Abbreviation (ISO4): Prog Geophy
Editor in chief:
The occurrence of earthquakes is often accompanied by abnormal ionospheric disturbances. To explore the correlation between earthquakes and ionospheric disturbances, this paper takes the successive earthquakes of magnitude 4.9 and 5.1 in Litang County, Sichuan Province, on September 22, 2016 as an example, based on the data of the tectonic environment monitoring network in Chinese, and combined with the ionospheric tomography technology to carry out analysis. Research has found that: (1) By analyzing the observation data of the Earth's electric field and the Global Ionospheric Map (GIM) data, significant limitations exist in analyzing moderate intensity earthquake ionospheric disturbances: GIM data is significantly affected by geomagnetic activity interference and cannot effectively identify earthquake related anomalies; (2) The use of ionospheric tomography technology revealed a sustained positive anomaly in the southern ionosphere of the epicenter before the earthquake. During the earthquake, a significant high-energy region was formed inside the ionosphere, accompanied by a dynamic process of many electrons migrating to higher altitudes. Research has shown that compared to traditional observation methods, ionospheric tomography technology can more intuitively analyze the spatiotemporal evolution characteristics of ionospheric structures during earthquake occurrence, providing important technical support for the study of earthquake ionospheric disturbance mechanisms.
The traditional method of describing precession nutation is spherical trigonometry. The defect of spherical trigonometry is that it is arbitrary in determining the positive and negative of an edge or Angle. When applied to the derivation of precession and nutation matrix, it is more complicated. In this paper, the coordinate system is established by defining the normal directions of different planes. Nutation matrix is given by vector method. Precession matrix is similar. The first group of nutation angles (nutation in longitude, nutation in obliquity) describes the changes of the instantaneous true equatorial plane with respect to the instantaneous ecliptic plane. The second group of nutation angles describes the change of the instantaneous true equatorial plane with respect to the ecliptic plane of the reference epoch. In this paper, the theoretical relationship between two sets of nutation angles is given, and an error in
This article provides an overview of recent advances in conventional earthquake prediction research alongside the current status of artificial intelligence applications in seismology. It explores the role of AI in the extraction of precursory anomalies, its application in short-term and imminent earthquake forecasting, its utility in medium-to long-term seismic hazard assessment, and its use in aftershock prediction. Research indicates that artificial intelligence technology can rapidly analyze real-time data and effectively identify seismic precursor signals. By integrating spatiotemporal features, AI can recognize subtle anomalous seismic patterns that are difficult to capture with non-AI methods, thereby improving the accuracy of earthquake prediction. However, current earthquake prediction research is still limited by factors such as data quality, model interpretability, computational cost, and cross-regional generalization ability. Future research should focus on exploring physics-informed artificial intelligence learning methods, enhancing multi-modal data fusion, and improving model interpretability. Emphasis should also be placed on translating prediction outcomes into practical applications for disaster early warning and risk assessment. Furthermore, AI technology shows significant potential in aftershock prediction research and, by leveraging the regular patterns of post-mainshock sequences, is poised to become a breakthrough area for the broader field of earthquake prediction.
The high-precision construction of geomagnetic reference map is the basis of geomagnetic matching navigation. In order to address the problem of modelling the geomagnetic reference maps with different line spacing and different geomagnetic anomaly features, this paper combines the geomagnetic anomaly features with the principal component analysis method to integrate the geomagnetic standard deviation, mean geomagnetic field value, mean cumulative gradient value, kurtosis coefficient, and standard deviation of gradient, and the five geomagnetic feature parameters, as a measure of the change of geomagnetic anomaly features, and then thins out the measured 500 m-high-resolution data. The thinned line data with different line spacings are modelled using five modelling methods, namely, multifaceted function, minimum curvature, Kriging interpolation, local polynomials and radial basis function, and the areas of gentle geomagnetic variations and complex areas are selected for accuracy analysis. The experimental results show that the algorithms should be reasonably selected in the light of the line spacing, geomagnetic complexity and the modelling region, and the overall modelling accuracy of the multifaceted function is the highest; the multifaceted function has the highest modelling accuracy at different line spacings in the gentle region; the multifaceted function has the highest modelling accuracy in the complex region at line spacings from 2 to 5 km and at spacings greater than 8 km lines, and the minimum curvature modelling accuracy is the highest at line spacings from 5 to 7 km lines.
