Representing shoreface reservoirs with a rule-based facies model: The GEOPARD algorithm

3D models from geological maps: strengths and weaknesses from the Pasubio Massif (Southern Alps)

3D geological models provide digital representations of subsurface architecture and are increasingly applied in both academic research and industry. Their construction can follow implicit or explicit approaches, depending on data availability and final modelling applications. While 3D models based on subsurface data are traditionally employed in several fields of study, models derived from outcrop data are increasingly being developed. In this study, newly acquired geological mapping data from the Pasubio Massif (Southern Alps, northern Italy), covering an area of ~36 km² within the geographic extent of the CARG sheet 081 “Rovereto”, were used to develop and test an iterative explicit workflow for 3D geological modelling using Move software. The workflow is based on the construction of a structured grid of geological cross-sections, followed by the interpolation of stratigraphic horizons and fault surfaces through Ordinary Kriging. Field observations indicate that the lithostratigraphic units cropping out in the study area form a gently NW-dipping monoclinal structure, exhibit overall constant thicknesses and are affected by the Schio-Vicenza fault system. Model validation first involved a qualitative comparison between mapped geological boundaries and faults with those obtained from the intersection of the modelled surfaces with the topography (DEM), followed by thickness maps evaluation as an internal consistency check. Where inconsistencies emerged, cross-sections were refined and the model was iteratively updated until geological geometries and thickness trends became consistent with field-mapped evidence. A final quantitative assessment was then performed by analysing thickness deviations from mean unit thicknesses and by measuring the spatial overlap between mapped and model-interpolated geological boundaries and fault traces. Comparison between the preliminary and validated models, supported by thickness deviation statistics, indicates that most residual discrepancies are constrained within ±10–20% of expected thickness values. The results highlight the critical role of iterative validation in ensuring geologically robust 3D models, emphasising common sources of uncertainty in explicit geomodelling workflows. This study provides a methodological framework for producing reliable 3D geological models in data-poor regions, complementing the recently published ISPRA guidelines for the organisation and standardisation of model datasets and supporting future applications in academic research and regional or national geological surveys.

Probabilistic imaging of sedimentary basins using spatial clustering of magnetotelluric model ensembles change-points

Mapping the internal structure of sedimentary basins, including the depth to basement, is valuable for a variety of geological applications, particularly the identification of natural resources such as groundwater and minerals, or of the geological structures associated with them. The magnetotelluric (MT) method has proven to be a reliable technique for imaging complete sedimentary sequences, especially in areas where thick sedimentary cover makes imaging challenging. However, due to the high regularisation required by MT inversion procedures, it is limited in its ability to precisely locate geological interfaces, which is crucial for exploration. Building on previous work where we imaged the basement using interface probabilities derived from 1D probabilistic inversion of MT data, we present a new method which allows for the simultaneous classification of multiple interfaces across an entire survey, incorporating constraints on the spatial relationships between models. As a result, we obtain spatially consistent model ensembles and classified interfaces corresponding to transitions between layers of consistent electrical resistivity. This classification effectively reduces the size of the model ensemble, decreasing uncertainty in the estimated depth of the interfaces of interest. We apply this method to the Eucla sedimentary basin in Western Australia, analysing 550 MT sites distributed across 12 profiles. The ensemble clustering clearly identifies three interfaces which are consistent with the sedimentary succession expected from this basin. The results show great consistency across all the survey, providing valuable insights into the geological setting of the area. This research demonstrates the capability to reliably image the structure of a sedimentary basin using MT, within a workflow that integrates probabilistic inversion and model ensemble classification.

