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.

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.

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".

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Severe changes in the local lithosphere, such as volcanic eruptions and earthquakes, can cause rapid changes in the regional lithospheric magnetic field. Geomagnetic scholars generally study the seismic magnetic relationship by monitoring changes in the regional magnetic field and selecting reasonable mathematical models.This article is based on the mobile geomagnetic observation data of the northern segment of the north-south seismic belt from 2020 to 2021. Through daily variation normalization correction, long-term variation correction, and stripping of the lithospheric magnetic field, the spatiotemporal distribution characteristics of the regional lithospheric magnetic field and the abnormal changes of magnetic elements at the locations of 7 moderately strong earthquakes are analyzed and studied. The results show that the horizontal vector of the lithospheric magnetic field in the study area forms several weak variation regions, The 7 moderately strong earthquakes discussed in this article all occurred within or at the edge of the weak variation zone of the horizontal vector of the lithospheric magnetic field, and there was a trend of turning, diverging, hedging, or magnitude mutation in the horizontal vector at the location of the earthquake; There is also a correlation between the annual variation contour lines of various elements of the lithospheric magnetic field and the location of earthquakes, as shown in the following: the "0" value line of the ΔD、ΔY、ΔZ、ΔF、ΔI、ΔX contour line shows a clear positional feature with moderate strong earthquakes, that is, the earthquake location is close to the "0" value line, and only from the distance between the earthquake location and the "0" value line, ΔD、ΔY、ΔZ is better ΔF、ΔI、ΔX, than three elements.This obvious change in seismic and magnetic characteristics has strong directionality in predicting the location of moderate to strong earthquakes; In addition, except for the Mangya earthquake with a magnitude of 5.5, all other earthquakes are relatively close to the anomaly areas designated based on the anomalies of the lithospheric magnetic field elements. Among them, the Menyuan earthquake with a magnitude of 6.9, the Malkang earthquake with a magnitude of 5.8/6.0, and the Zaduo earthquake with a magnitude of 5.9 are located within 30 km from the nearest abnormal change area of the lithospheric magnetic field, and the seismic effect is good.

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.

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.

The tight gas reserves of He8 member in Linxing area of Ordos Basin are large. However, the testing production of most wells in this member is low at the present stage. Recently, three wells have been tested, and the production yield after fracturing is more than 10000 m³/day, which reveals that the He8 member is of potential for development. According to the analysis of drilled wells, porosity is the key factor affecting productivity. Therefore, searching for high porosity reservoirs is of great significance for improving productivity of He8 Member. The rock physics analysis shows that V_p/V_s ratio in this area can distinguish sandstone from mudstone. According to the rock physics analysis result, this study predicts the distribution of sandstone in He8 member of Block A in Linxing area through pre-stack inversion. On the basis of pre-stack inversion result, the porosity prediction technology of tight sandstone reservoir is used to predict the porosity of thick reservoir by linear fitting for different reservoirs seperately for data in low P-impedance area. For the high impedance area, Bayesian discriminant analysis and co-simulation are used to predict the porosity of thin reservoirs. The porosity prediction results of thick reservoir and thin reservoir are combined to obtain the overall porosity prediction results of the reservoir. The results show that this method can effectively predict the porosity distribution of Class Ⅰ thick reservoirs and Class Ⅱ thick reservoirs, and simultaneously depict the porosity distribution of Class Ⅰ and Class Ⅱ thin reservoirs. By means of using different prediction method for different types of reservoirs, the description of reservoir porosity is more accurate, providing effective results for efficient development of tight sandstone reservoirs.

The development of fault and fracture systems usually leads to risks such as well leakage and water invasion during the drilling process. The complex geological structure、strong reservoir heterogeneity、developed faults and poor quality seismic data in the high sulfur area of the northeastern Sichuan Basin make it challenging for the fine delineation of the distribution laws of faults and fractures in the study area using the conventional technologies such as curvature and coherence et al, and cannot effectively guide the optimization design of drilling trajectories. Therefore, the fracture fine characterization technology of maximum likelihood attribute based on structure orientation is proposed according to the geological characteristics of the study area, and it can finely depict the distribution regularity of fractures. The analysis of the existing drilling trajectory design illustrates that the areas where well leakage and water invasion occur during the actual drilling process have a certain degree of consistency with the predicted fracture development area, indicating the accuracy of fracture characterization. In subsequent drilling trajectory design, the fracture area has been avoided effectively, reducing the risk of well leakage and water invasion. Therefore, the areas where faults and fractures developed in the study area can be accurately depicted using the technology, and then guiding the optimization design of the drilling trajectory. The proposed method can reduce the risks of well leakage, water invasion, etc. during the drilling process, which is of great significance for drilling design and drilling safety.

