The accurate classification of seismic signals is the key link in constructing seismic catalog, which is of great significance for seismic catalog cleaning, earthquake monitoring and alerting, and seismological research. Aiming at the existing seismic event classification algorithms with low accuracy and large computational overhead, this paper designs a deep learning network CL-MobileViT for automatic classification of seismic events. CL-MobileViT comprehensively considers the performance and efficiency of the algorithm, selects MobileViT as the main body of the network, adds the attention mechanism to improve the sensitivity of the network to effective features, and uses the idea of large kernel convolution decomposition to reduce the computing overhead of the network. At the same time, the AdamW optimization strategy is adopted to guarantee that the final model can maximize the performance of the network. Specifically, first of all, add Coordinate Attention in the skip connection of MobileViT block, so that the network can pay fine attention to the information of different locations, strengthen the interaction modeling between long-distance seismic phase features, and improve the classification accuracy; Secondly, the traditional convolution used in the local feature extraction part of MobileViT block is replaced by multiple small-size convolution kernels decomposed by a large kernel convolution, which improves the nonlinear fitting ability of the network while reducing the computation and parameter number, thus improving the accuracy of seismic event classification. Finally, AdamW optimizer is used to prevent network from being overfitted and improve the training effect. By comparison with 11 existing mainstream deep learning classification models, it is found that CL-MobileViT can reach 97.3% accuracy in recognizing three seismic events, namely natural earthquake, collapse and blasting, which is superior to the comparison methods. Moreover, the number of parameters of CL-MobileViT is only 1.19 M, which is far lower than the comparison methods. It is proved that the method in this paper has better ability of seismic event classification.

Global warming has prompted countries to reach a political consensus and take a series of actions to actively address climate change. Carbon dioxide geological storage injects the captured carbon dioxide into the storage site for long-term storage. The injected carbon dioxide may leak through potential pathways such as abandoned wellbores and faults due to a combination of pressure and itsbuoyancy. The change of physical parameters such as reservoir density before and after carbon dioxide injection provides a theoretical basis for gravity monitoring technology. The inversion of carbon dioxide plume distribution using gravity data aids in the analysis of fluid spatial movement and distribution over time. This paper focuses on the development and application potential of time-lapse gravity monitoring methods in the field of carbon dioxide geological storage from two perspectives: academic research and industrial applications. With the constant improvement of gravity observation instruments, the continuous innovation of data acquisition and processing technology, and the continuous progress of inversion interpretation methods, the application prospects for the time-lapse gravity monitoring method are also broader. At present, the development of time-lapse gravity monitoring needs to seize the opportunity of the ongoing expansion of the quantity and scale of carbon dioxide geological storage projects. Effective information on the distribution of carbon dioxide underground can be obtained by comparing the density determined by surface and borehole gravity before and after storage.The comprehensive use of various geophysical methods is the development trend of monitoring carbon dioxide geological storage projects in the future, taking into account the real needs of the project.

As a green, low-carbon, safe, high-quality and recyclable renewable energy, geothermal energy is of great significance to the adjustment of China's energy structure and the realization of the goal of "double carbon". As an environmentally friendly and non-destructive method, geophysical methods are widely used in the exploration of middle-deep geothermal resources, which can effectively detect deep hidden fault structures and obtain important information such as strata and buried depth. This paper systematically investigates the research and application status of common geophysical methods and exploration equipment at home and abroad in the detection of geothermal energy in medium and deep layers, analyzes the advantages of each geophysical detection method under its applicable conditions, and summarizes the research ideas of the detection of geothermal resources in medium and deep layers. According to the application examples of geophysical detection methods in geothermal areas, the adaptability, effectiveness and accuracy of geophysical detection methods in the detection of geothermal energy in medium and deep layers are expounded.

The GRACE (Gravity Recovery and Climate Experiment) satellite mission, a collaboration between NASA and the German Aerospace Center, was complemented by the launch of its successor, GRACE Follow-On (GRACE-FO), in May 2018. These satellites have crucially contributed to our understanding of Earth's long-term gravitational variations. However, gaps and interruptions in the time-variable gravity field series have arisen due to satellite battery issues, payload calibration errors, and the extended gap between the GRACE and GRACE-FO missions, affecting the continuity and completeness of the data. This paper provides an overview of the GRACE and GRACE-FO missions, data products, and the circumstances of data gaps. It categorizes the reconstruction methods for missing GRACE/GRACE-FO data into two main types: those based on mathematical statistics, the paper focuses on Singular Spectrum Analysis (SSA) and Least Squares Harmonic Analysis (LS-HE), comparing their applicability, strengths, and weaknesses with other methods such as the Autoregressive Moving Average Model (ARMA) and Multi-channel Singular Spectrum Analysis (MSSA). And those using auxiliary information, which employ other satellite data (like GNSS, Swarm, and SLR) and climate and hydrological data, often based on empirical regression relationships or deep learning. This paper evaluates these methods, comparing their applicability, strengths, and limitations, and presents a case study in the Yangtze River Basin using a combination of Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM), showing superior results over methods like Support Vector Machine (SVM), Random Forest (RF), and Iterative Singular Spectrum Analysis (ISSA). In conclusion, while mathematical statistical methods offer simplicity and low computational requirements, deep learning combined with various auxiliary data yields higher quality reconstruction results. In recent years, research both domestically and internationally in this field has also primarily focused on data reconstruction using various deep learning algorithms in conjunction with auxiliary information.The paper contributes to the ongoing research in this field, focusing on deep learning algorithms combined with surface mass models, climate, and hydrological data for data reconstruction, and provides insights for future approaches in filling data gaps for GRACE/GRACE-FO, enhancing the application and research in time-variable satellite gravimetry.

