Geophysical exploration has a wide range of applications in the fields of energy exploration, environmental monitoring and engineering survey by quantitatively processing the observed field source data for subsurface target detection. However, the geophysical exploration method using a single data has the problems of multiple solutions and low resolution in the inversion process. Therefore, joint inversion by synthesizing multiple geophysical observation data has more significant advantages than single inversion. Electromagnetic exploration and seismic exploration are two kinds of geophysical exploration methods based on different physical mechanisms, which typically differ in spatial resolution and sensitivity to the target. Therefore, the combination of the two can significantly improve the reliability of the inversion results. As an important means for quantitatively interpreting electromagnetic and seismic data, joint electromagnetic and seismic inversion can effectively reduce the multiple solutions of single data inversion and improve the prediction accuracy of formation parameters. This paper firstly summarizes the classification of electromagnetic and seismic joint inversion and its development history, then describes the principle and application examples of joint inversion method, and finally discusses its development opportunities and looks forward to the future research direction.

A cast of east-west trending continental slivers, which were composed of the Cimmerian Supercontinent, including the Lhasa continental block, started to rift apart from the northern margin of Gondwana in the late Paleozoic. According to this comprehensive tectonic scenario, several points of view have been proposed on when and from which continent the Lhasa block rifted away from the northern margin of Gondwana over the past decades, however, the issue still remains controversial. To this end, in this study, we aim to provide quantitative constraints on this topic by employing isotope-based geochronological dating and paleomagnetic techniques. Through carrying out an integrated study including zircon U-Pb geochronology, paleomagnetism, petrography, and rock magnetism on the early Middle Permian Luobadui Formation basalts and andesitic basalts developed in and around the northwest part of the Linzhou County of south Xizang (i.e., Tibet), it reveals that the Luobadui Formation volcanics was formed at ca. 272 Ma (i.e., Guadalupian stage of Middle Permian). Based on rock magnetic results carried out by previous scholars and petrographic investigations performed in this study, it shows that the Luobadui Formation volcanic samples experienced low-grade metamorphism along margins of some rock-forming minerals like plagioclase, quartz. Typical accessory minerals like magnetite particles (is also a kind of iron-bearing oxides) distributed in Luobadui Formation volcanic rock samples was also impacted by this low-grade metamorphism. Rock magnetic data sets also suggest that the dominant remanence carriers in the Luobadui Formation volcanic rocks are pseudo-single domain (PSD) magnetite grains in this study. Statistical analysis on filtered characteristic remanent magnetizations from 21 paleomagnetic specimens of the Luobadui Formation volcanic rocks reveals that the Fisherian mean direction and associated parameters over the sampling unit of the Luobadui Formation volcanic rocks are Ds±ΔD=124.2°±8.8°, Is±ΔI=34.5°±7.2°, k_s=20.3, α_{95 s}=7.2° after bedding correction. Thus, based on already obtained statistical mean direction over the selected 21 samples of the Middle Permian Luobadui Formation volcanics, the paleomagnetic pole can be computed as λ_p=16.8°N, φ_p=325.5°E, K=20.7, A₉₅=7.2°. The paleosecular variation has been adequately averaged out according to paleosecular variation (PSV) evaluation criteria in paleomagnetism for the sampled Luobadui Formation volcanics. Therefore, in combination of positive fold test results for the paleomagnetic data sets with rock magnetic and petrographic results, we believe that our obtained paleomagnetic results are primary origin. It can be calculated (reference site: 30.01°N, 90.99°E) that the Lhasa block was at 19.7°±7.2°S in southern hemisphere at ca. 272 Ma. In other words, the Lhasa block was still in middle-low latitude of southern hemisphere then. Combined with previously published results from the Lhasa block, we intend to think that the Lhasa continental block along with the Cimmerian supercontinent likely rifted away from northern periphery of Gondwana and started its long-lasting northward travel journey in early Permian or even much earlier period. Combined with previously published paleomagnetic and geological results, it shows that the total northward convergence is estimated as 1776±700 km of the Lhasa block travelling from low-middle latitude band of 19.7°±7.2°S at ca. 272 Ma to the equatorial area of 3.7°±3.4°S at ca. 180 Ma. This further indicates that the average motion rate of the Lhasa block was around 1.9 cm/a during the time period of 272~180 Ma. In other words, the Lhasa block likely rifted away from northern margin of Gondwana in Early Permian and immediately started its northward drift and motion journey.

High-precision Earth Orientation Parameter (EOP) forecasting, which encompasses parameters such as Polar Motion (PM), universal time, and length of day, is crucial for facilitating the transformation between terrestrial and celestial reference frames in various applications (e.g., satellite autonomous navigation, deep space exploration, and geodynamic research). However, the mainstream predictive methods (including Least squares, numerical decompositions) have serious limitations, such as tail effect, prior periods and poor estimations of model parameters. To improve the accuracy of EOP forecasting (1~360 days), this paper introduces an integrated model combining Singular Spectrum Analysis (SSA), Prony's method, and Autoregressive (AR) models, demonstrated through the case of PM parameter prediction: Firstly, the SSA is used to separate the principal components (e.g., trend, annual, and Chandler terms) and residual components from the original PM observations. Secondly, combined with the Prony method to model and extrapolate these principal components based on complex exponentials functions; and we combine the widely used AR method to predict their residuals. To verify this combined approach, we conduct multiple prediction experiments based on the IERS EOP 20 C04 data. The experimental results showed that the SSA+Prony+AR model effectively captures the time-varying characteristics and significantly mitigates the tail effects of the PM components. Compared with traditional LS+AR models and IERS Bulletin A forecast products, our proposed model exhibits superior performance in medium to long-term polar motion forecasting, particularly reducing forecast errors by nearly 40% in the X direction. These findings can also provide valuable insights into the forecasting of other EOP parameters.

To expand the application of Global Navigation Satellite System Reflectometry (GNSS-R) in the field of ocean monitoring, this paper, based on the CYGNSS wind speed products from 2020 to 2023, combined with the wind speed reference data, makes a systematic assessment of the accuracy of CYGNSS wind speed products and analyses the distribution characteristics of wind energy resources in the tropical and subtropical waters of China. Systematic assessment, and analyses of the distribution characteristics of wind energy resources in China's tropical and subtropical seas. The experimental results show that the extrapolated wind speed accuracy of the CYGNSS wind speed product is improved by about 47% after the correction of sea surface roughness, and it has a good matching accuracy with the reference data, which meets the needs of wind energy assessment. In the study area, wind energy resources are more abundant in the southeastern sea, among which the Taiwan Strait has the highest annual average wind power density, reaching more than 550 W/m². In the southern part of the South China Sea, wind energy resources are relatively scarce, and the annual average wind power density is lower than 250 W/m²; the wind power density of each sea area has significant seasonal differences, and the distribution of wind energy resources in the East China Sea is relatively stable in all seasons, with the distribution maintained at more than 300 W/m² all year round, while in the South China Sea there is a more violent seasonal fluctuation, and the difference between the average wind power density of the winter and summer seasons is significant; in terms of the potential for development, the wind power density in shallow waters in the southern part of the Yellow Sea is relatively high. In terms of developing potential, the southern Yellow Sea and other sea areas have high wind energy reserves and technological development capacity in shallow waters, while the northeastern part of the South China Sea and other sea areas show great development potential in deep waters.

