfaces were assumed orthogonal to the section plane, i.e. the dip direction is parallel to the section, with dip angles estimated at regular intervals along each interface according to the angles formed with the horizontal plane. Due to the plunge of the axis of the main nappe structure, the subsurface dip and dip directions resulting from this assumption may not be entirely correct in all instances. However, the approach taken was the only practical way in which some subsurface orientation data could be included, which in turn was absolutely necessary to successfully model the region. This assumption is not expected to have any implications for the utility of the model for hydrological and other environmental applications.

In total, 11 cross-sections distributed throughout the domain contributed to the model.

Following the completion of Steps 4 and 5, the surface and subsurface interface and orientation points could be visualised in 3D and their consistency with one another verified and, where necessarily, improved (Step 6). Figure 6 illustrates the final arrangement of digitised formation interfaces (both surface and subsurface) that were taken into account in the computation of the model.

Computing the model

In contrast to more traditional “explicit” or “surface-based” approaches, which essentially involve users generating geological surfaces or volumes based directly on the available data, GeoModeller takes an “implicit approach” to model estimation. Such an approach relies on algorithms that integrate observed data with geological interpretation. The particular theory and algorithms that are employed have been comprehensively described and exemplified elsewhere 72 , 74 , 75 , but to summarise: formation interface and orientation data are co-kriged to produce a 3D scalar field, or potential field 76 . Equipotential iso-surfaces with certain reference values represent formation interfaces, whilst the gradients of the scalar function describe their orientations. Several potential fields can be combined in the same model to, for instance, reconstruct complex erosive and/or onlap relationships between geological series (which are simply groups of individual formations). Faults can be represented by inserting discontinuities into the potential field. Once computed, the surfaces or volumes may be visualised by means of a marching cube methodology 77 .

The key benefits of taking an implicit approach are that certain conditions of geological validity (e.g. the forbiddance of overlapping interfaces) are directly enforced 78 , and that interface contact and orientation data can be considered simultaneously in the estimation of a continuous model 79 . Another positive feature is that a given model can be updated relatively quickly following the addition of new data. In our case, this allowed several “competing” models to be generated efficiently using slightly different subsets of the data, alternative interpretations of the relationship between formations in the geological pile, and varied parameter values. Drawbacks of the implicit approach include its relatively high memory usage, and the limited opportunity that exists for users to manually adjust individual modelled surfaces to match their expectations or expert knowledge.

The first stage of computing the model involved testing how the relations between the series (which are simply groups of formations) of the geological pile might best be represented. According to the legend of the Morcles map, erosive relationships exist between the Jurassic and the Cretaceous series, and also between the Cretaceous and Tertiary series. In early iterations these series were indeed treated separately with “Erode” relations, such that one or more would take precedence and cut over the others (depending on which were defined as “Erode” and which remained “Onlap”, as well as their relative positions in the pile). However, this prevented data from the upper or lower formation of one series from constraining formations in the next series, leading to some cross-cutting situations that were deemed unrealistic. Because the interfaces of all formations seem to generally follow one another, with few if any instances of formations within the modelled series having been completely eroded, it was eventually decided to treat all formations as part of a single series.

The only further inputs necessary to compute a model are the values of three (isotropic) interpolation parameters: the range, the nugget effect of geological interface data, and the nugget effect of geological orientation data. The range represents the spherical distance beyond a given location at which data points will have no influence on the model. The nugget effect parameters represent the variance between the values of observed data points that is not explained by separation distance, but could rather reflect measurement error or stochasticity (i.e. “noise”). Here, it can be thought of as the mismatch permitted between model and data.

Code availability

GeoModeller is a proprietary software owned by, and licensable from, Intrepid Geophysics (v.3.3.0 was used in this work). The approach GeoModeller takes has been thoroughly described in the peer-reviewed literature. Open source GIS tools (QGIS; https://qgis.org/en/site/ ) were applied for data preparation.

Data Records

Exporting the model.

Once an acceptable model had been produced (see Technical Validation), the model was exported in a regular voxel format at both 10 m and 50 m resolution. In this format, to reduce data volumes, the position of each voxel is given implicitly. In principle, the voxel model can be used directly for hydrogeological modelling. However, this would be rather inefficient with respect to the number of nodes and elements required. More contemporary practice would be to separately generate a finite element mesh of variable resolution, which allows appropriate representation of important features whilst minimising the total number of nodes, and then assign geological codes to each element in this mesh according to the geological model via a spatial query. Following this, one could attribute (based on knowledge of the lithology, etc.) reasonable initial estimates of the values of parameters describing physical properties throughout the domain. Such meshes could be comprised of layered prisms or, as are beginning to emerge, fully-unstructured tetrahedra. Various possibilities exist for refining or optimising the arrangement of elements according to surface or subsurface features, although this topic lies beyond the scope of this work.

3D shapes were also exported for visualisation. More specifically, the formation interface surfaces may be loaded as polygonal meshes in ParaView 80 ; an open source 3D visualisation software. In this environment, layers can be visualised or hidden at the user’s behest, and virtual interaction undertaken.

Finally, the final GeoModeller project was saved in its native format.

The model data ( Data Citation 1 ) is provided in a generic voxel format at two different resolutions: 10 m and 50 m (regular cubic cells) (Generic voxel format, Data Citation 1 ). The lithological code is listed for each cell. For ease of processing, no refinement of the vertical resolution has been made near the interface with the topographic surface in either case. The 10 m resolution voxel model is also provided in a modified voxel format that can be loaded into the freely-available visualisation software SGeMS 81 (SGeMS voxel format; also see Supplementary Figure 4 ). In addition, formation interface surface were prepared in a format that can be visualised in ParaView (Surfaces for ParaView, Data Citation 1 ). Finally, the entire GeoModeller project is provided in its native format; this can be viewed by readers with a GeoModeller licence (Native GeoModeller Project, Data Citation 1 ).

In the SGeMS format, the lithological formations are represented by the codes listed in Table 2 .

Technical Validation

The most relevant evaluation process was the visual comparison of how well lithological formation interfaces defined by the input data compared with the corresponding modelled interfaces – both on surface and on the vertical cross-sections. Accordingly, as part of the iterative computation and re-evaluation process, the adjustable parameters were systematically varied until a satisfactory balance between a smooth model and model that honoured the data was obtained. In practical terms, this was achieved by visually comparing the input data and the modelled interfaces on both surface and vertical sections. The final parameter values we arrived at are:

Range: 2,000  m

Nugget effect on geological interface data: 0.00001 (arbitrary unit of the potential field)

Nugget effect on geological orientation data: 0.1 (arbitrary unit of the potential field)

Comparisons of the input data and final modelled interfaces on the surface section and four randomly selected vertical cross-sections are shown in Fig. 7 . The close matches obtained indicate that the interpolation algorithm can reproduce the structures defined in the input data. If one then assumes that the original geological interpretation presented in the maps (i.e. the input data itself) is reasonable, one may suggest that the new 3D model is also geologically plausible. Taking slices through the domain between cross-section locations, and visualising the estimated volumes of individual formations with their corresponding input points, reinforced this assessment.