Porosity is an indispensable key physical parameter in reservoir evaluation, and there exists a complex and potential relationship between well logging curves and porosity. In previous studies, incomplete feature extraction of well logging curves and simple model construction have limited the accuracy of porosity prediction. To improve the prediction accuracy, this study innovatively combines Variational Auto-Encoder (VAE), Bidirectional Gated Recurrent Unit (BiGRU), and Attention mechanism to construct the VAE-BiGRU-Attention model. VAE can effectively learn the latent representation of data, enhancing data representation capability; BiGRU excels at capturing sequential data information, particularly suitable for handling the feature of porosity changing with depth; and the introduction of the Attention mechanism dynamically calculates the attention Attention weights of each time step, allowing the model to more accurately focus on key features and achieve better prediction results. To verify the effectiveness of the model, this paper is compared with Deep Neural Network (DNN), Recurrent Neural Network (RNN), and BiGRU-Attention through comparative experiments. The results show that the VAE-BiGRU-Attention model has a Mean Squared Error (MSE) of 0.995, Mean Absolute Error (MAE) of 0.698, and Root Mean Square Error (RMSE) of 0.998. Compared to other models, it exhibits significant improvement, effectively enhancing the accuracy of porosity prediction and providing a more reliable method for reservoir porosity prediction.

Water vapor is an important part of the atmosphere, and it is of great significance to realize the high-precision retrieval of water vapor content in the atmosphere for meteorological research. This paper used brightness temperature data of FY-3D/MWRI in July from 2019 to 2022 annually, with the water vapor data of ERA5 on Pacific as references, established six-channel and eight-channel random forest retrieval models (RF6 and RF8) for total precipitable water in maritime clear sky, based on the random forest algorithm. The experimental results indicated that compared to the empirical regression retrieval model, the random forest retrieval model have improved accuracy obviously, with the RF6 model achieving an improvement of about 22% and the RF8 model achieving an improvement of about 28%. Furthermore, when applying the RF6 and RF8 models which trained based on Pacific region data to the North Atlantic and South Indian Ocean, positive retrieval results were also obtained. Considering all factors, the RF8 model outperforms the RF6 model, and the RF6 model outperforms the empirical regression model.

The PmP' wave group with long offset has the characteristics of flat geometry, low apparent velocity and above the zero line of travel time in the seismic record section with the observation length of more than 250 km or more in the deep seismic sounding data of the western Cathaysia block. Because it is connected to the PmP reflected wave group and the waveform is similar, the conventional wave group identification is often used as a part of the PmP wave group. Using this wave group as part of the PmP wave group for theoretical seismogram simulation, it is difficult to obtain satisfactory fitting of travel time and amplitude at the same time. Taking the far-offset PmP' wave group of the wide-angle reflection/refraction profile SP3 of the Lianping—Heyuan—Haifeng earthquake in eastern Guangdong in 2021 as an example, the apparent velocity at the farthest end of the wave group is calculated to be 6.04 km/s, which is less than the average P wave velocity of the crust calculated by the travel time of the PmP wave group in the critical region of 6.27 km/s, indicating that the wave does not have the property of PmP wave. A new lower crust and Moho interface model is used to fit the two-dimensional theoretical seismograms of the PmP' wave group. The results show that this wave is a refracted wave from the lower crust, indicating that the lower crust of the Western Cathaysia block generally has a positive velocity gradient structure. This reflects the result of magma intrusion since the Mesozoic era in the southeastern margin of South China, which may be the direct evidence of newly crust.

Supercritical geothermal can extract more than ten times the energy of conventional Enhanced Geothermal System (EGS), and become the development direction of new energy. Although China has become the country with the largest direct utilization of medium and low temperature geothermal resources, the level of exploration and development of deep underground high temperature geothermal resources needs to be improved. In this paper, we analyze the research progress of high temperature and high pressure physics experiment, numerical simulation, geophysical exploration and monitoring methods for supercritical geothermal, and the rock-fluid-gas geophysical properties of three-phase medium are summarized and analysis. And we give the typical high temperature geothermal area in China for supercritical geothermal resource exploration potential evaluation preliminary discussions, The potential exploration areas of deep supercritical geothermal resources based on geophysical survey results are predicted to provide support for the commercial utilization of supercritical geothermal resources in China.

Currently, the manual method of annotating deep learning samples for Ground Penetrating Radar (GPR) with open-source tools such as LabelImg and Labelme is not only time-consuming and labor-intensive, but it also annotates images rather than radar data. This fails to satisfy the requirements of deep learning for large sample sizes and hinders the sharing and reuse of GPR data. It is essential to design a unified data storage format for the manual interpretation results of GPR data, establish a mapping relationship between hidden road defects and their GPR data, and enable autonomous retrieval, positioning, cropping, and automatic annotation of GPR data samples. Based on the YOLO network model, this study has developed an intelligent diagnostic software system for GPR images pertaining to hidden road defects. This system can automatically annotate GPR sample data pertaining to hidden road defects and implement methods such as adaptive gain adjustment, digital filtering, automatic zero drift removal, and background subtraction to enhance radar sample data, generating deep learning samples with different signal characteristics. Through a comparative analysis of the deep learning training performance of the YOLOv8n and YOLOv8x models on GPR samples pertaining to hidden road defects, a manual verification method for intelligent diagnostic results has been developed. The testing results of the algorithm and software reveal that automatic annotation and data enhancement of GPR data pertaining to hidden road defects can significantly expedite the generation speed of GPR deep learning samples and enrich the diversity of such samples. Compared with YOLOv8n, the YOLOv8x model achieves smaller training losses, higher training accuracy, and is more suited for intelligent diagnosis of GPR images pertaining to hidden road defects.

Yingxiongling is located in the western of the Qaidam Basin, with numerous ravines and complex structures. It is known as the "forbidden zone for seismic exploration" by oil and gas exploration experts from all the world. The complex near surface and underground conditions with thrust fractures have led to three major challenges in the area: severe static correction problems, low signal-to-noise ratio of field data and difficulty in imaging, which seriously constrain the progress of oil and gas exploration. On the basis of high-precision intelligent full arrangement of first arrival picking, a technology centered on adaptive weighted tomography and adjoint state tomography is proposed to improve the accuracy of static correction. According to the principle of "first strong then weak, first regular then random" and the idea of "six part method" for denoising, targeted researches on noise suppression strategies will be effectively carried out step by step to improve data fidelity. Combining high-precision grid tomography and multi-information constrained velocity modeling, a true surface full depth domain modeling and imaging technique is proposed to obtain high-quality migration results. The imaging results indicate that the series proposed technologies have improved the imaging accuracy of deep and complex structures, effectively solving the problem of well seismic inconsistency in the target layer, and providing an important guarantee for promoting risk exploration in the Yingxiongling area.