Located in the three tectonic domains of the Pacific plate, the Paleo-Asian Ocean and the Tethys, i.e., the northern part is blocked by the southward thrust of the Qinling orogenic belt and the North China block, the western part is pushed by the eastward flow of the uplift material of the Qinghai-Xizang Plateau, and the eastern part is blocked by the Cathaysian block. The eastern and western parts of the Yangtze block have different evolution characteristics. The western part still preserves a relatively stable quasi-craton block centered on the Sichuan continental core, while the eastern Yangtze has been involved in intracontinental orogeny since the early Paleozoic, and different degrees and styles of tectonic deformation have occurred. In this paper, we try to use the aeromagnetic anomaly to directly invert the magnetization vector, and realize the quantitative estimation of the magnetization intensity and magnetization direction in the Yangtze region. On this basis, the magnetic structure and difference of the Precambrian crystalline basement of the Yangtze Block are studied, and the deep structure and state of the orogenic belt within and around the Yangtze Block are analyzed. It is helpful to reconstruct its Precambrian composition and basic tectonic framework, and provides important support for the dynamic problems such as the tectonic relationship between the Yangtze and Cathaysian blocks and the North China plate and the influence of the eastward movement of the Qinghai-Xizang Plateau on it.Traditional magnetization inversion methods usually ignore the remanence and self-demagnetization effects. However, the actual geological conditions are often very complicated. Especially when there is strong remanence, the magnetization direction of the field source usually deviates greatly from the direction of the geomagnetic field. In this paper, the aeromagnetic data of the Yangtze region are collected, and the Direct Analytical Signal (DAS) is calculated by Hilbert transform. The direct analytical signal mode is used as input to invert the magnetization intensity of the region, and the magnetization intensity obtained by DAS mode inversion is used as a constraint. The penalty function is added to the objective function, and the joint objective function under the equivalent constraint condition is established. The inversion of the three components (Mx, My, Mz) of the magnetization intensity vector is realized, and the magnetization intensity vector structure of the Yangtze region is inverted. The magnetic structure of the Sichuan Basin, the Longmenshan tectonic belt and the surrounding area is analyzed.The following progress and understanding have been made: (1)The magnetization intensity of Longmenshan fault zone is different from that of Songpan-Ganzi block and similar to that of Yangtze block, so it is classified as Yangtze block. The northern Sichuan Basin and Hannan-Micang area have high magnetization, and the deep structure is a stable rhombic crystalline basement rock. There are large-scale NE-trending magnetic bodies in the deep underground of the central Sichuan Basin, which may be related to the Archean to Paleoproterozoic basement and the ancient continental nucleus below. The inversion results and the distribution characteristics of magnetic anomalies show that the main body of the Cambrian basement extends from the northern Sichuan Basin to the Micangshan Hannan coverage area.(2)In the negative magnetic anomaly area on the northern margin of the Sichuan Basin, the magnetization direction is opposite to the geomagnetic field, which may be related to the subduction and tectonic deformation of the Qinling orogenic belt. After the collision between the Yangtze plate and the North China plate in the late Late Triassic, the Qinling Mountains experienced a strong intracontinental orogeny, and the deformation expanded from the Qinling Mountains to the northeast of the Sichuan Basin, which may cause the change of magnetization direction.(3)There is a magnetic inversion phenomenon in the Longmenshan tectonic belt, which may be related to the remanence of the Neoproterozoic intrusive rocks. These intrusive rocks were formed in 830-740 Ma, mainly by the underplating of mantle-derived mafic magma. The magnetic minerals in the rock were reversely magnetized during the geomagnetic polarity reversal, and retained this state during the cooling and hardening process, forming an intrusive rock with remanence. In addition, the special tectonic position on both sides of the Longmenshan fault zone, that is, the strong interaction zone between the eastern margin of the Qinghai-Xizang Plateau and the Yangtze block, as well as the push of the eastward flow of the uplift material of the Qinghai-Xizang Plateau and the resistance of the underground rigid block of the Sichuan Basin, led to strong tectonic deformation and magnetization direction changes.

Geophysical exploration is a vital methodology for investigating the structural characteristics of geothermal systems. In recent years, Beijing geothermal geophysical exploration has made significant progress in the study of fault structure scale and extension characteristics, pre-Cenozoic basement morphology, reservoir burial depth and caprock thickness, spatial range of intrusive rocks and their transformation characteristics to reservoirs. Distinct integrated geophysical explorations are necessary for two typically different systems-namely convective type systems in uplifted orogenic belts and composite sedimentary basin systems, due to their contrasting geological configurations. Emerging technologies, particularly wide field electromagnetic methods and microtremor surveys, provide effective solutions for resolving complex geothermal geological structures and should be prioritized for implementation. The systematic collection and testing of rock physical property samples have provided foundational data for the quantitative assessment of geothermal resources and detailed structural inversion in the Beijing region.

Interferometric Synthetic Aperture Radar (InSAR), a highly effective technique for surface deformation monitoring, has been extensively employed in applications such as geological disaster monitoring, mining surface damage. Nevertheless, InSAR is limited by its ability to measure only one-dimensional deformation along the satellite's Line-of-Sight (LOS), which restricts its capacity to capture the full complexity of surface movements. This study focuses on Kilauea Volcano and utilizes three sets of Synthetic Aperture Radar (SAR) data acquired from different orbital directions before and after the volcanic eruption, in conjunction with data from several Global Navigation Satellite System (GNSS) monitoring stations. The two-orbit DInSAR observation method is used to calculate vertical and east-west deformations, whereas the three-orbit DInSAR observation method is employed to calculate vertical, east-west, and north-south deformations. The consistency of the vertical deformation and east-west deformation calculated by both methods is tested via the Intraclass Correlation Coefficient (ICC), and the deformation calculation accuracy is validated by monitoring data from two GNSS stations, KAMO and KTPM. To further discuss the computational accuracy of the two-orbit and three-orbit methods, the Probability Integral Method (PIM) is used in conjunction with SAR spatial parameters to simulate LOS deformations in different directions, allowing for 2D/3D deformation accuracy analysis. The experimental results indicate that: (1) the vertical (ICC=0.986) and east-west (ICC=0.989) deformations calculated by the two and three-orbit DInSAR observation methods are highly consistent, with maximum RMSE differences of 0.021 m and 0.013 m, respectively; (2) the absolute errors of the vertical, east-west, and north-south deformations calculated by the three-orbit method compared with the GNSS monitoring data from the KAMO and KTPM stations are relatively small, at 0.04 m, 0.035 m, 0.009 m, and 0.001 m, 0.023 m, and 0.0002 m, respectively; and (3) the simulation experiment reveals that, in the absence of spatial parameter errors, the three-orbit method can accurately calculate three-dimensional deformations; the error in the subsidence calculation using the two-orbit method is about 3% of the maximum subsidence value, and the error in the east-west horizontal movement is about 5% of the maximum horizontal movement value, indicating that the two-orbit method can provide relatively accurate vertical and east-west surface deformation measurements.