The thin lines with circles and arrows correspond to the input data extracted from the surface maps (surface section), or digitised directly from the formation interfaces illustrated on the cross-section diagrams (vertical sections). Specifically, the circles represent interface data points and arrows represent orientation data points. Input orientation data points are not shown on the surface section in the interests of clarity. The thicker, continuous lines correspond to the modelled surfaces. For the surface, the data points alone ( a ) and then data points with the interpolated interfaces underneath ( b ) are shown separately. The final filled volumes are also shown for all sections (panel c shows this for the surface section). Vertical cross-section letters follow the convention of the original inputs. In Section D, one of the faults that has been modelled is visible. Two disconnected circles (spheres in 3D) are visible; these are model artefacts, and are discussed in due course.

Although the number of input data points observations is high, given the inaccessibility of the subsurface, they are still unlikely to be sufficient to constrain a unique model 79 . For this reason, no data were explicitly withheld to provide independent evaluation data; keeping any observations aside would inevitably have had an adverse impact on the final model.

The resultant dataset ( Figure 8 ) represents 18 formations, including their associated folds and selected major faults, and covers a horizontal area of 9.6×13.4 km.

This figure was produced in ParaView. Coordinates are given in the CH1903 / LV03 system. The formation codes correspond broadly to those of the legend of the 58 Morcles sheet 53 , although the geological pile was slightly modified to reconcile it with the Diablerets sheet. Some colours were slightly adjusted to increase visual impact. In the western part of the domain, the stratigraphy is overturned, i.e. geologically older units are found above younger ones. Quaternary cover is not included in the model. Formations of the Ultrahelvetic zone are not included in the model, and thus the presence of “e6o1” (bright yellow) in the extreme north-west of the figure is not reflective of the real geology in this region (as indicated in Fig. 2a ). Similarly, the model is less well constrained in the extreme east of the domain (i.e. towards the centre of the nappe), which is away from the main area of interest.

A further visual comparison was made between a panoramic sketch of the geology of the eastern side of the Vallon de Nant (“Planche II” of Badoux 54 ) and a roughly equivalent view produced in the modelled environment, again with satisfactory results ( Supplementary Figure 3 ).

The interpolation approach employed to develop the 3D model is based on co-kriging. For this reason, the density of data points may have some effect on the final model. Whilst the relatively high-density surface points were extracted directly from the (pre-digitised) map data, and were therefore rather treated as rather “fixed” (although the density of vertices on relatively straight surface interfaces was increased so these features were not unduly de-weighted), the subsurface points had to be digitised manually from the cross-sectional diagrams. The density at which these points were inserted was at the discretion of the model developer, and was therefore somewhat arbitrary. This inevitably introduced a degree of subjectivity into the modelling process. On the other hand, it also provided a degree of flexibly to vary the extent to which data constrain the model regionally. For instance, where uncertainty in the position of an interface was considered lower (e.g. near an observable interface at the surface), the density of points could be increased. Furthermore, where the precise location of the interface was unknown in a particular region but the orientations could be inferred from adjacent layers, subsurface orientation data points could be inserted independently of interface data. Finally, as mentioned above, some additional interface points were even inserted onto the surface section where it was clear that a given boundary was continuous but was simply not marked on the map as a result of being obscured by Quaternary deposits. Accounting for these aspects, rather than simply computing the model based only on the available data, enabled us to produce a final model that both honoured the data and could be deemed geologically plausible.

The resultant model is deterministic. In regard to this, it may be noted firstly that the number of outcrops present in our highly eroded, mountainous study area mean that the geological structures are relatively easy to observe. Therefore, although the area is geologically complex, and the existing data (which formed our inputs) are naturally associated with some uncertainty (the vertical cross-sections naturally more than the surface data), this data uncertainty is arguably less pronounced than in other geological settings where bedrock outcrops are less conspicuous. The series of vertical cross-sections is also very dense. As such, it seems unlikely that attempting to refine the field data or geological interpretation, as expressed in the existing surface mapping or vertical cross-sections, would yield major improvements in the output.

Nevertheless, in future work, it would be possible to assess the uncertainty associated with the deterministic geometrical representation to be assessed by generating multiple realisations of the model. This could be achieved by perturbing the parameter values within a probabilistic framework. That said, it may be suggested that the high data density leaves little scope for model variability during the interpolation stage. Furthermore, before embarking on such a task, the relative magnitudes of various uncertainties that could arise throughout a wider modelling chain should be contemplated. For example, in our hydrogeological application, the uncertainty in the hydraulic conductivities of the various formations is likely to be much more substantial that the uncertainty in their geometrical structures. Exploring the capability of high resolution LiDAR terrain data to refine the input interface locations represents another potential avenue.

A number of challenges were encountered during the development process. Consequently, the final dataset is associated with some limitations. These limitations could motivate further improvements in 3D geological modelling algorithms in general, or the present model in particular. They are as follows:

The polarity (normal/overturned) of some surface orientation points in the frontal zone was not clear. In the horizontal plane, these points were located between vertical cross-sections. Given the extreme folding in the region, their polarities could not be easily confirmed. These points were omitted.

The subsurface orientation data derived from the vertical cross-sections were taken as if there is no plunge (i.e. as if the cross-sections are perfectly orthogonal to the main fold). In reality, the plunge angle is spatially variable, and the true dip and dip direction at any given location are dependent on the plunge angle and as well as the geometry of any secondary folds in those locations. Making the assumption of orthogonality was thus the only practical way in which subsurface orientations could be assigned, which in turn was important for successfully modelling this structurally complex region. Given its intended application(s), this assumption is not expected to have a major impact on the model.

In addition to the data prescribed, the implicit approach taken by GeoModeller inherently considers “geological concepts” when constraining the output. Whilst some of these constrains are highly beneficial (e.g. overlapping or “leaking” geological layers are not permitted), others, such as a tendency towards regularity in formation thickness throughout the domain, were more troublesome. This is because the thickness of some formations, most notably “i6_8”, does change drastically over short distances in our study area. Consequently, some interpolation artefacts such as disconnected “spheres” are present in areas of the model where the gradient in the potential field changes rapidly, particularly near the apices of sharp folds. In such a structurally complex environment, and with a large observation dataset, slightly improved results could have perhaps been obtained by assigning a greater weight to our observations and giving less freedom to the geostatistical interpolation.

When computing the model, the same range value was used for both the interface data and the orientation data. In GeoModeller, it is not possible to specify a different nugget parameter for, say, the interface data according to whether it was derived from the surface maps or the vertical cross-sections. In other words, it is only possible to make a distinction between the interface and orientation data when assigning range and nugget effect values, which are global parameters. This was unfortunate in our case because, being directly observable, the surface data points are much less uncertain than subsurface contacts, which are highly dependent on interpretation. Hence, all observations of a certain type carried an equal weight in our model.

The Quaternary cover is not represented by the model. The only available data pertaining to the unconsolidated Quaternary deposits are a series of shallow boreholes near the source of Le Rippaz, and reveal it to be discontinuous and heterogeneous. The Quaternary fill in the Vallon de Nant itself is likely to be a complex mixture of material having glacial, fluvial, and mass-movement origins. Reconstructing Quaternary cover in such environments is challenging in its own right. However, it is likely to be necessary for reliable hydrogeological flow simulations. As such, following geophysical surveys and their interpretation, plausible realisations of the heterogeneous structures could be generated using stochastic geostatistical simulation approaches such as Multiple Point Statistics 38 . An update of the present (bedrock only) model could potentially then be issued.

