Topographic free surfaces and seismic anisotropy significantly modify seismic wave propagation. With increasing demands for high-precision subsurface imaging, advanced seismic prospecting and seismological imaging methods require simultaneous consideration of topographic free surface and anisotropy effects on P-wave propagation. However, conventional finite-difference methods struggle to accurately simulate both topographic free surface and anisotropy in first-order acoustic velocity-stress equations, failing to capture complex wavefield responses. To address this challenge, we extend the existing curvilinear-grid finite-difference method, originally developed for the elastic wave VTI system of velocity-stress equations, to the acoustic VTI velocity-stress system with topographic free surfaces. The primary difficulty lies in setting the shear-wave velocity to zero in the elastic wave equations, which causes the free-surface velocity-derivative constraint matrix for acoustic topographic free-surface models to become rank-deficient or severely ill-conditioned. Therefore, we derive the velocity-derivative constraint specific to the acoustic VTI system and use the Moore-Penrose generalized inverse to obtain the normal velocity derivatives needed for updating the free-surface boundary condition. The resulting formulation includes body-fitted curvilinear grids, collocated-grid DRP/opt MacCormack discretization, and traction-image boundary treatment. Numerical experiments on flat-surface, Gaussian-topography, and BP benchmark models demonstrate stable performance under the tested conditions, while near free-surface waveforms computed by our approach closely match independent reference solutions. This method provides a reliable forward-modeling foundation for 2D acoustic-wave applications that require simultaneous, high-accuracy consideration of irregular topography and anisotropy.
High-altitude desert ecosystems exhibit extreme conditions, including intense UV radiation, strong diurnal temperature shifts, and aridity, shaping bacterial abundance and pedogenesis. This study investigates how episodic water availability shapes bacterial abundance and soil forming processes in a temporary lake system on the Barrancas Blancas plain (Ojos del Salado region). Using a multi-method approach, we combined bacterial abundance quantification via extracellular (eDNA) and intracellular (iDNA) DNA, physicochemical soil analyses and ground-penetrating radar surveys to assess subsurface stratigraphy. Meteorological and climatic data further characterized the environmental conditions. Our findings underscore the critical role of episodic water availability in pedogenesis and bacterial abundance. Soil moisture (P1: 31.1%–P2: 11.6%) and electrical conductivity (367–35 µS cm-1) decreased with distance from the lake, affecting redox conditions and bacterial biomass. Bacterial abundance peaked in surface soils at intermediate distance from the lake (P4, ∼21 m; iDNA = 1.21 × 108 gene copies g-1 soil), consistent with an optimal liquid water availability zone. Proximal sites, despite higher moisture inputs, experience prolonged freezing that delays thaw and limits bacterial abundace, while distal sites lack sufficient meltwater influence. This intermediate zone represents a thermal-hydric equilibrium that maximizes bacterial development. Across all transect positions, iDNA consistently exceeded eDNA (p < 0.001), indicating a predominance of potentially living bacterial biomass throughout the moisture gradient. Stratigraphic analyses revealed sedimentation and erosion cycles, demonstrating the lake’s long-term impact on landscape evolution. This study highlights the significance of high-altitude temporary lakes as natural laboratories for investigating the interactions between bacterial life, pedogenesis, and extreme hydrological regimes. Our results enhance understanding of geo-bio interactions in cryospheric desert environments and have implications for the search of potential life in extraterrestrial analog settings.