Deep Learning (DL)-based Ground Penetrating Radar (GPR) target recognition methods have been widely applied in geological exploration and infrastructure inspection. However, existing approaches face three main limitations: (1) Most GPR systems operate in single-polarization mode, leading to incomplete acquisition of target scattering information; (2) Traditional deep learning methods risk misclassification when handling B-scan images with similar hyperbolic features from different targets; (3) Direct input of 2D B-scan images into convolutional neural networks incurs high computational overhead. To address these challenges, this paper proposes a multi-polarimetric decomposition fusion method for GPR target recognition based on a lightweight MobileNetV3 network. The proposed method first acquires full-polarimetric GPR data (HH, VH, and VV polarizations) of subsurface targets. Subsequently, H-Alpha decomposition, Freeman decomposition, and Pauli decomposition are performed to extract eight polarimetric parameters characterizing the targets. These parameters are fused into an eight-dimensional feature matrix, which is then fed into a modified MobileNetV3 network integrated with a Squeeze-and-Excitation (SE) attention module for target identification. To verify the effectiveness of the method, four typical targets were classified in the experiments, and the results indicate that using the eight-dimensional feature matrix as the network input can enhance the target classification accuracy. The target classification accuracy can be further improved by incorporating the SE module into the network. Furthermore, compared to conventional ResNet18 and VGG16 networks, the improved MobileNetV3 achieves the highest recognition accuracy (98.75%) while significantly reducing parameter number and model size. The experimental results demonstrate that using an eight-dimensional feature matrix that includes target polarization information as the network input not only provides richer target information but also effectively reduces the redundant information of the input network. This improvement enhances the target classification accuracy while decreasing the matrix size of the input network. Additionally, the lightweight backbone network based on MobileNetV3, integrated with the SE attention mechanism, enhances critical feature extraction capabilities and strengthens discriminative power for target classification. The paper effectively addresses the challenges of insufficient feature discrimination and high computational load in GPR target classification.

Accurate observation system information is the foundation of high-precision seismic data processing. Due to the inability to use conventional GPS positioning for OBN receiver points, specialized technical means are usually required to determine the coordinates of the receiver points. Common secondary positioning methods for receiver points include least squares method, approximate tetrahedron method, search method, fitting surface method, equivalent velocity method, vector synthesis method, etc. However, they are all based on direct wave travel time for positioning. Shallow water OBN data only has direct waves within a small offset distance, making those methods difficult to apply. Another commonly used secondary positioning method is refracted wave secondary positioning, which has low positioning accuracy due to its inability to handle speed gradients, interface bending, and other situations. In response to the problem of inaccurate positioning of receiver points in shallow water environments, this paper proposes a positioning method based on first arrival travel time fitting. Firstly, the first arrival time is accurately picked up from the common receiver point gather. Then, a polynomial is used to fit the relationship curve between the first arrival time and offset. The coordinates of the receiver points are adjusted by the difference between the actual picked travel time and the corresponding value on the curve, and multiple iterations are carried out until convergence. Finally, based on the water depth values measured in the survey, the corresponding Z coordinates can be obtained by projecting the X and Y coordinates of the receiver points, thus obtaining accurate three-dimensional coordinates of the receiver points. The method proposed in this article has been applied to both simulated and actual OBN data, achieving ideal results.

In the West No.2 Mining Area of Longshou Mine, mastering the trends of surface subsidence and the development process of underground overburden is crucial for analyzing mining progress and assessing mining safety. This study proposes the use of SBAS-InSAR technology combined with bicubic interpolation to achieve continuous monitoring of surface subsidence trends. Based on the monitoring data, we analyze the rock stress changes induced by mining operations and further infer the formation and evolution of the underground overburden. The results of the study show that: (1)A typical subsidence funnel formed above the mining area, with significant uplift in the northeast direction of the mining area; (2)Different regions exhibited varying trend changes during the study period; (3)Through time-series analysis of characteristic points, key nodes of rock stress changes were identified, and the overburden formation process was divided into three stages: ①Early mining stage, where mining operations had a small impact and the overburden had not yet started to form; ②Formation stage, where the roof strata gradually collapsed and thickened to form the overburden, and surface subsidence accelerated significantly; ③Stable subsidence stage, when the overburden had been mostly formed, and the subsidence rate stabilized.The study concludes that SBAS-InSAR technology combined with bicubic interpolation can not only dynamically monitor surface subsidence trends but also indirectly reveal the evolution of the overburden. The results are highly consistent with those observed in GNSS and microseismic monitoring. This technology demonstrates unique advantages in mining safety analysis, providing a reliable and continuous time-series perspective for mining progress analysis and safety assessment.

Based on the CPC daily maximum temperature dataset from 1979 to 2021, this study identified the summer extreme high temperature events in Northeast Asia, and systematically analyzed the variation characteristics of the number of extreme high temperature days. Combined with NCEP/NCAR reanalysis data and GLDAS soil moisture data, correlation analysis and regression analysis were used to explore the possible influence of soil moisture anomaly in northern Eurasia on the change of extreme high temperature days in Northeast Asia in summer (June to August), and further reveal its potential physical mechanism. The results show that summer extreme high temperature events in Northeast Asia show a significant growth trend, and the high incidence areas are concentrated in northeast China and eastern Inner Mongolia. Further analysis shows that the soil moisture anomaly in the north of Eurasia shows a specific dipole distribution in spring and summer, that is, the soil moisture in the northeast of Europe is dry, and the soil moisture in the north of Lake Baikal and the outer Khingan Mountains is wet, and the land-atmosphere interaction affect atmospheric circulation patterns, contributes to the formation of "positive-negative-positive" zonal wave train structure over Eurasia. The eastward transmission of the wave train transmits the wave energy to the downstream region, contributes to the formation of anticyclonic circulation anomalies and positive geopotential height anomalies over Northeast Asia, which leads to the enhancement of atmospheric subsidence movement in the region, which leads to the surface warming, and finally increases the number of extreme high temperature days in Northeast Asia in summer.

To address the three-dimensional (3D) forward modeling problem of transient electromagnetic fields for grounded-wire source, this paper explores a finite element forward modeling method that employs a frequency-time transformation and background-scattered field separation strategy. It utilizes the vector finite element theory, and the 3D model is discretized using structured hexahedral mesh. Subsequently, the frequency-domain numerical response is computed using the MUMPS solver. Ultimately, the time-domain transient electromagnetic response is derived via cosine transformation. On this basis, the reliability of the forward algorithm is validated using both a one-dimensional layered model and an open three-dimensional model, and its computational accuracy is assessed. Theoretical examples show that the calculation results of the 3D transient electromagnetic forward modeling proposed in this paper are accurate and can meet the calculation requirements of 3D inversion research.

Carbon Capture, Utilization, and Storage (CCUS) has emerged as a effective strategy for mitigating atmospheric CO₂ emissions. However, the large-scale subsurface injection of CO₂ may induce ground surface deformation, potentially compromising the long-term integrity of CO₂ storage. To investigate surface displacement patterns in CCUS project areas following CO₂ injection, this study employed the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to analyze 45 ascending Sentinel-1A images acquired between January 2022 and August 2024, enabling comprehensive deformation monitoring over a 2.5-year period in a Chinese CCUS geological storage site. Recognizing the impact of tropospheric delay errors on InSAR measurements, we developed an enhanced atmospheric delay correction approach synergizing Global Navigation Satellite System (GNSS) data and ERA5 reanalysis data, specifically optimized for small-scale regional applications. Analysis of the phase-unwrapped interferograms confirms that our GNSS-constrained atmospheric correction approach significantly reduces tropospheric delay artifacts in localized interferometric observations. The derived InSAR deformation field reveals distinct surface displacement patterns in the CO₂ injection zone, with measured vertical deformation rates ranging from 0 to 10 mm/a. Deformation time series analysis at monitoring points adjacent to injection wells reveals a consistent response: initial surface uplift immediately following CO₂ injection, followed by gradual subsidence after several months. This pattern was confirmed through comparative analysis of InSAR-derived vertical displacements (converted from LOS measurements) and independent GNSS observations. This study demonstrates that the synergistic use of GNSS and InSAR technologies enables precise monitoring of millimeter-scale surface deformation in CCUS operational areas. The GNSS infrastructure serves dual purposes: (1) enhancing InSAR measurement accuracy through tropospheric delay correction, and (2) providing independent validation of InSAR-derived deformation results. These findings underscore the critical importance of integrated geodetic monitoring for ensuring the safety and efficacy of CO₂ geological storage operations.