Located in the three tectonic domains of the Pacific plate, the Paleo-Asian Ocean and the Tethys, i.e., the northern part is blocked by the southward thrust of the Qinling orogenic belt and the North China block, the western part is pushed by the eastward flow of the uplift material of the Qinghai-Xizang Plateau, and the eastern part is blocked by the Cathaysian block. The eastern and western parts of the Yangtze block have different evolution characteristics. The western part still preserves a relatively stable quasi-craton block centered on the Sichuan continental core, while the eastern Yangtze has been involved in intracontinental orogeny since the early Paleozoic, and different degrees and styles of tectonic deformation have occurred. In this paper, we try to use the aeromagnetic anomaly to directly invert the magnetization vector, and realize the quantitative estimation of the magnetization intensity and magnetization direction in the Yangtze region. On this basis, the magnetic structure and difference of the Precambrian crystalline basement of the Yangtze Block are studied, and the deep structure and state of the orogenic belt within and around the Yangtze Block are analyzed. It is helpful to reconstruct its Precambrian composition and basic tectonic framework, and provides important support for the dynamic problems such as the tectonic relationship between the Yangtze and Cathaysian blocks and the North China plate and the influence of the eastward movement of the Qinghai-Xizang Plateau on it.Traditional magnetization inversion methods usually ignore the remanence and self-demagnetization effects. However, the actual geological conditions are often very complicated. Especially when there is strong remanence, the magnetization direction of the field source usually deviates greatly from the direction of the geomagnetic field. In this paper, the aeromagnetic data of the Yangtze region are collected, and the Direct Analytical Signal (DAS) is calculated by Hilbert transform. The direct analytical signal mode is used as input to invert the magnetization intensity of the region, and the magnetization intensity obtained by DAS mode inversion is used as a constraint. The penalty function is added to the objective function, and the joint objective function under the equivalent constraint condition is established. The inversion of the three components (Mx, My, Mz) of the magnetization intensity vector is realized, and the magnetization intensity vector structure of the Yangtze region is inverted. The magnetic structure of the Sichuan Basin, the Longmenshan tectonic belt and the surrounding area is analyzed.The following progress and understanding have been made: (1)The magnetization intensity of Longmenshan fault zone is different from that of Songpan-Ganzi block and similar to that of Yangtze block, so it is classified as Yangtze block. The northern Sichuan Basin and Hannan-Micang area have high magnetization, and the deep structure is a stable rhombic crystalline basement rock. There are large-scale NE-trending magnetic bodies in the deep underground of the central Sichuan Basin, which may be related to the Archean to Paleoproterozoic basement and the ancient continental nucleus below. The inversion results and the distribution characteristics of magnetic anomalies show that the main body of the Cambrian basement extends from the northern Sichuan Basin to the Micangshan Hannan coverage area.(2)In the negative magnetic anomaly area on the northern margin of the Sichuan Basin, the magnetization direction is opposite to the geomagnetic field, which may be related to the subduction and tectonic deformation of the Qinling orogenic belt. After the collision between the Yangtze plate and the North China plate in the late Late Triassic, the Qinling Mountains experienced a strong intracontinental orogeny, and the deformation expanded from the Qinling Mountains to the northeast of the Sichuan Basin, which may cause the change of magnetization direction.(3)There is a magnetic inversion phenomenon in the Longmenshan tectonic belt, which may be related to the remanence of the Neoproterozoic intrusive rocks. These intrusive rocks were formed in 830-740 Ma, mainly by the underplating of mantle-derived mafic magma. The magnetic minerals in the rock were reversely magnetized during the geomagnetic polarity reversal, and retained this state during the cooling and hardening process, forming an intrusive rock with remanence. In addition, the special tectonic position on both sides of the Longmenshan fault zone, that is, the strong interaction zone between the eastern margin of the Qinghai-Xizang Plateau and the Yangtze block, as well as the push of the eastward flow of the uplift material of the Qinghai-Xizang Plateau and the resistance of the underground rigid block of the Sichuan Basin, led to strong tectonic deformation and magnetization direction changes.
