Search Results (91)

In characterizing unconventional reservoirs, conventional Digital Rock Physics (DRP) has long been constrained by three fundamental bottlenecks: the trade-off between imaging resolution and field of view, challenges in reconstructing multiscale pore topology, and the prohibitive computational cost of direct numerical simulation (DNS) at the pore scale. The deep integration of artificial intelligence and rock physics has given rise to a new paradigm—Intelligent Digital Rock Physics (IDRP). This paper provides a systematic review of the evolutionary trajectory of IDRP, with a focus on how machine learning is reshaping the end-to-end workflow from imaging and segmentation to reconstruction and simulation. First, we survey image super-resolution and 3D pore structure generation techniques based on convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, elucidating their mechanisms for surpassing optical diffraction limits and incorporating macroscopic petrophysical constraints. Second, we outline algorithmic strategies for fusing multi-source heterogeneous data (e.g., Micro-CT and SEM) and representing dual-porosity or multi-continuum systems. Third, we critically examine the application of machine learning surrogates in single- and multiphase flow prediction, highlighting how physics-informed machine learning (PIML) and reinforcement learning (RL)—by embedding governing equations such as Navier–Stokes or Muskat–Leverett into loss functions—achieve both computational acceleration and physical consistency. We further identify key limitations of current IDRP approaches, including insufficient validation of generated topological realism, narrow generalization across lithologies, inadequate representation of dynamic wettability, and limited model interpretability. Finally, we propose a forward-looking roadmap centered on multimodal foundation models for rocks, coupled with neural operators and uncertainty quantification frameworks, emphasizing the critical pathways for translating IDRP into engineering digital twins for unconventional hydrocarbon development, coalbed methane production enhancement, Enhanced Geothermal Systems, and geological CO₂ storage. This review offers a comprehensive reference for researchers at the intersection of geophysics, rock mechanics, and artificial intelligence. Full article

This study presents the diagnostic and conservation work carried out on the Roman mosaic of the South cubiculum in the Latomia dei Niccolini (Marsala, western Sicily). The mosaic, decorated with polychrome tesserae featuring a kantharos motif, presented severe structural damage, including fractures, subsurface voids, and progressive material loss. To assess the causes of deterioration and design an effective conservation strategy, an integrated approach combining non-invasive geophysical and 3D survey methods was applied. Ground-penetrating radar (GPR) was selected as the main diagnostic tool because it allows high-resolution subsurface imaging while preserving the integrity of the fragile mosaic surface. By utilizing high-frequency 2 GHz antennas and complementary video inspection, a significant subsurface cavity beneath the mosaic preparation layer was successfully mapped, determining its critical relationship with the main diagonal surface fracture. Simultaneously, laser scanning and close-range photogrammetry enabled the creation of accurate 3D models supporting both documentation and restoration planning. The conservation concluded with surface cleaning, mortar consolidation, and the successful structural detachment and relocation of the compromised section onto a lightweight support for future museum display. The findings demonstrate that integrating 3D digital and geophysical data provides a quantitative, low-risk roadmap for preserving highly vulnerable archaeological floorings, moving beyond qualitative technical documentation to establish a replicable preservation framework. Full article

On 4 February 1975, the Haicheng Ms 7.3 earthquake occurred in the Liaodong Uplift, northeastern China. To investigate its seismogenic structure and deep geological environment, we acquired broadband magnetotelluric data along two intersecting profiles across the epicentral region and performed three-dimensional inversion. Two orthogonal electrical sections were then extracted from the resulting 3D resistivity model to image the crustal structure beneath the Haicheng earthquake area. The model reveals that the northern segment of the Tanlu fault corresponds to a major electrical discontinuity between the Xialiaohe Basin and the Liaodong Uplift, suggesting that it may represent a deep-seated fault zone extending into the lithosphere. Beneath the Liaodong Uplift, a prominent mid-crustal low-resistivity layer is developed, and a synform conductive body is resolved beneath the source region. The Haicheng mainshock and relocated aftershocks are mainly distributed along the interface between this conductive body and the overlying high-resistivity upper crust. In addition, the Haichenghe–Dayanghe fault is imaged as a conductive zone that connects the mid-crustal conductor with shallower crustal levels. These electrical features suggest that deep crustal fluids, possibly related to Pacific Plate subduction and craton destruction, may have migrated upward along fault zones, weakened the seismogenic fault system, and promoted earthquake nucleation. Compared with the volcanic regions of the Jilin–Heilongjiang orogenic belt, where conductive anomalies extend into the upper mantle, the Haicheng region is characterized mainly by intracrustal conductors. This contrast highlights the role of crustal-scale conductive structures in the seismogenic environment of the Haicheng earthquake and provides geophysical constraints for comparing earthquake- and volcano-related deep processes in northeastern China. Full article