In conclusion, no 3D geological model the study region – which now forms the focus of a concerted interdisciplinary research effort – had been published or otherwise made available at the outset of our work. Whilst the development of a model with the requisite characteristics for hydrogeological and other applications was both technically challenging and time consuming, this data gap has now been addressed. The modelled formation interfaces correspond well with the location of these interfaces according to the input data, both at the surface and on available vertical cross-sections. Assuming the geological interpretation presented in the original maps and cross-sections is reasonable, the close matches obtained provide confidence that modelled representation of the geology also acceptable; a view that was upheld by additional comparisons with interpretive sketches.

In the sense that no new primary data has been collected, this work could be considered a data augmentation exercise. Our model thus demonstrates the considerable potential that exists to add value to existing geological data. We argue that it also amounts to more than the sum of its inputs; via the combination of the various input datasets and the process of geostatistical interpolation, it provides insight over the entire domain, ultimately forming an unprecedented geometrical description of the geological formations of the western and northern section of the Nappe de Morcles.

In contrast to certain previous publications which describe the development of 3D geological models only superficially, here, a detailed, step-by-step process has been presented. This should assist future researchers and practitioners in developing complex 3D geological models in future. Again in contrast to most previous instances, the data generated here are made freely-available, and care has been taken to ensure that they can be visualised in open source software. A range of applications across the earth and environmental sciences are likely to benefit if such work is conducted more consistently.

In one ongoing application, the geological model is being employed alongside various other datasets to parameterise physically-based, numerical simulations that integrate snow cover dynamics, surface-water flow, groundwater flow, and evapotranspiration. These simulations intend to elucidate the overall response of mountain hydrological systems to ongoing climatic change.

Finally, having been developed with the stringent, specific requirements of hydrological applications in mind, the model should also be suitable for a range of other applications, including rockfall hazard modelling, sediment provenance identification, hydro-chemical data interpretation, and pedogenic studies.

Usage Notes

Instructions to visualise the model data.

To visualise the surfaces in ParaView (as in Fig. 3 ):

Download the software from https://www.paraview.org/ , and install it.

Under “File”, select “Load State”

Navigate to “Geological_Model_State_File.pvsm”

Under “Load State Options”, select “Search file names under specified directory”, and ensure the directory path is correct.

Individual layers can be made transparent/non transparent by clicking on the “eye” symbol on the left hand side.

To visualise the voxel data in SGeMS:

Download the software from http://sgems.sourceforge.net/ , and install it.

Select “Load Object”, navigate to the file “10 m.SGEMS” and click “Open”.

In the “Select object type” dialogue box, choose “cartesian grid”, and click “Next”.

Provide a name for the grid, and enter the values shown in the following screenshot ( Fig. 9 ), then click “Finish”.

Parameters that should be entered in order to visualise the voxel data in SGeMS.

Once the data are loaded (which may take a few minutes), it may be viewed by checking the two tick boxes under the object tab.

The colour map can be changed if desired using the options under the “Preferences” tab. Also under this tab, by checking “Use Volume Explorer” and “Hide Volume”, a number of slices in the X, Y and Z planes can be visualised using the sliders.

Despite having specified the No-Data-Value on import, it does not seem possible make the grey area transparent. Nor does it seem possible to match the colour scheme in SGeMS to that used in this paper. An illustration of the model visualised in SGeMS is given above ( Supplementary Figure 4 ).

To work with the data in GeoModeller (licence required):

Launch GeoModeller

Open the project by navigating to the “.xml” file provided.

Additional information

How to cite this article : Thornton, J.M. et al . A 3D geological model of a structurally complex Alpine region as a basis for interdisciplinary research. Sci. Data . 5:180238 doi: 10.1038/sdata.2018.238 (2018).

Publisher’s note : Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

The work was conducted as part of the IntegrAlp project, funded by the Swiss National Science Foundation (SNF project CR23I2_162754). Discussions with J.-L. Epard and A. Parriaux are gratefully acknowledged.

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J.M.T. conducted the majority of the work, including sourcing and preparing the input datasets, generating and evaluating the model, preparing the figures, and writing the manuscript. G.M. advised on data processing and participated in the redaction of the manuscript. P.B. contributed the initial idea that a 3D geological model could benefit the planned interdisciplinary research and participated in the redaction of the manuscript.

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Thornton, J., Mariethoz, G. & Brunner, P. A 3D geological model of a structurally complex Alpine region as a basis for interdisciplinary research. Sci Data 5 , 180238 (2018). https://doi.org/10.1038/sdata.2018.238

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Modeling Geological CO2 Sequestration: Translations Across Spatial Scales and Advancements in Nonlinear Newton Solver

Sally M. Benson, primary advisor Hamdi Tchelepi, primary advisor Peter K. Kitanidis, advisor

Characterization of formation heterogeneity is the first step for constructing geological models that are used in simulating subsurface flow migration, such as CO 2 geological storage and oil/gas recovery. Technologies such as seismic survey, well logging, and core analysis reveal the formation heterogeneity at different scales. Due to the computational limitations on model construction and flow simulation, the gridblock size of a geological model is usually much larger than a core. The common industry practice is to assign core-scale properties directly to a geological model gridblock. The sub-grid- and sub-core-scale heterogeneities are neglected. The objective of this work is two-fold. First, we demonstrate that these small-scale heterogeneities can influence large-scale CO 2 migration during geological storage. Significant modeling error may occur if these heterogeneities are not accounted for properly. Second, we improve the nonlinear convergence performance of numerical simulation, which is a crucial tool for translating small-scale physics in large-scale modeling and for uncertainty quantifications during the scale-up. A new Newton solver is developed, which converges quickly by avoiding the oscillations and overshoots that are often encountered in traditional solvers.

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3D Geological Model of a Tunnel for Improving Construction Measures: A Case Study

• Original Paper
• Published: 25 August 2023
• Volume 42 , pages 975–989, ( 2024 )

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• Kang Wang 1 ,
• Weidong Guo 2 ,
• Shaoshuai Shi   ORCID: orcid.org/0000-0001-8984-9809 2 ,
• Ruijie Zhao 2 &
• Xin Wang 2  

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As the demand for information technology in tunnel construction continues to increase, geological three-dimensional visualization methods have become the forefront of tunnel construction research. This paper analyzes the requirements and main issues faced in three-dimensional modeling for tunnel engineering. To meet the high precision and high timeliness requirements of geological modeling in tunnel engineering, this study establishes a rapid three-dimensional reconstruction method for tunnel engineering models based on geological feature points and line data. Line data is used to construct the model boundaries, while point data is utilized for internal geological description through local Kriging interpolation. The adoption of a unified data format effectively improves the modeling speed. This modeling method integrates advanced geological prediction data, significantly enhancing the model's accuracy. Through the three-dimensional geological model, the geological conditions ahead of the tunnel face are analyzed. The information presented in the three-dimensional geological model is validated through drilling verification and found to be accurate. Subsequently, based on the information obtained from the three-dimensional geological model, the tunnel construction scheme is optimized. Additional localized radial grouting measures are supplemented on the basis of continuous advance peripheral grouting. As a result, tunnel safety construction is achieved by reducing the high geological risk length, thereby shortening the construction period. The approach presented in this paper can be widely applied in relevant tunnels and underground engineering projects.

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Acknowledgements

The authors thank the editor and anonymous reviewers for their valuable advices. This research was supported by National Natural Science Foundation of China (Grant Numbers 52278404 and 51979154) and Taishan Scholar Foundation of Shandong Province (Grant Number tsqn202103002) and Future scholars project of Shandong University, which are gratefully acknowledged.