Tourism destination image encompasses the multifaceted perceptions of visitors that influence their choice of destination. However, current computational methodologies often analyze visual and textual signals separately and seldom link image perception to the quality of recommendations. This study introduces multimodal destination image perception and recommendation (MM-DIPR), a framework utilizing public data that amalgamates visual encoding, natural language processing, geospatial context, and graph-based ranking into a cohesive perception-aware pipeline. MM-DIPR discerns seven interpretable perception attributes, namely, landmarkness, naturalness, cultural/historical salience, esthetic appeal, crowding proxy, sentiment affect, and activity affordance, through a dedicated perception bottleneck that facilitates cross-modal alignment and graph ranking. We establish a reproducible benchmark using solely public and openly licensed datasets (Google Landmarks Dataset v2, YFCC100M, Places365, Yelp Open Dataset, TREC Contextual Suggestion, Wikimedia/Wikidata/Wikivoyage, Overture Maps Places, and SNAP Gowalla/Brightkite) and assess MM-DIPR against 11 baselines (including spatiotemporal and knowledge-graph POI models) across six complementary tasks, namely, perception classification, image–text retrieval, personalized point-of-interest (POI) recommendation, cold-start recommendation, cross-city transfer, and explanation faithfulness. A formal spatial autocorrelation analysis confirms that all seven perception attributes exhibit statistically significant positive spatial autocorrelation (Moran’s I∈[0.289,0.421]; p<0.05), grounding the framework’s geospatial design choices. Experimental findings indicate that the perception bottleneck significantly enhances ranking accuracy (Recall@10 of 0.213 and NDCG@10 of 0.152 on the Yelp benchmark; p<0.01) and cold-start robustness compared to strong multimodal baselines. Ablation studies validate the independent contribution of each module, while diversity analysis reveals that calibrated re-ranking enhances long-tail exposure with minimal accuracy loss. A controlled annotation study with 2,000 images and three annotators confirms substantial inter-annotator agreement (κ∈[0.68,0.83]) for all attributes. Qualitative explanation panels ground recommendations in public visual and textual evidence, thus providing practical utility for destination management practitioners. All experimental code and derived benchmark artifacts will be made available to support reproducibility.
IntroductionDuring seismic data acquisition, noise interference and bandwidth limitations often degrade signal-to-noise ratio and resolution, thereby compromising geological interpretation.MethodsTo address this issue, we propose an adaptive dual-attention wavelet denoising and enhancement network (AdaWaveNet) based on a trainable wavelet feature extractor. The network is built upon trainable discrete wavelet transform (DWT) and inverse DWT (IDWT) kernels, which are constrained to satisfy the perfect reconstruction (PR) property. An adaptive thresholding function is designed to selectively suppress noise while preserving effective signals. The wavelet feature extractor enables multi-scale sub-band feature learning, and attention mechanisms are incorporated to focus on critical geological features.ResultsExperiments on synthetic post-stack time-domain data, the New Zealand Kerry3D dataset, and field seismic data from a practical survey demonstrate that AdaWaveNet consistently outperforms conventional wavelet thresholding, DnCNN-SDC, SeisGAN, and other mainstream methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), edge preservation index (EPI), and high-frequency energy ratio (HFR).DiscussionThe proposed method effectively restores event continuity and thin-bed reflection information, thus providing high-quality data support for subsequent seismic interpretation.
The efficient development of tight sandstone reservoirs is challenged by complex pore structures and heterogeneous remaining oil distribution. This study provides a quantitative pore-scale investigation into waterflooding mechanisms in the Changqing Oilfield’s C61 reservoir using Real Sandstone Micro-Models that preserve native pore geometry and wettability. Through integrated physical experiments and statistical analysis, we identified three displacement types (uniform, finger-like, reticulated) and found that permeability and pore-throat radius exhibit strong positive correlations with displacement efficiency (R2 > 0.75, p < 0.01), while heterogeneity indices show significant negative correlations. We further quantified diminishing returns for increased injection volume beyond two pore volumes under the tested conditions. To address the challenge of small sample size (n = 10), we employed a systematic machine learning framework incorporating data augmentation via SMOGN (Synthetic Minority Over-sampling Technique for Regression) and Gaussian noise addition, expanding the dataset to 60 samples. Four algorithms—Random Forest XGBoost, Support Vector Regression and Artificial Neural Network (Artificial Neural Network)—were evaluated using 10 × 5-fold cross-validation on the augmented dataset, with final validation on the original 10 samples using leave-one-out cross-validation (LOOCV). The Random Forest model outperformed the other algorithms under the tested conditions, achieving a cross-validation R2 of 0.85 ± 0.04, RMSE of 2.51% ± 0.42%, and MAE of 1.98% ± 0.35% in repeated 5-fold cross-validation on the augmented dataset, and a LOOCV R2 of 0.76 with RMSE of 3.18% on the original 10 samples. Feature importance analysis suggests that permeability (importance score 0.38) and pore-throat radius (0.27) are dominant controls, with the variation coefficient (0.18) indicating a detrimental role of pore structure heterogeneity. This work provides an exploratory, data-driven framework for optimizing waterflooding strategies under the tested conditions, demonstrating a potential approach to maximize insights from limited experimental data. Generalization to other reservoirs requires validation on larger, independent datasets.
