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.

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.

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.

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.

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.

Internal multiple suppression of seismic data has been a research hotspot and difficulty in the field of oil and gas exploration. The strong reflection interface in the subsurface will form internal multiples with strong energy, which seriously affect the identification of primaries. It also can reduce the authenticity and reliability of seismic imaging. The deep learning-based multiple suppression method can form more abstract high-level features by combining the low-level features to better discover the effective features of the data, and the multiple separation accuracy is high. In this paper, the attention mechanism is introduced for the problem of high training cost of traditional convolutional neural network, and an internal multiple suppression method based on the attention mechanism is proposed to reduce the training cost of neural network model. The data test shows that the method is not affected by the limitations of the traditional internal multiple suppression method and can avoid the regularization of seismic data, thus reducing the computational burdens and improving the computational efficiency, which has important theoretical and industrial application value.

Controlled-Source Audio Magnetotelluric (CSAMT) is a near-surface geophysical method that developed on the basis of Magnetotelluric method (MT). With the development of social economy,the data quality of CSAMT has also been seriously disturbed by noise interference. In practical exploration,the time series of electromagnetic field is usually superimposed with large-scale trend drift,short-term sudden strong interference and peak impulsive outliers,resulting in the distortion of the calculated resistivity spectrum. In this paper,an anti-interference processing method based on deep learning and joint de-noising is proposed to preprocess CSAMT time series. Firstly,a forward algorithm of electromagnetic time series of layered earth controllable source is proposed,which is used to generate standard electromagnetic signals without noise interference. Then,a Long and Short Term Memory Neural Network (LSTM) classifier is trained to recognize the noise. Finally,the improved Empirical Mode Decomposition (EMD) algorithm,correlation based data selection algorithm and robust statistical algorithm are jointly used to de-noise the CSAMT time series. The test results by simulated data show that the recognition accuracy of LSTM for noise interference can reach more than 95%,and the three noise reduction algorithms can reduce the data error from about 20% to less than 3%. Finally,the proposed method is applied to the actual data set of a metal mining area in Inner Mongolia. the accuracy of low-frequency resistivity and phase was effectively improved.

Instantaneous frequency is an important seismic attribute, which helps in identification of oil and gas reservoirs and is of great significance to reservoir prediction. This paper provides a detailed review of several common instantaneous frequency calculation methods and tests using analytical sinusoidal signals. The instantaneous frequency calculation results of each algorithm are compared with the analytical instantaneous frequency of sinusoidal signals, and various instantaneous frequency calculation methods are compared by comprehensively analyzing the correlation number, error between the calculated results and the theoretical analytical instantaneous frequency, and operation time. In order to further test the performance of each algorithm, we apply the more complex signals to each instantaneous frequency calculation method to analyze and compare. The test results of analytic and complex signals show that the performance of each algorithm varies for different types of data. The method that can relatively accurately calculate the instantaneous frequency of a simple analytical signal will have outliers, "negative frequencies", or inaccurate calculation results when calculating the instantaneous frequency of complex signals. Outliers and "negative frequencies" that appear during instantaneous frequency calculations will submerge the actual effective instantaneous frequency information. Further work is needed to overcome the problems of outliers and "negative frequencies".

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 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.

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.

Ocean resources are crucial for the sustainable development of a country, and improving marine resource surveys has become a focal point for many nations. Accurate detection of underwater targets has become a hot topic in the field of geophysics. The electromagnetic exploration method, based on differences in resistivity, can effectively extract and identify underwater high-resistivity or high-conductivity targets, making it one of the key technologies in marine geophysical exploration. Airborne electromagnetic methods, using airborne platforms, enable rapid data acquisition in marine areas and have been widely applied in marine resource surveys and environmental monitoring. However, the marine environment is complex and dynamic, and the forward modeling and inversion interpretation techniques based on one-dimensional marine-land geoelectric models struggle to characterize three-dimensional complex geoelectric structures. This severely hinders the detailed interpretation of multi-frequency electromagnetic data from airborne platforms. In this study, a hexahedral mesh spectral element method is used for high-precision multi-frequency airborne electromagnetic three-dimensional forward modeling of the marine-land geoelectric model. Numerical simulations and analysis of frequency-domain electromagnetic detection of underwater targets are conducted, summarizing the propagation characteristics of frequency-domain electromagnetic fields under different conditions. The study explores the identification capability of multi-frequency electromagnetic systems for underwater targets, providing theoretical references for the effective detection of high-conductivity targets in low-altitude marine areas using multi-frequency electromagnetic systems.