Shallow shear-wave velocity structures provide useful constraints on sedimentary architecture in coastal abandoned-estuary settings, yet laterally continuous velocity information remains limited in the Paleo-Yellow River Estuary, Yancheng, Eastern China. In this study, vertical-component ambient noise recorded by a dense linear array of 102 short-period stations over 27 days was used to derive Rayleigh-wave phase-velocity dispersion curves by the modified frequency-Bessel (MFJ) method. Sequential 1D S-wave velocity models were inverted beneath moving subarrays and interpolated to construct a pseudo-2D velocity profile along the survey line. For comparison, the conventional spatial autocorrelation (SPAC) method was applied to the same dataset using the same subarray length, usable frequency band, and inversion-layer parameterization. The MFJ method produces clearer and more concentrated fundamental-mode dispersion energy and suppresses high-frequency crossed artefacts more effectively than SPAC, which improves the stability of dispersion picking. The resulting velocity model reveals a laterally heterogeneous shallow sedimentary system and outlines a U-shaped low-velocity zone that is spatially consistent with the mapped paleochannel boundary. These results indicate that MFJ-based ambient-noise imaging can provide useful complementary geophysical constraints for paleochannel mapping and shallow sedimentary characterization in coastal abandoned-estuary settings. Full article

The archaeological remains of the Roman villa at Madonna del Piano are situated at the foot of the hill on which the municipality of Castro dei Volsci (Italy) now stands. This crucial region guarantees access to the coastal areas and is situated between the Via Latina and the Amaseno Valley. The first signs of the existence of archaeological structures can be seen in several historic aerial images, where anomalies are readily visible. The remnants of an imperial-era villa with varying periods of occupation were discovered during excavations carried out between the mid-1980s and the early 1990s. These remnants can now be identified in three distinct complexes that were previously linked as a component of a single complex. Given the site’s importance, a research project based on numerous studies and multi-scale approaches was launched in 2024 to collect new data and fill any knowledge gaps. The technique focused on the villa and its surroundings using LiDAR scans, geophysical prospections, 3D surveys of visible structures, archival research, and historical aerial photogrammetry. The findings provide new insight into the settlement by clarifying and elucidating its structure, relationships, and roles of the three complexes, and placing the results within a broader geographical context. Full article

Seafloor massive sulfide (SMS) deposits formed by hydrothermal circulation generate measurable self-potential (SP) anomalies in seawater, providing an effective geophysical indicator of sulfide mineralization. In this study, a remotely operated vehicle (ROV)-borne SP survey was conducted at the Yuhuang hydrothermal field on the Southwest Indian Ridge to investigate the spatial distribution of SMS mineralization. The survey operated at a near-bottom altitude of approximately 10 m, substantially lower than that typically achieved by autonomous underwater vehicles (AUVs) or towed systems, enabling high-resolution data acquisition with improved signal quality. To efficiently discretize complex seafloor topography under irregular data coverage, an adaptive octree mesh was employed, enabling computationally efficient three-dimensional inversion over a large survey area and recovery of the subsurface source current density distribution. The inversion results resolve a main anomaly zone spatially correlated with known SMS mineralization, as well as an additional anomaly zone that was not resolved by previous surveys and suggests potential mineralization. Anomalies associated with known mineralization show good spatial agreement with independent near-bottom observations and drilling results. The results demonstrate that ROV-borne SP surveying combined with adaptive meshing and three-dimensional inversion provides a reliable approach for imaging SMS mineralization in deep-sea environments. Full article