This research was supported by National Natural Science Foundation of China (Grant Numbers 52278404 and 51979154) and Taishan Scholar Foundation of Shandong Province (Grant Number tsqn202103002) and Future scholars project of Shandong University.

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Wang, K., Guo, W., Shi, S. et al. 3D Geological Model of a Tunnel for Improving Construction Measures: A Case Study. Geotech Geol Eng 42 , 975–989 (2024). https://doi.org/10.1007/s10706-023-02599-y

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3d mineral prospectivity mapping from 3d geological models using return–risk analysis and machine learning on imbalance data.

1. Introduction

2. study area and 3d geological models, 2.1. geological setting of the study area, 2.2. ore-controlling factors, 2.3. the 3d evidence layers of geological modeling, 3. methodologies, 3.1. borderline-smote, 3.2. random forest algorithm, 3.3. evaluation metrics, 3.4. return–risk analysis, 4. results and discussion, 5. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Peng, Q.; Wang, Z.; Wang, G.; Zhang, W.; Chen, Z.; Liu, X. 3D Mineral Prospectivity Mapping from 3D Geological Models Using Return–Risk Analysis and Machine Learning on Imbalance Data. Minerals 2023 , 13 , 1384. https://doi.org/10.3390/min13111384

Peng Q, Wang Z, Wang G, Zhang W, Chen Z, Liu X. 3D Mineral Prospectivity Mapping from 3D Geological Models Using Return–Risk Analysis and Machine Learning on Imbalance Data. Minerals . 2023; 13(11):1384. https://doi.org/10.3390/min13111384

Peng, Qingming, Zhongzheng Wang, Gongwen Wang, Wengao Zhang, Zhengle Chen, and Xiaoning Liu. 2023. "3D Mineral Prospectivity Mapping from 3D Geological Models Using Return–Risk Analysis and Machine Learning on Imbalance Data" Minerals 13, no. 11: 1384. https://doi.org/10.3390/min13111384

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• Published: 09 August 2017

Development of an updated geothermal reservoir conceptual model for NW Sabalan geothermal field, Iran

• Mirmahdi Seyedrahimi-Niaraq 1 , 2 ,
• Faramarz Doulati Ardejani 1 , 2 ,
• Younes Noorollahi 3 &
• Soheil Porkhial 4  

Geothermal Energy volume  5 , Article number:  14 ( 2017 ) Cite this article

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In this paper, a conceptual model has been developed for NW Sabalan geothermal field using exploration indicators. These indicators include the data and results of subsurface and surface investigations comprising geology, geophysics , hydro-geochemistry, hydrology and temperature and pressure distribution. The subsurface information was obtained from 10 deep exploration wells as well as from the results of previous studies in this field. All available data together with the stratigraphy and 1:20,000 geological map covering the study area were combined to produce a two-dimensional geological cross section. Also, a subsurface three-dimensional geological model was developed using available drilling logs. NW Sabalan geothermal field is one of the 18 detected potential areas in Iran that is located in Northwest of the country. This field includes a deep geothermal reservoir with a temperature range of 230–242 °C. The reservoir is covered by a cap rock with an approximate thickness of 500 m. There are four major geological units identified in the study area including Quaternary alluvium, fan and terrace deposits; Pleistocene post-caldera trachyandesitic Flows; Pleistocene syn-caldera trachydacitic to trachyandesitic domes and Pliocene pre-caldera trachyandesitic lavas, tuffs and pyroclastic. A hydraulic conductivity zone has been assessed by magnetotelluric surveys at deeper zones, suggesting that the main outflow direction is towards west and north of the area. The fluid chemistry is consistent with high chloride, neutral pH and immature liquids that partially equilibrated with host rock and they are classified as mixed water. The results of exploration and geological studies during a 10-year period have been integrated together to build a new overall conceptual model of the field.

A conceptual model provides a basis for a numerical model. The data available on geothermal systems are unified through the development of an appropriate conceptual model of the geothermal system of interest. They play a key role in all phases of geothermal exploration and development. Conceptual models are mainly based on surface and subsurface geological information, remote sensing data, results of geophysical surveys, the chemical and isotopic datasets of fluids in surface manifestations and reservoir fluid samples collected from wells, information on temperature and pressure conditions based on analysis of the available well logging data as well as other reservoir engineering information. A realistic conceptual model allows an explicit description of the main properties of a geothermal system such as hydraulic characteristics, groundwater flow, solute and heat transfer. A realistic and accurate conceptual model is probably not possible without a numerical model to prove its accuracy.

Northwest Sabalan (NW) geothermal field is one of the eighteen fields of identified potential and the fourth main high temperature areas in Iran that is situated in the northwest of Iran in Ardabil Province. Its distance from Tehran is 859 km (Fig.  1 a) (Noorollahi et al. 2009 ; Porkhial et al. 2015 ; Kosari and Sattari 2015 ). This field has practically been exploring and developing recently. The area has been under geological investigation since 1978 (Fotouhi 1995 ).

a Geographical situation of study area, b geological map of west and northwest of Mt. Sabalan (based on KML 1999a , detailed faults map is based on EDC 2010 )—geothermal wells and faults are shown in bold and bold italic , respectively. The letters A , B , C , D and E on geological map represent exploration well pads

The plan of electricity generation from NW Sabalan geothermal field has been presented since 1994. Afterwards emphasis has been put on this field as a top priority area. From 1998 to 2005, the detailed geo-based study was directed by the joint cooperation of renewable energy organization of Iran (SUNA) and Sinclair Knight Merz Ltd (SKM) of New Zealand. The NW Sabalan geothermal field was finally distinguished as an important potential for power generation purpose (Noorollahi et al. 2009 ).

Accordingly, during the time period of 2002–2004, three deep exploration and delineation wells (NWS-1, NWS-3 and NWS-4) have been drilled. Besides, a shallow injection well named NWS-2 (Fig.  1 b, PADs A, B and C) was drilled, in order to evaluate the subsurface geology and provide data for assessment and modelling the reservoir (Najafi and Ghobadian 2011 ; Porkhial et al. 2015 ). The location of these wells was determined based on the results obtained from a Magnetotelluric (MT) survey conducted in 1998 (SKM 2005a ).

The wells NWS-1 and NWS-4 were the first deep wells drilled in Sabalan volcanic area (Porkhial et al. 2015 ; Kosari and Sattari 2015 ). A maximum temperature of 242 °C was measured in well NWS-1 at a minimum depth of 832 m. Subsequently, from 2008 to 2011, SUNA performed new geophysical exploration studies. Based on the new results, the wells NWS-5 ( T max  = 240 °C, D min  = 1635 m) and NWS-11 ( T max  = 174.5 °C, D min  = 2701 m) on Pad A and C, respectively, NWS-6 ( T max  = 238 °C, D min  = 1144 m), NWS-7 ( T max  = 237 °C, D min  = 1341 m) and NWS-10 (maximum temperature 242 °C, D min  = 1890 m) on Pad D and NWS-8 and NWS-9 (maximum temperature 230.5 °C at a minimum depth of 2301 m), respectively, on Pad E were drilled (Fig.  1 b).

A few studies have been carried out in the past to present a conceptual model for the NW Sabalan geothermal field. The conceptual/geological models presented by KML ( 1999b ), Noorollahi and Itoi ( 2011a , b ), Porkhial et al. ( 2010a , b ), Khojamli ( 2012 ) and Sabzemeidani ( 2016 ) are all noteworthy.

