Abbreviation (ISO4): Prog Geophy
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Based on the three-component continuous seismic waveform data of 10 fixed stations in Tengchong volcanic network from 2020 to 2022, this study systematically studies the background noise characteristics of seismic observation in Tengchong volcanic area by using noise Power Spectral Density (PSD) and Probability Density Function (PDF) analysis methods. The results show that the high-frequency (> 1 Hz) noise of Tengchong volcanic network shows significant diurnal variation characteristics, and its intensity is highly consistent with the human activity cycle. The power spectral density of acceleration noise is about 20 dB higher than that at night due to the influence of industrial production, traffic and building construction during the day. Although the second type of microtremor noise in the 0.1~1 Hz frequency band originates from ocean waves, it is still the main noise component in the inland Tengchong area. The noise in this frequency band has obvious seasonal changes. The noise level in winter is about 2.5 dB higher than that in summer, and the peak frequency moves to the low frequency direction for about 1 s. Because the distance between each station is small and the distance from the coast is close, there is no significant spatial distribution difference in the noise of each station in the 0.1~0.5 Hz frequency band, but the noise level of the cave station is about 3 dB higher than that of the surface station. The analysis shows that the integrity of the cave bedrock may form a waveguide channel, which makes the energy propagate more efficiently in this frequency band, and the microtremor signal can be recorded more clearly. The results of this study have clarified the dominant factors of noise sources in different frequency bands of Tengchong volcanic network, revealed the influence mechanism of human activities, geographical environment and station installation mode on noise level, and provided an important theoretical basis for the optimization and transformation of the network and the improvement of data quality.
To enhance the capabilities of the Inner Mongolia Earthquake Early Warning System in data quality evaluation and frequency-domain anomaly detection, this study employs the Power Spectral Density (PSD) values corresponding to the 10th and 90th percentiles of the background noise power spectral density Probability Density Function (PDF) as a method to establish station background noise models. Utilizing the full year 2024 of continuous observation data from 48 upgraded reference stations in Inner Mongolia, we have quantitatively derived the background noise models (L-PSD and H-PSD) for these 48 stations for the first time. Based on these individual station models, we further established regional background noise models for Inner Mongolia (MODEL-PSD-1 and MODEL-PSD-2). These models are intended to support station operation monitoring, data anomaly identification, and scientific site selection. The findings demonstrate that: (1) Compared to global noise models (NHNM/NLNM), the locally developed station-level noise models exhibit higher sensitivity in data quality assessment, effectively identifying various anomalies, including pendulum cable faults in seismometers, changes in installation configurations, environmental interference, and differences in frequency response; (2) The established regional noise models show strong regional representativeness and seasonal adaptability, encompassing noise variation characteristics across diverse observational environments. They provide a reliable quantitative regional benchmark for site selection, observational environment assessment, and data quality inspection of new stations in Inner Mongolia and adjacent areas; (3) Noise source analysis indicates that the low-frequency band (0.02~0.05 Hz) is predominantly influenced by airflow and temperature variations, the microtremor band (0.1~0.5 Hz) is closely associated with oceanic activity, and the high-frequency band (1~20 Hz) is mainly affected by anthropogenic sources. The noise models developed in this study significantly improve the capacity for data quality evaluation and frequency-domain anomaly detection within the seismic network of Inner Mongolia and surrounding regions. They also establish a scientific foundation for future deployment of intelligent quality monitoring systems and the selection of sites for new stations.
On August 31, 2025, a magnitude MW6.1 earthquake occurred in the Hindu Kush Mountains region of eastern Afghanistan, near Kunar and Nangarhar provinces, causing a large number of casualties and property damage. The earthquake further exacerbated the risk of disaster in the region due to Afghanistan's location in the intra-continental deformation zone at the junction of the Eurasian and Indian plates, with active tectonic activity, and the country has long faced a humanitarian crisis and weak infrastructure. In order to reveal the disaster characteristics of this earthquake, based on the Sentinel-1 satellite data, this paper uses Interferometric Synthetic Aperture Radar (InSAR) to carry out coseismic deformation monitoring, and uses the coseismic deformation field as the constraint to invert the source parameters and static sliding distribution. The results show that the rupture depth of the earthquake is shallow, mainly concentrated within 8 km underground. The rupture is dominated by thrust motion and has a small tilt-slip component, with a maximum sliding amount of 2.5 m. At the same time, there were many strong aftershocks after the earthquake, the terrain of the earthquake area was steep, and many landslides occurred after the earthquake, and a large number of mud brick houses collapsed. As of September 4, 2025, the Afghan Red Crescent Society confirmed that the earthquake killed 3, 073 people and injured 3, 640, making it the deadliest earthquake event in Afghanistan in recent years. In view of the complex topography, poor building quality and dense population in the local area of the earthquake, this paper studies the population casualties and spatial distribution laws. The results show that the casualty population is highly concentrated in the river valleys and towns in the southern part of Kunar province and the northern part of Nangarhar province, which is closely related to the characteristics of local population aggregation along the river valley and the extremely poor seismic performance of traditional mud brick and adobe houses. Therefore, it is necessary to continue to pay attention to the secondary disaster hazards in the earthquake area and conduct multi-dimensional regional disaster risk assessment to reduce the systemic crisis in Afghanistan.
The vibroseis is one of the core seismic sources for onshore seismic exploration in the petroleum industry, characterized by controllable bandwidth and energy, environmental friendliness, and safety and efficiency. In 2024, the "Key Technologies and Equipment for Onshore Wideband, Wide-Azimuth, and High-Density Seismic Exploration" was awarded the First Prize of National Technology Invention Award, with the vibroseis seismic exploration acquisition technology as its core technology, which has a broad application prospect. This paper starts with the development history and working principle of the vibroseis, sorts out the progress of the vibroseis signal design and efficient acquisition operation methods, introduces the supporting operation and data processing technologies, and finally makes a prospect for the future development direction in combination with the advantages and limitations of the vibroseis seismic exploration acquisition technology.
Numerical simulation (forward modeling) serves as a core tool for analyzing complex physical processes and plays an irreplaceable role in multiple fields such as geophysics. However, traditional grid-based methods like the finite element method, despite their wide application due to flexible grid discretization, encounter difficulties in handling irregular geometries—especially those with sharp corners, the problem has been resolved by meshless methods, which only require the deployment of nodes without the need for mesh division. The element free Galerkin method, developed in the past two to three decades, holds a prominent position among meshless methods due to its rigorous theoretical foundation and algorithmic stability, and demonstrats remarkable advantages in numerical simulation. The shape functions constructed through moving least squares approximation inherently possess high-order continuity, thereby enhancing simulation accuracy. When simulating models with sharp inner angles, holes, and other complex boundaries, the element free Galerkin method does not rely on grid reconstruction, fundamentally eliminating the computational bias caused by grid distortion. This paper systematically reviews the theoretical framework, development history, application fields of the element free Galerkin method, and summarizes the differences in accuracy and computational efficiency among the Element-Free Galerkin Method, the Finite Element Method and the Finite Difference Method in their applications to the field of geophysics. Through this comprehensive review, it provides comprehensive materials for deeper research and engineering practice of the element free Galerkin method, with the aim of promoting its continuous development.