The prediction of in-situ stress is a critical parameter for guiding reservoir hydraulic fracturing, which influences the size, orientation, and morphology of reservoir fractures. Hydraulic fracturing of reservoirs is beneficial for the storage and transportation of oil and gas, and is of great significance for increasing oil and gas production. This paper proposes a method for predicting in-situ stress in HTI media based on Bayesian inversion. Firstly, considering the abundant vertical fractures in tight sandstone reservoirs, these are equivalent to HTI media. Based on the theory of HTI media anisotropy and Bayesian inversion, elastic parameters and anisotropy parameters of the underground media are obtained through pre-stack seismic data. Subsequently, the prediction of in-situ stress is achieved by utilizing the inverted elastic parameters and anisotropic parameters based on the principal stress calculation equation for HTI media. Finally, the Differential Horizontal Stress Ratio (DHSR) distribution is used to indicate areas in the reservoir that are easily amenable to fracturing and rich in oil and gas resources. Model data testing verifies the feasibility and noise resistance of the inversion algorithm, and application results from actual data demonstrate that the DHSR obtained from pre-stack seismic data inversion can effectively indicate areas in tight sandstone reservoirs that are easily fracturable, holding important implications for breakthroughs in the efficiency of tight sandstone oil and gas reservoir exploitation.

WHO(Wild Horse Optimizer) is a novel intelligent optimization algorithm. The Rayleigh wave dispersion curve inversion is a complex iterative optimization problem with multi-parameter and multi-pole. WHO was introduced into the Rayleigh wave dispersion curve inversion in this study, where a modification was presented to improve the local search ability of the algorithm, and the inversion success rate was also proposed to evaluate the performance of the algorithm in the inversion of Rayleigh wave dispersion curve. Among the calculation of three-layer theoretical models, the inversion success rates of WHO were just 52%, 43% and 37%. However, the inversion success rates of MWHO (Modified Wild Horse Optimizer) were increased to 62%, 80% and 63%. In the calculation of four-layer theoretical models, the inversion success rates of PSO(Particle swarm optimizer) were 53%, 63% and 59%, MWHO were raised to 60%, 75% and 63%. In contrast to the calculation of theoretical model with noise, the success rate of MWHO was higher than that of WHO and PSO: the success rate of MWHO was 67%, the success rate of PSO was 53%, and the success rate of WHO was 42%. On this basis, WHO, PSO and MWHO were used to calculate the measured Rayleigh wave data. The success rate of MWHO was still higher than that of WHO and PSO: the success rate of MWHO was 55%, the success rate of PSO was 46%, and the success rate of WHO was 41%. The trial calculation statement of theoretical models and measured data: MWHO has advantages in inversion success rate, calculation accuracy and other aspects, and also has certain practical and research value.

Because magnetospheric and ionospheric current source modelling is a prior step of global mantle conductivity imaging,we research on the current source modelling method and compare the C-responses excited by different sources. The study deduces a toroidal current source from time-varying geomagnetic fields with the IGRF-13 model and Green's function. Then we calculate the C-responses using the integral function method and verify the method on a layered Earth model. In quasi-dipole coordinate system,we compute the C-responses on the surface of the "1D+thin sheet" Earth model separately inspired by the toroidal current and its Y₁⁰ degree component,namely C_toro and C_Y1⁰ values,and compare them with the real values on the geomagnetic observatories. The outcome shows that ocean effect causes more disturbance on the real part of C_1d than its imaginary part and the source is mainly composed of its first-order part. At the GZH,QZH,and SSH observatories along the coastline,real part of C_toro is closer to C_real in around the period of 1~30 days,and real part of C_Y1⁰ is nearer in around the period of 30~100 days. Therefore,the local current may have a deep effect on the C-responses,its characteristics change with frequencies and it's influence may be evaluated carefully before GDS inversion.