In shallow geological exploration, drilling and geophysical prospecting are two distinct technical methods that play different roles in the acquisition of geological and geophysical data. With the continuous advancement of modern science and technology, the direction is towards green, intelligent, and precise exploration. Integrating drilling and geophysical prospecting technologies can achieve the acquisition of multiple information parameters, such as the structure and physical properties of rock and soil layers, in a single borehole. This integration provides a foundation for the construction of transparent geological models and the precise identification of underground space information. This paper combines the current state of research and development on integrated drilling and geophysical prospecting technologies and equipment to analyze future development trends of intelligent integrated drilling and geophysical prospecting technologies and equipment for shallow layers. It posits that the current period is crucial for the transition from traditional exploration to green and intelligent exploration. By integrating artificial intelligence, big data, and intelligent transmission technologies, and improving traditional drilling processes, the advantages of combined drilling and geophysical prospecting technologies can be effectively leveraged. This can enhance the level of automated and intelligent exploration of shallow geological conditions, providing new technical support for geological disaster prevention, complex condition engineering exploration, and construction. The technical research and engineering practice significance are substantial. Additionally, the paper offers insights and understandings based on drilling-while-prospecting methods, technologies, equipment, and engineering practices to provide a reference for the future development of precise and intelligent geological exploration.

Accurate identification of deep carbonate microfacies is crucial for reservoir characterization and sweet spot prediction. Deep carbonate reservoirs usually exhibit complex compositions and numerous microfacies types, leading to dramatic challenges and low accuracy in microfacies identification. This study employs conventional logging curves, elemental mud logging curves, and processed mineral interpretation logs from deep carbonate reservoirs as input. A Residual Long Short-Term Memory (ResLSTM) network-based supervised model is developed to establish nonlinear mapping relationships between logging data and carbonate microfacies for intelligent reservoir microfacies identification. The test results demonstrate that: (1) The residual structure incorporated in the ResLSTM network effectively mitigates gradient vanishing and explosion issues during network training. Compared with traditional LSTM networks, the proposed ResLSTM achieves over 10% improvement in prediction accuracy for deep carbonate microfacies. (2) For thin interbedded layers within the reservoir, the ResLSTM model achieves 92.4% microfacies prediction accuracy, demonstrating its strong robustness. These findings highlight the ResLSTM's superior capability in handling the heterogeneity and complex patterns inherent in deep carbonate reservoirs. Furthermore, the tests also demonstrate that the distribution of training data exerts a significant influence on prediction accuracy of ResLSTM. Specifically, in scenarios of input data imbalance, the ResLSTM tends to develop a pronounced predictive bias toward the majority lithofacies categories due to their numerical dominance in the training set. The systemic bias introduced by imbalanced lithofacies distributions presents a critical challenge in petrophysical machine learning applications, demanding urgent methodological innovations to enhance model generalizability across minority facies classes.

Geothermal heat flow is fundamental to understanding heat transfer and storage within the Earth's interior. However, it can only be measured at discrete locations, making continuous observation challenging and data acquisition costly. Consequently, the spatial distribution of available measurements is highly uneven. Traditionally, most heat flow maps have been constructed through direct interpolation of these measurement points, leading to significant biases due to uneven data distribution. Regions with dense measurements exhibit greater accuracy, whereas areas with sparse data suffer from reduced reliability in heat flow estimation. In contrast, machine learning techniques, when integrated with geological and geophysical parameters, provide a cost-effective and data-driven approach to generating accurate heat flow maps. This paper reviews the application of machine learning algorithms in geothermal heat flow prediction, systematically categorizing and summarizing existing methodologies. Key aspects of data preprocessing, feature selection, and model evaluation are examined, with particular emphasis on the impact of data quality and the selection of appropriate evaluation metrics. Additionally, the challenges associated with model underestimation are analyzed, and potential strategies for algorithmicimprovement and model optimization are discussed. In summary, advancing machine learning applications in heat flow prediction has significant implications for geothermal resource assessment, seismic hazard analysis, geodynamic research, and hydrocarbon exploration.

Geochemical logging has emerged as a cornerstone in the exploration and evaluation of unconventional oil and gas resources, owing to its ability to provide comprehensive data on reservoir characteristics. This paper aims to present a comprehensive review of the theoretical foundations, technological advancements, and future prospects in this field. Utilizing the Litho Scanner tool as a representative example, this study investigates its high-resolution and high-accuracy capabilities, enabling precise measurements of elemental concentrations, mineral compositions, and Total Organic Carbon (TOC). Recent progress in instrument development, including pulsed neutron sources and advanced scintillation crystals like LaBr₃ and CeBr₃, has significantly improved the reliability and resolution of geochemical logging tools. Additionally, the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms has revolutionized data processing methodologies, enhancing mineral quantification efficiency and reducing uncertainty. Case studies demonstrate the successful application of geochemical logging in complex reservoir characterization, highlighting its superiority in evaluating mineralogy, porosity, and hydrocarbon saturation while minimizing reliance on traditional core analyses. This paper also discusses emerging trends, such as the miniaturization and multifunctionality of logging instruments, the development of intelligent detectors, and the increasing use of real-time data analysis through cloud-based platforms. The synthesis between geochemical logging and other advanced logging techniques, such as nuclear magnetic resonance (NMR) and dielectric dispersion, is projected to further enhance reservoir evaluation capabilities. In conclusion, geochemical element logging is poised to play an increasingly critical role in unconventional resource exploration, environmental monitoring, and mineral prospecting. Driven by technological innovation and interdisciplinary integration, its continued evolution is expected to create new opportunities for accurate, cost-effective, and sustainable resource development.

The tight oil resources in the Triassic Yanchang Formation in the Ganquan area of the southern Ordos Basin have great potential. However, the research on rock mechanical parameters and in-situ stress analysis of its tight sandstone reservoirs is relatively lagging. In this paper, small cores with angles of 0° (parallel, H), 45° (oblique intersection) and 90° (perpendicular, V) to the bedding plane were drilled, and X-ray diffraction, scanning electron microscopy and triaxial stress experiments were carried out. The research shows that the minerals in the Chang 8 layer samples are mainly quartz (31.8%), plagioclase (22.6%) and clay (26.6%), with a total content of 81.0%. Scanning electron microscopy shows that there are quartz overgrowth, intergranular and grain-surface clay mineral filling in the core, and there are intergranular residual pores. Under different bedding directions, the Kaiser point stress value, peak load value and compressive strength decrease in turn, while the elastic modulus and Poisson's ratio first decrease and then increase. There is a logarithmic function relationship between the pore volume compressibility coefficient and the effective stress. Through the analysis and prediction of logging data and experimental data, the dynamic elastic modulus is higher than the static one, and the dynamic Poisson's ratio is lower than the static one. The prediction results of compressive strength, tensile strength and in-situ stress are consistent with the experimental results. The research results provide new indicators for the prediction of "sweet spots" in the integration of geology and engineering of tight oil reservoirs in the Chang 8 interval of the Ganquan area. They have important guiding value for the identification of comprehensive sweet spots in tight oil reservoirs in the southern part of the Ordos Basin, the optimization of horizontal well trajectories, the design of fracturing stages and clusters, and the optimization of construction parameters.