The lithology of volcanic rocks is diverse and the log characteristics are complex. Efficient lithology identification can improve the prediction efficiency of high-quality reservoir and reduce the exploration cost, thus laying a foundation for the efficient development of volcanic oil and gas resources in the later period. Aiming at the problems of low identification accuracy and complex model in the process of traditional machine learning lithology identification, this paper takes volcanic rocks in Wangfu fault Depression of Songliao Basin as the research object, comprehensively analyzes the geological characteristics of volcanic rocks reservoir, uses the corrected lithology data as the lithology sample label, and uses principal component analysis to screen out four characteristic logging curves sensitive to volcanic rock lithology identification as input. The lithology identification model is constructed by XGBoost algorithm to identify the lithology of volcanic rocks. After the lithology identification results are given by the model, the identification results are compared with those of random forest, KNN and SVM algorithms. The results show that the accuracy of XGBoost algorithm is 96.13%, while the accuracy of random forest, KNN and SVM algorithm is 93.15%, 91.68% and 91.24%, respectively. XGBoost algorithm can improve the accuracy of identification results by overfitting regularization term control algorithm, and improve the operation efficiency of the algorithm by multithreading parallel operation. The lithology identification model based on this algorithm can provide technical support for solving the problem of efficient lithology identification of volcanic rocks.

During the well-logging process, factors such as instrument malfunction and borehole collapse often lead to distortion or loss of density curves in certain well intervals, which in turn introduces errors in reservoir evaluation. To enhance the accuracy of reservoir evaluation, the reconstruction of density curves becomes essential. Traditional machine learning methods for curve reconstruction often fail to meet the required precision. To address this limitation, this paper proposes a novel method for density curve reconstruction that integrates Temporal Convolutional Networks (TCN), Bidirectional Gated Recurrent Units (BiGRU), and Multi-Head Attention (MHA) mechanisms. The proposed method utilizes the convolutional characteristics of TCN to capture the long-term dependencies in well-logging data, while the introduction of the MHA mechanism enhances the ability of BiGRU to selectively focus on critical features, thereby achieving precise density curve reconstruction. This method was applied to field data from the study area for reconstruction experiments. Initially, the impact of incorporating lithology indicators on the model's reconstruction capability was evaluated. Subsequently, a comparative analysis was conducted between the proposed network and Gardner's equation, multiple regression, Gated Recurrent Units (GRU), and Bidirectional Gated Recurrent Units (BiGRU). Finally, the generalization ability of the proposed network was validated through core calibration. The results indicate that the proposed density curve reconstruction method not only achieves higher accuracy but also demonstrates excellent generalization capabilities.

The accuracy of T₂ cutoff directly affects the accuracy of nuclear magnetic resonance calculation of bound fluid saturation, movable fluid porosity, and permeability. The pore structure of deep dense sandstone reservoirs is complex, characterized by low porosity, low permeability, and strong heterogeneity. The applicability of fixed T₂ cutoff values is poor. To improve the calculation accuracy of T₂ cutoff value in deep tight sandstone reservoirs, this study relied on nuclear magnetic resonance experimental measurements of deep tight sandstone samples and constructed a T₂ cutoff value calculation model that integrates Gaussian function and reservoir classification multi parameter fitting method. Program this model to achieve point by point calculation of T₂ cutoff values for deep tight sandstone reservoirs in offshore X zone. The research results indicate that when the bound water saturation is less than 41%, the Gaussian function method has higher accuracy in calculating the T₂ cutoff value, but when the bound water saturation is greater than 41%, the Gaussian function method is no longer applicable; For class Ⅱ and Ⅲ reservoirs (with bound water saturation greater than 41%), the T₂ cut-off value multi parameter fitting calculation model constructed using optimized sensitivity parameters has high accuracy; The T₂ cutoff values calculated by the fusion of Gaussian function and reservoir classification multi parameter fitting method are highly consistent with those determined by rock sample experiments, fully meeting the needs of nuclear magnetic resonance logging to evaluate pore structure in deep tight sandstone reservoirs.

How to accurately measure the speed of high-speed train has always been a hot issue in the research on high-speed railway. This paper introduces generalized cross-correlation time delay estimation algorithms to analyze dual-station recorded waves of ground vibrations induced by trains, and calculates train speeds. We capture waves of ground vibrations induced by trains along the high-speed railway between Yunnanyi station and Xiangyun station in the Guangtong—Dali railway section with seismographs. Then we analyze waves recorded in a pair of seismographs with three generalized cross-correlation time delay estimation algorithms, or PHAT weighting, SCOT weighting and ROTH weighting, and with basic cross-correlation time delay estimation algorithm respectively. The results of analyses show that compared to the other three algorithms the PHAT weighting effectively sharpens the maximum of cross-correlation function and the obtained train speeds based on this algorithm are more stable and accurate. Besides, PHAT weighting can avoid to a certain extent the problems met in the general methods of train speed measuring, such as the poor precision of instrument, the difficulties in instrument installation, etc., and can provide accurate train speed measurement for many areas where a prior information cannot be obtained. We calculate the train speed results of the three-component waveforms separately to determine which components are more accurate.When there are multiple railways, three-component autocorrelation waveform of train can be used to estimate the distance between the train and stations. For the obvious error of the train speed, we choose two wrong data to analyze the cause of the error, which is valuable in application. With the development of generalized cross-correlation technology, the calculation of time delay becomes more and more accurate.We will continue to follow the issues related to high-speed rail research and producing more valuable conclusions.

Accurate prediction of the ionospheric Total Electron Content (TEC) is of great significance for improving the accuracy of satellite navigation and positioning. To this end, a TEC short-term forecasting model that combines the Whale Optimisation Algorithm (WOA) with Long-Short Term Memory Networks (LSTM) is proposed in this study; The optimal fitness of WOA algorithm is obtained by LSTM model training, and the Optimal parameters of LSTM model are obtained by WOA algorithm optimization. Finally, combined with the TEC grid dot data provided by the Center for Orbit Determination in Europe (CODE), the proposed model is verified; the test results show that : in the geomagnetic calm state, the combined model is flat. The average correlation coefficient increased by 2.8%, 6.2% and 14.8% respectively compared with the LSTM model at low, medium and high latitudes; the average correlation coefficient of the combined model under geomagnetic active state increased by 6.6%, 9.2% and 7.9% respectively compared with the LSTM model in low, medium and high latitudes. And the prediction effect of the model is related to geomagnetic active state, season, solar activity level, etc. Under different geomagnetic active state, season and different solar activity level, the prediction effect of the combined model is better than that of a single LSTM model, which provides a reference for the practical application of the ionospheric TEC prediction model.

