Search Results (1,082)

Multisource remote sensing and Earth observation (EO) products provide scalable covariates for regional mineral prospectivity mapping, but their integration with incomplete and preferentially sampled occurrence records can produce biased prediction maps. We present a bias-aware machine learning workflow for porphyry copper prospectivity mapping that integrates satellite-derived alteration proxies, topographic variables, regional geology, structural context, and accessibility-related EO layers on a harmonized 1 km grid. The workflow separates remote sensing/geological predictors from survey-effort proxies and combines this decomposition with positive-unlabeled learning, stacked ensembling, rank-optimized blending, fold-wise calibration, and spatial block cross-validation. The case study covers the eastern Central Asian Orogenic Belt (CAOB) and uses porphyry Cu occurrences together with covariates derived from ASTER short-wave infrared information, Landsat 8 reflectance, SRTM topography, VIIRS night-time lights, GHSL population data, geological units, and active fault information. Across held-out spatial folds, the final RO-BAB ensemble provides a modest but exploration-relevant improvement in ranking relative to the all-covariate XGBoost baseline, increasing PR-AUC from 0.0297 to 0.0364 and recovering 26.75% of known deposits within the top 5% of ranked cells. The resulting maps delineate coherent remote sensing-supported prospective corridors while exposing regions where predictions may be influenced by historical accessibility and recording effort. The study demonstrates how machine learning that accounts for sampling bias can improve the reliability and interpretability of remote sensing mineral prospectivity products in the presence of only reference data. Full article

The Tajik Basin is located on the eastern edge of the Central Asian segment of the Tethyan tectonic domain. The basin underwent intense tectonic transformation during the Himalayan period, resulting in complex structural styles, unclear original sedimentary characteristics and oil and gas geological conditions, and a complex process of oil and gas accumulation, which restricts the further evaluation of the basin’s exploration potential. Studying the Tajik Basin in the macro background of the Tethys tectonic domain, the tectonic sedimentary evolution of the Tethys tectonic domain has a significant effect on the basin’s tectonic evolution, sedimentary characteristics, and oil and gas accumulation conditions. The Tajik Basin has gone through four stages of tectonic evolution: the Late Permian to Triassic was the stage of back arc foreland basin; the Jurassic period was the stage of back arc extensional faulting depression; the Cretaceous–Paleogene period was the stage of depression basins; and the Neogene is the stage of the regenerated foreland basins. Through field geological surveys and analysis of outcrop samples, it has been determined that the Tajik Basin has developed three sets of source rocks: the Middle and Lower Jurassic, Cretaceous, and Paleogene. Among them, the organic matter abundance of the Middle and Lower Jurassic is relatively high, most of them are in the mature stage, and they are primarily gas-generating source rocks. The Cretaceous and Paleogene source rocks are mainly oil generating and in a low-mature state. There are four sets of reservoirs developed in the Tajik Basin: Middle-Upper Jurassic carbonate rocks, Lower Cretaceous clastic rocks, Upper Cretaceous carbonate rocks and Paleogene carbonate rocks. Comprehensive research shows that the Tajik Basin mainly develops three types of oil and gas reservoirs: Jurassic carbonate gas reservoirs, distributed in the southwestern Gissar Uplift and Surhan Depression in the western part of the basin; Paleogene carbonate reservoirs, distributed in the southern Vakhsh Depression and the eastern Kuliabu Depression; and multi layer–multi lithology oil and gas reservoirs, distributed in the northern Dushanbe Depression. The primary controlling factor for the three types of oil and gas reservoirs is tectonic movement, which forms traps and simultaneously reshapes the reservoirs, ultimately leading to effective accumulation of oil and gas. The distribution of oil and gas in the Tajik Basin is characterized by “west gas and east oil, west more and east less, west pre-salt and east post-salt, and pre-salt gas and post-salt oil”. Affected by the regional tectonic movements of the Tethys rich oil and gas tectonic domain, the basin has high-quality hydrocarbon source rocks, reservoirs, and cap rock conditions. The pre-salt Jurassic has the potential to form large natural gas reservoirs, while the post-salt Cretaceous and Paleogene still have further potential for exploration. Full article