Seismic data acquisition serves as a critical foundation for seismic exploration. Since it accounts for the majority of the costs in seismic exploration, the optimization of acquisition technology is of great significance to the entire seismic exploration workflow. Constrained by the Nyquist sampling theorem, traditional acquisition methods require high sampling rates, lack flexibility in adapting to actual field conditions, and incur substantial exploration costs. Compressed sensing theory has been introduced into seismic exploration for more than a decade. Acquisition methods based on this theory allow for random deployment of receivers, reduce operational time, lower costs, and improve the efficiency of seismic exploration. However, existing compressed sensing based acquisition design methods cannot guarantee a consistent number of traces per shot, which adversely affects reconstruction quality. This paper proposes a two-step sampling method to overcome the inconsistency in the number of sampled traces per shot. The first step employs regular sampling to generate fixed sampling positions and control the spacing between adjacent receivers. The second step introduces random sampling to fill the gaps within the fixed scheme, enhancing the randomness of sampling, and subsequently generates sampling schemes that maintain a consistent number of traces per shot. Data experiments demonstrate that the reconstruction results from the two-step acquisition method achieve a higher signal-to-noise ratio compared to those from random sampling and piece-wise random sampling on average. Therefore, this method holds practical application value in seismic exploration.
The integration of deep learning methods with physical equation constraints has become a dominant development trend of seismic Full-Waveform Inversion (FWI). At present, physically constrained deep learning-based FWI is mostly implemented based on automatic differentiation frameworks. However, when applied to large-scale numerical simulations, such frameworks suffer from excessive gradient memory consumption, poor adaptability of gradient preprocessing, and difficulties in model segmentation computation, which limit their practical application for subsurface velocity modeling. To address these issues, this study abandons the gradient calculation scheme of automatic differentiation and adopts the adjoint gradient computation of conventional FWI as the core algorithm. Combined with deep learning reparameterization technology and network optimization training strategies, a more practical deep learning parameterized FWI method is proposed. Relying on high-performance computing resources, the adjoint-state gradient of the convolution-based objective function is efficiently solved. A Long Short-Term Memory Neural Network (LSTM-NN) is employed to characterize subsurface model parameters. Leveraging its superior capability in capturing long-range dependencies, the network establishes a more stable parameter mapping relationship and effectively improves the performance of network reparameterization. Furthermore, targeting the strong nonlinearity inherent in FWI, a multi-scale inversion strategy is introduced. Independent LSTM-NN parameterized training is performed at different inversion scales, enabling the network to characterize subsurface medium information layer by layer and effectively enhance the global convergence performance of the algorithm. Numerical test results verify that compared with the conventional convolutional neural network-based parameterized inversion method, the proposed LSTM-NN multi-scale inversion method can clearly delineate subsurface structural interfaces, compensate for the deficiencies of geometry illumination, and significantly improve the identification accuracy of fault details. It ultimately delivers inversion results with higher accuracy, providing a feasible technical solution for the practical application of high-precision seismic velocity modeling.
Total Organic Carbon (TOC) is a critical parameter for evaluating the enrichment of organic matter and the hydrocarbon generation potential of shale reservoirs. Conventional seismic TOC prediction techniques typically rely on high-accuracy density inversion. However, density inversion methods based on isotropic assumptions often yield substantial uncertainties in shale formations exhibiting strong Vertical Transverse Isotropy(VTI), thereby degrading the reliability of TOC estimation. Previous rock-physics studies have demonstrated that the VTI anisotropy parameters ε and σ exhibit strong correlations with TOC. In this study, we propose a novel TOC prediction methodology that integrates anisotropy-parameter seismic inversion with neural-network modeling. First, rock-physics analyses are conducted using laboratory measurements and wireline logs to elucidate the quantitative relationships between TOC and the anisotropy parameters. Second, an optimal three-term approximated reflection-coefficient equation for VTI media is selected to perform joint seismic inversion, and the anisotropy parameters ε and σ are subsequently derived through stepwise numerical computation. Finally, a Backpropagation (BP) neural network is constructed to establish a nonlinear mapping between TOC and the anisotropy parameters ε and σ, enabling seismic prediction of TOC. Synthetic tests indicate that the proposed approach maintains strong robustness under moderate noise levels. Application to field data shows that the inverted TOC results near wells exhibit good agreement with logging-derived TOC, thereby confirming the practicality and reliability of the method for quantitative characterization and spatial prediction of TOC in shale reservoirs.
Shale oil is an important component of China's national energy strategy. Research on onshore and offshore shale oil in China is advancing in parallel, and investigating the rock mechanical properties of oil shale is fundamental to achieving efficient shale oil exploration and development. In this study, Chang 7 oil shale from the Yanchang Formation in the Ordos Basin and the second member of the Liushagang Formation (Liu 2) oil shale from the Weixinan Sag were selected as research targets. A comprehensive investigation of their physical and mechanical properties was conducted from the perspectives of rock strength, anisotropy, microstructure, failure characteristics, and hydration behavior. The mechanical characteristics of the oil shales were clarified, and the similarities and differences between onshore and offshore oil shales were analyzed. The results show that: (1) Both oil shales exhibit pronounced anisotropy, and rock strength varies systematically with bedding angle. The Liu 2 oil shale has more developed bedding and stronger anisotropy than the Chang 7 oil shale. (2) Anisotropy significantly affects failure modes. Shear failure occurs at a bedding angle of 60°, whereas splitting failure dominates at 90°. The influence of anisotropy on failure is more evident for the Liu 2 oil shale. (3) The Chang 7 oil shale exhibits higher strength than the Liu 2 oil shale. However, Chang 7 oil shale is more susceptible to hydration, showing a greater degree of strength degradation after hydration. These findings provide technical support for wellbore stability studies and hydraulic fracturing design.
For fault-controlled channel sandbody reservoirs, where small faults are prevalent, sandbodies are thin, and the data have a relatively low signal-to-noise ratio, we propose an improved 3D ResU-Net++ network that integrates an enhanced DenseASPP module, CBAM based attention optimization, and depthwise separable convolutions to address the identification of low-order faults in complex settings. First, a DCNv2-enhanced DenseASPP module is introduced into the ResU-Net++ backbone. By densely connecting multi-scale DCNv2 convolution branches, the network's receptive field is markedly expanded, improving multi-scale contextual awareness of fine-grained faults and thin-layer sandbodies. Second, CBAM recalibrates channel and spatial attention weights to further highlight fault textures and structural boundaries in addition, standard convolutions are replaced with depthwise separable convolutions to boost the model's resolving power and computational efficiency. Finally, transfer learning is employed to enhance generalization. Applied to the Tong-61 fault block, the method achieved segmentation metrics of IoU and Dice up to 85.6% and 92.2%.Well-seismic comparisons and case studies indicate that the proposed approach can accurately delineate complex fault structures and finely depict the distribution of thin sandbody reservoirs, providing an efficient and effective technique for integrated structural-lithologic interpretation and high-resolution hydrocarbon exploration and development.
The small-scale carbonate fracture-cavity reservoirs developed in the deep Tarim Basin exhibit a typical heterogeneous "beaded" seismic response signature, but the accuracy of their reservoir identification and spatial delineation is susceptible to interference from background seismic reflections of surrounding strata. Therefore, building upon conventional SVD for extracting fracture-cavity reservoir responses, this study addresses uncertainties in iterative selection of truncated singular values. We develop a filtering operator through DTW-based similarity constraint of right singular vectors. With energy-ratio constraints, enabling simultaneous seismic response separation and denoising. This establishes a novel singular vector similarity-constrained SVD method. Based on testing and validation with both synthetic and actual seismic data, it is found that the truncated singular value corresponding to the "inflection point" in the DTW similarity distance between right singular vectors, which can effectively distinguish background formation reflections from seismic responses of fractured-cavity bodies. This method further highlights seismic responses of fracture-cavity bodies while significantly suppressing background reflections and random noise. The enhanced beaded weak signals provide critical support for subsequent seismic facies identification and reservoir prediction.