Distributed Acoustic Sensing (DAS) technology leverages the linear response of Rayleigh backscattered light in optical fibers to external acoustic fields, enabling distributed sensing and collection of vibrations. This technology has seen extensive application in seismic exploration. However, conventional straight optical fibers exhibit sensitivity primarily along the fiber axis, limiting their effectiveness in detecting vertically incident seismic waves, thus constraining their use in surface seismic surveys. To address this limitation, a Helically Wound Cable-Based Distributed Acoustic Sensing (HWC-DAS) technique has been proposed. In this approach, the fiber is helically wrapped around a deformable elastic medium, improving directional sensitivity and enhancing its ability to detect vertically incident reflections, significantly boosting sensitivity. Nevertheless, systematic comparisons between HWC-DAS and traditional seismic geophones remain sparse, and the differences in their sensitivity, Signal-to-Noise Ratio (SNR), and frequency-dependent responses are not yet clearly understood. To fill this knowledge gap, we conducted controlled laboratory experiments, including shaking table and vibration-sensing tests, to systematically compare the performance of HWC-DAS with conventional seismic geophones. Results from shaking table experiments indicate that HWC-DAS exhibits vibration response capabilities comparable to those of geophones across various frequency bands. Furthermore, vibration-sensing tests demonstrated that HWC-DAS achieves higher sensitivity and improved SNR compared to single-point geophones. Our study validates the effectiveness and feasibility of HWC-DAS for seismic exploration through laboratory experimentation, laying foundational groundwork for its practical deployment in industry.

Aerial magnetic surveying serves as a critical technical approach in various fields, including mineral resource exploration, geological structure analysis, detection of abandoned oil and gas wells, and localization of underground magnetic anomalies. With the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, its application has expanded significantly into the domain of aerial magnetic gradient detection. Considering the limitations of current UAV-based magnetic gradient detection systems—such as narrow magnetic field measurement ranges, substantial magnetic interference from the carrier, and pronounced low-frequency noise, which collectively hinder high-precision detection—this study proposes a quadrotor UAV-based vertical magnetic gradient detection system capable of effectively acquiring vertical magnetic gradient data. The system establishes an airborne magnetic measurement platform by integrating a quadrotor UAV with a Bartington Mag-03 fluxgate magnetometer and a data acquisition system. A specially designed variable vertical linkage structure enables the magnetometer to be vertically mounted 6 meters beneath the UAV. During flights under wind conditions of level 4 or lower, the magnetometer remains sTable and vertically oriented, with magnetic interference from the UAV motors being negligible. To address the challenge of magnetic compensation under higher wind conditions, which necessitates shortening the connecting rod, field experiments were conducted, including UAV magnetic interference tests and pipeline detection trials. The collected magnetic data were processed and analyzed to implement magnetic interference compensation. The experimental results demonstrate that all system performance indicators meet the technical specifications required for aeromagnetic surveys, enabling safe, efficient, and high-quality vertical magnetic gradient detection. The issues of magnetometer instability and magnetic interference compensation difficulties have been effectively resolved, resulting in significantly improved magnetic measurement data quality. This system provides advanced technical support for China's aerial vertical magnetic gradient detection capabilities and holds great promise for future applications.

The Anninghe fault zone is a near north-south fault in the fault active system of the Sichuan-Yunnan block, and its deep structure and seismic activity have attracted much attention. In order to evaluate the seismotectonic environment and study the "relics" of mantle plume action in the northern section of Anninghe fault zone, this paper, based on the Jiulong-Zhaojue magnetotelluric sounding data, processed the data by various means, established the deep electrical structure model of the study area, and analyzed the geological structure data, drawing the following conclusions: (1) The deep electrical structure of the area is relatively complex, the surface and shallow strata are mainly distributed with medium and high resistivity, with uneven depth and thickness, the low resistivity layer of the middle and lower crust is developed, and there are relatively upwelling high resistivity bodies in the upper mantle of the Anninghe fault zone.(2)The main fault zones in the area show obvious electrical interface/electrical gradient zone in terms of electrical structure, and the low resistivity of the region near the fault zone may be related to the saline fluid filled inside.(3) The seismic sources in the study area are mainly distributed at the edge of the high-resistivity body, the high-low resistivity contact zone, and inside the low-resistivity body. The high-resistivity body has stronger rock mechanical properties, and the high conductor corresponds to the weak material in the crust. Under the dynamic background of the continuous southeast expansion of the Qinghai-Xizang Plateau, these forces are transmitted through the weak material and concentrated at the edge of the high-resistivity body, causing it to brittle fracture, thereby promoting the occurrence of earthquakes. (4) The axial high resistance body in Panxi tectonic belt is also a high speed, high density and high magnetic anomaly body, which may be the eruption channel of the Emeishan large igneous province and the accumulation channel of the thick basic ultrabasic rocks.

Magnetotelluric (MT) inversion is a key technology for exploration underground electrical structures. However, conventional methods such as the Gauss-Newton method and Occam method are limited by high computational complexity, sensitivity to initial models, and strong non-uniqueness. In recent years, deep learning has significantly improved the efficiency and accuracy of inversion through end-to-end nonlinear mapping, multimodal data fusion, and noise enhancement strategies. This paper systematically reviews the core progress of deep learning in MT inversion: Firstly, it reviews five traditional MT inversion methods, including the Gauss-Newton method, quasi-Newton method, Occam method, conjugate gradient method, and nonlinear conjugate gradient method. These conventional MT inversion methods face problems such as high computational resource requirements, significant sensitivity to initial models, and strong non-uniqueness. Then, in response to these difficulties, this paper mainly reviews the application of deep learning methods such as MT2DInv-Unet and MT-MitNet in MT inversion. MT2DInv-Unet avoids the dependence on initial models through end-to-end nonlinear mapping, reduces the risk of falling into local optimal solutions, and thus alleviates the problems of initial model sensitivity and strong non-uniqueness; MT-MitNet accelerates forward modeling, significantly shortening the forward calculation time and effectively reducing computational complexity. The application of deep learning methods effectively improves the predicament of traditional inversion methods. Finally, in the face of challenges such as scarce measured data, non-Gaussian noise interference, and three-dimensional anisotropic modeling in complex geological scenarios, this paper combines the advantages of deep learning methods and proposes targeted suggestions for optimizing MT inversion.

The continents and the oceans are the two members of the earth family. They are all "Earths". The "Earths" cultivate, lived and multiplied on the land and sea, and together maintain social progress, economic prosperity, and the development of science and technology. And continue to create the future. The ocean occupies 70.8% of the total area on this earth, and contains rich and diversified various metal mineral resources. It is a strategic base for the sustainable development of the demand and supply of metal mineral resources in human society. To this end, the main issues discussed in this article are: (1) my country's inland important mineral resources are in short supply, the situation is grim and where to go! (2) Marine mineral resources are abundant, diverse, and have great potential, (3) Submarine metal nodule mineral resources gather, (4) Discovery of many metal sulfide deposits and natural gold in the Indian Ocean, (5) Marine mineral resources in China Exploration and discovery. Obviously, marine mineral resources, exploration, development and utilization are the inevitable trajectory of my country's construction and sustainable development!

The Silurian reservoir in the Kataklonk Uplift area of the Tarim Basin is characterized by alternating sandstone and mudstone depositions, exhibiting strong heterogeneity with generally thin individual sand bodies. Traditional seismic waveform indication inversion techniques, while capable of identifying reservoir characteristics through impedance parameter calculations, lack sufficient sensitivity to minor lithological differences in complex formations with frequent sandstone-mudstone alternations, thus limiting reservoir prediction accuracy. To address this issue, this paper innovatively introduces a method combining Principal Component Analysis with Seismic Waveform Indication Inversion (PCA-SMI). First, through sensitivity analysis, Gamma Ray (GR) logging curves were determined as the input feature parameters for seismic waveform indication simulation inversion. Then, the first three principal components reflecting the main reservoir characteristics were extracted using principal component analysis to reconstruct the gamma ray curve, enhancing the feature curve's ability to identify different lithologies. Finally, the reconstructed gamma ray curve was used as input for lithological inversion via waveform indication simulation. Results demonstrate that this method significantly improves the identification accuracy of sandstone-mudstone interbedded reservoirs, optimizing the sandstone-mudstone discrimination threshold from 74.56 API to 57.6 API, effectively resolving issues of low vertical resolution and lateral discontinuity in inversion results. In practical applications, this method shows significant advantages in identifying individual sand bodies and precisely characterizing reservoir distribution, with prediction results achieving a 91.76% match with actual logging sandstone thickness. This achieves dual improvements in reservoir prediction accuracy and resolution, providing powerful technical support for oil and gas exploration under complex geological conditions.