IntroductionInterlayer slip is a key mechanism during fold evolution; however, its effects on preexisting or contemporaneous secondary structures (e.g., cleavage and fractures) remain poorly constrained. This study investigates a well-exposed outcrop in the southern Yidun Arc, northwestern Yunnan, to decipher the modification of fold-related structures by interlayer slip and the resulting tectonic sequence.MethodsWe combined detailed field-based structural analysis with stereographic projections to systematically measure and analyze the orientations and geometries of bedding, cleavage, interlayer small folds, and joint sets. Kinematic indicators and stress orientations were reconstructed from the collected structural data.ResultsAxial planar cleavage, formed during the late-stage of major folding, is systematically deflected adjacent to bedding interfaces by subsequent interlayer slip, providing a distinct kinematic indicator for syn-folding ductile deformation. Interlayer small folds, sharing a hinge line with the major fold, are direct products of ductile interlayer slip. Two joint sets (J1 and J2) are developed, with orientations of 235°∠70° and 35°∠75°, respectively. Their Mode I (extension) geometries indicate that the minimum principal stress (σ3) was oriented approximately NE-SW during J1 formation and NW-SE during J2 formation. These joints represent independent brittle fracturing events post-dating folding.DiscussionIntegrating geometry, kinematics, and deformation mechanisms, we propose a three-stage evolutionary model: Stage I involves ductile contraction and syn-folding flow, forming the major fold; Stage II features small folds and late-stage cleavage development, where ongoing slip modifies earlier cleavage; Stage III is characterized by exhumation and post-folding brittle fracturing, forming transecting joint sets. This work not only clarifies the intrafolding sequence of structures but also provides a critical outcrop-scale example and methodology for unraveling polyphase tectonic overprinting.
Under climate change, the increasing frequency of extreme rainfall events has made soil cut slopes more prone to progressive instability. Understanding their deformation–failure mechanisms and reinforcement effects is therefore of engineering significance. However, previous studies have mainly focused on overall stability evaluation or mechanical behavior of anti-slide piles, while the progressive displacement propagation of actual slopes and the redistribution of lateral earth pressure within the sliding mass before and after reinforcement remain insufficiently addressed. In this study, a typical cut slope in Liaoyuan City, Jilin Province, China, was investigated using field surveys, engineering drilling, laboratory tests, and rainfall data. Based on laboratory test results, a discrete element model was established using PFC software to compare the deformation evolution and lateral earth pressure response of the slope under natural conditions and after reinforcement with double-row anti-slide piles. The results show that, under natural conditions, displacement first initiates at the slope crest and then propagates toward the slope toe along the potential sliding surface, eventually forming a through-going shear band and showing clear progressive instability. The double-row anti-slide piles effectively suppress displacement propagation, prevent shear band coalescence, and transform the slope from continuous deformation to a relatively stable state. Meanwhile, reinforcement alters the transmission path of lateral earth pressure within the sliding mass, reduces earth pressure fluctuations, and shares the landslide thrust. These findings provide an engineering reference for the stability analysis and anti-slide pile design of similar cut slopes.