The dominant azimuth angle (α-angle) of telluric field rock cracks is an important parameter in geoelectric research, holding significant meaning for identifying seismic precursors. Traditional methods for calculating the α-angle based on harmonic amplitude ratios have certain limitations, particularly regarding the depiction of dynamic evolution processes and dependency on original data quality. This study aims to propose a new α-angle calculation method based on trend fitting to enhance the characterization capability of the dominant direction of rock cracks and the sensitivity of anomaly identification. This method separates the natural electric field from the telluric field, combines the phase information of various orders of electric field harmonics, and utilizes full-day data for trend fitting to calculate the dominant azimuth angle. Using observation data from 14 geoelectric stations within a 560 km radius of the 2017 Jiuzhaigou MS7.0 earthquake epicenter (103.82°E, 33.20°N) for verification, a comparison with traditional calculation results shows that: For stations with well-developed rock cracks (Δα≤10°), such as the Pingliang and Gufeng stations, the results of the new method are similar to those of the traditional method, with minimal differences in polarization stability indicators (average SDS and MECM were 0.62% and 0.26%, respectively), verifying the correctness of the new method. More importantly, for stations exhibiting suspected pre-seismic anomalies (such as Tianshui, Chengdu, and Hanwang stations), the new method reveals anomaly phenomena, such as increased variation range or jumps in the α-angle, more significantly than the traditional method, demonstrating stronger anomaly detection capability. Therefore, the trend-fitting-based α-angle calculation method can not only effectively reflect the dominant direction of telluric field rock cracks but may also possess higher sensitivity to suspected pre-seismic anomalies, thus providing a useful supplement to pre-seismic geoelectric anomaly analysis. However, its universality across different geological environments still requires further verification.
Gravity, magnetic potential fields, surface heat flow, and GPS velocity fields are common types of geophysical spatial data that often require interpolation for regional-scale analysis. These spatial data often exhibit different variation patterns in different directions, known as spatial anisotropy. However, most interpolation methods do not sufficiently consider the influence of spatial anisotropy on interpolation results, which can lead to suboptimal results. Although Kriging interpolation, which is fundamentally based on variogram modeling, is capable of capturing directional variation in spatial data, in practical applications, directional influences are often neglected, and interpolation weights are typically determined solely based on distance. Consequently, the effects of spatial anisotropy in variograms on interpolation results remain insufficiently insight. To address this issue, a series of comparative experiments were conducted using four datasets: three synthetic datasets—all incorporating observational noise and representing, respectively, isotropic, simple anisotropic, and complex anisotropic spatial structures—and one real-world measured dataset. These experiments were designed to assess the interpolation accuracy of Kriging under both isotropic and anisotropic variogram models across a variety of spatial distribution scenarios. Quantitative analysis revealed that when spatial anisotropy is weak, the interpolation results from isotropic and anisotropic variogram models are comparable. In contrast, under conditions of pronounced spatial anisotropy, the choice of variogram model significantly affects interpolation performance, with anisotropic variograms providing notably higher accuracy. These results suggest that incorporating variogram anisotropy into Kriging interpolation is not only necessary but also crucial, as it enables a more accurate characterization of directional spatial variability and leads to more reliable interpolation outcomes.
Interferometric Synthetic Aperture Radar (InSAR), a highly effective technique for surface deformation monitoring, has been extensively employed in applications such as geological disaster monitoring, mining surface damage. Nevertheless, InSAR is limited by its ability to measure only one-dimensional deformation along the satellite's Line-of-Sight (LOS), which restricts its capacity to capture the full complexity of surface movements. This study focuses on Kilauea Volcano and utilizes three sets of Synthetic Aperture Radar (SAR) data acquired from different orbital directions before and after the volcanic eruption, in conjunction with data from several Global Navigation Satellite System (GNSS) monitoring stations. The two-orbit DInSAR observation method is used to calculate vertical and east-west deformations, whereas the three-orbit DInSAR observation method is employed to calculate vertical, east-west, and north-south deformations. The consistency of the vertical deformation and east-west deformation calculated by both methods is tested via the Intraclass Correlation Coefficient (ICC), and the deformation calculation accuracy is validated by monitoring data from two GNSS stations, KAMO and KTPM. To further discuss the computational accuracy of the two-orbit and three-orbit methods, the Probability Integral Method (PIM) is used in conjunction with SAR spatial parameters to simulate LOS deformations in different directions, allowing for 2D/3D deformation accuracy analysis. The experimental results indicate that: (1) the vertical (ICC=0.986) and east-west (ICC=0.989) deformations calculated by the two and three-orbit DInSAR observation methods are highly consistent, with maximum RMSE differences of 0.021 m and 0.013 m, respectively; (2) the absolute errors of the vertical, east-west, and north-south deformations calculated by the three-orbit method compared with the GNSS monitoring data from the KAMO and KTPM stations are relatively small, at 0.04 m, 0.035 m, 0.009 m, and 0.001 m, 0.023 m, and 0.0002 m, respectively; and (3) the simulation experiment reveals that, in the absence of spatial parameter errors, the three-orbit method can accurately calculate three-dimensional deformations; the error in the subsidence calculation using the two-orbit method is about 3% of the maximum subsidence value, and the error in the east-west horizontal movement is about 5% of the maximum horizontal movement value, indicating that the two-orbit method can provide relatively accurate vertical and east-west surface deformation measurements.