To mitigate the adverse effects of strong interference on the data quality and detection performance of electromagnetic methods, and to address the suboptimal signal-noise separation performance resulting from inappropriate parameter settings in deep learning approaches. This paper proposes an electromagnetic signal-noise identification method based on deep learning optimized by the Hybrid Strategy Improved Hippopotamus Optimization (HSIHO) algorithm. First, multiple optimization strategies are evaluated through comparative analysis, and a hybrid strategy combining lens opposite-based learning and adaptive T-distribution perturbation is selected to enhance the original hippopotamus optimization algorithm. Subsequently, the improved HSIHO algorithm is employed to optimize key parameters of the deep learning model, including the learning rate, number of neurons, and number of iterations. Finally, the optimized model is applied to electromagnetic signal-noise identification, and its performance is compared with that of other deep learning models. Experimental results demonstrate that, in benchmark function tests, the proposed HSIHO algorithm exhibits superior convergence speed, solution accuracy, flexibility, and scalability compared to other intelligent optimization algorithms. In practical applications involving electromagnetic data processing, the HSIHO-optimized deep learning model achieves significantly higher accuracy in signal-noise identification than probabilistic neural networks, convolutional neural networks, long short-term memory networks, and gated recurrent unit. The processed electromagnetic data show enhanced quality and greater stability in electric field curve morphology. The proposed method provides effective technical support for the inversion and interpretation of electromagnetic survey data.
The tight sandstone gas reservoir in the second section of the Xujiahe Formation in the western Sichuan Depression is characterized by deep buried, ultra-low porosity, high density, strong heterogeneity, and significant differences in the development of planar fractures. Because the reservoir has been tight before the main reservoir formation period, faults and fractures have become advantageous migration channels for highly mature gas, as a result, the relationship between gas and water is complex, and the identification of logging fluid properties is difficult. Due to the significant impact of reservoir complexity on resistivity, and acoustic waves, traditional fluid identification methods based on resistivity-porosity intersection can not effectively distinguish gas and water layers. However, the existing nuclear magnetic resonance logging fluid property identification technology, which uses relaxation time cut-off values to identify "bound water" and "movable water" as the core idea, suffers from the contradiction between "deep invasion of mud filtrate" and "shallow radial detection depth of instruments" caused by overbalanced drilling, resulting in ineffective differentiation between water and filtrate on the relaxation spectrum and reduced reliability of fluid identification. To deal with this issue, a calibration is conducted between the results of 2D NMR and constant-rate mercury injection experiments on core samples, the microscopic geological significance of the T2-T1 relaxation spectrum is clarified, by correlating the degree of natural gas charging during accumulation with the distribution of gas and water, research is carried out based on the layout of signals in the relaxation spectrum, the relative magnitude of signal intensities, and their correspondence to gas well production conditions. It is shown that throats occupy a considerable proportion of the reservoir space in tight sandstone reservoirs, the first independent peak on the right side of the clay water signal in the two-dimensional relaxation spectrum is the throat retention water signal, the ratio of natural gas signal to throat retention water signal can reflect the degree of natural gas injection during the reservoir formation period, the reservoir formation characteristic coefficients calculated using the two-dimensional relaxation spectrum have a good correlation with the production characteristics of gas wells, thus a new thinking for identifying fluid properties in tight sandstone gas reservoirs with the core of characterizing the degree of natural gas injection during the reservoir formation period is formed. The method is used for gas-bearing evaluation for the second section of Xujiahe Formation in the western Sichuan Depression, favorable result is achieved. It effectively overcomes the issue of low distinguishability between gas and water layers' conventional logging response, which confirms the rationality and accuracy of T2-T1 spectra in characterizing the microscopic geological features of reservoirs.
With the advancement of development, oil-gas-water three-phase flow has emerged in near-horizontal wells of the XJ Oilfield. Differences in phase flow rates and fluid physical properties have complicated the three-phase flow in the wellbore, rendering conventional methods ineffective. To solve this problem, this paper first conducts simulation experiments under different well conditions in near-horizontal wells with oil-gas-water three-phase flow using the Flow Imaging Tool (FIT) array flow imager. The common flow patterns in the experiments are summarized based on experimental data and theoretical flow patterns. Subsequently, the imaging processes of simple inverse-distance weighting interpolation, Gaussian radial basis function interpolation, and ordinary Kriging interpolation are introduced by utilizing the local gas holdup and water holdup characteristics obtained from array optical fibers and array resistors. These three methods are then implemented in MATLAB for cross-sectional imaging and average holdup calculation. Through the comparison of imaging results and analysis of relative errors, the average holdup calculated by the optimal method is combined with the high-density cross-sectional segmentation method to achieve three-dimensional holdup imaging along the wellbore. Finally, the coverage range of the FIT instrument in the experimental wellbore is analyzed. The research results indicate that the data measured by the array flow imager can reflect the flow patterns and behaviors of each phase in the wellbore during the oil-gas-water three-phase flow experiments in near-horizontal wells. In fully horizontal wells, the Gaussian radial basis function method is more suitable for calculating the water-hydrocarbon interface and water holdup, while the Kriging method has a better effect on estimating the gas-liquid interface. The three-dimensional wellbore imaging obtained by these methods can intuitively reflect the distribution of oil, gas, and water media in the well and the total holdup value on the wellbore cross-section, providing a certain reference for dynamic monitoring in the oil-gas-water three-phase flow of near-horizontal wells.