KML ( 1999a ) built a hydrogeological geothermal model for the Mt. Sabalan volcanic complex based on multi-disciplinary combination of geological, geochemical and geophysical data. The heat source, exploration wells, meteoric waters, deeply circulated meteoric waters, magmatic volatiles and condensates are illustrated. Based on this model, one may explain that a large residual magma mass is shown to underlie the Sabalan caldera complex with intrusive apophyses developing upwards from the magma mass to shallow depths controlled by caldera ring-faults. KML ( 1999a ) further explained that the thermal features might all have a common origin within the Sabalan caldera.

Noorollahi and Itoi ( 2011a , b ) have presented a conceptual model from the available three deep exploration wells data as a basis for their reservoir numerical model. The simulation predicted a power plant equivalent to be designed with a capacity of 50 MWe and an age of more than 30 years. Now construction of the first pilot plant is running with a capacity of 5 MWe, the produced fluid temperature of 86 °C and maximum flow rate of 58 l/s (average flow rate: 46 l/s) in the NW Sabalan Site. This plant is located between Pad A and Pad D with geographical coordinates x  = 739,108, y  = 4,238,580 and elevation of 2635 masl (Fig.  1 b).

Literature review indicates that despite several researches related to the development of conceptual models for NW Sabalan geothermal field, a comprehensive conceptual model based on the combination of geological, geophysical, hydrogeochemical, hydrological and deep well data has not been yet presented to provide a basis for developing a numerical model for the study geothermal reservoir. The weakness of these works (KML 1999a , b ; Porkhial et al. 2010a , b ; Noorollahi and Itoi 2011a , b ; Khojamli 2012 ; Sabzemeidani 2016 ) is that they only employed the information obtained from three exploration wells for the development of the conceptual model. Besides, they ignored some of important exploration indicators to be considered. Therefore, these models cannot fully explain the processes of Sabalan geothermal system, such as conduction and convection zones. In addition, they presented an incomplete explanation about the fluid flow cycle. Moreover, they did not define an exact boundary for the geothermal reservoir due to the lack of adequate data. In the present work, attention has been focused on the development of a conceptual model which takes all geothermal system variables into account and provides a visual representation of the geothermal system. The structure of the conceptual model presented here was completed by the use of geological and thermal data obtained from the new deep exploration wells. Conduction and convection zones and the top of the reservoir were added to this model by the incorporation of the geophysical results together with subsurface temperature distribution. To derive the conceptual model, Datamine Studio3 Version 3.21.7164 and ArcGIS Desktop software were used as tools.

NW Sabalan geothermal field is located in the northwest part of Mt. Sabalan. A geological map at a scale of 1:20,000 of Sabalan region is shown in Fig.  1 b. Mt. Sabalan is a large stratovolcano comprising a wide-ranging central structure which is constructed on a likely tectonic horst of underlying intrusive and effusive volcanic rocks. According to KML ( 1999a ), the output magma had a great role in forming a collapsed caldera about 12 km in diameter and a depression of about 400 m.

The Mt. Sabalan region sits on the south Caspian plate, which underthrusts the Eurasian Plate to the north and it is in turn underthrust by the Iranian plate, which causes compression in a NW direction. Besides, a dextral rotational motion resulted by the movement of Arabian plate beneath the Iranian plate makes the geological structures in the region more complicated. As a result of this tectonic structure, the Cenozoic geological history and stratigraphy of the region under consideration are complicated and the geological units in this region might have different structural characteristics (Mckenzie 1972 ; Amidi 1978 ; TBCE 1979 ; ENEL 1983 ; Nejad 1987 ; Manouchehri 1989 ; Emami 1994 ; Noorollahi et al. 2008 ).

The lava flows are typically trachyandesite and trachydacite with alternating explosive phases (KML 1999b ). There are four major geological units identified in the NW Sabalan area that include (1) quaternary alluvium, fan and terrace deposits; (2) pleistocene post-caldera trachyandesitic flows; (3) pleistocene syn-caldera trachydacitic to trachyandesitic domes and (4) pliocene pre-caldera trachyandesitic lavas, tuffs and pyroclastic (SKM 2005a , b ).

Two major geological structures have been identified in the study area that comprise a set of linear faults and several ring-faults (Fig.  1 b) (KML 1999a ; SKM 2005a , b ; Noorollahi et al. 2008 ; Ghaedrahmati et al. 2013 ). The results of interpretation of Centre National d’Etudes Spatiales (SPOT) satellite images and aerial photographs showed a WNW trending structural zone passing through the Moeil Valley. The faults strike mostly towards the NW and NE (KML 1999a ).

Stratigraphy

By increasing data due to adding new exploration wells and constructing Pads D and E, the realisation of the deep volcanic succession in the NW Sabalan geothermal field became easier. These data have been used in this paper to understand the stratigraphy in details. It is noted that the deepest geothermal wells in NW Sabalan are NWS-1 and NWS-3 with approximate depths of 3177 and 3197, respectively.

A three-dimensional geological model (Fig.  2 ) was generated using both the available wellbore and surface geological data. Datamine Studio3 Version 3.21.764 software was used as a tool. Figure  3 shows a two-dimensional cross section including surface and subsurface geological data. The geological map (Fig.  1 b), wellbore data and the three-dimensional model (Fig.  2 ) were employed to build this section.

Subsurface three-dimensional geological model (by Datamine software). Dip diorite Porphyri, Diz dizu formation, Epa Epa formation, Hfs hornfels, Mon monzonite, Nga Ngab formation, Pla plat formation, Val valhazir formation). a All geological units, b indicator geological units, c transparent model

NW-SE geological cross section of the NW Sabalan site with the exploration wells and stratigraphy

Monzonite basement rock was revealed at the depth of 1021 m of NWS-1 well in southeast. This rock was further approved at the depth of 2140 m of NWS-6. However, it appeared as the monzonite dykes at the depth zone of 1978–2700 m of NWS-9 well. The outcrop of monzonite extends as a bound, 5–10 km wide, for 100 km with a general NW strike. Mineralogical study of this rock showed minerals comprising plagioclase, orthoclase and slight amounts of quartz, biotite and minor hornblendes. Also, the wells NWS-4 and NWS-5 reached to Diorite at an approximate depth of 1840 m. This rock type appeared as old and young diorite intrusive in NWS-1.

Epa Formation

The thickness of Epa formation varies from a few hundred metres to 2200 m in the study area and it includes trachyandesite, trachybasalt, andesite, altered andesite and lahar. Name of this unit has been taken from the local geological map at a scale of 1:100,000 (Emami 1994 ). This formation has a separate weathering horizon at its upper surface. This unit identifies an unconformity with the overlying Valhazir formation (Fig.  3 ) (SKM 2005c ).

Valhazir formation

This formation has been introduced by Bogie et al. in ( 2000 ). Its thickness varies between 300 and 2000 m with a variable alteration zone (from 420 to 970 m). This formation includes Pliocence pre-caldera trachy-andesitic lava flows, tuffs and pyroclastic breccia as the oldest volcanic rocks identified in the study area. According to Bogie et al. ( 2000 ) these volcanic units are more fractured and affected by faulting than the younger units.

The subsurface studies identify that pyroclastics is dominant in the upper parts of the unit with lavas in lower portions (SKM 2005a , b ).

The Valhazir formation is equivalent with Qpad of the geological map of Meshginshahr (Emami 1994 ). Samples of Valhazir formation taken through the exploration well is totally altered to clay, silica and pyrite with slight amounts of chlorite and Fe-oxides. This means that the initial lithology of Valhazir is not clear, hence a term of altered volcanic is used for this formation (Eshaghpour 2005 ).