To address the limitations of conventional empirical formula-based pore pressure prediction methods in engineering practice, such as high dependency on velocity, numerous required empirical parameters, and significant human influence, this study proposes an intelligent pore pressure prediction model based on eXtreme Gradient Boosting (XGBoost). By incorporating the ratio of actual P-wave velocity to the normal compaction trendline as a feature parameter in model training, the prediction accuracy and generalization capability of pore pressure are significantly improved. Furthermore, an enhanced method is introduced, which replaces the normal compaction trendline with the Dv curve for pore pressure prediction, effectively mitigating the computational complexity and subjectivity associated with establishing the normal compaction trendline. The effectiveness of this improved method is also validated across other machine learning regression models. The results demonstrate that the proposed intelligent pore pressure prediction model and its enhanced method exhibit high prediction accuracy and generalization ability, providing efficient and reliable data support for drilling safety. This approach holds significant engineering application value and broad prospects for future use.

As an important component of Transient Electromagnetic (TEM) prospecting, the performance of a TEM transmitter almost determines the effectiveness of this prospecting method. In order to accurately prospect shallow and even very shallow layers using multi-turn small coils, this paper designs an improved TEM transmitter based on SiC MOSFET. SiC MOSFET is a third-generation semiconductor that offers significant improvements in voltage withstanding, current withstanding, heat dissipation, and response speed compared to widely used Si IGBT. The shut-off time, as a critical parameter of the transmitter, essentially determines the degree of coupling between the primary and secondary field signals. To prevent losing information in even very shallow and relatively shallow layers, the transmitter designed in this paper supports switching transmission between large and small currents. When prospecting in even very shallow or relatively shallow layers, a small current of 1.1 A can be selected, with a shut-off time of only 4 μs. When prospecting in shallow layers, a large current of 16.2 A can be selected, with a shut-off time of 35 μs. To provide transmitting current data use for post-processing, this paper designs a current acquisition system that supports dynamic sampling rate to collect the current waveform in the whole time. When the current waveform is in the rising or falling edge area, the acquisition system automatically selects a high sampling rate of 1.8 MSPS for sampling. When the current waveform is in the steady-state area, the acquisition system automatically selects the lowest sampling rate of 50 KSPS for sampling. Tests show that by sampling with dynamic sampling rate, the acquisition accuracy and data volume can be effectively balanced, thereby ensuring the stability of the storage system. In addition, the transmitter board has a small size of only 255 mm×192 mm, and supports 12 V battery power supply, so it has good portability and can improve field prospecting efficiency to a certain extent.

Multi-beam Backscatter Strength (BS) data can be used to identify the types of the seafloor and the distribution of seafloor geological hazards, but the traditional method for BS data interpretation is time-consuming and subjective. We applied K-S test techniques to automatically interpret the multibeam BS data. After eliminating the effect of incidence angle, our statistics analysis show that four typical sediments have the Gaussian distributions, and there is a good correlation between BS and the grain size of seafloor sediments. Based on the distribution trends, five other BS distributions were constructed the missing typical seafloor without seafloor samplings. Then, we performed a single sample K-S test method to classify the seafloor sediments for the whole surveyed region. The unknown types of seafloor sediments can be judged and classified by compared their BS distribution with the known typical Gaussian distributions and measuring the similarity between the two. Through experimental comparisons, we determined the optimal window size for experiments on this area to be 30 m × 30 m. At the same time, we set the classification confidence level to 90%, and we obtained results from our experiments that the overall recognition rate (the ratio of the identified area to the total area) reached 92%, and the classification results also matched all the sampling results with high classification accuracy, and the method achieved good results. The results illustrate our automatic method can replace the conventional in-house BS interpretations and reduce offshore operation costs. It requires only a small amount of seafloor samples to achieve automatic seabed classification for the entire area. In addition, the reliability of the classification can be evaluated by a parameter of statistics analysis. The high accuracy of the classification results of this method is particularly suitable for areas where large areas of typical substrate are distributed.

In recent years, the exploration of deep volcanic rocks in Songliao Basin has achieved a breakthrough in the new formation series (Huoshiling Formation) and new rock type (intermediate-basic volcanic rocks). The physical response of deep volcanic rocks is complex and multi-solution because of the variety of mineral composition, no obvious boundary and relatively compact rocks. In order to quantitative analysis the physical responses and differences of deep intermediate-basic volcanic rocks and improve the accuracy of volcanic rock lithology identification and reservoir prediction, 40 typical samples of volcanic rocks from Huoshiling Formation in the southern Songliao Basin are selected, the rock mineral composition and microstructure are quantitatively analyzed, the eight types of rocks are classified into three types by introducing lithofacies and rock structure information, that is effusive andesite, explosive tuff and volcanic breccia, the petrological characteristics and petrophysical parameters under formation pressure and fluid conditions are tested, and the relationships between petrophysical parameters and lithofacies, lithology, physical properties and fluid are clarified. The results show that the petrophysical properties of deep volcanic rocks are controlled by the combination of lithofacies and lithology, the rock physical properties of explosive tuff are the best, density and p-wave impedance can be used as the sensitive parameters for the optimization of high quality reservoirs, the identification threshold and quantitative relationship are different with the types of volcanic rocks, that is density is small than 2.582 g/cm³ for the effusive andesite, 2.509 g/cm³ for the explosive tuff and 2.555 g/cm³ for the volcanic breccia. There are obvious differences in fluid identification ability of rock physical parameters under different rock types, the combination parameters λ-0.28 μ、Ip²-2.15 Is²、λ/μ can be used to identify the fluid properties in the effusive andesite, the explosive tuff and volcanic breccia rocks respectively based on the tested petrophysical parameters and sensitivity analysis results of the fluid saturated rock samples, and the identification effect is good when the sensitive parameters are used in the actual study area.