The integration of seismic physical simulation with structural analog modeling represents a significant frontier in experimental geophysics, yet it is hampered by a fundamental material science challenge. Traditional seismic models, fabricated from solidified polymers or resins, are static constructs that lack the dynamic, gravity-driven deformation processes inherent to natural tectonics. Conversely, structural analog models, which expertly simulate faulting and folding using loose granular materials like dry sand, are typically seismically opaque due to intense wave scattering and attenuation at grain-to-grain contacts filled with air. This study was initiated to develop and validate a novel methodological framework that bridges this divide. Its primary objective is to create a physical modeling system wherein the kinematic evolution of geologic structures and the concurrent seismic wavefield response can be observed simultaneously, thereby enabling a direct study of the interplay between tectonic processes and their seismic expression. To achieve this goal, we conducted a comprehensive investigation into the acoustic properties of potential modeling materials. A suite of standard samples, including paraffin-composite materials, mastic, and various water-saturated granular materials (e.g., quartz sand, glass microbeads, brown fused alumina), was prepared using different protocols. A key innovation was the development of an "underwater molding" technique for saturating granular materials, which was systematically compared against immersion and permeation methods. The acoustic characterization was performed using an ultrasonic transmission and reflection testing system. We meticulously analyzed the transmitted waveforms' first-arrival clarity, amplitude stability, and frequency content, as well as the reflection energy and continuity from engineered interfaces within layered models. The research specifically focused on quantifying the effects of two critical parameters: (1) the settling time of the saturated granular pack, which governs gravitational compaction and pore-fluid homogenization, and (2) the particle-size contrast between adjacent layers, which controls the genesis of acoustic impedance contrasts. Our systematic experiments yielded several key findings. First, the underwater molding method proved superior in eliminating entrapped air within pore spaces, thereby producing transmitted signals with sharp first arrivals, high amplitude, and minimal post-signal coda, unlike the oscillatory and noisy signals from samples prepared by other methods. Second, the settling time was identified as a critical control on signal quality. Prolonged settling (up to 72 hours) dramatically enhanced high-frequency (450~600 kHz) content, suppressed low-frequency noise and reverberations, and sharpened the transmitted wavelet. This is attributed to compaction-driven enhancement of grain-contact stiffness and homogenization of the pore-fluid distribution. Third, for interface imaging, the mineralogical density difference between layers was found to be a less effective generator of reflection coefficients compared to particle-size contrast. A significant difference in grain size catalyzes the inter-fingering of fine and coarse grains at the boundary, creating a distinct, dense transition zone with a sharp porosity gradient and a sufficiently high acoustic impedance contrast to produce strong, continuous reflections in seismic profiles. Finally, in a practical demonstration, a multi-layered model over 10 cm thick, constructed with optimized particle-size contrasts and subjected to extended settling, yielded a reflection profile with markedly enhanced continuity, energy, and resolution of internal interfaces, confirming the synergistic effect of compaction and size differentiation. This research successfully establishes a robust and effective methodology for "seismo-tectonic" physical simulation using water-saturated granular materials. The method fundamentally resolves the long-standing trade-off between mechanical realism and acoustic transparency in analog modeling. By providing a material system that deforms under gravitational and boundary stresses while permitting high-quality seismic imaging, it opens new avenues for investigating the dynamic evolution of seismic wavefields throughout tectonic deformation processes, such as thrust faulting or extensional graben formation. The findings offer profound insights for interpreting seismic data from natural unconsolidated sequences, including subaqueous sediments and glacial tills, where similar granular and saturated conditions prevail.
Focal mechanism solutions of microseismic events during Hot Dry Rock (HDR) hydraulic fracturing are critical parameters for revealing fracture propagation mechanisms. However, conventional methods struggle to address their characteristics of low energy, high event density, and complex genesis. Based on extensive microseismic data from HDR fracturing, this study develops a focal mechanism inversion method specifically for HDR microseismic events. By combining P-wave first-motion polarity, P/S amplitude ratios, and full-waveform information, a multi-constraint objective function is constructed to calculate the source moment tensor parameters. Finally, combined with iterative inversion of the in-situ stress field, the optimal fault plane is automatically determined, thus avoiding human-induced uncertainty. Applied to a carbonate-type HDR reservoir in eastern China, the method successfully calculated focal mechanisms for 145 microseismic events (MW>-0.5). Practical application demonstrates that this framework enables efficient batch processing of microseismic focal mechanisms, providing reliable technical support for fracture characterization and fracturing optimization in HDR reservoirs.
Pre-stack AVO inversion represents an effective approach for retrieving formation elastic parameters, which has significant implications for fluid identification and reservoir property prediction. However, conventional pre-stack AVO inversion methods based on linearized approximations of the Zoeppritz equation suffer from low accuracy and strong dependence on initial models, and pre-stack stochastic inversion approaches are hindered by poor stability, low computational efficiency, and slow convergence rates. To address these challenges, this study proposes an Adaptive Multi-chain Markov Chain Monte Carlo (AM-MCMC) method for elastic parameter inversion. This method incorporates well-log constrained prior information within a Bayesian framework and constructs a hybrid posterior probability distribution and objective functional based on the exact Zoeppritz equations. By introducing adaptive strategies into the MCMC algorithm, the proposed distributions of the inversion parameters are dynamically adjusted. Multiple Markov chains are employed for stochastic simulation, simultaneously computing statistical characteristic parameters including posterior means, covariances, and confidence intervals of elastic parameters, thereby achieving quantitative characterization of both elastic parameters and their associated uncertainties. Synthetic model tests demonstrate that the stochastic inversion method proposed in this study exhibits superior accuracy, stability, noise immunity, and convergence rate, while also providing reliable uncertainty quantification of the inversion results. Application to field data demonstrates that the near-wellbore seismic inversion results exhibit strong correlation with well-log measurements, validating the method's capability to accurately characterize the elastic properties of gas-bearing tight sandstone reservoirs. This approach shows considerable potential for widespread application in reservoir prediction and reserve evaluation.
To address the limitations of traditional lithology identification methods in accuracy and efficiency, as well as the poor generalization of existing machine learning models under complex geological environments, this study proposes a logging lithology identification method based on granular-ball space, termed LogGBS. This method maps raw well log data into the granular-ball space and constructs multiscale granular-ball representations through an Adaptive Granular-Ball Generator (AGBG). Using "granular-ball purity"as the key control parameter, LogGBS ultimately performs lithology identification by a Granular-Ball kNN (GBkNN) classifier. To evaluate the effectiveness of the proposed method, well log data from the Hangjinqi Gas Field (clastic reservoir) and Sulige Gas Field (carbonate reservoir) in the Ordos Basin, as well as the Xushen Gas Field (volcanic reservoir) in the Songliao Basin, are selected as research objects. The performance of LogGBS is compared with that of eight traditional machine learning and deep learning methods, and the influence of granular-ball purity on classification accuracy is analyzed. The results show that when the purity is not less than 0.9, the average accuracy of each reservoir exceeds 90%; when the purity reaches 0.95, some indicators even surpass 99%. LogGBS demonstrates superior identification accuracy and generalization ability in all three types of reservoirs, providing a new idea for lithology identification based on well logs under complex geological conditions and contributing to the improvement of oil and gas exploration and development efficiency.
The coupled effects of temperature difference and hydration on the mechanical properties and energy characteristics of shale are closely related to its load-bearing capacity and structural failure. The evolution of these characteristics is of great significance for the evaluation of deep wellbore stability and the optimization of deep geothermal energy development. Therefore, experiments were conducted to investigate the effects of different temperature difference and hydration durations on the mechanical properties and energy characteristics of shale. The evolutionary patterns of shale energy characteristics under coupled temperature difference-hydration effects were clarified, and a damage constitutive model was proposed from the perspective of energy. Main findings are as follows: (1) At the same hydration duration, with increasing temperature difference, the peak strength, elastic modulus, total energy, and elastic energy of shale decrease, while the proportion of dissipated energy exhibits the opposite trend, indicating thermal damage and structural deterioration. (2) At the same temperature difference, as the hydration duration increases, the peak strength, elastic modulus, total energy, and elastic energy of shale decrease, whereas the proportion of dissipated energy increases. (3) The influence of temperature difference on shale is greater than that of hydration duration. (4) A larger temperature difference or shorter hydration duration results in more pronounced brittle behavior of shale. (5) An energy-based damage constitutive model of shale under the coupled effects of temperature difference and hydration is proposed, which demonstrates high predictive accuracy and provides theoretical guidance for the prevention and control of wellbore instability during deep and ultra-deep drilling.