Coarser-grained pyroclastics comprise lapilli tuff and tuff breccia. However, their fine cutting size cannot be easily recognised. Lithic clasts are dominant and include a variety of andesites with either biotite or hornblende as the main mafic minerals. The andesite at the base of the formation includes euhedral phenocrysts of plagioclase and hornblende in a dark and very fine-grained matrix (SKM 2005a , b ).

Plat and Ngab formation

These units have been named by Emami ( 1994 ) and were identified in NWS-3 well, only. The main indication for the presence of these units in NWS-3 is the existence of olivine phenocrysts in the altered rocks with trachybasalts. Trachybasalts have been first reported in the stratigraphic sequence in the Ngab unit, which is indicated in the cross section of geological map presented by Emami ( 1994 ). The Ngab unit is underlined by the Plat unit (Fig.  3 ). Plat formation includes Pliocene Lahar, Crystalitic Tuffs and Andesitic Breccia with a thickness of 114 m (SKM 2005c ).

Dizu formation

The Dizu formation has been identified to be the upper stratigraphic sequence at NW Sabalan geothermal field (Bogie et al. 2000 ). It is equivalent to the Q t1 and Q t2 units (Amini 1988 ).

This formation comprises terrace deposits displaying a mixture of poorly sorted sand, granules, pebbles, cobbles and boulders with some sand intercalations. The terrace deposits show a blend of fluviatile processes, mainly during flash floods and by mass flow from the surrounding slopes (SKM 2005a ; Mousavi and Darvishzadeh 2010 ).

The rounded clasts of andesite, trachyandesite and subordinate trachydacite are dominant in this formation. The andesite includes phenocrysts of plagioclase and hornblende in a grey matrix. The trachyandesite also comprises plagioclase and hornblende phenocrysts, but further includes K-feldspar phenocrysts showing a brown matrix. The trachydacite consists of coarse phenocrysts of sanidine along with plagioclase and hornblende and displays a light grey vesicular matrix. The coarser grains are very poorly sorted and clasts range from granule to boulder size, with a sand matrix. They are uncemented and show no indication of hydrothermal alteration. However, the rare clasts of chalcedonic quartz are the erosional products of the hydrothermally altered sequences of the Valhazir formation (SKM 2005a , b ).

Geophysical surveys including Direct Current (DC) resistivity, Transient Electromagnetic (TEM) and MagnetoTelluric (MT) were conducted in the Mt. Sabalan region by the Renewable Energy Organization of Iran (SUNA) in 1998. The surveys were carried out at 212 resistivity stations over an area of about 860 km 2 . 110 of these stations were located in NW of Mt. Sabalan. These surveys resulted in five anomalies that the largest one is located at Moeil Valley (Fig.  1 b) (KML 1999a , b ; Bromley et al. 2000 ; SKM 2005a ; Noorollahi et al. 2008 ; Ghaedrahmati et al. 2013 ).

The resistivity of geological layers at shallow depths was provided by DC resistivity data, and the magnetotelluric soundings with a spacing of 50 m were employed to provide resistivity data for deeper layers. In order to provide resistivity data for those layers at a depth zone of 100–300 m and for the treatment of “static-shifts effects”, the TEM surveys have been conducted. The natural-source MT method with a frequency range of 8 kHz–0.02 Hz was employed to estimate the exploratory drilling depth. The MT method identified an area in the Moeil valley (NW Mt. Sabalan) with a low resistivity anomaly which is accessible at a depth range of 300–3000 m (Bromley et al. 2000 ; Noorollahi et al. 2008 ). Three deep exploratory wells (namely NWS-1, NWS-3 and NWS-4) were consequently drilled between 2002 and 2004 (at Pads A, B and C, Fig.  1 b). Their locations were chosen based on a 4 Ωm low resistivity anomaly zone. However, this drilling program was not successful to identify the exact location of the upflow zone (SKM 2005a ).

Subsequent interpretation of good quality MT data by SKM ( 2003 ) and Talebi et al. ( 2005 ) revealed a new location for outflow zone near the well NWS-3. The anomalous zone with low resistivity extended to southeast of NWS-1 (Pad D, E, Fig.  1 b) that suggests a location for likely upflow zone. They recommended further investigation of the upflow zone location applying low-frequency magnetotelluric data and further drilling operation in this location.

A new MT study incorporating 78 deep soundings with a frequency range of 385–0.0005 Hz was conducted by EDC (EDC 2008 ) in 2007, to determine the location of likely reservoir and possible drilling targets for the development of a geothermal power plant. The results showed a shallow resistivity anomaly as the likely centre of geothermal system on the east of Pad E and west of the young trachyandesite domes of post-caldera volcanic formation. Due to inadequate coverage of MT stations, especially, at the east of the study area, another MT survey was designed and conducted in 2009 for more identification of the geothermal system (EDC 2010 ). Based on the joint interpretation of new MT sections and two-dimensional geological models (EDC 2010 ), a final analysis was performed between years 2007 and 2009 to confirm the presence of a hotter zone in the east side of Pad D.

Ghaedrahmati et al. ( 2013 ) developed a three-dimensional invention code using a long-period MT data to obtain a realistic resistivity model. The resistivity slices of the different horizons from their model are shown in Fig.  4 . A low resistivity zone [zone (1), on the resistivity slice map related to the depths below 300 m] has extended from northwest to the centre of the area. This zone is almost parallel to the general structural trend and faults of the study area. This zone has been identified as an alteration zone due to the upward thermal flows along the faults and permeable structures. These structures have been delineated from wells NWS-1 and NWS-4 and further validated by the presence of thermal springs (Fig.  1 b).

The resistivity slices at different depths (modified from Ghaedrahmati et al. 2013 ), the black dots on maps show MT stations and the labels 1 , 2 , 3 , 4 , 5 and 6 display conductive (anomaly) zones related to geothermal resource obtained from 3-D modelling results. New location of the hotter zone (as an ellipse ) and its previously identified location by EDC ( 2010 ) (as a circle ) are also illustrated on the 4600 m depth slice map

The resistivity cross section given by EDC ( 2010 ) with a direction almost similar to that of geological section (Fig.  1 b) was considerably modified and is shown in Fig.  5 . The indicators of alteration zone identified in wells of Pad D and Pad E are also illustrated in this figure. Joint interpretation of Figs.  4 and 5 reveals that the geothermal resource is associated with the hydraulic conductivity zones (3) and (4) at an approximate depth of 4600 m. This deep conductive zone could be related to fluid flow within the zones consisting of high fracture density.

Resistivity cross section modified from EDC ( 2010 ) incorporating wells data and alteration indicators. Sm smectite, Il ilite, Ser sericite, Ep epidote, Me metamorphic

Also, two additional hydraulic conductivity zones can be highlighted in southern and south-eastern area in the 1000 m depth resistivity slice map [zones (5) and (6) in Fig.  4 , first row, right figure]. The results of 3-D modelling approximated a depth more than 1500 m for these two conductive zones (see Fig.  4 , second row, left figure). These zones are probably altered due to interaction of thermal fluids and country rocks along the main fractured zones and faults (EDC 2008 , 2010 ; Ghaedrahmati et al. 2013 ).