The role of geophysical inversion in seismic research and prediction is of paramount importance. This paper seeks to comprehensively outline the constraints associated with conventional inversion techniques, focusing on the introduction of a Bayesian-based uncertainty inversion method. Bayesian inversion involves computing posterior distributions utilizing diverse prior distributions and likelihood functions, with special emphasis on established techniques like the Markov Chain Monte Carlo (MCMC) and variational inference methods, thereby augmenting the reliability of inversion outcomes.The manuscript furnishes an elaborate exposition on pivotal techniques within Bayesian inversion, notably delving into regularization methods (such as Laplace and von Karman regularization) that confine the parameter space in seismic inversion, validated through rigorous case studies. Moreover, it expounds on sampling methodologies (including the Metropolis-Hastings algorithm and Gibbs sampling) that facilitate parameter space sampling and approximate posterior distributions. The application of the Metropolis-Hastings algorithm in seismic inversion is meticulously elucidated.The discussion accentuates the criticality of model parameter selection, notably the influence of uncertainty associated with fault geometric shape selection on inversion results. Additionally, it probes into the challenges encountered in constructing finite fault source models and presents a Bayesian-based case study evaluating the credibility of different slip model clusters.In conclusion, the paper summarizes the limitations inherent in the Bayesian approach and delineates potential avenues for future research directions. In the realm of geophysical inversion, the application of Bayesian methods presents novel prospects for overcoming the constraints of traditional methodologies.

Seismic samples are typically designed on a perfect Cartesian grid. However, field constructions can disrupt the sampling geometry, resulting in the samples missing or off-the-grid. Our research goals are to simultaneously regularize off-the-grid samples and interpolate missing data for 3D seismic data under the framework of compressive sensing, which combines a 3D curvelet transform, a fast iterative threshold algorithm, and a merging sampling operator. The new sampling operator combines a binary mask with a barycentric Lagrangian operator for simultaneous interpolation and regularization. The fast iterative threshold algorithm is helpful to improve the interpolation accuracy and efficiency while solving the ill-posed problem. Finally, we demonstrate the effectiveness of the proposed approach by simulated and field datasets.

Ionospheric correlation plays a key role in ionospheric data assimilation, which describes the statistical relationship between different ionospheric parameters or locations, capturing the dependencies and similarities between different regions of the ionosphere. Ionospheric correlation time is an important representation of ionospheric correlation. The ionospheric correlation time is an important parameter, which contains the temporal variability, structure and dynamics information of the ionosphere. This parameter can be directly used to improve the ionospheric data assimilation model. Therefore, this paper proposes an optimization scheme for the ionospheric assimilation system based on ionospheric correlation time. For the same ionospheric assimilation system, different values of correlation time will have a significant impact on the assimilation results. This paper compares the assimilation results when the ionospheric correlation time is 1.5 hours and 2.5 hours. The results show that in mid-to-high latitudes It is more reasonable to use a larger ionospheric correlation time; while it is more reasonable to sample a smaller ionospheric correlation time in mid-and low-latitude areas. Therefore, this paper recommends classifying and calculating the correlation times in different regions to make the ionospheric assimilation results closer to the real values. In-depth research on ionospheric correlation time will help improve the accuracy of ionospheric assimilation systems and help researchers gain a more comprehensive understanding of ionospheric correlation.

As a significant occurrence of geothermal resources, the energy stored in subsurface hot dry rock reservoirs can be extracted via Enhanced Geothermal System (EGS). Highly efficient exploration for deep hot dry rock would assist the achievement of energy transition and dual carbon goals. Some benefits of seismic prospecting include high resolution, super deep exploration and controllable deployment, which means this technique cannot be replaced in meticulous depiction of reservoirs and identification of natural fractures zone. So as to investigate the updated progress in seismic exploration for hot dry rock and provide certain valuable references, we have classified the application of active source seismic methods in terms of different strategies. Data processing, interpretation and inversion techniques are utilized in accurate imaging of structures, estimation of rock physical parameters, and dynamic monitoring of EGS exploitation. Besides, we verified that integrated seismic and other geophysical exploration can promote the exactitude in determination of well location and receivers' layout. The summarized concepts in this paper may be useful for researchers to acquire the information of seismic methods for hot dry rock prospecting effectively.

The Qaidam Block is located in the northeastern part of the Qinghai-Xizang Plateau, situated between the Qilian Block and the Songpan-Ganzi Block. The restoration of its paleogeography is very important for understanding the formation of the Qinghai-Xizang Plateau and the northern of China. However, the paleogeographic location of Qaidam block during Carboniferous period is still controversial. Paleomagnetism as one of the most effective methods for reconstructing the positions of ancient continental blocks, has played an irreplaceable role in the exploration of the Early Carboniferous paleogeographical location of Qaidam block. The results of rock magnetism experiments can provide foundational data for conducting systematic paleomagnetic studies, and their significance cannot be ignored. This paper selects sandstone samples from the Lower Carboniferous Chengqiangggou Formation in the Qaidam Block to conduct detailed rock magnetic experiments, petrographic experiments, as well as demagnetization experiments, to identify the type and characteristics of magnetic minerals in the Chengqianggou Formation sandstone. The results show that the main magnetic minerals in the sample from Chengqianggou Formation sandstone are Single-Domain (SD) and Multi- Domain (MD) hematite and magnetite. The demagnetization curves of some samples exhibit two-component behaviors, and the stable remanence direction of the high-temperature section can be effectively isolated. Combined with the results of petrographic experiments, it is concluded that the main magnetic minerals in the sandstone samples of the Chengqianggou Formation have the capability to record a stable primary remanence during its sedimentary period. Capable of conducting further research on tectonomagnetism.

To address the issue of large observation blind zones in Transient Electromagnetic (TEM) surveys, caused by excessive transmitter coil inductance and suboptimal emission current waveform, this study proposes a rapid shutdown TEM method, which minimizes coil inductance and enables rapid primary-field shutdown. An experimental prototype was designed and tested, demonstrating that the proposed method significantly reduces the turn-off time and the coil inductance, while improving emission current waveform fidelity. These enhancements effectively minimize blind zones, indicating strong potential for practical applications.