Elastic full-waveform inversion serves as a fundamental technique for recovering subsurface elastic parameters. However, its application is hindered by mode coupling between P- and S-waves, multi-parameter crosstalk, and a strong dependence on the initial model, all of which degrade the accuracy of velocity model building at depth. The conventional inversion methods employing P-wave sources rely on PP and PS reflections, yet the PS converted wave typically exhibits low energy, making it difficult to jointly construct accurate initial P- and S-wave velocity models. To address this limitation, the present work introduces a traveltime-based inversion framework for P- and S-wave velocities built upon the S-wave source wave equation. First, a curl source function is designed to simulate pure shear-wave source excitation. Second, high-fidelity P- and S-wave mode separation is accomplished through vector decomposition combined with a wavenumber-domain correction for staggered-grid spatial migration. Subsequently, a traveltime minimization objective functional for SS and SP reflected waves is formulated, and the adjoint equations together with the corresponding velocity gradient expressions for P- and S-wave velocities are derived using the Born approximation and the adjoint-state method. The traveltime sensitivity kernels of SS and SP reflections are then extracted via wavefield separation, and the cross-correlation interference among multiple wave modes is decomposed. A stepwise inversion strategy is employed: S-wave velocity is first inverted from SS reflections, after which P-wave velocity is inverted from SP reflections; finally, the two velocity models are jointly refined to mitigate parameter crosstalk. Synthetic tests demonstrate that the proposed method effectively separates the SS and SP traveltime sensitivity kernels, which exhibit smooth shapes and favorable symmetry. Unlike conventional full-waveform inversion, which is prone to being trapped in local minima, this approach can accurately retrieve the long-wavelength components of both P- and S-wave velocities, thereby providing a high-quality initial model for subsequent full-waveform inversion and significantly improving the migration imaging accuracy of multicomponent seismic data.
Deep learning (DL)-based three-dimensional (3D) gravity inversion has emerged as a promising approach for subsurface density reconstruction in near-surface resource exploration, offering significantly lower computational cost than conventional inversion methods for large datasets. However, existing convolutional neural network (CNN)-based gravity inversion studies are largely limited to binary or constant-density models, thereby limiting their robustness in reconstructing realistic subsurface density models. In this study, we evaluate the performance of three encoder–decoder CNN architectures, UNet, UNet++, and ResUNet, for 3D gravity inversion. We modify both the training approach and the network architecture by using synthetic models with varying geometric complexity and density contrasts and by incorporating a Softplus output activation function in the output layer of all three networks. These modifications enable the networks to simultaneously reconstruct subsurface geometry and density contrasts. Among the three architectures, UNet++ consistently achieved the highest Dice scores, the lowest gravity-data misfits, and the lowest prediction uncertainty for the synthetic models. We further validate the proposed framework by applying it to the San Nicolás massive sulfide deposit, demonstrating its applicability to real gravity data and its potential for near-surface exploration.
Reservoir water level monitoring and early warning are critical components of flood control and disaster mitigation in river basins, as they can effectively prevent dam failures or downstream flooding. However, existing reservoir monitoring systems generally suffer from response delays and ineffective early warnings when dealing with sudden anomalies in water level readings (such as those caused by extreme hydrological conditions, instrument malfunctions, or localized damage). This study proposes a method for the automatic identification and early warning of abnormal water level changes that integrates the quantile regression model and the isolated forest algorithm (QR-IForest framework). Taking two series-connected reservoirs in the Yonghan River Basin in Guangdong Province as the study subjects, the method performs anomaly detection on real-time water levels based on data from actual monitoring stations in the reservoir area. The results indicate that: (1) The fused method achieves higher accuracy in anomaly detection than either method alone, with a precision rate of 92.24%, a recall rate of 0.8992, and an F1-score of 0.9106. The true positive rate (TPR) for anomaly detection reaches 99.15%, while the true negative rate (TNR) reaches 89.92%. (2) Simulation tests were conducted for two different scenarios (heavy rainfall and river channel blockage). The results show that the model’s anomaly detection capability can provide rapid, early warnings, detecting anomalies as early as 2 hours before an incident occurs. (3) This integrated model can automatically classify detected anomalies. When identifying anomalies related to monitoring equipment or those caused by deviations in hydrological patterns, the detection accuracy exceeds 85%. The method developed in this study provides a reliable basis for efficiently identifying anomalies in water level monitoring data from small and medium-sized reservoirs, thereby ensuring the safe management and operation of these reservoirs.