In response to the challenges of unknown seismic noise distribution and difficulties in constructing paired samples, we propose a self-supervised blind denoising method integrating dynamic noise modeling with collaborative optimization. The method constitutes a dual-network collaborative architecture comprising a noise modeling subnetwork and a denoising subnetwork. Both subnetworks adopt U-shaped network structures incorporating wavelet transforms and Swin Transformers. The noise modeling subnetwork is optimized through mean squared error, while the denoising subnetwork is trained using a SURE-based loss function. The dual-network collaborative framework overcomes limitations of traditional supervised learning by: (1) introducing a non-prior modeling strategy that dynamically estimates noise distribution characteristics through data-driven noise modeling sub-network; and (2) sampling predicted noise distributions to construct nosier-noisy sample pairs for training the denoising subnetwork. The two subnetworks undergo collaborative training with mutual promotion and synchronous convergence, achieving blind noise modeling and blind denoising respectively. Numerical simulations and practical seismic data experiments demonstrate the proposed algorithm's effectiveness.
Full Waveform Inversion (FWI), as an advanced method for velocity reconstruction, stands out in the current stage for its high-precision results. Typically, FWI employs the L2 norm of the residual between observed and synthetic seismic records as the objective function, iteratively updates the velocity model to make the objective function get small. However, due to the limited amount of observation data compared to unknowns, the inversion problem is ill-posed. Applying regularization constraints to the objective function is crucial for mitigating the ill-posedness. This paper introduces an exponential regularization term to the L2 objective function, thereby partially alleviating the ill-posedness. This method introduces layer boundaries during the updating process, resulting in a more favorable blocky structure of the model. Comparison to conventional L1 regularization, the method proposed in this paper does not need to address the problem of non-differentiability of the L1 norm regularization term with respect to the model parameter when computing the gradients, while directly calculating the gradient of the regularization term with almost no additional computational cost. The application testing on synthetic data of the salt dome model and Marmousi model demonstrate that the proposed method can more accurately depict velocity models, which validates its effectiveness.
Carbonate reservoirs exhibit strong heterogeneity, with their primary reservoir types being fracture-cave reservoir bodies controlled by strike-slip faults. However, the fine characterization of such reservoir bodies remains a challenging issue at present. To address this challenge, the following approaches are adopted.Firstly, considering that conventional post-stack seismic attributes possess strong capabilities in identifying fracture development zones composed of numerous fractures or large faults, the Likelihood attribute and the ant-tracking attribute are preferred for identifying fault-fracture bodies.Secondly, based on the analysis of seismic response characteristics of fracture-cave reservoir bodies, karst cave bodies exhibit distinct features on seismic profiles. A model-regularized post-stack impedance inversion method is employed, and the residual impedance attribute is utilized to characterize the development characteristics of karst cave bodies. This method can ensure the sparsity of inversion results while improving their lateral continuity.Finally, a multi-attribute fusion technique is applied to integrate the Likelihood attribute, ant-tracking attribute, and residual impedance attribute into a new attribute-the fracture-fault-cave body attribute. This attribute can delineate the spatial association between faults and fracture-cave reservoir bodies, facilitating the characterization and prediction of carbonate fracture-cave reservoirs.Practical application examples demonstrate that this attribute can effectively characterize the relationships between fault systems and cave/vuggy reservoirs.