Ordos Basin is one of the important uranium energy bases in China. In recent years, the exploration research shows that there are abundant sandstone type uranium deposits in Luohe Formation in Pengyang area, southwest of the basin. Some scholars found that the metallogenic causes of sandstone-type uranium deposits are related to deep oil and gas through geochemical research methods. However, there are relatively few geophysical studies. On the basis of previous studies, this paper describes the fault distribution in the study area through structural analysis of seismic data. By using seismic research methods such as logging, seismic attribute analysis and reservoir wave impedance inversion, the geophysical response characteristics, mainly reflected in high gamma, strong amplitude, low frequency, low mid-high wave impedance and low velocity (compared to similar uranium-free formations), etc. Of oil and gas reservoirs and uranium-bearing sandstones are identified, so as to divide uranium-bearing sandstones and favorable oil and gas areas, and discuss the relationship between uranium mineralization and deep oil and gas from the perspective of geophysics. The final study shows that the deep oil and gas in Pengyang area of Ordos Basin will migrate along the fault to the shallow layer, and REDOX reaction will occur with the uranium-bearing fluid, and then reduce and mineralization in the shallow sandstone layer.

Tight sandstone gas reservoirs, as a crucial component of global unconventional natural gas resources, face challenges in efficient development due to the unclear water production mechanisms. This paper systematically reviews the research methods for studying water production mechanisms in tight sandstone gas reservoirs through a literature survey, providing more comprehensive theoretical and methodological support to address this issue. The research methods are primarily categorized into three types: theoretical and model analysis, experimental analysis, and produced water characteristic analysis. Theoretical analysis integrates theories related to gas and water occurrence, revealing the distribution, flow, and interactions of gas and water in reservoirs at both macro and micro levels. Experimental analysis verifies the flow characteristics of gas and water and the water production mechanisms through methods such as Nuclear Magnetic Resonance (NMR) experiments, capillary pressure experiments, and gas drive water experiments. The produced water characteristic analysis method uses field production data and water sample chemical compositions to determine the type of water production and, combined with the first two methods, specifically analyzes the gas-water occurrence state and water production mechanisms. The results indicate that analyzing water production mechanisms in tight sandstone gas reservoirs requires a comprehensive application of multiple methods to enhance predictive accuracy. The paper concludes with a discussion and outlook on the development trends and existing achievements in reservoir water production mechanism research methods, proposing improvements in predictive accuracy through cross-validation, refining experimental processes, and employing chemical or isotope analysis techniques combined with big data and artificial intelligence algorithms to further enhance the accuracy and applicability of water production mechanism research.

The Ocean Bottom Node (OBN) seismic exploration technology has gradually become an important technical means in offshore oil and gas exploration. It is not constrained by ocean bottom cables and can collect high fidelity and wide azimuth multi-component data, making it easy to study PS wave information. Making full use of the field data of seismic exploration and finely studying the joint inversion method of P-wave velocity and S-wave velocity is the key research direction in this field. However, due to the special observation mode of OBN technology, the problem of inconsistent elevation of shot-receiver points will occur in the processing, and the conventional migration imaging method will not be able to be used. In this paper, the wave equation datum correction method is combined with the conventional migration method, which can effectively solve the problem that the shot points and receiver points are not in the same datum. At the same time, in the process of velocity update, the PP and PS wave layer depth residual constraint is introduced into the P-wave and S-wave tomographic inversion to constrain the update of S-wave velocity, and realize the P-wave and S-wave joint tomographic velocity inversion method of multi-component data of ocean bottom nodes, which provides accurate velocity field for subsequent migration imaging processing.

Due to the special nature of the aquatic environment, the placement of electrodes for electrical exploration is difficult and the data collection efficiency is low. Currently, 2D electrical exploration is mainly used in aquatic areas, making it difficult to accurately locate the target bodies. 3D DC exploration is rarely reported. Based on the fast acquisition technology of parallel electrical exploration and the joint 3D inversion method of multiple survey lines, this article conducted a 3D DC electrical exploration of the original bottom boundary of a quarry in aquatic conditions. A numerical model similar to the actual geological conditions of the survey area was constructed, and the feasibility of 3D DC electrical exploration was verified through numerical simulation. During actual exploration, electrodes were arranged on the water surface, and a pseudo 3D observation system data composed of multiple 2D survey lines was used for 3D inversion. By obtaining multiple XZ resistivity profiles, the depth data of the quarry bottom in a 30 m×5 m grid was obtained, forming contour maps of the quarry bottom boundary. The accuracy of the results was verified through drilling calibration, satellite photo comparison, actual pumping exploration, and other means.

Interferometry Synthetic Aperture Radar (InSAR) is capable of accurately capturing the minute deformation of the surface, particularly the alteration of the edge of the subsidence funnel. Nevertheless, with regard to the precipitated land subsidence in the mining area, the interference signal will lose coherence on account of the excessive sedimentation gradient, leading to heightened uncertainty of the monitoring results and making it arduous to accurately reflect the actual settlement circumstances. To address this issue, a novel method for surface settlement detection is proposed by us. This method combines the advantages of SBAS InSAR technology with probability integration method, enhancing its precision in detecting complex deformation patterns. The method uses probability integral method to invert the center of the basin and integrates with the settlement monitoring results obtained by SBAS to obtain the complete surface subsidence results of the mining area. To verify its effectiveness, we used SBAS InSAR technology to perform time series processing on 49 Sentinel-1A data images from Yuncheng coal mine in Heze City, Shandong Province, and the land subsidence rate and cumulative deformation subsidence in the study area were obtained. The feasibility and accuracy of the method are verified by using the results from December 2016 to June 2018 and the measured data of working face leveling. The results show that the surface subsidence of Yuncheng coal mine is relatively serious. Two subsidence funnels appeared during the monitoring period, and the amount of ground subsidence increased year by year. The maximum subsidence monitored was 2477 mm, and the maximum subsidence rate reached -194.09 mm/a. The fused results of SBAS-InSAR and the probability integration model had a maximum relative error of settlement of 3.7% and 5.2% in terms of strike and dip compared to the leveling data. This method provided more accurate and reliable subsidence information than the single SBAS-InSAR processing, significantly improving the reliability of mining subsidence monitoring and offering a new application reference for mining area subsidence monitoring.