The Huadian area in Jilin Province records multiple tectonic phases from the Paleozoic to the Mesozoic, which have generated a complex geological framework. However, Jurassic magmatic rocks are extremely scarce in this region, resulting in a significant gap in the geological record for this critical period. This study presents integrated geochemical, zircon U-Pb geochronology, and Hf isotopic data for the Wangjiadian monzonitic granite. The intrusion yields an emplacement age of 185.1 ± 2.1 Ma, corresponding to the Early Jurassic. Geochemically, it is characterized by high SiO2 (73.60-74.28 wt%), elevated alkali contents (Na2O+ K2O = 7.71-8.24 wt%), and high Al2O3 (13.28-13.62 wt%), but is relatively depleted in MgO (0.18-0.21 wt%) and CaO (0.70-0.73 wt%). It exhibits strong enrichment in light rare earth elements (LREEs), a slightly moderate negative Eu (δEu = 0.65–0.79), and typical geochemical features of high-K calc-alkaline I-type granites. Trace element patterns display enrichment in LREEs, Rb, Th, U, and K, coupled with depletions in Sr, P, and Ti. The Hf isotopic compositions (εHf(t) = −10.98 to −4.61; tDM2(Hf) = 1,518–1915 Ma) indicate that the magma was generated by partial melting of Paleoproterozoic crustal basement at lower crustal levels. In combination with coeval Early Jurassic granites in the upper Yanji area and bimodal magmatism in the Xiaoxing’anling-Zhangguangcai Ridge, these results suggest northwestward subduction of the Paleo-Pacific Plate beneath the northeastern margin of the Sino-Korean Block during the Early Jurassic.
The 1930s Dust Bowl Drought (DBD) in midcontinental North America was a severe environmental event, with large-scale farm failures and ∼500,000 residents displaced during a global depression. Drought conditions were initiated with mode changes in ocean circulation including Pacific Ocean cooling and North Atlantic warming, and concomitant development of a persistent high-pressure ridge over western U.S., weakening the Great Plains Low Level Jet. Drought conditions were further exacerbated by the expansion of cereal crops in the 1920s and drought denudation in the 1930s which increased surface albedo, and decreased evapotranspiration, convection and precipitable moisture. The increase in bare sandy surface soils increased dust loads, which further extended the footprint and severity of the DBD. Agricultural census and remote sensing analyses of 1930s aerial photography show that only 30%–35% of the DBD area was cultivated, consistent with the broader Great Plains, and less, 10%–15% on the western edge. In situ dust emissivity experiments show that there are multiple sources of dust from cultivated and uncultivated surfaces, depending on vegetation and soil conditions. The DBD had >100 dust storms/year lasting hours to days with visibility <100 m, with corresponding PM10 concentrations >5,000 μg/m3, posing a severe health risk. This dust storm occurrence was broadly analogous to contemporary dust events for the most arid areas in Asia and Middle East. DBD dust storms occurred under anticyclonic circulation, predominance of southward meridional flow, with reduced moisture flux from oceanic and gulf sources. Future droughts may be more severe with greater climate variability and hydrometeorological deficits reflecting added drought severity from greenhouse gas warming with new dust sources across the Great Plains, though potentially mitigated by extreme intervening wetter periods, continued irrigation and vegetation changes that can locally enhance evapotranspiration.
The safety status of temporary steel trestle bridge will deteriorate rapidly due to severe riverbed erosion caused by natural disasters such as floods and storm surges, rapid degradation of supporting structures, and the superposition of heavy vehicle loads, which can easily lead to collapse accidents. This paper develops a physics-based Gaussian-process regression (GPR) framework with active learning for rapid safety assessment of temporary steel trestle bridges. A parameterized Euler-Bernoulli beam model on elastic supports is used to calculate structural responses under moving construction vehicles. Seven uncertain parameters are considered: vehicle gross mass, vehicle speed, local scour depth, support stiffness factor, deck damage ratio, lane eccentricity, and braking force ratio. The resulting active-learning GPR (AL-GPR) surrogate selects new simulations near the safety limit state and uses the simulation budget efficiently. On an independent 500-sample test set, the AL-GPR model achieved a coefficient of determination (R2) of 0.997, a root mean square error (RMSE) of 0.017, and a safe/unsafe classification accuracy of 0.996. Compared with passive sampling under the same simulation budget, active learning reduced the classification error by 33.33%. The results show that local scour depth, vehicle mass, and support stiffness are the dominant risk drivers, and that the trained surrogate can generate speed-load safety maps for real-time construction traffic management.