During tunnel construction, geophysical advanced detection technologies are essential for identifying hidden geological hazards and implementing timely preventive measures. In recent years, significant progress has been made in adapting these technologies to rapid and continuous tunnel boring machine (TBM) excavation. The advances are mainly demonstrated in two aspects: (1) Ground Penetrating Radar (GPR) and seismic exploration have essentially achieved real-time dynamic monitoring during excavation. The former accomplishes circumferential scanning through cutterhead integration, while the latter utilizes rock-breaking vibrations as a seismic source. (2) For electrical exploration, the optimization of observation systems and integration with TBM have effectively mitigated the limitations posed by confined construction spaces and strong electromagnetic interference, significantly reducing the downtime required for detection. However, the current research still faces many challenges. For instance, the contradiction between the advanced detection range and resolution of a single geophysical method remains prominent. The effective extraction of signals in a strong noise environment remains a key factor restricting the detection accuracy. Moreover, the construction of an intelligent interpretation and real-time decision-making system for multi-source data also requires key breakthroughs. Therefore, this paper systematically reviews advanced detection methods for adverse geological structures and water-inrush hazards, analyzing and summarizing the technical characteristics and limitations of various observation systems. It provides an outlook on the integration and intelligent detection of the comprehensive advanced detection system. The aim is to provide a reference for the development of the integrated exploration and excavation technology system.
With the increasing complexity of geotechnical engineering projects, traditional continuum mechanics theories struggle to accurately characterize the discontinuous deformation behavior of rock masses under complex environments and strong disturbances. As a numerical simulation method well-suited to the discrete nature of rock masses, the Discontinuous Deformation Analysis (DDA) method offers unique advantages in handling dynamic responses under conditions of large deformation, large displacement, and complex boundary constraints. This paper first reviews the theoretical framework and improvements of DDA, with particular attention to advances in contact detection, boundary conditions, and multi-physics coupling modeling. It then summarizes the achievements of DDA applications in various dimensions, including slope engineering, underground construction, and responses to seismic and blasting loads. Finally, it analyzes the current challenges and future research directions in both theoretical development and engineering practice. The results show that, owing to its adaptability to block motion and contact behavior, DDA demonstrates remarkable advantages in a wide range of rock engineering problems. It not only elucidates instability mechanisms such as block toppling and sliding, and accurately simulates hazardous block trajectories, but also improves the precision of sliding process modeling through the incorporation of viscous boundary conditions and friction attenuation mechanisms. These advances enable dynamic reconstruction of the entire process from blast cavity expansion and fracture propagation to muck pile formation. Future research should focus on refining contact algorithms and friction constitutive models, developing multi-field coupling frameworks, advancing high-performance parallel computing, and integrating intelligent modeling with machine learning for engineering risk prediction. Such efforts will enhance the adaptability and practicality of DDA for catastrophic event analysis in complex rock masses, providing robust theoretical support and numerical tools for disaster prediction and risk mitigation in complex rock engineering.
During the development of urban underground space, ground subsidence triggered by legacy coal mine goafs poses a severe threat to the safety of rail transit engineering. Taking the Shengjing mining area traversed by Jinan Metro Line 8 as the study area, this paper aims to construct an integrated research framework encompassing refined geophysical detection, comparative multi-method settlement analysis, and fluid-solid coupling numerical simulation. The objective is to provide a theoretical basis for deformation risk assessment and anti-deformation design of urban rail transit in water-rich mining areas.A comprehensive geophysical strategy, primarily utilizing the Transient Electromagnetic Method (TEM), was applied to delineate the distribution of water-filled goafs, the reliability of which was verified by five boreholes. Subsequently, the Probability Integral Method (PIM) and FLAC3D three-dimensional numerical simulation were employed to calculate and compare the surface settlement caused by the No. 3 and No. 9 coal seam goafs. The 3D numerical model systematically accounted for the lithological differences of 22 strata and the weakening effects of faults. Furthermore, fluid-solid coupling simulations were conducted under two scenarios: dry season (groundwater depth of 50 m) and wet season (groundwater depth of 20 m), to quantitatively evaluate the impact of groundwater level fluctuations on subsidence.(1) TEM clearly identified a "high-low-high" three-layer geoelectric structure, and the delineated low-resistivity anomaly zones were highly consistent with the water-filled goafs revealed by drilling. (2) The maximum surface settlement predicted by PIM was 864.12 mm, whereas the FLAC3D result was 915.3 mm. PIM carries a risk of systematic underestimation as it fails to account for the overlapping disturbance of multiple seams and fault activation. The numerical simulation also revealed that the F11 fault causes discontinuous deformation, and multi-seam mining transforms the floor of the upper coal seam from heaving to settling. (3) Groundwater significantly intensifies subsidence: compared to the dry condition, the maximum surface settlement increased by 9.96% (reaching 1006.5 mm) in the dry season and by 32.3% (reaching 1210.9 mm) in the wet season. The primary controlling mechanism is the increase in total stress on the rock mass due to elevated pore water pressure.It is recommended that PIM and numerical simulation be used jointly for mutual verification. This study quantified the reactivation weight of groundwater on legacy goafs, addressing the limitations of traditional empirical formulas in water-sensitive strata. The findings provide a scientific reference for settlement prediction and safety evaluation of similar underground engineering projects in water-rich mining areas.
The signal of transient electromagnetic observation is influenced by the excitation source mode, geological body conductivity, coupling mode between the emitting part and the receiving part, damping characteristic of the receiving coil, and the capacitive and inductive parameters of the receiving coil, among other factors. When studying the laws of transient electromagnetic response, it is generally assumed that the above system parameters are ideal and not considered, which makes it difficult for the actual transient electromagnetic signal to match the theoretical calculated value. This study proposed a model of transient electromagnetics, set up an equivalent circuit of the transient electromagnetic emitting system, geological body, and receiving system, and developed a basic process simulation system for ground central loop source TEM by establishing the mutual inductance and coupling relationship between the emitting source, geological body, and receiving coil. The system fully considers the parameters of the emitting unit (the inductance and distributed capacitance of the emitting coil and the switching time) and the parameters of the receiving unit (internal resistance, matching resistance, inductance, and distributed capacitance), and by arbitrarily changing a single parameter or multiple parameters, the influence of these parameters on the transient electromagnetic attenuation curve can be studied. The output results of the model are compared with the theoretical output results obtained from the receiving coil control equation to complete the accuracy verification. Based on this, the effects of the switching time, emitting coil inductance and capacitance, receiving coil damping factor, and impedance characteristics on the transient electromagnetic receiving signal are studied. The results indicate that reducing the turn-off time, loop inductance, and capacitance on the transmission end helps improve signal quality. On the receiving end, the impedance characteristics of the receiving coil are the main factor determining the received signal. It was concluded that the signal quality is better when the receiving coil resistance is slightly underdamped or critically damped. Finally, the transient electromagnetic signal is simulated by setting the instrument's measured parameters, and it is compared with the actual data of the instrument. The developed model can be used for calibration of the measured parameters and signals of the instrument, as well as forward design of the instrument. The comparison with measured results further verified the reliability of the developed simulation system for TEM, contributing to the advancement of efficient parameter research and instrument optimization in TEM applications.
Acceleration-domain seismic data acquired by digital nodal instruments, when converted to the velocity domain through time integration, can effectively enhance low-frequency signals and thereby benefit deep imaging. However, this process also nonlinearly amplifies unstable components such as surface waves, external interferences, and low-frequency random noise, leading to a reduction in low-frequency Signal-to-Noise Ratio (SNR), limitations in velocity model building, and further masking of deep reflection energy. Therefore, how to suppress the low-frequency noise amplified during integration while preserving effective low-frequency signal energy has become a key technical challenge in the processing of digital nodal seismic data. To address this issue, this study proposes a multi-feature adaptive low-frequency noise suppression technique. According to the generation mechanisms and characteristics of different types of noise, a dispersion-curve-based surface-wave suppression method is employed to accurately separate and attenuate surface-wave interference by extracting and analyzing surface-wave dispersion characteristics. In addition, an external interference suppression method based on MAD-Trimmed vector median filtering is introduced, and a dynamic-statistics-based threshold optimization technique is incorporated to suppress low-frequency background noise. The processing results demonstrate that the proposed method can effectively suppress complex low-frequency noise while preserving the integrity of effective low-frequency signals. Compared with conventional denoising approaches, the proposed technique achieves a better balance between noise attenuation and signal fidelity, improving the signal-to-noise ratio of velocity-domain data by approximately 1.3 times, and providing more reliable, high-quality input data for subsequent seismic data processing.