Lithology forms the foundation for evaluating high-quality reservoirs and is crucial for effective hydrocarbon development. Deep tight sandstone reservoirs are characterized by significant burial depth, complex lithology and strong vertical heterogeneity, leading to challenges for traditional well-log lithology identification. This study focuses on the deep tight sandstone reservoirs of the Jurassic Sangonghe Formation within the Taibei Sag, Turpan-Hami Basin. Integrating core, thin section, laboratory analyses, and conventional well logs through core-log calibration, reservoir lithology was classified into siltstone, fine sandstone, medium sandstone, coarse sandstone, and sandy conglomerate based on median grain size. The results demonstrate that the Sangonghe Formation reservoirs exhibit complex and diverse lithologies, dominated by fine, medium, and coarse sandstones. The primary pore type is intragranular dissolution pores. A machine learning-based lithology prediction model was developed using gamma ray (GR), deep resistivity (RD), shallow resistivity (RS), acoustic transit time (DT), bulk density (DEN), and compensated neutron log (CNL) curves as input features, with median grain size as the prediction label. The model achieved a high coefficient of determination (R²) of 0.897 on the test dataset, showing strong agreement with core-measured data and enabling continuous vertical lithology classification in single well. Application to blind wells confirmed the model's generalization capability and prediction reliability, overcoming limitations imposed by scarce core data on reservoir evaluation. Further coupling analysis of lithology and physical properties reveals that lithology significantly controls reservoir quality, with medium and coarse sandstones exhibiting optimal properties and representing the primary lithology for high-quality reservoir development. This study provides theoretical and technical support for sweet spot prediction and efficient gas reservoir development in tight sandstones of the Sangonghe Formation, Taibei Sag.

Three-dimensional gravity inversion is the process of obtaining the location, shape, and physical parameters of underground anomalies using gravity anomaly data observed at the surface. In recent years, the rapid development of data-driven methods has led to widespread interest in applying Deep Learning (DL) techniques to gravity inversion problems, yielding certain results. Research based on the U-Net network that employs an Attention Feature Fusion (AFF) mechanism for three-dimensional gravity inversion enhances the vertical resolution of the inversion results. However, the reconstruction effect for deeper models still needs improvement. Building on this, the use of Global Receptive Convolution (GRC) to replace certain standard convolutions aims to ensure that the convolution process meets the conditions of spatial position and channel indexing. This approach integrates global and local features into a pixel-wise representation, further learning the original features to improve the reconstruction resolution of deeper models. The network's inversion results show enhanced vertical resolution, and experimental results from the San Nicolás deposit in Mexico demonstrate that the inversion network can clearly predict the basic location and approximate shape of the sulfur deposit, aligning well with known geological data.

In response to the challenges of unknown seismic noise distribution and difficulties in constructing paired samples, we propose a self-supervised blind denoising method integrating dynamic noise modeling with collaborative optimization. The method constitutes a dual-network collaborative architecture comprising a noise modeling subnetwork and a denoising subnetwork. Both subnetworks adopt U-shaped network structures incorporating wavelet transforms and Swin Transformers. The noise modeling subnetwork is optimized through mean squared error, while the denoising subnetwork is trained using a SURE-based loss function. The dual-network collaborative framework overcomes limitations of traditional supervised learning by: (1) introducing a non-prior modeling strategy that dynamically estimates noise distribution characteristics through data-driven noise modeling sub-network; and (2) sampling predicted noise distributions to construct nosier-noisy sample pairs for training the denoising subnetwork. The two subnetworks undergo collaborative training with mutual promotion and synchronous convergence, achieving blind noise modeling and blind denoising respectively. Numerical simulations and practical seismic data experiments demonstrate the proposed algorithm's effectiveness.

The traditional Gravity-Geologic Method (GGM) is an approach that combines shipborne bathymetric data and marine gravity anomalies data to invert seafloor topography by resolving short-wavelength gravity anomalies using the Bouguer plate formula. To further enhance the accuracy of GGM in seafloor topography inversion, an Improved Gravity-Geologic Method (IGGM) has been developed in this paper. A weighting parameter was introduced into the Bouguer plate formula, and short-wavelength gravity corrections were calculated at control points to refine the long-wavelength gravity field modeling further. A seafloor topography model with a spatial resolution of 1′×1′ was constructed for the reef area near the Nansha Islands (112°E—116°E, 8°N—12°N) using the Improved Gravity-Geologic Method. The accuracy of the model was evaluated using shipborne bathymetric data provided by the National Oceanic and Atmospheric Administration of the United States (NOAA). Results indicate that the IGGM model more accurately reflects fine topographic variations, with the standard deviation of 175.68 m in depth differences from measured data, representing a reduction of 7.01 m, 20.67 m and 19.68 m compared to the GGM model, the SIO V25.1 model and the SRTM15+V2.0 model, respectively. Compared to the GGM model, the IGGM model shows a significant accuracy advantage in complex reef areas with depths ≤1000 m, reducing the standard deviation of depth differences by 10.20 m. This validates the effectiveness of the Improved Gravity-Geologic Method in enhancing inversion accuracy, especially in reef regions, where it more accurately reflects the trend of seafloor topography changes, providing essential support for seafloor topography modeling in these areas.

Natural Remanent Magnetization (NRM) of geological samples contains critical records to understand the evolution of geomagnetic fields. Self-Reversed Remanent Magnetization (SRM) refers to the phenomenon that certain magnetic materials acquire a remanent magnetization opposite to the direction of external magnetic field. SRM may occur in various geological settings. Understanding the SRM mechanism is essential for accurately establishing magnetostratigraphy, reconstructing plate tectonics, and interpreting past geomagnetic field variations. However, current researches on SRM in geological samples are mostly case studies, lacking a comprehensive synthesis of existing knowledge. This paper reviews the historical development of SRM research on geological samples, highlighting representative case studies on both natural and synthetic samples. Emphases are laid on features and physical mechanisms of SRM in different geological contexts, outlining the methodologies and techniques used for investigating SRM, and addressing current challenges and knowledge gaps. Future research should utilize interdisciplinary approaches to better understand the microscopic physical mechanisms and to advance the study of SRM.