IntroductionThe dynamic mechanisms of Youjiang Basin low-temperature mineralization and the source of ore-forming fluids remain controversial, particularly regarding Indosinian versus Yanshanian tectonic driving forces and deep crustal versus sedimentary fluid origins.MethodsThis study focuses on the Badu Sb-Au deposit and conducts LA-ICP-MS in-situ trace-element and sulfur-isotope analyses of pyrite, sphalerite, stibnite, and ankerite to constrain ore-forming processes and resolve key regional metallogenic debates.ResultsTwo generations of pyrite are recognized: supergene-altered Py Ⅰ developed within quartz-sulfide veins (δ34S = 17.93‰–19.53‰, avg. 18.70‰), and hydrothermal euhedral disseminated Py Ⅱ (δ34S = 17.73‰–18.68‰, avg. 18.12‰), representing early hydrothermal mineralization. Sphalerite exhibits Cd enrichment, In-Sn-Co depletion, and Ga/In ratios <1, indicating typical Mississippi Valley-type (MVT) fluid characteristics. Trace-element evidence confirms that ankerite precipitated under sedimentary-hydrothermal transitional conditions (La = 514.08–514.98 ppm; Eu = 73.60–93.64 ppm), while stibnite formed during subsequent dominant hydrothermal stages (La = 183.05–223.89 ppm; Eu = 12.51–13.93 ppm). The significantly elevated Eu concentrations in stibnite represent primary geochemical signals of reducing, Eu2+-rich hydrothermal fluids. Stratigraphically ordered hydrothermal minerals yield averaged δ34S values of 14.58‰ (ankerite), 14.66‰ (sphalerite), and 13.98‰ (stibnite). The overall sulfur-isotope variation, coupled with trace-element redox indicators, records fluid mixing and progressive hydrothermal evolution rather than a simple monotonic trend.Discussion and ConclusionIntegrated mineral paragenesis and geochemical fingerprints demonstrate that ore-forming fluids were dominated by modified sedimentary pore fluids with minor deep-source contributions, and mineralization was driven by Yanshanian asthenosphere upwelling and lithospheric extension, overprinting early Indosinian tectonic frameworks. This study establishes a clear data-driven genetic model for Sb-Au mineralization in the Badu deposit, clarifies long-standing dynamic and fluid-source controversies in the South China LTMD, and supplements the regional low-temperature metallogenic theory.
The southwestern margin of the transition zone between the Southwestern Tarim Depression and West Kunlun Mountains, known as the West Kunlun Piedmont Thrust Belt, exhibits intense compressional deformation. As a critical oil and gas exploration area in the Southwestern Tarim Depression, the internal architecture and structural styles of this piedmont thrust belt have long been key geological challenges hindering exploration progress. This study establishes a structural interpretation model for the Kekeya segment of this belt on the basis of integrated analysis of surface geological outcrop observations, seismic data, and continuous electromagnetic profile (CEMP) data, and analyzes its structural evolution process and petroleum geological significance. It is proposed that the Kekeya segment can be interpreted as a piedmont thrust wedge structure bounded by a basin–mountain boundary fault (rear-edge fault) and a low-angle detachment fault in the middle-upper crust (basal fault). The internal thrust wedge is composed of 4–5 major reverse faults and the wedge-shaped fault blocks they divide, superimposed in varying configurations, with additional faults of different scales and characteristics developed within. Most major faults exhibit multiphase activity of different natures, and the superposition of multiple structural deformations results in complex structural styles and layered, zonal, and segmented variations within the thrust wedge. From the Tiekelik Uplift in the northern edge of the West Kunlun Mountains to the axial part of the Southwestern Tarim Depression, the thrust belt sequentially manifests as a basin-margin thrust uplift zone, basement-involved thrust zone, and cover detachment thrust-fold zone, reflecting a gradual weakening of compressional deformation intensity and shallowing of deformation-involved strata. This study further suggests that the thrust wedge underwent multiple “opening–closing” structural evolution cycles from the Nanhua period–Early Paleozoic (Caledonian), late Paleozoic–Triassic (Hercynian–Indosinian), and Jurassic onward (Yanshanian–Himalayan), with the superposition of multiphase deformation leading to complex internal structural styles. Notably, major faults within the basement-involved thrust zone often exhibit multiphase kinematic characteristics, serving as boundary faults for both Carboniferous–Permian rifted basins and later thrust anticlines and fault blocks. These fault zones are favorable for the formation of self-sourced Carboniferous–Permian hydrocarbon reservoirs, representing a significant target for current oil and gas exploration.