Transient Electromagnetic (TEM) method, as a highly and non-destructive geophysical exploration technology, has unique advantages in near-surface exploration fields such as engineering geology, urban underground space development, and engineering detection. However, traditional transient electromagnetic instruments have problems such as insufficient resolution, near-surface blind zones, and cumbersome equipment in near-surface exploration. To address these problems, this paper has developed a near-surface high-resolution TEM receiving system, making it possible to accurately measure the secondary field signal over the entire period. To broaden the measurement frequency band of the acquisition system and accurately capture the rapid abnormal response caused by high impedance objects, this paper designs a high-speed receiving system based on the hardware architecture of "AD7760+XC7K325T". The early signals reflect very near-surface and relatively near-surface information, while the late signals reflect near-surface information. To accurately collect both the early strong induction signals and the late weak induction signals, this paper proposes a dual-channel synchronous acquisition scheme. In this scheme, the first channel collects the early strong signals through an attenuation circuit. When the signal amplitude is below a certain threshold, the second channel is enabled by an analog electronic switch and collects late signals through a multi-stage amplification circuit. The receiving system inevitably suffers from DC offset and random noise interference. In this paper, based on the bipolar transmitting square wave, a superposition algorithm is implemented in the FPGA to suppress the noise interference in real time. Test show that the receiving system not only achieves a sampling rate of 2.5 MSPS, but also has short-circuit noise levels of 45 μV and 0.8 μV respectively, which can well meet the full-period acquisition requirements of the secondary field signals. Furthermore, by normalizing the voltage attenuation curves corresponding to large and small currents, it is found that the attenuation curves of the two can be well integrated, which provides the possibility for reflecting geological structures throughout the entire period. In addition, the circuit board size of this receiving system is relatively small, with a maximum size of only 191 mm×133 mm, and supports 12 V lithium battery power supply, making it very suitable for the instrument miniaturization and integration.
This study addresses the issue of insufficient accuracy in Ground Penetrating Radar (GPR) for urban underground pipeline census and safe operation and maintenance by introducing the GprMax forward numerical simulation software to systematically quantify the influence mechanisms of key parameters, including pipeline burial depth, diameter, material, and filling medium on electromagnetic wave field responses. The research reveals that the relative permittivity of subsurface media serves as the core controlling factor determining radar wave field energy attenuation and reflection intensity. Building upon this finding, and targeting the current challenge of accurately characterizing the three-dimensional spatial structure of complex underground pipelines, this study proposes a joint analysis method integrating "three-dimensional slice reconstruction+structured feature extraction along slice B-scan+three-dimensional attribute volume fusion." This method effectively overcomes the limitations of traditional two-dimensional profiles that can only display images or single attributes, enabling precise revelation of pipeline spatial orientation in the thickness extension direction. It provides a solid theoretical basis for optimizing on site detection schemes and data interpretation, while also establishing a comprehensive data foundation for constructing high-precision three-dimensional visual geological models.
The corrosion electric field generated by equivalent electric dipoles of an underwater vehicle in multilayer seawater environment was simulated using ultra-low frequency dyadic Green's function for stratified media. The influence of the seawater's conductivity, depth, and stratification state on different components of the corrosion electric field was analyzed. By combining the dyadic Green's function with the Particle Swarm Optimization (PSO) algorithm, the location and intensity of equivalent electric dipoles with different number were inverted. The calculation results showed that the intensity of the corrosion electric field in seawater will decrease with increasing seawater's conductivity and they are inversely proportional to each other. Since the normal component of the electric field is discontinuous at layer interfaces, this component can be used to determine the interface position of stratified seawater. The inversion results of the corrosion electric field become more and more accurate with increasing number of equivalent electric dipoles. The consistency between the inversion results and the simulation results of the corrosion electric field of the underwater vehicle indicated that it is feasible to replace the underwater vehicle's electric field with the equivalent d ipole's electric field.
Continuous development and innovation in technology application are essential for enhancing ocean management capabilities and building China to a strong maritime nation. In recent years, with the advancements and widespread use of Unmanned Underwater Vehicle (UUV) technologies, such as submersibles and gliders, there is an urgent need to conduct research and deepen technologies related to detection, tracking, and disposal in complex marine environments, focusing on the aforementioned mobile targets, in order to address the issue of monitoring underwater moving targets under complex conditions. Based on existing ship seismic wave detection technology, this paper presents a vector wavefield direction-finding and tracking algorithm based on absolute azimuth correction. Furthermore, through direction-finding and tracking experiments conducted in shallow water (120 meters deep) conditions using Underwater Unmanned Submersible Vehicles (UUVs) as targets, the effectiveness of the algorithm in continuously direction-finding and stably tracking underwater moving UUV targets is verified. The experimental results show that by utilizing the low-frequency part of the UUV's own radiated noise during maneuvering, a single vector sensor can achieve continuous direction-finding and stable tracking of the UUV, Accuracy reaches ≤10°@6 km, laying a technical foundation for the next step of multi-node networking positioning detection.
The Yinggehai Basin is rich in low-permeability gas reservoirs and represents a key area for offshore natural gas reserve growth and exploration in China. The prediction of "sweet spot" reservoirs is crucial for the effective exploration and development of such low-permeability reservoirs. However, the low-permeability reservoirs in this basin are influenced by multiple factors including sedimentation, diagenesis, and subsequent alteration, resulting in complex petrophysical properties, strong heterogeneity, and insufficient understanding of their spatial distribution. These challenges lead to notable difficulties in sweet-spot prediction using conventional geophysical methods, characterized by low accuracy and high ambiguity. To address these issues, this study systematically conducts targeted research. First, systematic petrophysical experiments based on core samples are performed to identify key geological factors controlling reservoir permeability. Second, petrophysical patterns are analyzed, and a seismic response template characterizing sweet-spot reservoirs is established. Furthermore, integrated techniques such as AVO forward modeling, pre-stack seismic inversion, and seismic facies-constrained permeability prediction are employed to develop a geophysical evaluation method suitable for low-permeability gas reservoirs in the Yinggehai Basin. Practical exploration results demonstrate the effectiveness of the proposed method, providing support for the efficient exploration and development of offshore overpressured low-permeability gas reservoirs.
The Beibuwan Basin is rich in oil and gas resources. However, due to the complex faults, the seismic imaging of the Weizhou Formation and Liushagang Formation in the Paleogene is poor, which restricts the precise implementation of deep traps. Analysis suggests that the key reasons for the poor imaging of complex faults in this area are the acquisition with narrow azimuth, low coverage fold, weak illumination, and processing difficulties with amplitude preservation denoising and characterizing rapid velocity changes. In order to resolve this difficulties, quantitative evaluation techniques for acquisition parameters with azimuth-offset-coverage is propoesd, and anisotropic migration imaging based on full waveform inversion velocity modeling are proposed for the OBN seismic data. The research significantly improved the quality of seismic data in the complex fault zones of Beibuwan Basin and achieved good application results.