Deep coal seams are classified as ultra-low permeability gas reservoirs, characterized by inherently low permeability and micro-scale pore-throat structures. During production, these reservoirs are highly vulnerable to external fluid intrusion, resulting in water-locking damage. To tackle water-locking damage associated with the low permeability of deep coal seams, this study elucidates the microscopic mechanisms of water-unlocking agents, addressing the prior lack of microscopic quantitative characterization. By integrating Low-Field Nuclear Magnetic Resonance (LF-NMR) with physical simulation experiments, we quantitatively analyze fluid retention patterns across various pore-throat scales. Five water-unlocking agents (nonionic, anionic, cationic, zwitterionic, and fluorocarbon) were selected, and their efficacy was evaluated via surface tension, contact angle, and permeability recovery tests. Results indicate that, at a bound water saturation of 53.30%, the water-locking damage rate reaches 87.04%, indicating severe water-locking. LF-NMR analysis shows that fluid retention within small pore-throats (0.001~2.5 ms) accounts for 86.51% of total retention, serving as the primary contributor to water-locking damage. Upon addition of water-unlocking agents, surface tension decreases while contact angles increase. The fluorocarbon-based agent (Type V) exhibited the best performance, reducing surface tension to 19.60 mN/m, increasing contact angle to 48.85°, and achieving a water-locking prevention contribution rate of 81.20%. Following Type V agent treatment, fluid retention in small pore-throats decreased by 31.51%, and permeability recovery reached 28.49%. Small pore-throats serve as the primary seepage channels. As fluid occupancy in smaller pores decreases, gas-phase seepage channels expand, resulting in increased macroscopic permeability. This study accomplishes microscopic quantitative characterization of water-locking damage via LF-NMR, demonstrating that water-unlocking agents alleviate capillary forces to unblock small pore-throat channels, thereby offering novel strategies for efficient deep coal seam development.

Seismic first arrivals automatic pickup technology is of great significance in seismic exploration. The traditional manual pickup method has problems such as low efficiency and weak noise immunity, so the industry has been looking for more efficient and automated methods. In recent years, deep learning techniques, especially convolutional neural networks, have attracted much attention in seismic wave first arrivals pickup, and by training a large amount of data, the deep learning methods can automatically identify the features and classify them efficiently, which improves the limitations of the traditional methods. In this study, a deep fully convolutional Segnet semantic segmentation network is used to directly output the probabilistic map of the first arrivals location through semantic segmentation of effective waves and noise based on the advantages of fully convolutional neural networks. The method has the advantages of high efficiency and strong noise resistance, and it has achieved good results in the orthogonal data and real dataset tests.

In the current field of log interpretation based on machine learning, due to the difference in data distribution, it is difficult to directly apply the model trained on the existing log data to the new log interpretation. This paper focuses on the intelligent interpretation of geophysical logging, and proposes a semi-supervised model fine-tuning method, Log2FT, with the help of machine learning. In this method, the model is trained on the source domain, and then fine-tuned with a few labels in the target domain to improve the adaptability of the model. In order to verify the effectiveness of this method, we selected four wells D, E, F and G located in Jiyang Depression, Bohai Bay Basin, and conducted four groups of experiments D→E, E→D, F→G, and G→F, respectively. Through a series of experimental designs, including parallel repeated experiments, contrast experiments, ablation experiments and related interpretation analysis, the effectiveness and practicability of the proposed method are fully verified. The experimental results show that this method significantly improves the accuracy of logging interpretation.Thisresearchhelpsto overcome the problem of data distribution difference in the existing logging interpretation, and provides a feasible and effective method for interpreting new logging data.

Seismic inversion, a critical method for inferring subsurface petrophysical parameters (acoustic impedance) from seismic data, plays a vital role in hydrocarbon exploration and geological research. However, conventional seismic inversion methods heavily rely on low-frequency background models, exhibit sensitivity to noise interference, and suffer from low computational efficiency, making them inadequate for high-precision inversion under complex geological conditions.In recent years, Convolutional Neural Networks (CNN) have been widely adopted in seismic impedance inversion due to their strong nonlinear modeling capabilities and end-to-end learning approaches, enabling effective mapping of seismic data to impedance values. Nevertheless, deep CNN architecture still face challenges such as gradient vanishing, gradient explosion, and overfitting, which compromise the stability and accuracy of inversion results. To address these limitations, this study proposes a ResUNet-based inversion approach by integrating residual blocks into a UNet framework, thereby mitigating gradient vanishing and information loss issues while enhancing inversion accuracy and stability. Synthetic seismic data generated through forward modeling were used to train the ResUNet model, and real seismic data were subsequently input into the trained network to derive impedance inversion results. However, the intelligent inversion outputs may exhibit discrete artifacts and "spiking" phenomena near complex geological features (fault), impairing the smoothness and continuity of the results. To optimize the inversion outputs, a post-processing smoothing technique was further introduced to suppress anomalies and improve stability. Theoretical model testing and real-data validation demonstrate that the ResUNet combined with smoothing effectively enhances the precision and stability of seismic acoustic impedance inversion, offering a feasible novel method for intelligent seismic inversion applications.

The Xiangshan uranium ore field in Jiangxi Province is the largest volcanic-type uranium ore field in China. Mineralization is primarily controlled by volcanic structures, and its origin is inseparable from volcanic activity. Therefore, the identification of paleovolcanic vents/volcanic conduits in Xiangshan is of great significance for deep uranium resource exploration. Combining the existing geological physical property data in the Xiangshan area with the latest rock magnetism results, and considering the advantages of multi-scale decomposition of wavelet analysis method in boundary identification, this paper employs rock magnetism and wavelet analysis to separate and interpret the fourth-order detail fields of gravity and magnetic anomalies in the Xiangshan area, Jiangxi Province. A detailed comparison was conducted with other gravity and magnetic boundary identification methods, and a systematic identification of paleovolcanic vents in the study area was achieved. The results verified a paleovolcanic vent on the left side of Xiangshan Main Peak, located at a depth of approximately 2 km underground. In addition to porphyroclastic lava, the Xiangshan paleovolcanic vent may also contain rhyolitic dacite with low magnetic susceptibility from the Daguding period. Meanwhile, the paleovolcanic vent in the northwestern part of Furong Mountain was verified, with its eruptive materials primarily composed of rhyolitic dacite. The eruptive materials of paleovolcanic vents in Niutouling, Julong'an, Zoujiashan, and the northeast of Yaogang are mainly porphyroclastic lava. Furthermore, it is speculated that a subvolcanic vent may exist southwest of Yunji, with its eruptive materials dominated by porphyroclastic lava and located at a depth of approximately 2 km underground. These results provide support for refining the understanding of the deep structure of Xiangshan and facilitating deep uranium exploration.

The dispersion and attenuation of seismic waves are critical physical properties of hydrate reservoirs, essential for the precise identification and quantitative characterization of hydrate reservoirs. The coupled effects of hydrate saturation and occurrence modes typically exert significant influences on seismic wave velocity dispersion and attenuation characteristics. However, most existing attenuation characterization theories are established based on assumptions of single or limited hydrate morphologies, making it challenging to accurately describe hydrate reservoirs with complex occurrence states. To address this, this study integrates the generalized effective medium model and two-phase media theory to develop an attenuation theoretical model for hydrate reservoirs that simultaneously considers four occurrence modes: contact-cementing, grain-coating, matrix-supporting, and pore-filling. A comparison of P-wave attenuation characteristics modeled using Biot theory (focusing on global fluid flow in porous media) and BISQ theory (Biot-Squirt theory, incorporating both global fluid flow and local squirt mechanisms) under different hydrate morphologies reveals that the attenuation predicted by Biot theory is significantly lower than that of the BISQ model. This discrepancy arises because the BISQ framework accounts for additional energy loss mechanisms, such as microscopic fluid squirt between hydrate-coated grains, which are critical in heterogeneous hydrate-bearing sediments. Based on the BISQ-derived attenuation model, a morphology-constrained attenuation rock physics template is developed, and a crossplot of P-wave velocity, attenuation, and hydrate saturation is constructed to identify hydrate occurrence modes. Furthermore, the developed morphology-constrained attenuation rock physics template is applied to logging data from Sites 1247B and 1250F of the Ocean Drilling Program (ODP) Expedition 204. Field results demonstrate that the constructed crossplot of P-wave velocity and attenuation versus hydrate saturation accurately identifies hydrate occurrence modes, and the attenuation rock physics template aligns well with the distribution characteristics of logging data. These research outcomes provide a new theoretical foundation and technical methodology for utilizing seismic attenuation attributes to identify and evaluate hydrate reservoirs, confirming the capability of attenuation theory to accurately diagnose occurrence states and estimate reservoir physical parameters.