The Changbai Mountain area, the largest Cenozoic intraplate volcanic field in eastern China, features abundant high-temperature hot springs and high geothermal potential. However, the genesis and aggregation patterns of its geothermal systems remain poorly understood. This study recalculates crustal and residual deep/mantle heat- flow components along a representative profile and synthesizes published geological, geophysical, geochemical, and geothermal evidence to characterize the main geothermal system elements, including caprock, reservoirs, water source, and migration pathways. Controlling factors are examined from three dimensions: deep dynamics, magmatic heat source, and fault characteristics. Results reveal a “Cold crust–Hot mantle” thermal structure. The heat-flow calculation indicates that crustal radiogenic heat contributes approximately 40% of the surface heat flow, implying a dominant deep heat contribution. The available evidence suggests the presence of potential hydrothermal reservoirs in carbonate and clastic rocks, possible HDR targets in deeper metamorphic rocks, and locally effective basaltic sealing units. Fault systems and meteoric recharge likely control fluid circulation. Geothermal systems are controlled by mantle upwelling and lithospheric thinning due to western Pacific Plate subduction, multi-source heat coupling, effective caprock sealing, and fault-controlled water–heat conduction. These results provide a conceptual framework for future geothermal exploration and testing. This study elucidates the aggregation patterns and genetic mechanisms, providing a theoretical basis for exploration and development. Full article

The Jiaduoling area is located in the northern segment of the Southwest Sanjiang Metallogenic Belt, a region characterized by complex geological structures and abundant mineral resources. This study systematically identifies the spatial correlation between subsurface magnetic bodies and tectonic structures by utilizing 1:50,000 high-precision aeromagnetic data. Advanced processing techniques—including upward continuation, vertical derivatives, total gradient modulus, and Euler deconvolution—were integrated to refine the structural framework and clarify the mechanisms of fault-controlled mineralization. The results indicate that the aeromagnetic anomaly pattern is predominantly governed by NW-trending faults. Specifically, the deep-seated major fault F₁ (with a calculated depth exceeding 3 km) served as the primary migration channel for ore-forming fluids, while secondary faults created localized ore-hosting spaces. Physical property analysis reveals a significant magnetic contrast, where Mesozoic intermediate-acid magmatic rocks act as the essential source for mineralization, providing both material and thermal energy for the formation of porphyrite-type iron deposits. Based on these findings, a three-dimensional “aeromagnetic anomaly-structural framework-mineralization” correlation model was established. Finally, two high-potential metallogenic prospective zones (P1 and P2) were delineated, providing precise geophysical evidence and strategic guidance for regional mineral exploration and the targeting of concealed ore bodies. Full article

The mineral system model formalizes the critical geological processes and mappable parameters that control ore formation, which can then be translated into spatial predictors used as input features in machine learning (ML)-based mineral prospectivity mapping (MPM). In most MPM studies, exploration evidence features are indeed derived from the mineral system model of the targeted deposit type. However, not all features produced in this way are necessarily informative or favorable for prospectivity analysis. This challenge can be addressed by using feature selection frameworks to identify the most relevant features before applying ML and deep learning (DL) algorithms for mathematical integration. To address this need, this study employs an unsupervised variational autoencoder (VAE) framework to evaluate and rank exploration evidence layers. The VAE quantifies feature importance through a systematic strategy that measures the sensitivity of reconstruction-error components, mean squared error (MSE), mean absolute error (MAE), and Kullback–Leibler (KL) divergence, to individual feature variations. In this way, the VAE ranks the exploration features and helps to identify those that are the most useful for prospectivity mapping. The proposed approach was applied to a real geo-dataset from a porphyry copper district in Iran. Based on the conceptual model of porphyry copper mineralization, 15 evidence layers were generated, including proximity to phyllic, argillic, propylitic, iron oxide, and silicification alteration zones; proximity to intrusive rocks, faults, and fault intersections; and geochemical maps of Cu, Mo, Sb, Pb, Zn, As, and W. The VAE-based ranking indicated that evidence layers related to hydrothermal alterations, intrusive rocks, and faults were the most influential exploration features, whereas geochemical evidence layers showed lower relative importance. Based on this evaluation, two modeling scenarios were considered: in the first, all available features were used, and in the second, only the features selected by the VAE framework were included. In both cases, the final prospectivity model was produced by an autoencoder (AE). For comparison, the prediction-area (P–A) plots of the two prospectivity models were generated using 14 known mineral occurrences as positive ground-truth labels, indicating that the model based on the selected features achieved a higher prediction rate (80%) than the model based on all features (72%). These results demonstrate that the evidence layers derived from the mineral system approach can benefit from unsupervised VAE-based evaluation, leading to improved performance of the prospectivity modeling. Full article