Hydraulic conductivity zone is one of the main indicators of hydrothermal activity in Mt. Sabalan area with resistivity values less than 20 Ωm which have been identified at elevation zones varying from 1200–2800 masl. The presence of hydraulic conductivity zones at deeper horizons suggests that the main outflow directions in Mt. Sabalan area are towards the west and north. These conductive zones have about 600–1000 m thickness below Pad D (Fig.  5 ).

In addition, the data obtained from well NWS-7 show that smectite and illite–smectite units are coincident with the hydraulic conductivity zones and also epidote unit is coincident with the increase in resistivity values (>30 Ωm). From the well NWS-8 data, it is obvious that smectite, chlorite and illite units have resistivity values less than 30 Ωm, whereas illite and smectite units are coincident with the resistivity contour lines above 30 Ωm (modified from Ghaedrahmati et al. 2013 ).

• Geochemistry

The surface geochemistry of Mt. Sabalan area was first time interpreted by Stefansson ( 1989 ) and further reported by Fotouhi ( 1995 ). Subsequently, the geochemistry of hot springs has been studied by Khosrawi ( 1996 ). Similar investigations have been carried out by Stelbitskaya and Radmehr ( 2010 ) and Kosari ( 2011 ).

The natural thermal features at NW Sabalan have been discovered as hot springs, most of which are located on the outskirts of the Moeil valley with a temperature range of between 25 and 85 °C. This area is characterised with the most thermal features in Mt. Sabalan (Fig.  1 b).

The spring waters are acid SO 4 , acid Cl–SO 4 and natural Cl–SO 4 types (Table  1 ). Hydrothermal alteration can be seen at the land surface. The main hydrothermal alteration is associated with the Valhazir formation (Rahmani 2007 ).

Springs chemistry

All thermal features in NW Sabalan arise mainly from the gravels of the Dizu formation (Fig.  1 b). There is no spring reported in downstream areas at lower elevations. Their water is characterised with a simple dilution trend indicating mixing with varying amounts of shallow groundwater (Table  1 ). They show a strong seasonal cyclic variation in flow rate; however, very slight seasonal deviation in temperature or chemistry can be seen. Despite the elevated Cl concentration, isotopic data categorised the waters to lie on the local meteoric water line. Mg concentrations exhibit a weak inverse correlation with Cl. This may suggest that geothermal fluids containing elevated Cl concentrations are diluting with the shallow groundwater aquifer consisting of high Mg concentrations.

Assuming the fact that all of the springs are not boiling, quartz geothermometer without stream loss (Fournier 1977 ; Fournier and Potter 1982 ) may be acceptable and a temperature range of 80–156 °C was calculated using this geothermometer. The Na–K geothermometer resulted in temperatures greater than 250 °C. However, elevated Ca and Mg concentrations reject application of this geothermometer. The Na–K–Ca geothermometer calculated a temperature range of 75.5–213 °C. Accordingly, recent temperature estimation applying various geothermometers by the authors is given in Table  2 . As well shown, the reservoir temperature at NW Sabalan varies between 88 and 244 °C.

According to the chemistry of the springs water and the different temperature ranges estimated by various geothermometers and also comparison them with the measured temperature of the reservoir (242 °C, in well NWS-10), it seems that the agreement between the calculated and measured reservoir temperature is somewhat close.

Reservoir chemistry

Although not given, several samples were collected from well NWS-1 in May and June 2004 and from well NWS-4 in September 2004. The samples were collected by SUNA. The samples were both untreated and acidified water. Chemical analyses of pH, Cl, HCO 3 , SO 4 , Ca, B and CO 2 were carried out in the site laboratory of SUNA and for Li, Na, K, Ca, Mg and SiO 2 in laboratory of GNS Wairakei (New Zealand). Stable isotope analysis (δ 18 O and δ 2 H) was conducted in the laboratory of GNS Wellington (Stelbitskaya and Radmehr 2010 ).

In the second stage of development and reservoir assessment, the discharged fluids from four wells, including NWS-6D, NWS-7D, NWS-9D and NWS-10D were analysed. The water samples have been classified as high chloride, neutral pH and mature liquids showing partial equilibrium with host rock. The analytical data of liquid samples comprise Ca, Na, K, Mg, Li, Fe, Mg, Mn, B, Cl, F, SiO 2 , SO 4 , CO 2 , H 2 S, HCO 3 and NH 3 concentrations in the liquid phase of Webre and Weir box samples. The steam samples were analysed for all gas components containing CO 2 , H 2 S, He, H 2 , Ar, O 2 , N 2 , CH 4 and NH 3 (Kosari and Sattari 2015 ).

Liquid chemistry

Based on SKM reports on production wells NWS-1 (SKM 2005a ) and NWS-4 (SKM 2005b ) and further interpretation by Rahmani ( 2007 ), the discharging fluid chemistry of wells NWS-1 and NWS-4 was classified as an alkaline-Ph, medium salinity and sodium chloride water. The chemistry of discharging fluid was almost identical for most of the chemical concentrations except Ca, which slightly varies from well NWS-4 (25–30 ppm) to well NWS-1 (15–30 ppm). In well NWS-1, Ca concentration decreased compared with Cl over the first week of discharging. Kosari ( 2011 ) suggested that the initial water discharged from the wells may have originated from deeper and different inflow zones with relatively different compositions apparently with higher Ca concentration.

The calculated calcite saturation of deep liquid phase in the aquifer water through wells NWS-1, NWS-4, NWS-6, NWS-7, NWS-9 and NWS-10 (Fig.  6 a, b) revealed that the deep liquid is generally super-saturated at lower temperatures than reference temperatures and under- saturated at temperatures more than reference temperatures (see reference temperatures in Table  3 ) (SKM 2004a ; Kosari and Sattari 2015 ).

Calcite saturation states of the NW Sabalan reservoir fluid ( a SKM 2004a , b Kosari 2015 )

Figure  7 shows Na–K–Mg ternary diagram (Giggenbach 1991 ) related to the springs of NW Sabalan. It is well indicated that most of the samples are plotted in the partial equilibration and mixed waters domains. Applying two geothermometers including K–Mg and Na–K (Giggenbach 1988 ) on this geo-indicator reveals that the deep fluid temperatures range is from 235 to 320 °C and between 285 and 335 °C, respectively. Table  3 shows the reservoir temperatures calculated by several geothermometers and compared with the reference temperatures (Strelbitskaya and Radmehr 2010 ; Kosari and Sattari 2015 ). This table further indicates that there is a close agreement between temperatures calculated by quartz geothermometers and the reference temperatures.

Na–K–Mg ternary diagram and geo-indicator representing equilibrium state of discharging waters from NW Sabalan wells (Kosari and Sattari 2015 )

The chemistry of discharging stream from the wells indicated that about 98% of the total gas content is CO 2 , while H 2 S, N 2 , Ar, H 2 and CH 4 form the remaining steam components (Kosari 2011 ). The temperature calculated by gas geothermometers, particularly H 2 S geothermometer changes from 269.1 to 277.1 °C (with an average of 274.2 °C), is fairly well correlated with that of Na–K geothermometer (279 °C). However, CO 2 geothermometer estimated temperatures ranging from 287.3 to 308.9 °C (having an average of 301.8 °C) which is higher than those calculated applying all the other gas and solute thermometers (Table  3 ). The elevated CO 2 concentration in deep liquid is the main reason for this overestimation (Kosari and Sattari 2015 ).