Oldoinyo Lengai volcano is located within the Natron Rift, a young magmatic segment of the East African Rift System, where tectonic extension and magmatic processes are tightly coupled. Understanding how stress, deformation, and magma transport interact in such settings remains a key challenge. Here, we use local shear-wave splitting measurements to image seismic anisotropy and constrain the distribution of stress and deformation across the Natron Rift. We analyze ∼4,400 station–event pairs from the dense SEISVOL seismic network, identifying anisotropy with an average delay time of ∼0.12 s and a dominant fast-axis orientation of ∼21°N, broadly consistent with the regional extensional stress field. However, pronounced lateral variability reveals distinct anisotropic domains across the Natron Basin, the region east of Gelai, and the magmatically active zone in the vicinity of Oldoinyo Lengai. In all areas, anisotropy is dominated by a shallow crustal layer (∼5–6 km), attributed to stress-aligned crack systems, with evidence for additional complexity from anisotropic layering and lateral heterogeneity. In the magmatically active region, we observe significant deviations from the regional pattern, including localized rotations of fast-axis orientation and increased delay times. Depth-dependent analysis indicates an additional anisotropic contribution at mid-crustal depths (∼11–15 km), consistent with a previously inferred magma storage region. Temporal variations in splitting further correlate with periods of elevated volcanic activity, suggesting that the carbonatite and silicic plumbing systems are strongly coupled and the corresponding melt and fluid transport dynamically modifies the stress field and fracture network. We interpret these observations as the expression of a magmatically influenced transfer zone, where stress rotation, shear accommodation, and focused magma ascent interact. Our results demonstrate that seismic anisotropy captures both regional stress and its local modification by magmatic processes, highlighting the key role of magma–tectonic feedbacks in controlling deformation and segmentation in young continental rifts.
Earthquake Early Warning Systems (EEWS) are now operational across multiple jurisdictions, including Canada, issuing messages designed to trigger protective behaviour such as Drop-Cover-Hold-On in the seconds before damaging waves arrive. Because most recipients will have no prior experience of an EEW alert, the experiential background they bring to a first alert is general earthquake comprehension, the phenomenon this study examines. Existing models frame recipients as rational decision-makers, yet the empirical record of lived earthquake experience suggests that the early seconds are dominated by confusion, and what recipients actually need from messaging in those moments has remained underexamined. Through a phenomenological-linguistic analysis of nearly 1,000 first-hand accounts from the United States Geological Survey’s Did You Feel It (DYFI?) survey across five North American earthquakes, this study traces how respondents describe and make sense of their experiences. Confusion was the dominant emergent feeling and was almost always paired with active resolution efforts; the patterns yielded a four-phase Earthquake Comprehension Cycle that converges with foundational phenomenological and perceptual cycle models. EEWS messages function primarily as early comprehension aids, interjecting into the cycle ahead of ground shaking and supplying a theory that the recipient would otherwise need to generate themselves. The reframing applies broadly to other early warning systems facing confusing emergent events and offers a phenomenological lens for risk communication design.