According to the measurement of the China Earthquake Networks Center, at 17:00:08 on June 1, 2022, Beijing time, a M_S6.1 earthquake occurred in Lushan County, Sichuan Province. Three minutes later, another M_S4.5 aftershock (the largest aftershock of this earthquake sequence) occurred. From the epicenter announced by the China Earthquake Networks Center, the epicenter of M_S6.1 mainshock is very close to that of the 2013 Lushan M_S7.0 earthquake (about 10 km), and the seismogenic fault of the former event is still controversial nowadays. The focal mechanisms of the 2022 mainshock and its largest aftershock may shed more light on this problem. In this paper, firstly based on the waveform data recorded by the broadband seismic stations deployed by the Sichuan Seismic Network, we use ISOLA software for waveform fitting to obtain the double couple focal mechanism of two earthquakes. The result of 2022 mainshock as follows: 222°/43°/104° (nodal plane Ⅰ: strike/dip/slip) and 23°/48°/76° (nodal plane Ⅱ), centroid depth 16 km, and moment magnitude M_W5.9. The result of aftershock is shown as 237°/36°/109° (nodal plane Ⅰ), 34°/56°/76° (nodal plane Ⅱ), centroid depth 17 km, and moment magnitude M_W5.0. These two earthquakes are mainly trust type. Secondly, based on the continuous waveforms recorded by the seismic stations within 150 km epicentral distance during 2022-06-01 to 2022-06-10, we use LOC-FLOW software to identify and relocate the aftershocks. A total of 838 aftershocks are obtained, most of which are distributed in the northwest of the mainshock in the horizontal map. In the depth profile, on one hand most aftershocks are distributed along the up-dip direction of the mainshock, on the other hand it indicates that the fault plane southeastward inclines. Theoretically, the recorded waveform can be calculated by the convolution of source time function, propagation path effect and instrument response function. Assuming two events closely nearby each other are recorded in the same seismic station, the difference between two waveforms approximately reflects the difference of earthquake rupture process due to the same instrument response and similar propagation path effect. In this paper, the mainshock waveforms of two Lushan events recorded by 31 azimuthal stations are compared and analyzed, and it turns out two waveforms at each station show good consistence in the period range 10~20 s, indicating the rupture process for these two events may have similarities to some extent. The study area locates in the southern Longmenshan fault belt, the surface is mainly dominated by the faults with NE-SW strike and NW dip. However, according to the focal mechanisms and aftershock analysis in this study, we speculate that the 2022 Lushan earthquake should occur on a blind thrust fault inclining to SE. It is consistent with the NW-SE tectonic stress field in this area that has long been subjected to lateral extrusion on the Tibetan Plateau.

Converting acceleration-domain seismic data into velocity-domain seismic data using numerical integration introduces integration noise, which adversely impacts subsequent seismic data processing. This paper initially analyzes the reasons for integration noise in both time-domain and frequency-domain integration. It then conducts comparative analyses of various methods for suppressing integration noise, including high-pass filtering, polynomial fitting, and EMD-based methods, using real seismic data. Additionally, the paper examines the practical applicability of these methods and briefly discusses the feasibility and cost-effectiveness of using time-domain integration combined with EMD for conversion of acceleration-domain seismic data into velocity-domain seismic data in on-site processing. This technology has been successfully applied in the X project in South America. During processing, acceleration-domain seismic data collected by digital nodes were transformed into velocity-domain seismic data and merged with velocity-domain seismic data received by analog nodes, resulting in improved data consistency and an enhanced signal-to-noise ratio of the weak seismic signal from deep, thus confirming the method's effectiveness.

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.

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.

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 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.

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.

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.

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.

Accurate fault identification is crucial to oil and gas exploration and development. Traditional fault identification technology based on coherence volume attribute has poor effects in complex structural zones. Conventional convolutional neural network based on image segmentation is also difficult to make up for the feature information lost in down sampling. Therefore, building a global information extraction attention mechanism can not only introduce information extraction in the concatenation part of the U-Net full convolutional network structure, compensates for the lack of information in the downsampling process and enhances the network's learning ability. It can also enhance the bottom level feature information and improve interpretation accuracy by using information scaling at the bottom level of the network. Moreover, this attention block does not add additional parameter information and has a low memory requirement. The experimental results show that the test accuracy of the neural network model with attention mechanism reaches 96%, and the loss function converges to 7%. The description of the main fault of the actual seismic data is better than the conventional U-Net. The attention mechanism of global information extraction provides a new idea for 3D fault intelligent recognition based on convolutional neural network.