Common Conversion Point (CCP) gather extraction is an important step in converted wave seismic data processing. It is also the main difference between converted wave processing and P-wave processing,and directly affects the results of velocity modeling and imaging. The conventional CCP gather selection and sorting method is still based on the whole trace selection and sorting,mainly the asymptotic approximation trace classification method,that is,the Asymptotic Conversion Point (ACP) gather extraction. This kind of method can satisfy the imaging requirements of deep strata to a certain extent,but it is difficult to guarantee shallow imaging effects. To solve this problem,this paper proposes a spatiotemporally variable conversion point gather extraction method. By calculating the spatial position of the CCP and combining the converted wave two-way traveltime,the mapping relationship between the input converted wave data and the output CCP gather is determined,and reflected seismic signals are mapped to accurate spatiotemporal locations. It can effectively handle asymmetric converted wave propagation paths,thereby effectively improving the extraction quality of CCP gathers. Numerical simulation and field data calculation results show that the CCP gather extraction method proposed in this paper has good practical application effects.

In response to the data quality assessment issues faced by the integration of seismometer, strong motion seismograph, and intensity meters, research and analysis work on background noise of regional early warning networks has been carried out. Using the noise power spectrum method, maximum probability acceleration noise peak (PGA), velocity noise peak (PGV), and displacement noise peak (PGD) standards, scientifically evaluate the background noise of the three types of sensors that make up the network, and analyze the differences in their noise levels; Quantitatively evaluate the noise level of stations using the noise power spectral area ratio method, and display the noise level of each station based on different color codes, making it convenient to visually judge the operation status of the station; Based on actual statistics, select appropriate proportions to determine the high and low baseline of various sensor noise models and the upper and lower limits of normal noise PGA, PGV, and PGD, and identify stations with suspected abnormal waveform records. The research results indicate that among the three types of seismic monitoring instruments, seismometers can fully and effectively record environmental noise in the full frequency band, with the lowest noise level; The strong seismic instrument can record environmental noise above 0.1 Hz, and the frequency band below 0.1 Hz is mainly due to instrument self noise, with a higher noise level than the seismometer; The recording of the intensity meter is basically the self noise of the instrument, which cannot record the Earth's pulsation and has the highest noise level. The noise power spectral area ratio method divides the noise level into four levels, which can effectively select stations with high-quality records. Detecting abnormal stations through frequency and time domains greatly improves the reliability of detection results, while also facilitating the detection of false alarms such as calibration and abnormal large pulses.The quality evaluation results of the observation data of the network can be used as a reference for maintenance personnel to focus on high-quality stations, ensure the Completeness of the data of high-quality stations, and record abnormal stations that should be repaired in a timely manner, providing an important guarantee for the reliability and accuracy of earthquake early warning and intensity quick report products.

Loose asphalt concrete is a commonly encountered defect in road surface layers, often leading to premature issues such as pavement cracking and potholes, thereby compromising the overall performance and lifespan of the road. Leveraging the statistical characteristics of asphalt concrete's multiphase, discrete, and random distribution, we employed a quantitative constraint multiphase discrete random medium modeling approach to develop models of asphalt concrete loosening with varying porosity rates. Additionally, we conducted GPR (Ground Penetrating Radar) forward modeling to investigate the GPR wavefield characteristics and intuitive diagnostic techniques associated with loosening. Our research findings reveal that, in comparison to traditional layered uniform medium models, the multiphase discrete random medium model offers a more precise portrayal of the actual state of asphalt concrete loosening. Furthermore, its numerical simulation outcomes align more closely with measured radar data. As the degree of asphalt concrete loosening increases, the inhomogeneity of the medium becomes more evident, resulting in a stronger amplitude of the corresponding GPR wave, a more chaotic waveform, more intense variations in regional amplitude curves, and more prominent diffraction waves on both sides. By analyzing the GPR waveform chaos, amplitude intensity, and intensity of change, we can more intuitively and accurately identify loose areas in asphalt concrete, qualitatively assess the degree of loosening, and provide a solid foundation for targeted treatment and repair measures.