Min County experiences intense debris flow activity due to extreme weather and geological events. This study analyzes debris flow activity in Min County using GIS spatial analysis, time-series statistics, correlation analysis, periodic fitting, and field investigations across four event-based key periods (2002, 2012, 2013, and 2020). Long-term meteorological records (1951–2020) are introduced to support climatic trend analysis. Results indicate that stratigraphic lithology and fault tectonics control about 85–90% of the spatial distribution of debris flows, while extreme short-duration rainstorms trigger large-scale outbreaks and strong earthquakes further intensify activity. The high-occurrence cycle of debris flows (7–8 years) does not fully align with the annual wetness cycle (12 years). On a short time scale (years to decades), extreme earthquakes and rainstorms exert more significant impacts than normal precipitation patterns. This study preliminarily infers potential future peak periods of debris flows in Min County, with uncertainty from climate fluctuations and uncertain seismic events considered. The coupled mechanism of seismic weakening and rainfall triggering, together with lag-time characteristics, is revealed to support disaster prevention and mitigation. Full article

The Tonglushan ore field is an important component of the polymetallic mineralization belt in the middle and lower reaches of the Yangtze River in China. The skarn-type Cu, Fe, Au, and Mo molybdenum deposits are mainly developed in the contact zone between the rock mass and the strata, as well as in the contact zone between residual and capturing bodies in the rock body. The distribution of ore bodies is controlled by faults and strata, but there is a lack of large-scale geophysical information on the contact relationship between the ore-forming geological body and the host rock and on the deep spatial morphology of the ore-forming structure and intrusion rock. The study uses the JS-WEM2 wide-field electromagnetic instrument and the RS230 spectrometer to conduct the ground frequency domain electromagnetic and radiometric spectrometry measurements on four profiles. The measurement results indicate that the fault distribution in the Tonglushan ore field is predominantly in the NW-trending and NE-trending directions. The NW-trending Tonglushan–Lijiashan fault (F2) is a steeply dipping fault; the NE-trending faults are minor, with steep dips, generally extending no deeper than −1000 m. The Tonglushan stock exhibits the northeastward uplift, characterized by southward overlap and southeastward dip. The deep resistivity is greater than 3000 Ω·m, while the resistivity below −1000 m is less than 2000 Ω·m due to the fault influence. The ore bodies are mainly distributed along the contact zones where variations in the occurrence of the rock intersect with the strata. On resistivity profiles, these zones show the gradient variation in resistivity and the distorted shape of the resistivity contour line. The radioactive element contents of wall rock above the ore bodies are characterized by high U, high Th, and low K. The Wide-Field Electromagnetic Method (WFEM) can effectively detect the distribution and morphology of rocks and faults, and combined with the radioactive characteristics of geological bodies, it can effectively identify concealed faults and the favorable mineralization target areas. Novelty: The study combines the WFEM with radiometric measurements to reduce uncertainty in exploration compared to using only one method. It improves the detection accuracy and target identification ability of deep hidden ore bodies, providing the new technical method for deep mineral exploration in complex structural areas. Full article

Orthorhombic normal fault networks in sedimentary basins and their formation mechanisms are of significant geological importance. Orthorhombic normal fault networks and planar H-shaped normal fault networks (HNF) developed in distinct locations along the Linshang dextral transtensional fault, with the HNF on the eastern hanging wall and the orthorhombic normal fault networks on the western footwall. Using 2D and 3D seismic data, we investigated the geometry, evolution, and formation mechanisms of these fault networks within the regional tectonic context. The HNF consists of systematically arranged E–W-trending normal faults and N–S-trending cross normal faults. The orthorhombic normal fault networks comprise four sets of normal faults. Expansion indices and balanced cross-section analyses indicate that both these networks formed contemporaneously during the E₂s³ stage. Mechanical analysis suggests that differences in the local stress field led to the development of these networks in different segments of the Linshang Fault. The HNF formed sequentially within a single tectonic phase. In contrast, the orthorhombic normal fault networks developed within a 3D strain field driven by the combined effects of dextral transtension along the Linshang Fault and footwall tilting. Hydrocarbon exploration results confirm that these normal fault networks exert significant control on hydrocarbon migration pathways and accumulation patterns. Full article

Tight gas is a critical unconventional energy resource, yet the geological characteristics and accumulation processes of tight gas in China’s Songliao Basin remain poorly documented. This study aims to investigate the tight gas system in the Songliao Basin as a representative continental basin, with key objectives including evaluating source rock and reservoir properties via mineralogical and geochemical analyses, characterizing lithologies and pore types, determining the gas charging mechanism in tight media, and identifying the main controlling factors for accumulation. Geochemical results indicate that the Shahezi Formation contains medium to good mudstones and excellent coals. Reservoirs consist of tight sandstones and conglomerates deposited in fan delta and braided river delta systems, with pore spaces dominated by dissolution pores and microfractures, resulting in ultra-low porosity and permeability. Conventional buoyancy-driven migration is ineffective; instead, gas charging is driven by hydrocarbon generation expansion force, creating overpressure that expels pore water and forces gas into reservoirs through fault-sand conduits. Accumulation is controlled by continuous gas supply from thick, highly mature source rocks, dissolution-enhanced and fracture-dominated reservoir space, and sufficient source–reservoir pressure difference. This study elucidates tight gas characteristics and accumulation mechanisms in continental basins, providing data applicable to both continental and marine settings. 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