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.

Bedding is one of the most typical and important characteristics of clastic rocks, and its development density (bedding density) reflects the changes of sedimentary environment and sediment to a certain extent. Aiming at the identification of shale bedding using micro resistivity scanning logging, first, the logging response of the bedding is extracted by trend filtering. Secondly, the quantitative relationship between the bedding interface and the first and second derivative of the logging curve is established. In other words, the bedding interface usually corresponds to the "half amplitude point" of the logging curve, which is approximately represented by the extreme point of the first derivative or the zero point of the second derivative in mathematics. Then the zero search algorithm is used to automatically pick up the bedding interface and calculate the bedding density. The method in this paper is applied to the bedding evaluation of the shale gas reservoir in Longmaxi formation, south Sichuan, and the calculated results are consistent with the bedding fractures observed in the core. Meanwhile, the comparison with the organic carbon content shows that the bedding density of the "sweet spot" segment with better source rock quality shows an obvious decreasing trend, indicating the effectiveness and feasibility of the bedding parameter evaluation in the shale gas reservoir evaluation.

The slowness information of near-wellbore formation is of great significance to oil-gas exploration and development, which can reflect the properties of rock. However, the near-wellbore formation presents radial heterogeneity with a variable slowness profile, due to the effects from mud filtrate invasion, wellbore stress, drilling machinery damage and fracturing operation. Accordingly, extracting radial slowness profile from acoustic logging inversion has important application value in identifying invasion depth, evaluating wellbore stability, guiding well completion design, and evaluating fracturing effect. Therefore, we have investigated the current inversion methods constructed by researchers in this paper and divided them into three categories: the inversion with arrival time of waves, the inversion with slowness-time coherence (STC), and the inversion with wave dispersion. After comprehensive research and analysis, we found that the usability and performance of the methods is different due to the difference of target mode waves and their algorithms. Based on the obversions, we further concluded the advances of the methods in extracting the slowness profile near a wellbore from array acoustic logging data, and compared their performances in real applications.

Aiming at the problem that the full bridge inverter transmitter system can only send jumping rectangular wave signals and is vulnerable to electromagnetic coupling spike interference and harmonic pollution in the receiving circuit, an arbitrary waveform electrical (magnetic) signal transmission device based on digital power amplifier is designed. For the low-pass filtered rectangular wave amplification output, it can significantly reduce the edge change rate of the rectangular wave output from the transmitter, suppress the electromagnetic coupling interference caused by the high-order harmonic current in the artificial source signal in the input of the receiver, or directly suppress the electromagnetic coupling interference on the sinusoidal wave or combined sinusoidal wave amplification output, and eliminate harmonic pollution. In this paper, the generation mechanism of electromagnetic coupling is analyzed, and the suppression effect of rectangular wave after low-pass filtering on the coupling peak of the receiving circuit is simulated and calculated. A signal transmission system based on digital power amplifier is built, which can amplify and output arbitrary waveform signals including the combined rectangular wave signal after low-pass filtering. Taking dual frequency signal as an example, the inductive coupling suppression experiment is carried out, It has achieved good experimental results and has important theoretical and practical application value.

About 50 km away from Dayawan Nuclear Power Station, a M_L4.6 earthquake occurred at the Shanwei-Chaozhou fault on 11 March 2022. There was an earthquake as large as M6.0 occurred in same site. In this paper, based on the geomagnetic observation data of 10 geomagnetic stations in coastal areas of South China, the integration effect of the diurnal variation distortion period was used to quantify the distribution of anomalies.we discussed the degree of interference and the decisive factors in the calculation according to the prediction range of the day-to-day ratio threshold, and the internal source ring current is used as the source to explain the source current mechanism related to the geomagnetic day-to-day ratio anomaly. The results show that filtering the geomagnetic station data with large noise interference can effectively remove its noise, improving the credibility of the day-to-day ratio calculation. It is proposed that the diurnal variation amplitude of the geomagnetic vertical component is mainly controlled by the Sq current system, which may be related to the space weather activity of the ionosphere and is obviously dependent on local time. Estimating the skin depth of the current change in the three-layer conductor and combining with the results of other geophysical explorations, we believe that the position where the ring current is generated may be located at the most intense exchange between the asthenosphere hot material upwelling to the lithosphere material, about 100 km underground. Therefore, we calculate the induced magnetic field distribution caused by the ring current superimposed on the surface at a depth of 100 km. The magnetic field generated by this internal source current weakens the vertical geomagnetic field caused by the Sq current. Themagnitudeofthe Z-component changes day by day, which eventually leads to an anomaly in the day-to-day ratio of the geomagnetic field.