Joint inversion of gravity and magnetic can directly obtain the underground density and magnetic distribution by synthesizing the characteristics of gravity and magnetic data,and effectively reveal different lithology distribution and underground structure,which is an important means of mineral resources exploration. The actual surface of the earth and the observed surface of the airborne gravity and magnetic survey that fluctuates along the terrain are both undulating. In order to realize the highly efficient joint inversion of the undulating observation surfaces,we have established a fast joint physical inversion method for gravity and magnetic data under the constraints of undulating observation surfaces. Firstly,the data is flattened according to the maximum observed height. Then,Block-Toeplitz-Toeplitz-Block(BTTB)-FFT is used to achieve fast inversion,and the space between the converted observation plane and the actual observation plane is used as a constraint to eliminate the multiple solutions caused by the calculation of invalid grid cells. Therefore,this method can achieve high efficiency inversion without increasing the multiplicity of solutions due to additional partition elements. Model tests show that this method can effectively improve the computational efficiency by more than 32 times without losing the accuracy of inversion calculation,and has good stability for noisy data inversion. Finally,we applied this method to Sankeshu Depression,Tonghua Basin,eastern Jilin Province,China,and obtained the distribution of basement and igneous rocks in this area. The average depth of basin basement in this area is about 2.5 km,and the development of igneous rocks is mainly concentrated in the area with large basement depth,which provides important basic geological information for the next oil and gas exploration.It also provides important guiding significance for oil and gas exploration and deployment in the eastern peripheral new area of Songliao Basin.

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 spatial association relationship of shale oil fractured reservoir is complicated, the seismic wave field characteristics of favorable fractures are complex, the seismic response characteristics are complex and diverse, and the seismic interpretation of shale oil fractured system is strong, which seriously affects the reliability of seismic identification of shale oil. Based on the geological, well logging and seismic data, this paper takes the Niuxie 55 well area of Jiyang Depression, Shengli Oilfield as an example. The fracture model of shale oil reservoir is established by comprehensive use of geological, logging and seismic data, by using the seismic forward modeling technology, the characteristics of fracture models with different orientations, angles and densities are analyzed, the seismic response mechanism of shale oil reservoir fractures is defined, and the shale oil fracture identification template is formed. The results show that the azimuth Angle has little effect on the seismic response characteristics of fractures. With the increase of fracture density, the intensity of seismic response and the degree of fracture fragmentation increase, and intermittent reflection and chaotic reflection appear. The reflection intensity of seismic response of fractures increases with the increase of inclination Angle. Low Angle fractures show weak reflection, high Angle fractures cause abnormal dithering of interlayer reflection, and vertical fractures show chaotic reflection. According to the established shale oil fracture identification template, improve the reliability of fracture identification, and lay a foundation for shale oil exploration and development.

Parker C. Chen (born July 27, 1898, Xinchang county, Zhejiang Province- died March 4, 1960, Beijing) was the founder of geomagnetism in China and the founder of the Chinese Geophysical Society. Professor Chen had made outstanding contributions not only in geomagnetism, but also in the solar-earth relations, space physics, as well as initiation of artificial satellites program in China. His contributions and leadership had promoted scientific status internationally and paved the way for related scientific disciplines and technological progress.

Cement mixing piles are common method of treating soft soil foundation in coastal areas. However, there are some problems in practical engineering, such as cutting corners and unstable pile quality. Current methods for detecting mixed piles are high cost, have a low sampling-ratio and can permanently damage the integrity of the pile body. Non-destructive and accurate detection methods are urgently needed. In this paper, three methods of time-lapse cross-hole full waveform inversion (separate inversion, continuous inversion and double difference inversion) are proposed by combining cross-hole seismic, full waveform inversion and time-lapse seismic exploration. These methods are applied to the expansion project of Singapore Changi Airport, and the detection effects of various methods are analyzed and compared. The test results show that the cross-hole wave velocity test is easily disturbed by the environment and it is difficult to reflect the actual range of the pile foundation. The background field has a certain influence on the resolution of full waveform inversion. Continuous inversion can effectively highlight the location and scope of the pile foundation and double difference inversion can further weaken the influence of background field on the basis of continuous inversion, so as to improve the imaging accuracy.

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.

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.