The microscopic pore structure of tight sandstone reservoirs exhibits strong heterogeneity, and the characteristics of hydraulic fracturing fluid damage vary widely. By finely classifying the types of tight sandstone reservoirs and clarifying the microscopic damage characteristics of different types of tight sandstone reservoirs, effective guidance can be provided for hydraulic fracturing treatments of tight sandstone reservoirs. Based on this, the study focuses on the tight sandstone reservoirs of the Shanxi Formation in the northeastern part of the Ordos Basin. Building upon a refined characterization of reservoir types, nuclear magnetic resonance (NMR) quantitative characterization technology was employed to study the microscopic damage characteristics of different types of reservoirs. The results show that the tight sandstone reservoirs of the Shanxi Formation can be classified into three types: type Ⅰ, type Ⅱ, and type Ⅲ. Type Ⅰ reservoirs exhibit good porosity and permeability properties, high pore-throat selectivity coefficients, and a high percentage of movable fluids. Type Ⅱ reservoirs have relatively good porosity and permeability properties, good pore-throat selectivity coefficients but poor fluid mobility, while Type Ⅲ reservoirs exhibit overall poor physical characteristics. The results of NMR T₂ spectra and T₁-T₂ spectra of hydraulic fracturing fluid microscopic damage show that Types Ⅰ and Ⅲ reservoirs suffer more significant fluid damage, which is closely related to the development of large pores and microfractures in these two types of reservoirs.

As domestic oil and gas fields enter their late development stages and the exploration of unconventional oil and gas fields progresses, the industry's demand for higher resolution in seismic exploration continues to rise. This necessitates the consideration of the stratum's viscoelastic absorption impact during the migration imaging process. Facing the complexities of medium parameter modeling, stability in the compensation process, and noise suppression issues, the stationary-phase QPSTM (Deabsorption Prestack Time Migration) technique offers a fundamental solution through the use of effective Q parameters obtained by scanning technology and adaptive migration apertures. However, since the absorption and propagation path of seismic waves are closely intertwined, the accuracy of using effective Q to address viscoelastic absorption issues in the context of complex paths still requires improvement. Currently, accurate modeling of Q parameters in layers remains a challenge for the industry. In light of this, we have adopted a new approach by grouping Offset Vector Tile (OVT) gathers and combining them with QPSTM technique, to develop an OVT domain Q prestack time migration method. This method, based on grouping effective Q by offset and azimuth, achieves more precise compensation for viscoelastic medium absorption and finds a better balance between Q parameter compensation threshold and noise suppression. Test results using field data have demonstrated that this method can improve existing deabsorption prestack time migration by avoiding the modeling of Q parameters in layers, and achieve a more balanced resolution and signal-to-noise ratio in the migration results. This provides an effective industrial solution for viscoelastic medium prestack time migration.

With the deepening of oil and gas exploration, new discoveries have been made in ultra-low permeability tight reservoirs of Chang 8 reservoir in the lower Yanchang formation, Ordos Basin in recent years. However, there are distinct differences in oil charging among wells and the reservoir characteristics and main controlling factors need to be further clarified. Through new drilling, mud logging, fluid experimental and lab test data, as well as geochemistry and wireline logging analysis, comprehensive study were carried out to reveal the main controlling factors of hydrocarbon migration and accumulation and differential distribution law in Chang 8 reservoir on the basis of exploration data as well as previous research results in southwestern margin and adjacent region of Ordos Basin. The results show that contrast to inner basin, the fluid properties is worse, hydrocarbon charging scale and degree is smaller in southwestern margin. The major formation reasons of enrichment difference are source rocks quality and reservoir forming power. The sand body and reservoir physical properties is not so important. Structural adjustment influence secondary oil-gas migration or damage in southwestern margin contrast to basin center, resulting in lower enrichment degree of marginal reservoir. Unlike the high quality source, high pressure and strong charging mode in the basin center, the oil and gas at the edge of the basin should adopt a low generation, low pressure, and low charging reservoir formation mode. In order to achieve best selection and optimization in the poor edge area, more attention should be paid to the fine evaluation of the source rocks and the analysis of preservation conditions.Through the above research, comparative study should strengthen more in order to further understand the distribution law of low permeability to tight oil and provide effective guidance for tight oil exploration of complex marginal area.

A spectrum observation device has been designed for measuring the electrical spectrum parameters of outdoor geological outcrops. The signal sending end adopts multi stage current expansion output or voltage power amplification output to increase the load capacity of the sending signal; On the basis of common clock synchronization, the receiver and transmitter improve the anti-interference ability of the device through coherent detection, and use weak signal detection technology to design preamplifiers, signal conditioning and other circuits to improve the signal-to-noise ratio of the channel. By calibrating the observation device using a standard resistance capacitance network model and conducting on-site experiments, spectral observation of underground conductive media was achieved. The results show that the device has signal transmission mode and multi gear switching function, automatic frequency conversion measurement, simple operation, and the observation results play a certain supporting role in selecting exploration methods, establishing geophysical models, and geological interpretation.

The Permian volcanic rocks in the western Sichuan Basin have broad prospects for exploration. However, the volcanic rocks in this area have the characteristics of large lateral thickness difference, rapid lithofacies change and strong heterogeneity. Moreover, its seismic response is chaotic and complex, making it difficult to accurately identify the volcanic rocks and predict the seismic facies. In order to clarify the distribution and exploration prospects of volcanic rocks in this area, a technical process of depiction and seismic facies classification prediction is proposed based on the well-drilled data, seismic data and geological research. Firstly, based on the data of instantaneous phase and coherence attributes of high-resolution processing seismic data, the volcanic rock indication profile was constructed to accurately identify the volcanic rock development area. Secondly, combined with the seismic response characteristics of volcanic rocks, the seismic strata of the top and bottom interfaces of volcanic rocks were explained, and the volcanic rock mass in the study area was finely depicted. Then, based on the seismic waveform classification analysis, the seismic response characteristics and seismic facies classification prediction in the overall study area (macro-characteristics) and the key study area (local characteristics) were studied. By carrying out favorable seismic facies prediction and exploration direction analysis in a study area in central Sichuan, the seismic strata interpretation and the depiction were successfully achieved, the seismic reflection patterns of volcanic rocks with different lithofacies were analyzed, and the seismic waveforms were classified.The conclusion is that: a set of technical processes of depiction and seismic facies analysis of Permian volcanic rocks in the western Sichuan Basin based on high resolution processing, seismic response characteristics identification, seismic facies classification and prediction has been proposed. The plane distribution characteristics of explosive facies volcanic rocks have been effectively predicted.