The late-time signals of semi-airborne transient electromagnetic method are relatively weak and easily masked by environmental noise. To better reduce the impact of noise and ensure the accuracy of subsequent imaging analysis and related research, this paper proposes a Kalman Filtering (KF) improved by Sparrow Search Algorithm (SSA) for denoising semi-airborne transient electromagnetic signals to improve the Signal-to-Noise Ratio (SNR) of raw data. Since the performance of conventional Kalman filtering is affected by the setting of noise covariance matrix, fixed covariance matrix cannot accurately adapt to noise changes in complex noise interference scenarios. Based on this, this paper establishes a dynamic noise covariance matrix and then uses sparrow search algorithm to globally optimize the intensity parameters in the covariance matrix, and adopts the filter with optimal parameter combination for denoising. The denoising effect of the proposed SSA-KF denoising algorithm is compared with traditional methods such as Empirical Mode Decomposition (EMD), Wavelet Transform (WT) and Singular Value Decomposition (SVD) for different noises. Experimental results show that the SNR of the proposed algorithm is improved by 30.57 dB, 24.61 dB, 33.01 dB and 20.03 dB respectively for Gaussian white noise, atmospheric noise, power frequency noise and mixed noise, and the denoising effect is better than other algorithms. In the application of measured data of underground rivers in Lichuan City, the apparent resistivity imaging results of the data denoised by SSA-KF algorithm show clearer abnormal characteristics compared with direct imaging and imaging results after SVD denoising.
The traditional plate tectonic theory has limitations in explaining continental earthquakes, and the thermal structure of the lithosphere serves as a crucial window for exploring the mechanism of intraplate earthquakes. Based on geothermal data from Ningjin and its adjacent areas in North China, combined with the crustal layered structure revealed by deep seismic sounding profiles, this study employs a 2D finite element method to numerically simulate the deep temperature field of the study area, characterize the features of the lithospheric thermal-rheological structure, and explore its relationship with seismic activity. The results indicate that from the Taihangshan Uplift to the Linqing Depression, the mantle heat flow exhibits a low-to-high trend, the "thermal" lithospheric thickness shows a thick-to-thin variation, and the Moho temperature displays a high-to-low-to-high pattern. The crust-mantle heat flow ratios in the eastern Jizhong Depression and Linqing Depression range from 0.66 to 0.72, indicating intense deep-seated activities. Seismic events in the study area are spatially concentrated in terrestrial heat flow gradient zones, local Moho uplift zones, and areas with well-developed deep and large faults. Vertically, they display a two-layer distribution, with the boundary defined by the Brittle-Ductile Transition Depth (BDTD) in the middle crust. Lateral variation zones of the thermal structure are concentration areas of thermal stress; BDTD demarcates the two-layer distribution of earthquakes; and deep and large faults controlled by the thermal structure serve as the key carriers for the upwelling of mantle thermal materials and stress transfer. Under the influence of the subduction of the Pacific Plate, mantle upwelling triggers back-arc extension, which results in lithospheric fragmentation and disturbs the intracrustal temperature and stress fields. The coupling of thermal stress and tectonic stress further forms the two-layer seismic fracture pattern.
The Shengyuan volcanic basin and the Tianhuashan volcanic basin are adjacent to each other, but they are located in the eastern segment of the Qin-Hang metallogenic belt and the North Wuyi metallogenic belt, respectively, and therefore have important geoscientific significance. In this study, a short-period dense seismic array was deployed in the region, and seismic ambient noise imaging was used to construct a three-dimensional shear-wave velocity structure of the upper crust shallower than 5 km. The results show that the Gan-Hang tectonic belt and the North Wuyi metallogenic belt exhibit significant shear-wave velocity differences: the former is generally characterized by low velocities, whereas the latter is dominated by high velocities. With increasing depth, the southern boundary of the Gan-Hang tectonic belt shifts northward as a whole, providing velocity-structure constraints on the shallow north-dipping thrust geometry of the Jiangshan—Shaoxing fault in the study area. At a depth of 5 km, the shear-wave velocity contrast between the Gan-Hang tectonic belt and the North Wuyi metallogenic belt decreases, indicating that the shallow velocity differentiation between the two tectonic units weakens with depth. In combination with the regional tectonic background, this feature may be related to the Caledonian compressional reworking after the Neoproterozoic amalgamation of the Yangtze and Cathaysia blocks, as well as the Yanshanian extension and magmatism associated with the NW-directed subduction of the Paleo-Pacific plate.
Temporary broadband seismic observation plays a crucial role in the study of the deep structure and dynamic processes of the South China Block. However, although scholars have been deploying a large number of temporary broadband seismic stations across the South China Block and have achieved abundant research outcomes, systematic research on the quality of observation data from these stations remains insufficient yet necessary. Ambient seismic noise is an important parameter for evaluating the data quality of broadband seismic station, which can be quantitatively characterized by calculating the Power Spectral Density (PSD) of the observation data and statistically analyzing the corresponding Probability Density Function (PDF). Therefore, this study firstly obtains the ambient seismic noise levels at 7 temporary broadband seismic stations located in the western South China Block by using the PSD PDF method. Subsequently, taking station LH23 as a typical example, this study analyzes the main sources of ambient seismic noise in different frequency bands and their characteristics. In addition, this study also investigates the origins of high outliers in the PSD results, along with the morphological features of the corresponding curves. The results of this study indicate that cultural noise, microseism, and ground tilt noise are the main sources of ambient seismic noise in different frequency bands at the temporary broadband seismic stations in the western South China Block. These noise sources exhibit different diurnal or seasonal variations, and the interference of the same noise source on horizontal and vertical components also shows discrepancies. Seismic events, intense near-station anthropogenic activities, and system transients can all result in high outliers in the PSD results, while the corresponding deformation of curves induced by these different reasons present distinct characteristics. Based on the above results, this study further proposes suggestions for the deployment of temporary broadband seismic stations in the South China Block, from the aspects of station site selection, vault construction, as well as seismograph performance testing and station maintenance.
