Aug 2026· Geophysical Journal International· 0 citations
Abstract
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.
The electrical resistivity tomography (ERT) method is a powerful tool for inferring subsurface structures in landslide investigations. However, its effectiveness is often limited by the inherent nonuniqueness of inversion and the severe smearing artifacts caused by conventional regularization, particularly for delineating thin structures like slip zones. To address these challenges, this article proposes a unified and application-oriented constrained inversion framework for ERT that flexibly integrates multisource prior geological information. Building on established constrained inversion concepts, the framework combines structurally guided directional constraints, borehole resistivity constraints, differentiated regional constraints, and localized borehole-derived interface and dip constraints within a consistent Gauss–Newton inversion workflow. The framework integrates different types of constraints into the inversion process, including structural orientations, known resistivity points from boreholes, and predetermined interface positions. We first demonstrate the efficacy of each constraint type through numerical simulations on a theoretical landslide model. The method was then validated with a comprehensive field study at the Huangtupo landslide in the Three Gorges Reservoir area of China. In situ resistivity tests within a tunnel system provided characteristic resistivity values for key structural elements (slip zone, slip mass, and bedrock), forming the basis for the reference model. Constrained inversions were performed on four ERT survey lines by incorporating interfaces from seismic ambient noise tomography (S-ANT) and orientations from multiple boreholes and tunnels. The results show that, compared with unconstrained inversion, the proposed constrained strategy reduces inversion ambiguity associated with equivalence and suppresses the smearing effects, enabling a clearer identification of multilevel landslide structures that are typically obscured in standard ERT images. Finally, a detailed and reliable 3-D model of the landslide was constructed by integrating the interpreted 2-D profiles. The proposed constrained inversion approach mitigates the common pitfalls of standard ERT and provides a practical methodological basis for landslide characterization and stability analysis.
Fan Li, Kai Lu, Guangtian Niu et al.· IEEE Transactions on Geoscie...· 0 citations
The Upemba Rift, located in the southeastern Democratic Republic of Congo, constitutes a sedimentary sub‑basin that remains largely underexplored despite its presumed petroleum potential. Covering an area of approximately 46,519 km², it recently attracted attention during the licensing rounds launched in 2022 by the Ministry of Hydrocarbons of the Democratic Republic of Congo. However, the absence of modern seismic data as well as detailed geological and geochemical surveys significantly constrains the assessment of its hydrocarbon prospectivity. The only available information derives from the magnetic survey conducted by CGG in 1986, which has already been interpreted in various studies.
Within this context, the present study – Geophysical characterization of structures in the Upemba Graben and estimation of sedimentary thickness using the Source Parameter Imaging (SPI) method: Reprocessing and integrated analysis of magnetic data – aims to propose a preliminary sedimentary model and to shed light on the morpho‑tectonic evolution of the basin, thereby further highlighting its petroleum potential as well as the maximum sedimentary thicknesses of this intra‑cratonic basin.
The results obtained are based on an integrated methodological framework combining a multimodel numerical approach, critical and comparative analysis, and an in‑depth morpho‑tectonic interpretation, together with sediment thicknesses derived from the SPI method, emphasizing their quantitative contribution. Taken together, these elements provide a robust preliminary argument that contributes to a better understanding of the morpho‑tectonic history and sedimentary dynamics of the Upemba Graben and its petroleum system, thereby paving the way for increased interest from potential partners and renewed momentum in future exploration campaigns.
Kenny Lose Manzembele, Bavon Diemu Tshiband, Dominique Westhondo Osomba et al.· Romanian Journal of Petroleu...· 0 citations
The Khewari Block, located in the structurally complex Lower Indus Basin of Sindh, Pakistan, occupies a tectonically favorable yet insufficiently explored sector of a Mesozoic–Cenozoic rifted basin known for its hydrocarbon prospectivity. This study employs an integrated geophysical geospatial workflow to confine subsurface architecture and assess reservoir potential. Extensive interpretation of 2D seismic profiles identifies a system of NE-SW oriented normal faults, horsts, grabens, and local compressional structures, producing a wide range of trap geometries, including fault-bounded closures, rollover anticlines, and tilted fault blocks. Time and depth structure mapping refines horizon geometries and identifies structurally isolated blocks with enhanced trapping potential. Bouguer and residual gravity anomaly maps
further resolve density differences associated with uplifted carbonate platforms and sediment filled troughs, confirming segmentation of the structure based on seismic data. Surface dataset complements, such as DEM-based elevation, slope models, and land-use classification, offer further limitations on terrain accessibility, drainage systems, and operational capabilities. The integration of surface and subsurface datasets reduces the uncertainty in the interpretation process, improves the delineation of traps, and contributes to the most effective well-placement planning. The multidisciplinary approach illustrates the effectiveness of integrating seismic reflection, gravity modelling, and surface-based GIS analysis to create a coherent structural framework of frontier basins. The workflow is transferable to other data-limited regions and provides a robust foundation for evidence-based hydrocarbon exploration and decision making.
Muhammad Junaid, Muhammad Sajid, Taimoor Ahmad et al.· Rudarsko-geološko-naftni Zbo...· 0 citations
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.
Wenzhao Meng, J. Chong, Wei Wu· Urban Lifeline· 0 citations
Unsustainable rates of groundwater (GW) depletion make GW management a priority. Effective GW management is hindered by the uncertainty in the predictions of aquifer models, but the increase of geodetic surface deformation data can improve aquifer characterization. We integrate surface deformation measurements into a Bayesian inference framework to infer aquifer permeability in a poroelastic model. We demonstrate the applicability of this technique using a Nevada pumping test with both Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) surface deformation data. We infer the lateral permeability variations in the aquifer with high spatial resolution and identify the information content of each data set. For the Nevada case, a single InSAR surface deformation map provides significantly more information than multiple GPS time series. As the availability of global InSAR data continues to grow, geomechanical inversion of geodetic surface deformation data will become a valuable technique for aquifer characterization and management.
A. Alghamdi, M. Hesse, Jingyi Chen et al.· Geophysical Research Letters· 1 citation
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