The Shahejie Formation is endowed with high-quality hydrocarbon source rocks which is an important rocky oil and gas reservoir in Bohai Bay Basin. Rock mechanics, which is an important geomechanically property of reservoirs, holds great significance in accurately evaluating the drilling and completion process and fracturing construction design. In this paper, typical shales, sandstones, and mudstones of the Shahejie Formation are taken as the research objects, and thin-section appraisal and triaxial compression tests are carried out to clarify the petrographic and mechanical properties and the damage modes of the reservoirs. The influence of confining pressure and laminar orientation on rock strength anisotropy and damage characteristics is analyzed. The results show that: The rocks of different lithological reservoirs show different mechanical properties. Sandstone has high compressive strength and modulus of elasticity, strong hard brittleness characteristics, and mainly undergoes shear damage. The second is shale, which is controlled by the influence of laminar orientation with significant anisotropy in mechanical strength and fracture pattern, and good fracturability. Mudstone is the least mechanically stable. Layer orientation has a significant effect on the anisotropy of reservoir compressive strength. The mechanical strength of the rock is most stable when the dip angle of the laminae is 0°, followed by 90°. The compressive strength and modulus of elasticity of the rock are the lowest when the dip angle of the laminae is 60°. The modified Gaussian model can effectively characterize the changing trend of compressive strength with laminar inclination angle β. The orientation of the laminae affects the compressive fracture pattern of shale. When the orientation of the laminae is 0°, the damage mode is a composite damage controlled by tension through the laminae and shear along the laminae surface. At 30° and 60°, the specimens showed shear damage. When the orientation of the laminae is 90°, the specimens are characterized by tensile splitting damage across the end face. Both sandstone and mudstone show shear damage mode. The research results can provide theoretical support to the evaluation of reservoir rock mechanics and highly efficient exploration and development of oil and gas.

The MEMS seismograph developed based on Micro-Electro-Mechanical System (MEMS) technology has the advantages of easy integration, low maintenance cost and low power consumption, and is widely used in the field of seismic monitoring. However, the integrated software and hardware resources of MEMS seismograph are limited, and it is greatly interfered by the instrument's own noise and other factors, resulting in low-quality seismic signal measurements, which requires higher embedding algorithm. To solve this problem, this paper proposes an improved Short Term Average/Long Term Average (STA/LTA) algorithm that is more suitable for MEMS seismographs. Firstly, the characteristic function of Anti-interference (AR) is constructed to suppress the interference of baseline drift and low-frequency noise, and improve the anti-interference ability of the STA/LTA algorithm to pick up seismic events. Secondly, the method of "delay time window" is proposed to improve the computational efficiency and picking accuracy of the STA/LTA algorithm, and reduce the occupation of MEMS integration resources by the STA/LTA algorithm. Finally, the influence of different time window sizes on the picking efficiency of STA/LTA algorithm is further explored by combining the time window position. The simulation results of actual seismic data show that the improved STA/LTA algorithm proposed in this paper has higher computational efficiency, stronger real-time and anti-interference ability, and is more suitable for integrating MEMS seismometers with limited resources.

Pre-stack AVO inversion is one of the key methods for reservoir characterization, from which abundant elastic parameters in underground media can be obtained, which is conducive to the identification of oil and gas reservoirs. The inverse problem of pre-stack angular track set recording to elastic parameters is challenging in terms of adaptability and resolution. To solve these problems, a pre-stack AVO inversion network based on Transformer framework is proposed in this paper to solve the velocity and density of P-S wave. Inversion results are unstable and transverse continuity is poor in the network that uses pre-stack seismic data as one-way input. Therefore, prior knowledge constraints are introduced in training to improve the stability and accuracy of inversion results. In order to reduce the dependence on well data inversion, this paper uses transfer learning strategy to transfer the trained model to the real data inversion. In the data preprocessing stage, the data augmentation method is used to expand the training samples, so that the proposed network can fully extract the pre-stack trace set information, and establish the complex nonlinear mapping relationship between the pre-stack trace set and the elastic parameters. In this paper, the method of multi-task learning is used to realize simultaneous inversion of P-wave velocity, S-wave velocity and density, so as to improve the inversion accuracy and calculation efficiency. Through inversion testing of Marmousi2 synthetic data and actual data, and comparing with classical deep learning frameworks, the multi-task Transformer framework proposed in this paper has higher accuracy and high-resolution inversion results.

In recent years, Full Polarimetric-Ground Penetrating Radar (FP-GPR) has been developed to enhance the recognition capabilities of GPR. In order to extract the polarimetric properties of the targets, many polarimetric decomposition techniques in Synthetic Aperture Radar (SAR) are applied to FP-GPR. H-Alpha decomposition can obtain two parameters, H and α, for classification. However, due to the differences in measurements between FP-GPR and SAR, the classic H-Alpha classification template in SAR may not be suitable for FP-GPR. This paper proposes to use the Support Vector Machine (SVM) and Particle Center Supported Plane (PCSP) to analyze and obtain the rules from the FP-GPR data and establish new classification criterion suitable for FP-GPR. The training and testing of four kinds of targets verified the feasibility of this two methods. Furthermore, the comparison about the accuracy of the two method was performed. Finally, the SVM and PCSP were applied to the landmine detection, and two new methods of landmine identification was proposed. The results of the two methods are compared.

Orthogonal modelling is currently of great importance in a number of disciplines, including the exploration and development of unconventional oil and gas reservoirs, nuclear waste storage, carbon dioxide sequestration and groundwater hydrology. The most common approach to modelling mixed fluid saturation is to replace an ensemble of multiphase fluids with an equivalent fluid. This paper investigates the effect of the equivalent fluid model on the elastic properties of a saturated porous medium based on a rock physical model of the effect of intersecting and non-intersecting fractures on the elastic properties of the rock physical model.This paper focuses on the case of partial saturation of a gas-water two-phase mixed fluid and introduces the dimensionless parameter q to quantify the degree of saturation of such a mixed fluid. This approach builds on single fluid theory and further explores the complex interactions of two-phase fluids and their physical manifestations. The parameter q is employed to characterise the pressure variations within the fluid, as well as the fluid mixing. It represents an effective method whose value affects the stiffness of the equivalent fluid. The use of the equivalent fluid and parameter q allows for the investigation of the impact of water and gas components on the elastic properties, anisotropy parameters, and the generation and variation of P-wave and S-wave velocities in the rock physical model. This enables a more accurate simulation of the properties of fractured reservoir media.