The dispersive characteristics of high-frequency(≥2 Hz) Rayleigh-wave phase velocities have been widely used to determine the subsurface shear velocity. However, the shear velocity of the surface layers (e.g., < 0.2 m depth) is poorly constrained in the dispersion inversion within tens of hertz. Furthermore, the inverted shear velocity at deeper depths can be biased due to inaccurate shear velocity at surface in dispersion inversion. In practice, both the concrete road under urban environment and rigid ballast bed of railway should be presented as a thin layer of distinct high (>1000 m/s) shear velocity at the surface, the inverted results from dispersion inversion would be largely biased under these abnormal surface environments. Compared to the phase velocity, the Rayleigh-wave Horizontal-to-Vertical (H/V) ratios of particle motion are particularly sensitive to shallower shear velocity. Based on this idea, we propose a method for constructing V_s profiles based on the joint inversion of the Rayleigh-wave phase velocity dispersion and H/V from active-seismic records. According to the inversion results of synthetic data based on the model with high-velocity top layer, the accuracy ofthe shearvelocity[LM]in the surface layer and underlying layers is considerably improved by joint inversion including H/V. The proposed method has great engineering significance for the detection of urban road and railroad bed diseases under high-velocity surface layer conditions.

With the depletion of near surface resources in mines, deep underground space has become a key area for exploration work. At present, conventional surface geophysical methods cannot meet the requirements of both depth and accuracy for deep mining exploration. Therefore, moving high-precision geophysical methods to the well for geophysical exploration is an important means to solve this problem. This article discusses the feasibility of implementing equivalent anti magnetic flux transient electromagnetic method in wells through theoretical and physical experiments. Firstly, the measurement method of equivalent anti magnetic flux transient electromagnetic method in wells was introduced, and the physical principle of this method was analyzed. The theoretical calculation formula of this method in a full space environment was derived, and the response signal during the measurement process of this method was analyzed. Then, the feasibility of this method in measuring the full space environment of the well was further verified through physical experiments, and the response signal was analyzed. Finally, the effectiveness of this method is analyzed through a case study of well exploration work in a mining area in Gejiu City, Yunnan Province. By comparing the geophysical results with known geological data, it is shown that this method can effectively detect the location and spatial distribution of geological anomalies such as ore bodies and structures when applied in wells.

The Magnetotelluric (MT) method is widely used in the exploration of oil and gas resources. The time-lapse MT method can monitor reservoir dynamic distribution and interface changes by observing the time-lapse MT response caused by the change of underground electrical structures and interfaces. Traditional time-lapse MT methods monitoring simulation are based on isotropic theory and regular grids. However, the induced polarization (IP) effect is widely present in reservoirs, ignoring which can lead to interpretation errors and difficulties in time-lapse monitoring of reservoirs, especially for unconventional reservoirs with complex terrain and high water content in the later phases of development. First of all, in order to solve these problems, the Cole-Cole complex resistivity model is used in the vector Helmholtz equation to characterize the IP effect of the reservoir medium. Then, the vector Helmholtz equation discretized by the Galerkin finite element method with unstructured tetrahedral grids, has been implemented to a 3D time-lapse electromagnetic algorithm that considers the IP effect. Thirdly, the analytical solution of the ID model considering IP effect is used to verify the correctness of the 3D algorithm in this paper. Further, different IP parameter variations are set and analyzed to determine the characteristic responses of the reservoirs and discuss the resolution ability of the time-lapse electromagnetic monitoring. At last, a realistic reservoir model in the Fuling area is set up to analyze the time-lapse electromagnetic anomalies generated before and after the displacement of the reservoir. The results show that the significant differences in the time-lapse MT responses caused by changes in the IP parameters of the reservoir can be used to infer the time-lapse change process of the reservoir. In the process of reservoir displacement enhancement, the response difference is distinguished by the sensitivity to the boundary of the displacement swept region, where △ρ_xy^a can effectively respond to the change of the interface in the x direction, while △ρ_yx^a targets the y-direction.