Model-based polarimetric SAR (PolSAR) algorithms for bio- and geophysical parameter estimation rely on the effective separation of the combined scattering response of vegetation canopies and the soil surface through physically based models. However, the interpretation of polarimetric features derived from physical models is still subject to some ambiguity. Another strategy for complementing the model-based approaches for scattering mechanisms characterisation deals with the separation of the polarised and depolarised contributions of the PolSAR data according to their degree of polarisation. In this paper, we propose a two-component decomposition for estimating the depolarised and polarised components within the target and their corresponding coherency matrices. The method requires the previous calculation of the backscattering powers given by the model-free three-component (MF3C) decomposition, which in turn relies on the 3-D Barakat degree of polarisation. This quantitative information allows us to construct an inversion algorithm to retrieve the proportion of the polarised and depolarised contributions for all the elements of the observed coherency matrix under the reflection symmetry assumption. In essence, the proposed decomposition can be regarded as an extension of the MF3C method and, as a consequence, it enables the exploitation of both model-free and model-based approaches by using a physical rationale driven by the capability of the 3-D Barakat degree of polarisation. Therefore, practical applications can benefit from this approach as the retrieval of target parameters could presumably be done in a more accurate way by directly applying existing scattering models to both components. Indoor multi-frequency datasets acquired over three vegetation samples from the European Microwave Signature Laboratory (EMSL) and P-, L-, and C-band AIRSAR images over a boreal forest in Germany have been employed for testing the proposed decomposition. Performance analysis was performed using different polarimetric tools applied to the outcomes of the two-component decomposition, namely, the eigendecomposition and the copolar cross-correlation analysis of polarised and depolarised components, as well as histograms and a correlation analysis among backscattering powers. Overall, it has been observed that the method outputs are consistent with the theoretical expectations for the depolarised and polarised scattering components for a wide range of scenarios and sensor frequencies. Full article

Shallow subsurface structure detection in coastal zones serves as a critical foundation for resource development and engineering construction. However, conventional geophysical methods exhibit significant limitations in land–sea transition zones, where pronounced “boundary effects” create substantial “exploration gaps” due to difficulties in merging terrestrial and marine datasets. To achieve truly seamless subsurface imaging across the coastal boundary, this study develops and implements an integrated cross-boundary survey approach utilizing nodal seismometers and seismic ambient noise. At Dong’ao Island, Zhuhai, we deployed a comprehensive seismic profile spanning hillside, sandbeach, and seafloor environments to evaluate the method’s applicability in complex coastal settings systematically. Results demonstrate substantially stronger ambient noise energy in submarine environments compared to terrestrial settings. All stations recorded abundant and stable high-frequency (>1 Hz) noise signals, which are adequate for shallow subsurface imaging. Rayleigh wave dispersion curves extracted via the advanced Frequency-Bessel transform method enabled inversion of a continuous 2D shear-wave velocity profile along the survey line. Bedrock interface depths determined using the Horizontal-to-Vertical Spectral Ratio (HVSR) method showed remarkable consistency with the bedrock morphology revealed by the shear-wave velocity structure, validating the reliability of our approach in coastal environments. This research successfully demonstrates the feasibility of seismic ambient noise imaging as a bridging technique for land–sea exploration, providing an efficient, environmentally friendly, and continuous technical solution to overcome coastal zone exploration challenges. Full article

Saline water intrusion poses a major threat to groundwater security in arid and semi-arid regions, reducing freshwater availability and challenging sustainable water resource management. Accurate delineation of the fresh-saline water interface is therefore essential; however, conventional hydrochemical and laboratory-based assessments remain costly, invasive, and spatially limited. Resistivity methods have long been used to infer subsurface salinity, as low resistivity typically reflects clay-rich saline water and higher resistivity reflects freshwater-bearing sand or gravel. Yet, resistivity values for similar lithologies frequently overlap, causing ambiguity in distinguishing fresh and saline aquifers. To overcome this limitation, Dar–Zarrouk (D–Z) parameters are often applied to enhance hydrogeophysical discrimination, but previous studies have relied exclusively on one-dimensional (1D) D–Z derivations using vertical electrical sounding (VES), which cannot resolve the lateral complexity of alluvial aquifers. This study presents the first application of electrical resistivity tomography (ERT) to derive two- and three-dimensional D–Z parameters for detailed mapping of the fresh-saline water interface in the alluvial aquifers of Punjab, Pakistan. ERT provides non-invasive, continuous, and high-resolution subsurface imaging, enabling volumetric assessment of aquifer electrical properties and salinity structure. The resulting 2D/3D models reveal the geometry, depth, and spatial continuity of salinity transitions with far greater clarity than VES-based or purely hydrochemical methods. Physicochemical analyses from boreholes along the ERT profiles independently verify the geophysical interpretations. The findings demonstrate that ERT-derived 2D/3D D–Z modeling offers a cost-effective, scalable, and significantly more accurate framework for assessing fresh-saline water boundaries. This approach provides a transformative pathway for sustainable groundwater monitoring, improved well siting, and long-term aquifer protection in salinity-stressed alluvial regions. Full article