The Mid-Lithospheric Discontinuities (MLD) are an important interface recently discovered in the continental lithospheric mantle. They are closely related to the formation and tectonic evolution of the continental lithosphere. However, their origin remains unclear and has not reached a consensus. The South China Block has a very complex lithospheric structure. Its internal structure and evolution have long been major issues in geoscience. The mechanism of regional lithospheric thinning in the eastern part is still highly debated. Recent seismological studies have found evidence of MLD in the lithospheric mantle of the central and western South China Block. To further investigate the spatial distribution of MLD within the South China Block, this study uses seismic data recorded by national network stations distributed across the block. The single-station S-wave receiver function stacking technique and the k-means+[KG-*2/5]+clustering analysis method are applied to systematically detect MLD in the South China Block. The results show that MLD in the South China Block is mainly distributed in the Upper Yangtze Block. The depth ranges from 75 to 130 km. Beneath some stations in the Sichuan Basin, two possible MLD signals are observed in the mantle lithosphere. The MLD may have originally formed as the continental Lithosphere-Asthenosphere Boundary (LAB) at shallow depths. The ancient magmatic ocean LAB might have been captured or preserved. As the lithosphere cooled, it migrated to greater depths and now appears as the current LAB. In the eastern part of the South China Block, the lithosphere may have delaminated along the original MLD due to the influence of the subduction and retreat of the ancient Pacific Plate. Later, under thermal erosion and extensional forces, the lithosphere continued to thin. The MLD interface was destroyed. As a result, no MLD is observed in the eastern part of the South China Block. This study provides new insights into the spatial distribution and possible formation mechanisms of MLD in the South China Block. It also contributes to understanding the complex lithospheric evolution of the region.

Submarine mud volcanoes are uplifted landforms formed by the escape of submarine fluids. Studying their evolution process is of great significance for the exploration of marine oil and gas resources, the protection of marine biodiversity, and the prevention of marine geological disasters. Based on the existing research, this paper systematically reviews and summarizes the identification characteristics of submarine mud volcanoes under different acoustic detection methods using submarine acoustic instruments and platforms. It also summarizes the morphological features, causes, and evolution stages of submarine mud volcanoes, and analyzes and discusses the progress of quantitative research on the morphology of submarine mud volcanoes. The investigation results show that theacoustic data obtained by combining multiple acoustic methods can not only be used to obtain the morphological and profile characteristics of submarine mud volcanoes, but also serve as supporting data for quantitative research on the morphology of submarine mud volcanoes. Due to the low detection resolution and narrow detection range, the submarine mud volcanoes identified in China at present are mainly large and medium-scale and dome-shaped. Small-scale and complex-shaped submarine mud volcanoes are difficult to identify. The morphological parameters of submarine mud volcanoes are mainly water depth, diameter and slope. There is less research on other parameters such as the slope of the two wings and the depth of the depression. It is suggested to continuously improve the detection technology to enhance the detection resolution, and to use multiple carrier platforms to expand the detection range of the seabed to obtain higher-resolution and more abundant seabed topographic and geomorphic data. At the same time, on the basis of the existing detection technology and detection resolution, the quantitative research on the morphology of submarine mud volcanoes should be deepened to obtain rich morphological quantitative parameters and achieve precise quantitative research. Automatic identification methods such as deep learning should be introduced to realize the automatic identification of submarine mud volcanoes and improve the efficiency of identifying submarine mud volcanoes.

The cementation index is an important parameter for calculating water saturation. Its main influencing factors include the following aspects: formation temperature, pressure, formation water salinity, reservoir space type, mud content, wettability, dispersion characteristics, particle shape and size. This article provides a brief overview of the influencing factors and their characteristics and results. The results are: The cementation index of oil wet reservoirs is lower than that of water wet reservoirs. The higher the mud content, the smaller the cementation index. The higher the frequency, the higher the cementation index. The cementation index is influenced by the type of storage space and varies greatly. The larger the content of micropores, the smaller the cementation index. Rock with good pore structure has a lower cementation index. The smaller the pore throat ratio, the lower the cementation index. The cementation index decreases with the degree of particle sphericity, and the m value steadily increases. The smaller the equivalent particle, the higher the cementation index. In addition, the reasons were analyzed and practical application suggestions were provided, aiming to provide reference for the research and analysis of rock electrical experiments and cementation index, in order to obtain accurate cementation index and lay the foundation for accurate calculation of water saturation.

The development and utilization of urban underground space present significant challenges due to complex and unknown geological conditions as well as potential engineering risks. Ground Penetrating Radar (GPR), with its high resolution, adaptability, and non-invasive detection capabilities, has become one of the primary tools for underground space exploration. However, GPR data collected by antennas of different frequencies exhibit distinct advantages and limitations: high-frequency antennas provide higher resolution, which is critical for detecting small-scale or shallow targets, but their detection depth is significantly limited. In contrast, low-frequency antennas offer greater penetration depth, enabling the detection of deeper targets, but at the cost of reduced resolution. This trade-off limits the comprehensive application of GPR technology in urban underground exploration, where both resolution and depth are crucial. To address this limitation, our study proposes a low frequency match filter method. This method first performs spectral analysis on two sets of GPR signals collected by antennas with different frequencies to identify their respective advantageous frequency bands and determine the boundary point between them. The signal collected by the high-frequency antenna is then used as the input, with its high-frequency components remaining unchanged, while the low-frequency components are replaced by the low-frequency signal as the desired output. Based on this, a frequency-segmented matching filter is constructed. Finally, the filter is applied to all radar data collected by the high-frequency antenna, achieving low-frequency compensation for the high-frequency signals. The results from both model experiments and real-world applications demonstrate that the proposed frequency-segmented matching filter significantly broadens the spectrum of high-frequency GPR data, enabling more effective extraction of deep signals while preserving the high resolution of shallow layers. This method greatly enhances the overall capability of GPR in urban underground space detection, addressing the limitations of traditional high-frequency GPR systems with insufficient detection depth in complex geological conditions. In conclusion, the low frequency match filter technology proposed in our study offers an efficient and cost-effective approach for extracting deep signals from GPR, showing promising application potential.

Microseismic monitoring is widely used in the analysis of engineering disasters such as mines and tunnels, all of which are based on microseismic source parameters. The calculation of microseismic source spectrum is the basis of source parameter calculation. Once the source spectrum is determined, most of the source parameters can be calculated accordingly. However, research into the calculation process and method of microseismic source spectra is limited. Therefore, we investigated the basic steps and methods of microseismic source spectrum calculation in detail. This includes four main steps: waveform processing and source location; signal spectrum calculation; signal spectrum correction and source spectrum determination. Signal processing involves removing the instrument response, filtering and arrival picking with localisation to provide basic parameters for subsequent calculations. Signal spectrum calculation involves not only common methods such as the discrete Fourier transform, but also the lag-window spectral technique and multi-taper methods. Signal spectrum correction involves geometric spreading, site effects, and attenuation, which are all influenced by multiple and complex factors. Source spectrum determination initially requires the theoretical source spectrum model to be determined, followed by the selection of the objective function and fitting method. This study briefly analyses the impact of these factors on the calculation of the microseismic source spectrum and suggests possible future research directions. These results can be used as a reference for calculating the source spectrum, thereby establishing a foundation for calculating source parameters and ultimately providing theoretical support for standardising and normalising engineering microseismic monitoring and the quantitative warning of engineering disasters.