Ground subsidence in abandoned gypsum mines represents a typical geological hazard in evaporite regions, posing a direct threat to infrastructure and public safety. This study investigates a collapse-induced seismic event (ML3.4) that occurred on 8 March 2025, in an abandoned gypsum mine in Hunan Province, China. The primary objective is to propose a multi-scale conceptual model for this stratal instability. To achieve this, an integrated approach was employed: high-resolution unmanned aerial vehicle (UAV) photogrammetry was applied to map surface deformation, transient electromagnetic method (TEM) surveys were conducted to image subsurface structures, and microseismic monitoring was deployed to track dynamic instability processes. The application of these methods yielded several key findings: UAV-derived orthomosaics delineated a collapse-affected area of ∼75,000 m2, featuring ground cracks, subsidence ponds, and structural damage to buildings. TEM inversion imaging revealed prominent low-resistivity anomalies corresponding to water-saturated fracture zones, which contrast sharply with the high-resistivity host gypsum layers. These anomalies are inferred to act as primary conduits facilitating persistent water infiltration from the surface. Microseismic monitoring (>200 events) revealed that seismic sources were predominantly concentrated at the basal level of the mined-out zone and along collapse-induced fractures, with the highest event density spatially correlated with the surface drainage channel. Integrated analysis of the survey indicates that while long-term gravitational stress provided the background loading, rainfall infiltration and surface water flow significantly accelerated the mechanical weakening of the fault slip planes. This spatial convergence supports an evidence-based conceptual model wherein hydro-structural coupling reduced the effective shear strength of the structural planes, acting as the primary trigger for the final collapse. The integrated multi-source framework provides a reproducible methodology for goaf stability assessment and hazard mitigation in similar geological settings.
The Sinian Dengying Formation in the western Deyang-Anyue Rift Trough is a key target for deep carbonate gas exploration, yet its reservoir controls remain debated. Integrating core, thin-section, and geochemical data, this study clarifies the coupled mechanisms governing reservoir development. Results reveal that reservoirs predominantly occur in platform-margin and high-energy shoal facies, with pore systems comprising intercrystalline, dissolution, and fracture pores. Primary porosity is largely obliterated by deep-burial compaction and cementation, yielding an overall low-porosity, low-permeability matrix. However, structurally influenced platform-margin exposure zones and fracture-affected mound–shoal bodies outside strongly cemented fault cores locally contain higher-quality reservoirs because of dissolution and fracture-enhanced connectivity. Vertically, reservoirs are zoned: tight lower intervals transition upward into dissolution-enhanced, comparatively porous middle-upper sections. Critically, reservoir evolution is dictated by a “sedimentation–diagenesis–tectonics” coupling: sedimentary architecture and thickness define macroscopic reservoir distribution; compaction and cementation induce densification, while dissolution and fracturing create secondary pore-fracture networks. Hydrothermal activity superimposes a “dissolution-filling alternation,” drastically intensifying heterogeneity. This multi-scale, multi-stage coupling ultimately controls the pronounced spatial variability of the Dengying Formation reservoirs and provides a geological framework for screening favorable targets in deeply buried carbonate successions.
The shallow shear-wave velocity structure obtained from surface-wave dispersion inversion is essential for seismic site characterization; however, deterministic inversion provides only point estimates without uncertainty, while existing probabilistic approaches often lack coverage calibration, systematic robustness evaluation, and physical-consistency checks. A mixture density network is trained on a large-scale public dispersion-inversion benchmark to output a depth-wise probability distribution of shear-wave velocity Vs; conformal calibration is introduced to provide coverage guarantees, an augmentation-calibrated robust variant handles distribution shift, and dispersion forward modeling examines the physical consistency of predictions. On the test set, the shear-wave velocity prediction attains a coefficient of determination R2 of 0.904 and a root-mean-square error of 0.199 km/s; this calibration corrects the empirical coverage of the nominal 90% interval from an over-covered 0.950 to a precise 0.903 while narrowing the interval width by 14.3%. Under observational noise and out-of-distribution geology, the standard conformal coverage degrades, decreasing to 0.829 for faulted sites, and augmented calibration partially restores it to 0.868; a contrast between generic and frequency-dependent physics-guided perturbations yields nearly identical coverage, indicating that the recovery stems mainly from augmentation breadth rather than perturbation spectral shape, with residual under-coverage remaining under severe shift. A dispersion-consistency diagnostic reveals a positive correlation (0.558) between physical residual and predictive uncertainty, showing that the uncertainty captures physical inconsistency. The framework delivers calibrated, robust, and physically consistent uncertainty quantification for probabilistic surface-wave dispersion inversion.