In NW Sabalan area, the magmatic intrusions are permeable due to the abundance of fractures, typically in the upper parts. Field investigations have shown that the volcanics of the Oligo-Quaternary succession made up mainly of lava at shallow depths are also permeable. Pyroclastic deposits and altered zones have medium-to-low permeability (Khosrawi 1996 ). The permeability of these rock units eases the movement of recharging water to the subsurface. However, the hydrogeology of the NW Sabalan is mostly controlled by faults.

The average annual atmospheric temperature is about 8 °C while in higher elevations, it decreases to 0 °C. The average temperature is very low from December to February and it reaches to 0 °C or even below (−15 °C in January). The maximum value of average monthly temperatures is obtained in May and April. The average monthly precipitation has been recorded in synoptic stations between 1996 and 2015. The precipitation occurs mainly in the form of snow in the winter. Based on available data, the maximum average monthly precipitation is 72.79 mm in May and its minimum is 12.14 mm in August (Fig.  8 ).

Average monthly precipitation from 1996 to 2015

Subsurface distribution of temperature and pressure

The reservoir thermodynamic data including temperature and pressure were measured from the exploration boreholes.

In a geothermal system, there is a need to specify the temperature distribution in assessing reservoir characteristic. To achieve the goal, it is necessary to measure downhole temperature under an equilibrium condition and to be described under the stable state of reservoir. To study the reservoir temperature changes vertically and horizontally, the contour plots and vertical cross sections have to be provided, accurately. These plots are useful to show how the interaction of hot and cold fluids occurs, so they are very important in developing a proper framework for the hydrogeological model of the system (Abdollahzadeh Bina 2009 ; Porkhial et al. 2015 ).

Figure  9 shows the stable temperature profiles for ten wells including NWS-1, NWS-3, NWS-4, NWS-5, NWS-6, NWS-7, NWS-8, NWS-9, NWS-10 and NWS-11. According to a measured water level equal to 2413 masl at well NWS-6, a representative ‘Boiling-Point-for-Depth’ (BPD) curve is also shown. Since, all of the temperature profiles are below the BPD curve, one may say that the reservoir in the study area contains water only. At an altitude of about 1800 masl, behind the casing of well NWS-1, the condition is near the BPD curve. It means that there is probably a two-phase fluid condition. For wells NWS-6, NWS-7 and NWS-10, the temperatures below the altitude of +1500 masl are near the BPD curve. The temperature reverse mode is slightly observed between +700 and −200 masl. Below the altitude of −200 masl at well NWS-1, +600 masl at well NWS-3 and +300 masl at well NWS-11, the temperature increases with depth. The highest temperature can be estimated at about 240–243 °C in well NWS-1 and in the altitude of about +1800 masl and also at well NWS-10 at an altitude of about 850 masl. There is a deep upflow zone between the wells NWS-1, NWS-6, NWS-7 and NWS-10, vertically and in southern direction.

Stable well temperatures for the NW Sabalan wells

By the use of measured temperatures in all exploration wells, temperature contours were plotted in a cross section along the section AB of Fig.  1 b. The result is shown in Fig.  10 a. As illustrated in this figure, the temperature increases from north to south. This means that there is possibly a high temperature upflow zone in the southern part of the area under investigation which is located below the Pad D. At a depth zone of about 1300–1750 masl, between wells NWS-6, NWS-7 and NWS-10 and NWS-1 located between Pad D and Pad A, the highest temperature of the reservoir (>240 °C) can be estimated. Also, a hot outflow from there originates and flows to SE-NW direction towards the well NWS-3. Besides, the cap rock with a thickness of about 500 m can be well recognised in the range of about 2300–1800 masl. The main flow below the drilled area covers at least a 2200 m thick convection zone approximately below the altitude 1700 masl.

Distribution of subsurface pressure and temperature for cross section AB of Fig.  1 b ( a temperature, b pressure)

Several faults, namely NW3, NW5, NE5, NNW2 and NW4 build the upflow zone for the reservoir system. Figure  10 b shows the cross section of estimated pressure using available data obtained from 10 deep wells. This cross section was built using the pressure profiles of the wells which are equivalent to the pressure formed at the major feed zones, only.

Conceptual model

Figure  11 shows the final conceptual model for the cross section AB of Fig.  1 b. This model has been developed based on the geological, geophysical, geochemical, temperature data described in the previous sections. The results of geophysical survey revealed that the geothermal reserve is associated with the hydraulic conductivity zones below the Pads E and D, 1000 m below sea level. In addition, the hydraulic conductivity zones were identified below the Pad D and E by resistivity values of less than 20 Ωm at elevation zones varying from 1200 to 2800 masl. The results of the temperature profiles near the reservoir show that the top of the reservoir is at about 1800 masl, below which there are little temperature changes in the convecting reservoir. The model includes a deep geothermal reservoir with a temperature range of 230–243 °C. Faults NW3 and NE5 possibly control the conductivity of the reservoir. Moreover, they have important role in the creation of upflow zone below the wells NWS-6, 7, 10 (Pad D) where the heat and pressure differences cause a single-phase flow travel through these faults and the other fractures by convection phenomenon. It is noteworthy to say that there is a good correlation between the presence of hot springs and faults in the study area. It seems that the upward flow takes place through faults NNW2 and NE5 and NE1 and eventually discharges to surface as hot springs. According to the results of stable temperature profiles (Fig.  9 ) and distribution of subsurface temperature (Fig.  10 a), it is remarkable to express the idea that the infiltration of meteoric water creates a shallow cold zone and this mixes with the upflow zone moving upwards to the shallower zones in subsurface. By penetrating meteoric waters to deeper zones, the geothermal system is fed and the fluid flow circle is eventually completed.

Overall conceptual model of NW Sabalan geothermal field

Conclusions

Identifying physical and chemical processes involved in a particular problem and creating an accurate conceptual model are the most critical issues in the development of a successful numerical model. An appropriate conceptual model can be used to describe the crucial features of geological situations and define the principal processes in a geothermal system. In this study, a conceptual model has been presented based on the available geological, exploration and drilling data for NW Sabalan geothermal field. The model includes a deep geothermal reservoir with a temperature range of 230–243 °C. This model can provide useful information regarding temperature, pressure, outflow zone and fluid flow circulation within the system that can be successfully used to improve the previous numerical models and provide a basis for sustainable development of geothermal field for imminent targets. However, timely updates of this conceptual model based on new surface and subsurface exploration data are critical for successful development planning, well siting and resource assessment of NW Sabalan geothermal field. Faults NW3 and NE5 possibly control the conductivity of the reservoir rocks. Furthermore, they have important role in the creation of upflow zone below the wells NWS-6, 7, 10 on pad D.

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Authors’ contributions

MS carried out the modelling and developing the results. MS also drafted the manuscript. The second and third authors, FD and YN, supervised the research and guided the interpretation of results. FD and YN also considerably edited and improved the drafts. SP advised the research and gave technical support during the data acquisition process. All authors read and approved the final manuscript.

Acknowledgements

The authors thank the School of Mining, College of Engineering, University of Tehran for supporting this research. The technical support given by Renewable Energy Organization of Iran (SUNA) during the data acquisition process is acknowledged.

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Seyedrahimi-Niaraq, M., Doulati Ardejani, F., Noorollahi, Y. et al. Development of an updated geothermal reservoir conceptual model for NW Sabalan geothermal field, Iran. Geotherm Energy 5 , 14 (2017). https://doi.org/10.1186/s40517-017-0073-0

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DOI : https://doi.org/10.1186/s40517-017-0073-0

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