Geospatial Site Amplification Model with Geotechnical Adjustments for the Basin and Range Province of the Western United States

Ground-motion model (GMM) site terms developed from continuously available geospatial data and enhanced with site-specific measured geotechnical data have been demonstrated to be an effective approach for site term development in California (Roberts et al., 2025; Roberts et al., 2026). This study extends this methodology to the Basin and Range physiographic province in the western United States. Ground-motion data from 16,852 recordings at 433 stations in the Next Generation Attenuation-West3 database (Buckreis and Stewart, 2025) are used for model development. A base site term model is developed using mapped geospatial variables (e.g., sediment thickness, elevation, and surficial geologic units) to capture trends in soil stiffness and basin effects. The target site amplifications of peak ground acceleration, peak ground velocity, and pseudospectral accelerations from 0.01 to 10 s are decomposed from the residuals of the Boore et al. (2014) (BSSA14) GMM. A linear mixed-effects regression model is then developed to predict each target site amplification using geospatial variables. The resulting model is a linear geospatial site term that provides a consistent site term for all locations in the region. The geospatial site term shows a substantial reduction in site-to-site variability; on average, an 8.5% reduction is achieved compared with BSSA14. This base geospatial model is then enhanced with local geotechnical information where available. The proposed geotechnical site term adjustment models are developed using microtremor horizontal-to-vertical spectral ratio data (Anbazhagan et al., 2025) and measured VS30 data (Buckreis and Stewart, 2025). Additional reductions in the site-to-site variability are achieved when the geotechnical adjustments are applied. This study illustrates that the approach of incorporating broadly available geospatial data before site-specific geotechnical data is effective in regions outside of California and demonstrates how geospatial site amplification models with explicit uncertainty characterization can be developed where ground-motion data are sparse.

Characterization of stratigraphic interfaces and embedded limestone cavities using integrated geophysical and geological data

Accurate identification of stratigraphic interfaces and embedded cavities is critical for geological modeling, engineering design, and infrastructure maintenance. However, integrating geophysical and geological data remains challenging due to discrepancies in spatial resolution, signal-to-noise ratio, and geological representation. This study presents a stratigraphic U-Net model that combines geophysical and geological information to improve subsurface interpretation. An initial U-Net model is trained using a preliminary P-wave velocity model derived from conventional seismic inversion. The predicted interfaces, however, show noticeable discrepancies with borehole observations because of manual velocity picking and limited borehole data. To address these limitations, the stratigraphic U-Net model is retrained using the initial predictions together with both drilled and synthetic boreholes. This iterative strategy significantly improves the identification of stratigraphic interfaces, particularly at greater depths. The stratigraphic U-Net model is further integrated with an opening detection model to identify embedded cavities within geological layers. Model evaluation shows that geological diversity captured by borehole data contributes more to prediction accuracy than simply increasing the borehole number. A field case study demonstrates that the proposed framework accurately identifies stratigraphic interfaces and subsurface cavities, providing a robust workflow for reducing geological uncertainty and improving the spatial coverage and reliability of subsurface characterization.

Mountain geoecosystems. GIS modelling of rockfall and protection

A Novel Similarity-Based Upscaling Method for Highly Heterogeneous Reservoir Models

The paper presents a novel method for upscaling geological model grids of hydrocarbon reservoirs, particularly well-suited to highly heterogeneous formations. To determine the optimal number of layers in the upscaled model, the Lorenz coefficient was used. A rapid decline in its value was interpreted as a significant loss of geological information, providing a quantitative criterion for limiting vertical coarsening. Hydraulic Units (HU) were calculated based on the analysis of Reservoir Quality Index (RQI) and Flow Zone Indicator (FZI) parameters. These units served as the basis for transforming the reference model into a rock-type model. Advanced image comparison techniques based on deep artificial neural networks were used to compute the similarity between adjacent layers. This enabled the identification and merging of geologically similar layers while preserving the reliability of the upscaled model. The proposed method outperforms conventional upscaling approaches in terms of both accuracy and computational efficiency. Notably, it eliminates the need for time-consuming optimisation procedures, which are commonly required in standard workflows. Furthermore, the developed algorithm is grounded in robust theoretical principles related to reservoir rock classification and allows continuous improvement of results via retraining or replacement of the image analysis module.