Declining gold discovery rates require prospectivity workflows that are statistically transparent at the regional scale, while remaining adaptable to project-scale geological interpretation. We present a robust sampled weights-of-evidence (sampled-WoE) workflow for gold prospectivity mapping in the Yilgarn Craton, Western Australia. Mine and deposit records were cleaned using 400 m spatial deduplication, yielding 7203 representative mineralized points from 12,036 strict training records. Unlabeled background points were repeatedly sampled within the Yilgarn Craton and stratified by lithology, greenstone-belt membership, and structural-density class. Ten evidence variables were evaluated diagnostically, and an independence audit based on Spearman correlation and Cramér’s V defined a six-layer regional stack comprising lithological setting, fault density, magnetic anomaly, gravity anomaly, K, and Th. Stress tests showed limited sensitivity to random seeds and background-sample sizes, whereas larger exclusion buffers systematically inflated several weights. The conservative no-buffer scenario was therefore selected as the primary model. The highest-ranked 5% of the study area captured 49.23% of the valid representative mineralized points, corresponding to a descriptive 9.85-fold enrichment over random spatial selection; spatial-block out-of-sample validation retained a 45.4% Top-5% capture and 9.1-fold enrichment. The reproducible regional baseline provides a consistent basis for separate project-scale geological refinement and validation. Under spatial-block cross-validation, the out-of-sample Top-5% capture was 45.4% (9.1-fold enrichment), and a non-spatial random cross-validation (49.3%) confirmed that spatial blocking removes the optimism introduced by spatial autocorrelation. Full article

Aiming at the technical problem of asymmetric large deformation of mining roadways under the influence of fault tectonic stress and excavation disturbance, and taking the return roadway of the left first face of 14^# coal seam in the East No.2 Mining Area of the Pinggang Coal Mine as the engineering background, this research on the deformation mechanism and control technology of a near-fault roadway was carried out by combining on-site monitoring, theoretical analysis, numerical simulation and on-site practice. The results show that under the superposition of fault tectonic stress and excavation disturbance, the surrounding rock presents asymmetric deformation characteristics of fault sidewall > roof > floor > mining sidewall, with the roof-to-floor convergence peak of 991 mm and the two-side convergence peak of 968 mm; the critical instability range of the near-fault roadway is nonlinearly negatively correlated with the surrounding rock Geological Strength Index (GSI) and nonlinearly positively correlated with the disturbance factor (D). The critical instability range of this roadway is 3.44 m, and the peak values of stress and deformation of pillars during the excavation and mining are concentrated 0~4 m from the sidewall; the pillar width is linearly negatively correlated with the stress peak and nonlinearly negatively correlated with the deformation peak. When the pillar width is greater than 16 m, the stress superposition effect of the fault and mining is weakened, and the surrounding rock deformation tends to be stable. Based on the deformation mechanism, a control scheme of “coal pillar size optimization + surrounding rock grouting modification + high-strength anchor cable strengthening” was proposed, which optimized the pillar width to 16 m, adopted grouting reinforcement, and added long and short anchor cables to form a high-strength active support system. On-site practice shows that after the application of this scheme, the two-side convergence and roof-to-floor convergence of the roadway are reduced by 82.4% and 84.5%, respectively, compared with the original support; during the mining period, the two-side convergence is 397 mm and the roof-to-floor convergence is 484 mm, realizing the safe and stable operation of the roadway and the efficient mining of the working face. The research results provide a theoretical basis and engineering reference for the control of asymmetric large deformation of typical near-fault roadways. Full article

With the transition to deep coal mining, the transparent detection of hidden geological hazards in the floor strata is fundamental for production safety. In mine seismic exploration, gliding waves—inhomogeneous plane waves propagating along the coal–rock interface—offer a unique advantage for penetrating high-velocity floors via the skin effect, overcoming the total reflection limitations of conventional in-seam waves. This study investigates the propagation laws and anomaly response characteristics of floor gliding waves using super-critical incidence theory and high-order staggered-grid finite difference simulations. The results demonstrate that the apparent velocities of gliding P and S-waves are bounded by those of the coal and host rock, exhibiting minimal dispersion. Quantitative analysis using a penetration depth model reveals that while penetration depth is frequency-dependent—with lower frequencies providing deeper reach—high-frequency components remain essential for high-resolution imaging. Crucially, the proposed method was validated through a field Case Study at the 11123 working face. By utilizing a specialized deep-hole excitation strategy to ensure super-critical incidence, the inversion successfully identified a hidden fault extending up to 60 m below the floor, which was subsequently confirmed by rock roadway excavation. These findings establish a robust physical basis for designing underground floor-detection systems and provide a significant theoretical reference for addressing detection blind spots in deep mining environments. Full article