Deep seismic reflection cannon single-coverage profiling, evolved from hydrocarbon exploration, has been widely used since the mid-20th century to study deep continental and marine structures. Traditional multi-fold stacking techniques, however, face significant challenges in complex tectonic environments due to severe signal attenuation and cumulative static correction errors exceeding two and a half kilometers. To address these limitations, the Large-charge Single-fold Deep Seismic Reflection Profiling technique employs high-energy explosive charges and extended receiver arrays to implement single-fold acquisition. This approach effectively mitigates signal decay and static error accumulation, achieving a twenty percent improvement in crustal imaging resolution and a forty percent reduction in static correction errors compared to conventional methods.The methodology encompasses fundamental wavefield excitation principles, instrumental innovations, acquisition parameter optimization, and advanced data processing workflows. Controlled detonation of large-charge sources generates broadband wavefields penetrating beyond fifty kilometers, while broadband three-component seismometers operating across a wide frequency spectrum maximize signal fidelity. Acquisition parameters such as shot spacing and temporal sampling are refined through ray-tracing simulations to balance resolution and penetration depth. Data processing integrates nonlinear static correction, adaptive noise suppression, high-precision velocity modeling, and amplitude-preserving migration to deliver enhanced imaging accuracy. Case studies across tectonically diverse regions validate the technique's effectiveness. In the eastern Qinghai-Xizang Plateau, it successfully traced Moho discontinuities and subduction morphologies with two kilometer vertical resolution, revealing complex slab rollback patterns linked to the India-Eurasia collision. Within the North China Craton, the technique precisely delineated deep-seated fault systems extending from the Moho to the lithosphere-asthenosphere boundary, directly correlating with Mesozoic mineralization events. In the Bohai Bay Basin, it identified mantle delamination associated with hydrocarbon reservoirs, fundamentally transforming basin evolution models. These applications demonstrate its critical value in resolving orogenic evolution, Moho morphology, deep fault systems, and seismic mechanisms, with profound implications for geodynamics, resource exploration, and seismic hazard mitigation.Despite its achievements, the technique encounters persistent challenges in energy penetration efficiency—where signal loss exceeds sixty percent at Moho depths—and noise suppression under low signal-to-noise ratio conditions. Future development should prioritize AI-driven acquisition systems with real-time adaptation, integration of gravity, magnetic, and magnetotelluric data for multi-physics modeling, and exascale computing-enabled full-waveform inversion to achieve sub-kilometer resolution. These advancements will establish digital twins of Earth's deep interior, advancing our understanding of crust-mantle dynamics and resource distribution.The twenty percent resolution enhancement and forty percent error reduction solidify this technique as an indispensable tool for high-fidelity deep Earth imaging. By resolving previously obscured structures such as crust-penetrating fault systems, Moho discontinuities, and mantle delamination, it provides a systematic foundation for Earth science research and resource security applications. This work delivers essential methodological guidance for advancing deep structural exploration and addressing fundamental questions in Earth sciences.
The Handan-Xingtai region is a major ore concentration area for large-scale skarn iron deposits in China, and ore prospecting in the deep and peripheral areas of existing mines constitutes the primary task at the current stage. The high-density and high-magnetic characteristics of iron ore targets relative to surrounding rocks provide crucial physical property support for delineating iron ore prospective areas using gravity and magnetic data. However, practical applications are still confronted with issues such as irrelevant signal interference, strong remanent magnetization, and non-uniqueness in interpretations. Based on high-resolution ground gravity and aeromagnetic data in the study area, we first employ equivalent source-based field separation technology to eliminate interference from deep-seated sources and adjacent geological bodies, thereby obtaining residual gravity fields at different depths. Meanwhile, various magnitude magnetic transforms (e.g., total magnitude of three magnetic anomalous vector, horizontal total gradient, normalized source strength) are calculated to mitigate the impact of strong remanent magnetization. Subsequently, a comprehensive analysis and evaluation are conducted by integrating geological information, locations of known iron ores, and distributions of density and magnetic susceptibility. The results indicate a strong correspondence between high gravity anomalies and high magnetic anomalies associated with igneous bodies and iron ores, with iron ore occurrences predominantly distributed along the boundaries of high-value zones or transition zones between high and low values of residual gravity anomalies and magnitude magnetic transforms. Finally, prospective areas for iron ores are delineated based on the aforementioned findings, which can provide effective constraints and references based on gravity-magnetic data for subsequent iron ore exploration in the study area.
To clarify the deep electrical structure of the Mujicun porphyry copper deposit in the northern Taihang Mountains, explore its coupling with the "source-transport-storage" processes of the mineralization system, and further advance the understanding of the deep background of porphyry copper deposits within the North China Craton, this study carried out two-dimensional inversion using the nonlinear conjugate gradient algorithm, based on 13 Magnetotelluric (MT) stations traversing the Mujicun ore concentration area. An electrical model down to a depth of approximately 40 km was constructed. The model clearly reveals two sets of high-conductivity channels extending into the middle-lower crust, along with a NNE-trending fault system that controls these electrical features; both are interpreted as conduits for deep ore-bearing fluids. Additionally, the model identifies a mineralization source zone or a remnant magma chamber at a depth of about 25 km. The obtained electrical structure finely delineates the spatial architecture of the mineralization system, indicating that this "channel-reservoir" coupling system was governed by the reactivation of the Taihangshan deep fault zone under a Late Mesozoic extensional setting, accompanied by magma emplacement. It thus stands as a typical example of crust-mantle interaction and mineralization during the destruction of the North China Craton.
The Miaoling—Jiuzhanggou gold belt in Song County, Henan Province, is situated in the eastern part of the Xiong'er Mountain—Waifang Mountain gold and polymetallic ore concentration area, serving as a major gold-producing zone in western Henan. All gold deposits discovered within this gold belt are situated in concealed rock mass regions inferred from gravity and magnetic data, and the formation of these deposits is strictly controlled by steep to moderately inclined fault structures. Studying the geophysical field characteristics of this gold belt, inferring the spatial distribution of concealed rock masses and ore-bearing fault structures, constitutes an effective approach for deep exploration and prospecting and is key to achieving breakthroughs in mineral exploration. In response to previous issues in this gold ore belt, such as limited geophysical exploration investment, shallow detection depth, and low observation precision, which hindered a deep understanding of the deep geological structural characteristics, this study interpreted regional gravity and magnetic anomaly features. Comprehensive profiling surveys using low-cost, deep-penetration, high-resolution magnetotelluric sounding, microtremor sounding, and high-power time-domain induced polarization sounding were carried out in the gold ore belt and its typical mining areas. Electromagnetic, microtremor, and induced polarization anomaly information within the profiles were obtained. Combined with existing results, the deep geological structures of the Miaoling—Jiuzhanggou gold ore belt and its gold deposits were essentially clarified. The deep spatial distribution characteristics of Mesozoic intrusive rocks, the Taihua Group metamorphic basement, and Cenozoic basin strata, as well as the deep morphological features of major ore-controlling structures, were analyzed and delineated. Furthermore, deep prospecting potential predictions and target area determinations were conducted. The research findings can provide valuable references for deep prospecting in this gold belt and other related studies.
The stepwise metallogenic model established through theoretical innovation in the Jiaodong area has been successfully applied to deep exploration of the Zhaoping Fault Zone and other relevant areas. This work has achieved major prospecting breakthroughs and set a benchmark for deep gold exploration across China. As an important gold production base of the country, the Xiaoqinling area has long seen slow progress in deep prospecting activities.Comparative analysis of basic geological data indicates that both the Xiaoqinling and Jiaodong areas are located on the margin of the North China Craton. They share similar macroscopic tectonic settings and have undergone intense multi-stage magmatism, possessing great potential for deep gold mineralization. In recent years, funded by the Shaanxi Provincial Geological Exploration Fund, our project team has carried out a new round of deep prospecting supported by integrated geophysical techniques and made important progress.Through detailed interpretation of gravity-magnetic anomalies and wide-field electromagnetic data, this paper clarifies that the deep structure of the Xunmadao Fault in the Xiaoqinling area is highly similar to that in the Jiaodong area, showing an overall stepwise spatial pattern with alternating steep and gentle fault segments. Guided by the Trinity prospecting prediction theory, the deep steep-gentle transition zones of the fault are identified as the optimal metallogenic positions, and four deep prospecting targets are delineated accordingly.Subsequent deep drilling has successfully revealed altered rock-type gold orebodies within the predicted targets, with a true thickness of 10 meters and a maximum gold grade of 13.4 g/t. This study verifies the existence of the stepwise metallogenic regularity in the Xunmadao Fault Zone. The findings not only realize a major breakthrough in regional deep gold prospecting, but also provide a valuable reference for geological research and mineral exploration in areas with similar tectonic features.
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