The Jinsha River,located in the upper reaches of the Yangtze River,is prone to frequent geological hazards on both sides due to its unique terrain and geological conditions. Firstly,the article employed the SBAS-InSAR technique to capture the surface deformation characteristics of landslides in the Gongjue County of the Jinsha River Basin in the Tibet Autonomous Region,spanning the period from October 2014 to October 2018, spanning from Shadong Township to Xiongsong Township. Subsequently,by integrating ascending and descending orbit DInSAR datasets,the two-dimensional deformation information was obtained in this region. Building upon this foundation,the Shadong landslide was selected as the research subject,the surface parallel flow constraint model was introduced to conduct three-dimensional deformation monitoring studies on the landslide.The results show that: (1) The study area is characterized by fragmented terrain and the development of geological hazards. Utilizing ascending and descending orbit Sentinel-1A data,nine and thirteen deformation regions were detected,respectively. Among them,the maximum deformation rate in certain areas reached 150 mm/year.(2) The two-dimensional deformation results reveal that the maximum deformation rates in the east-west and vertical directions are 147 mm/year and-70 mm/year,respectively. The spatial distribution characteristics of landslide deformation vary significantly at different locations. (3) The presentation of the three-dimensional deformation results shows the movement direction of the Shadong landslide in various locations,with the slope mainly moving in the northeast direction and accompanied by a sinking state. (4) Based on the rainfall factors in the region,the correlation between typical landslide deformation and rainfall was analyzed. The results show that Intense rainfall is critical driving factor for accelerating landslide deformation.

Geomagnetic activity, which refers to the time and space variations of Earth's internal and external magnetic fields, has always been a research focus in the field of Earth sciences and acts as an important medium in exploring the Earth's space environment. In recent years, the impact of geomagnetic activity on human health has gained widespread attention, becoming a new research hotspot. Based on existing literature in this field, this paper systematically reviews the effects of geomagnetic activity on human health: It first summarizes the negative impacts of geomagnetic phenomena like magnetic storms caused by solar activity on cardiovascular and cerebrovascular diseases, mental health disorders, and other physiological states (illnesses), along with a medical interpretation of these effects' mechanisms. The paper then looks forward to the future from three aspects: the establishment of a theoretical system linking geomagnetic activity and human health, the prediction and early warning of geomagnetic events, and protective measures against the hazards of geomagnetic activity. This provides a feasible reference for researchers further exploring this area and has significant practical significance.

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.

Accurate precipitation data of satellite is very important for real-time precipitation monitoring and weather forecasting. This paper takes the Chinese mainland and its surrounding sea areas as the research area, and takes the DPR(Dual-frequency Precipitation Radar) precipitation data as the reference value to verify and evaluate the AMSR2(Advanced Microwave Scanning Radiometer 2) precipitation product from July to September 2022 by using classification statistical indicators and accuracy evaluation indicators. The results show that the AMSR2 precipitation product has the best observation effect over the ocean, with the probability of detection of 0.659 and ETS score of 0.546, and the correlation coefficient with DPR is 0.679, RMSE is 4.598 mm/h; The observation effect over the land is second, with the false alarm ratio of 0.277 and ETS score of 0.357, and the correlation coefficient with DPR is 0.325, RMSE is 2.793 mm/h; The observation effect over the coast is relatively poor, with the low probability of detection of 0.361 and ETS score of 0.307, and the correlation coefficient with DPR is 0.329, RMSE is 4.527 mm/h. At the same time, as the rainfall level increases, the estimation error of AMSR2 precipitation product for precipitation is also increasing. It is easy to overestimate precipitation in light rainfall level, and in moderate rainfall level it is easy to underestimate precipitation over the coast, to overestimate precipitation over the sea and land, while in heavy rainfall and heavy rainstorm level it is easy to underestimate precipitation, and the degree of underestimation increases with the increase of rainfall level.

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 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.