Ancient underground voids present non-trivial hazards to urban redevelopment, particularly where groundwater conditions change during construction. We propose a staged, groundwater-aware workflow that integrates in-void mapping with area-scale geophysics and explicitly links water state to imaging performance. Following exposure of an undocumented masonry tunnel in a foundation pit in Wuhan (China), we acquired underwater CCTV and sonar during water-filled conditions, and, after drainage, collected ground-penetrating radar (GPR, 75–150 MHz) and ultra-high-density electrical resistivity tomography (UHD-ERT, 1 m electrode spacing) data. Calibration lines over the breach anchored the depth/geometry and reduced interpretational non-uniqueness. Analytical estimates using Archie-type and CRIM relations, together with observed signatures, indicate that drainage increased resistivity and reduced electromagnetic attenuation, improving UHD-ERT contrast and GPR penetration. The merged evidence resolves a straight-walled arch (~1.8 m wide × ~1.9 m high) at ~4–5 m depth with a sealed end 4 m south of the breach. Sonar confirms a northward segment measuring 45 ± 2 m to a sealed wall; a GPR void-type anomaly at ~57 m along trend represents a candidate continuation that remains unverified with current access. Within the resolution and sensitivity of the 2D survey, no additional voids were detected elsewhere on site. This case demonstrates that coupling in-void CCTV/sonar with post-drainage GPR and UHD-ERT, organized by hydrologic stage, yields engineering-grade constraints for risk control. The workflow and boundary conditions provide a transferable template for water-influenced, urban environments. Full article

While 3D seismic reflection is well established in hydrocarbon exploration at the kilometer scale in relatively simple offshore settings, its application to shallow faulting in continental basins is rare, owing to difficulties in adapting acquisition and processing to rugged terrains and complex near-surface conditions. We present the first high-resolution 3D seismic study of a seismogenic fault in a structurally complex intramontane basin at depths < 200 m. The survey focuses on the Pantano–Ripa Rossa Fault, ruptured during the 1980 Mw 6.9 Irpinia earthquake, the largest Italian event of the past century. This fault cuts across the Pantano di San Gregorio Magno, a small basin filled with Quaternary sediments and showing modest cumulative displacement. Our results demonstrate that in such environments, where morphotectonic analysis and 2D geophysics provide limited constraints, high-resolution 3D seismic imaging is crucial to resolve fault geometry and to assess surface-faulting hazard. The 3D volume reveals a ~35–40 m wide intra-basin deformation zone beneath the 1980 rupture, composed of synthetic and antithetic splays, and highlights lateral variations in fault geometry and stratigraphy. Deformation is distributed and complex, with fault-controlled depocenters, variable sedimentary architectures, and rapid basement-depth changes—features unresolved by 2D data. We infer that the Pantano–Ripa Rossa Fault is relatively young, active since the late Middle Pleistocene, and developed in the hanging wall of the NE-dipping southern basin-bounding fault, challenging previous models that located the master fault along the northern basin margin. Full article

Monitoring environmental risks in operational landfills that contain closed cells requires non-invasive techniques capable of accurately characterizing subsurface contaminant dynamics. Electrical Resistivity Tomography (ERT) was selected because it enables continuous imaging across capped cells without intrusive drilling, with high sensitivity to the strong conductivity/resistivity contrasts that differentiate leachate (very low resistivity) from landfill gas or dry waste (high resistivity). This study employed ERT to spatially characterize contaminant distribution in closed cells within a landfill system in the Caribbean region of Colombia. Fifteen geophysical survey lines were acquired using Wenner, Dipole–Dipole, and Gradient arrays and processed through 2D, 2.5D, and 3D inversion models. The results revealed extensive low-resistivity zones (<2.1 Ω·m) in the southeastern sector, interpreted as leachate accumulations, some reaching the surface. Conversely, high-resistivity anomalies (>154 Ω·m) were identified in the southwestern area, associated with potential biogas pockets. Although these high-resistivity volumes represent <1.1% of the total modeled volume, their location and depth may pose geoenvironmental risks due to internal pressure build-up and preferential migration pathways. Existing leachate and gas collection systems showed adequate performance, though targeted corrective actions are recommended. ERT proved to be a precise, scalable, and cost-effective method for mapping subsurface contamination, offering critical insights for post-closure landfill management in tropical settings. Full article