Long horizontal wells in high-permeability fault-block reservoirs may intersect multiple faults, leading to complex pressure-transient responses, strong parameter coupling in conventional well-test interpretation, inefficient manual history matching, and pronounced non-uniqueness in fault-property identification. To address these challenges, this study proposes a physics-regularized neural inversion framework based on a PINN parameterization and low-weight physics regularization for well-test parameter inversion in long horizontal wells intersecting multiple faults. The proposed method takes the multiple-fault pressure response of a long horizontal well as the target problem. Both the pressure–drawdown curve and the pressure–drawdown derivative curve are used as data constraints. At the same time, parameter scaling and stage-wise training are introduced to jointly invert the reservoir permeability, fault transmissibility coefficient, skin factor, and effective producing length of the horizontal well. Considering that the simplified line-source forward model is not fully consistent with the two-dimensional pressure-diffusion equation and the fault-interface residuals, a physics-loss consistency test is performed to determine safe weighting ranges for the PDE residual and the fault-interface residual. These residuals are then incorporated into the training process as low-weight physics regularization terms to improve the physical plausibility of the inversion results. Results from the base case, different fault types, multiple-fault combinations, noise-robustness tests, ablation experiments, and method comparisons show that the proposed method can stably fit pressure–drawdown and pressure–drawdown derivative curves and effectively identify key well-test parameters in single-fault cases and some multiple-fault cases. In single-fault cases, the order of magnitude of the fault transmissibility coefficient can be identified stably. Reliable inversion performance is obtained for medium- to high-transmissibility faults and some multiple-fault combinations. In contrast, ambiguity remains between sealing faults and strong-baffle faults in multiple low-transmissibility fault combinations. The results further indicate that, under multiple random initializations, the physics-regularized neural inversion framework provides improved inversion stability in the tested synthetic low-transmissibility multiple-fault cases compared with the traditional least-squares method. Therefore, the proposed framework can serve as an intelligent auxiliary tool for well-test parameter inversion and fault-connectivity evaluation in complex fault-block reservoirs. Nevertheless, fine discrimination of low-transmissibility faults and interpretation of highly noisy field data still require joint constraints from geological, seismic, and production-dynamic information. A preliminary reduced field PINN fitting test using the well X falloff event further provides an engineering-scale applicability check for real pressure-transient data, with a pressure NRMSE of 2.457% for the extracted shut-in response. Full article

Small, dispersed, and vegetated creeping landslides in hilly areas of eastern China hinder traditional remote sensing and detection. To address this, this study takes Yixian County (Anhui Province) as a representative area. Based on high-resolution GF-2 satellite images, three deep learning models embedded with the Squeeze-and-Excitation (SE) attention mechanism (ResNet18-SE, VGG13-SE, UNet-SE) were developed and compared with the original UNet model. Combined with field investigation, landslide mapping and accuracy assessment were conducted to evaluate the feature extraction capabilities of four models. The results indicate that the UNet-SE model achieved optimal performance (Precision: 0.911, Recall: 0.685, F1-score: 0.782, Kappa: 0.730, IoU: 0.643). Its F1-score exceeds ResNet18-SE, VGG13-SE, and the original UNet by 8%, 3%, and 5%, respectively, proving superior regional adaptability and generalization performance. Additional verification on creeping landslides in Kecun Town (Yixian County) and post-earthquake landslides in Lushan County (Sichuan Province) further confirms the reliability of the UNet-SE model. Furthermore, Frequency Ratio (FR), Random Forest (RF), and SHapley Additive exPlanations (SHAP) were adopted to reveal the correlation between landslide occurrence and seven geological-environmental factors. Landslides are most susceptible to develop at elevations of 400–500 m, NDVI values of 0.4–0.5, slopes below 10°, east and northeast aspects, 300–500 m away from rivers, 500–1000 m away from faults, and areas covered by soft sedimentary lithology. Distance from faults, distance from rivers, and elevation are identified as the three favorable conditional factors. In conclusion, the proposed landslide detection framework can provide reliable spatial data and technical references for regional geological hazard prevention, ecological conservation and sustainable development in hilly areas. Full article