Wyrtki jet is an easterly jet that occurs during the monsoon transition period in the equatorial Indian Ocean. It generates abundant zonal redistribution of heat, salt, and water masses, which plays an important role in ocean thermohaline circulation of the eastern Indian Ocean and even global ocean. The intraseasonal variability of Wyrtki jet is related to the equatorial Kelvin waves and has a significant impact on the intraseasonal variability of the Indonesian throughflow in the outflow straits. It is also a triggering factor for the Indian Ocean Dipole. In addition, intraseasonal variability of Wyrtki jet can regulate the seasonal and interannual variabilities of Wyrtki jet across time scales, making it a potential important participant in climate variation of the tropical Indian Ocean and even the global world. Therefore, conducting in-depth research on the intraseasonal variability of Wyrtki jet is of great scientific significance for a profound understanding of the ocean circulation in tropical Indian Ocean as well as the process and mechanism of air-sea interaction in the Indian Ocean basin. This article mainly introduces the current research status of the intraseasonal variability of Wyrtki jet, and summarizes its generation mechanism as well as its seasonal and interannual variation characteristics. On this basis, future research on the intraseasonal variability of Wyrtki jet is prospected. We suggest that future research on the intraseasonal variability of Wyrtki jet can be conducted from two aspects: the correlation between the intraseasonal variability of Wyrtki jet and the Asian monsoon, and the correlation between the intraseasonal variability of Wyrtki jet and large-scale air-sea interaction events. Furthermore, it is pointed out that in the future, various methods such as observation, ocean circulation model and machine learning technology should be used to obtain data of Wyrtki jet. Based on these data, we can further clarify the impact of the intraseasonal variability of Wyrtki jet on local and global ocean and atmosphere, thereby improving understanding of the tropical Indian Ocean and global ocean circulation.

Compared with traditional cable based acquisition, the use of node instruments has higher collection efficiency and lower cost. In order to adapt to the transformation of onshore seismic acquisition methods from cable to node, on-site data processing and quality control technology also needs to shift from shot domain to detection point domain. Therefore, this article proposes a real-time and efficient quantitative quality control technology for common detection point gathers collected by node instruments. Firstly, the integrity of detection points and data is checked in the detection point domain through precise positioning technology such as automatic matching of gun channels, "first checking both ends, then checking the middle", and other methods. Then, through the multi attribute quantitative analysis technology of common detection point gathers, various attributes such as abnormal interference, 50 Hz industrial electricity statistics, and low-frequency abnormal energy statistics are automatically counted to control the quality of collected data. Finally, the common detection point gathers are classified and rated based on their energy, background interference, and other attribute statistics. The quality control method proposed in this article has been applied in the entire node collection work area of complex mountain areas in the south, effectively shortening the data recovery time, improving the efficiency of collection and processing, and ensuring the accuracy and reliability of node instrument data collection.

Cross-sea bridges and tunnels play a significant role in alleviating transportation bottlenecks. Accurate and comprehensive geological survey data is essential for selecting and optimizing cross-sea transportation engineering plans, designing improvements, and formulating construction plans. It also serves as a vital safeguard for engineering risk decision-making. However, traditional survey methods face limitations in underwater tunnel projects due to the complex marine traffic environment, aquatic conditions, and variable topography and geology. This study, based on the Jintang Subsea Tunnel survey project for the Ningbo-Zhoushan Railway, introduces three-dimensional seismic exploration technology into the field of engineering surveys, exploring new approaches to surveying underwater tunnels under complex geological conditions. The application of three-dimensional seismic reflection in the Jintang Subsea Tunnel survey project solves the challenges associated with data collection in shallow water areas through a collection scheme that includes single-cable, parallel, and multi-line closely spaced acquisitions. Advanced processing techniques, such as tidal correction based on high-precision measurement data, multiple wavelet suppression techniques using pre-stack predictive deconvolution and post-stack wavefield extrapolation, and well-constrained depth conversion techniques, significantly improve the vertical and lateral resolution of three-dimensional seismic exploration in water areas and enhance the accuracy of survey results. This approach successfully addresses the spatial distribution of the seabed, Quaternary strata, and underlying bedrock (submarine mountains) in complex geological sections, as well as the range of structural features and fractured zones. Furthermore, the well-constrained geological modeling technique based on three-dimensional seismic reflection results accurately simulates the spatial contact relationships between formations and structures. By integrating engineering models, it enables a visual analysis of the geological conditions along the tunnel, effectively improving the quality of survey results and achieving high-precision three-dimensional geological exploration. The application study of the Jintang Subsea Tunnel survey project demonstrates the feasibility of high-precision three-dimensional seismic reflection exploration technology in water areas. It provides accurate and reliable geological survey results for water-based engineering geological surveys, design optimization, and engineering risk decision-making, offering a fresh perspective for complex engineering geological surveys in water areas and showing excellent practicality and potential for broader adoption.

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.