In recent decades, technological developments in archaeological geophysics have led to growing data volumes, so that an important bottleneck is now at the stage of data interpretation. The manual delineation and classification of anomalies are time-consuming, and different methods for (semi-)automatic image segmentation have been proposed, based on explicitly formulated rulesets or deep convolutional neural networks (DCNNs). So far, these have not been used widely in archaeological geophysics because of the complexity of the segmentation task (due to the low contrast between archaeological structures and background and the low predictability of the targets). Techniques based on shallow machine learning (e.g., random forests, RFs) have been explored very little in archaeological geophysics, although they are less case-specific than most rule-based methods, do not require large training sets as is the case for DCNNs, and can easily handle 3D data. In this paper, we show their potential for geophysical data analysis. For the classification on the pixel level, we use ilastik, an open-source segmentation tool developed in medical imaging. Algorithms for object classification, manual reclassification, post-processing, vectorisation, and georeferencing were brought together in a Jupyter Notebook, available on GitHub (version 7.3.2). To assess the accuracy of the RF classification applied to geophysical datasets, we compare it with manual interpretation. A quantitative evaluation using the mean intersection over union metric results in scores of ~60%, which only slightly increases after the manual correction of the RF classification results. Remarkably, a similar score results from the comparison between independent manual interpretations. This observation illustrates that quantitative metrics are not a panacea for evaluating machine-generated geophysical data interpretation in archaeology, which is characterised by a significant degree of uncertainty. It also raises the question of how the semantic segmentation of geophysical data (whether carried out manually or with the aid of machine learning) can best be evaluated. Full article

With strategic mineral exploration extending to deep and complex geological settings, traditional methods increasingly struggle to dissect metallogenic systems and locate ore bodies precisely. This synthesis of current progress in muon imaging (a technology leveraging cosmic ray muons’ high penetration) aims to address these exploration challenges. Muon imaging operates by exploiting the energy attenuation of cosmic ray muons when penetrating earth media. It records muon transmission trajectories via high-precision detector arrays and constructs detailed subsurface density distribution images through advanced 3D inversion algorithms, enabling non-invasive detection of deep ore bodies. This review is organized into four thematic sections: (1) technical principles of muon imaging; (2) practical applications and advantages in ore exploration; (3) current challenges in deployment; (4) optimization strategies and future prospects. In practical applications, muon imaging has demonstrated unique advantages: it penetrates thick overburden and high-resistance rock masses to delineate blind ore bodies, with simultaneous gains in exploration efficiency and cost reduction. Optimized data acquisition and processing further allow it to capture dynamic changes in rock mass structure over hours to days, supporting proactive mine safety management. However, challenges remain, including complex muon event analysis, long data acquisition cycles, and limited distinguishability for low-density-contrast formations. It discusses solutions via multi-source geophysical data integration, optimized acquisition strategies, detector performance improvements, and intelligent data processing algorithms to enhance practicality and reliability. Future advancements in muon imaging are expected to drive breakthroughs in ultra-deep ore-forming system exploration, positioning it as a key force in innovating strategic mineral resource exploration technologies. Full article

The increasing demand for critical minerals is forcing the mineral exploration industry to search for deposits beneath deeper cover and over larger areas. MobileMT, an airborne passive, broadband, total-field AFMAG-class system, couples three-component measurements of airborne magnetic field variations with a remote electric-field base station to image electrical resistivity from the surface to depths of >1–2 km. We present a workflow that integrates MobileMT data with the parallelized, adaptive finite-element 2.5D open-source inversion code MARE2DEM, accompanied by automated mesh generation procedures, to create a rapid and scalable workflow for deep ore exploration. Using this software on two field trials, we demonstrate that (i) high-frequency (>4 kHz) data are essential for recovering not only shallow geology but also, when combined with low frequencies, for refining deep structures and targets and that (ii) base station effects modify the shape of the apparent conductivity curve but have negligible impact on the inverted sections. The proposed workflow is a reliable and effective approach for identifying mineralization-related features and refining geologic models based on data from extensive airborne geophysical surveys. Full article