The calculation of sensitivity is a key link in the linear inversion of marine controlled source electromagnetic data, which determines the quality and efficiency of electromagnetic data inversion, so it is necessary to examine and analyze the sensitivity calculation. In this paper, we derive the formula for calculating the sensitivity in the controlled-sources electromagnetic 2.5-dimensional inversion in the frequency domain, and use the adaptive unstructured finite element algorithm to calculate the electromagnetic response, and the adjoint reciprocal method to calculate the electromagnetic sensitivity, which only needs to calculate two basically the same bound-value problems (the primary field and the adjoint field), and then integrate the dot product of the two corresponding fields to get the deviation of the observed electromagnetic field components from the conductivity parameter. The algorithm is examined using a one-dimensional isotropic model, and the accuracy of the sensitivity calculation method in this paper is verified by comparing it with the one-dimensional isotropic modeling algorithm. Then the one-dimensional anisotropic model and the two-dimensional anisotropic models are computationally analyzed to characterize the sensitivity distributions of different electromagnetic field components in high- and low-resistance anisotropic media.

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.

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.

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.

Loess has very obvious collapsibility and water sensitivity. During the process of water infiltration, the hydraulic conditions of loess undergo significant changes, which is highly prone to inducing a large number of geological disasters such as loess landslides. This feature is more obvious under special extreme climate conditions, such as rainstorm, temperature upheaval, etc., which can induce a variety of geological disasters. Through rapid geophysical exploration, it is of great significance to determine the changes in the physical properties of loess at different depths, as well as the distribution and changes of cracks, for the analysis, prediction, and early warning of disasters. This article analyzes and summarizes the applicability of different detection techniques in detecting the characteristics of loess landslides. Through typical application cases, the actual application effects of different methods are demonstrated, and the development trend of future loess disaster detection is prospected, providing reference and suggestions for the development of geophysical detection technology for loess disaster in the next step.

Fine-grained magnetite with single domain and its neighborhood is one of the dominant magnetic-carrying minerals in paleomagnetism. Its magnetic properties depend strongly on particle size, crystal form, shape and oxidation degree, etc., regardless of which will result in inaccuracy of paleomagnetic data recording and ambiguities in corresponding geological interpretations. In view of the complexity of natural magnetite particles, the computational limitation of micromagnetic modeling, and the locality of microscopic observation, we firstly elaborates on the significance of fine magnetite synthesis in rock magnetism, and then reviews the research status, applications, and challenges of fine-grained magnetite synthesis in rock magnetism. By introducing the synthesis methods of fine-grained magnetite in material magnetism, we then expound the paleomagnetic significance in geological applications. Finally, the applications of integrated magnetic synthesis method are put forward, including the study of the "magnetic unstable" particles, paleointensity and the forward and inversion of rock magnetism. This paper will provide systematic reference for the synthesis of fine-grained magnetite and its rock magnetic applications, and deepen our understanding of mineral magnetic properties and related geological processes.

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.

Rock grain size plays a significant role in the analysis of hydraulic conditions and the identification of depositional environments. Traditional methods for grain size measurement, for instance sieve analysis and laser diffraction, are time-consuming, costly, and suffer from discontinuity in depth due to limited core recovery during drilling. Although the combination of well log curves and machine learning methods can compensate for the limitations of rock physics experimental techniques, existing studies mainly focus on one-dimensional characteristic values of grain size, lacking a comprehensive representation of the two-dimensional grain size distribution. In this study, we propose a machine learning approach that combines the automatic hyperparameter optimization framework (Optuna) with gradient boosting algorithms (LightGBM and XGBoost) to address the challenge of predicting two-dimensional grain size distribution in reservoirs. Based on well log data and grain size distribution experimental data from a certain block in the Chengdao oilfield, we compare eight different machine learning methods, including linear regression, Support Vector Regression (SVR), k-Nearest Neighbors (k-NN), random forest, Gradient Boosting Decision Tree (GBDT), XGBoost, LightGBM, and Convolutional Neural Network (CNN). By optimizing the machine learning parameters, we identify the most appropriate method for predicting reservoir grain size distribution. The research results demonstrate significant differences in the accuracy of grain size distribution prediction among the ten machine learning methods. When using nine well log parameters, including natural potential, sonic, wellbore diameter, compensated neutron, natural gamma, formation resistivity, deep lateral resistivity, micro lateral resistivity, and shallow lateral resistivity, as inputs, the proposed method achieves the highest accuracy in predicting the two-dimensional grain size distribution in reservoirs, with R² coefficients approaching 0.7 and smaller errors. Furthermore, linear regression, SVR, as well as GBDT attain lower accuracy in predicting reservoir grain size distribution, which are not eligible for grain size prediction in reservoirs.