To investigate the relationship between the pore structure of coal cleats and the resistivity response, and to enhance the effectiveness and accuracy of evaluating coal cleat porosity using resistivity logging, this study analyzed the distribution characteristics of the coal cleat system based on X-CT scanning imaging technology: face cleats extend further, while butt cleats are distributed between face cleats and are approximately orthogonal to them. Digital coal rock models containing different cleat structures were generated by combining Fractal Brownian Motion(FBM) and successive Random Addition Algorithms (SRA). On this basis, the Finite Element Method(FEM) was used to simulate and study the influence of coal cleat type, dip angle, and porosity on resistivity. Finally, starting from the basic principle of the dual-laterolog iterative method, we proposed incorporating the power-law relationship between cleat porosity and the formation factor into the cleat porosity calculation process. This optimized the quantitative calculation method for evaluating coal cleat porosity using resistivity logging, forming a complete research path from microscopic structural characterization to macroscopic logging response, which breaks through the limitations of traditional methods that rely on idealized geometric models. The results show that: (1) The more developed the face cleats are, the more significantly the resistivity decreases; butt cleats also reduce coal resistivity to some extent, but their influence is less pronounced than that of face cleats. (2) When the cleat dip angle is constant, coal resistivity decreases following a power-law function as cleat porosity increases. When the cleat porosity is constant, the resistivity of coal containing low-angle cleats is significantly lower than that containing high-angle cleats. However, when the cleat porosity increases beyond a certain level, the influence of the cleat dip angle gradually weakens. (3) The cleat porosity calculated by the optimized dual-laterolog iterative method is numerically closer to the inversion results from NMR logging than the values obtained using the Aguilera cube model. Although certain deviations in the variation trend persist at different depth points, which may be attributed to factors such as localized resistivity anomalies caused by coal seam heterogeneity, the influence of fracture connectivity differences on conductive pathways, and limitations in X-CT scanning resolution, the overall evaluation effectiveness of coal cleat porosity based on conventional resistivity logging is improved. The calculation accuracy meets the requirements for coal seam log interpretation and can provide an important reference for coal reservoir resource assessment.
In order to effectively utilize logging data to improve the gas layer identification accuracy of low-porosity, low-permeability sandstone reservoirs, this paper focuses on the Shanxi Formation reservoirs in the eastern Ordos Basin as the research object, and, based on a review of the literature, conducts detailed studies on gas layer identification in combination with actual logging data.Regarding the reservoir characteristics of the study area, conventional identification methods exhibit poor accuracy and a high misjudgment rate in recognizing gas layers. This paper introduces a Northern Goshawk Optimization-based Random Forest model (NGO-RF) to conduct a refined identification of gas layers.First, the core parameters of the random forest model (number of decision trees, minimum number of leaves) are optimized using the NGO algorithm. Then, the optimized parameters are applied to the random forest model to complete the prediction process for atmospheric layer identification.To verify the effectiveness of this model, the study selected three commonly used models for comparison: the unoptimized random forest model, the particle swarm optimized support vector machine model, and the BP neural network model.The research results demonstrate that the NGO-RF model exhibits the best performance in gas layer identification in the study area, achieving an accuracy of 99.45% on the training set and 96.25% on the prediction set. Both accuracies surpass those of the other three comparison models, fully confirming the suitability of the NGO-RF model for gas layer identification in low-porosity, low-permeability sandstone reservoirs of the Shanxi Formation in the eastern Ordos Basin.
This paper is based on the shallow-water three-dimensional Ocean Bottom Cable (OBC) seismic data from a basin in China and conducts an application study on diving wave Full Waveform Inversion (FWI) velocity modeling for VTI media using real seismic data. Before conducting FWI, the applicability of full waveform inversion was analyzed from three aspects: frequency, offset, and signal-to-noise ratio. A targeted seismic data preprocessing workflow was designed to improve the signal-to-noise ratio of the data, and different wavelet estimation methods were discussed and compared through forward modeling tests, incorporating water-layer multiples as effective signals for the inversion. In the actual inversion process, to avoid inaccuracies in anisotropic parameters caused by the coupling effect of multi-parameter simultaneous inversion, a stepwise inversion strategy was adopted. The Normally Move Out velocity was inverted first, and δ was obtained using logging prior information. On the basis of the two, the final parameter η was inverted, ultimately completing the 12 Hz VTI anisotropic full-waveform inversion iteration modeling. The application achieved good modeling and migration imaging results.
With the rapid advancement of oil and gas exploration technologies, the precise prediction of small-scale fractures has become increasingly vital for optimizing reservoir development and improving overall production efficiency. Conventional fracture prediction methods are primarily designed to detect larger-scale fractures, which often results in insufficient accuracy and resolution, thereby failing to meet the rigorous demands for precise small-scale fracture prediction. In order to improve the accuracy of fracture prediction, this paper focuses on high-precision fracture prediction by leveraging OVT domain wide-azimuth seismic data, enabling detailed analysis of fracture networks and their spatial distribution. The research begins by generating high-quality azimuthal seismic data volumes using a comprehensive series of advanced processing techniques, ensuring precise and reliable inputs for subsequent fracture analysis and interpretation. Specifically, offset OVT gathers are systematically transformed into azimuth gathers, enabling detailed fracture analysis. This process involves meticulous noise prediction and suppression, amplitude variation with angle-guided compensation for accurate reflectivity, and partially stacking in azimuth to enhance signal clarity and improve the precision of fracture prediction. This meticulously designed workflow ensures the preservation of azimuthal anisotropy information, which is indispensable for effective fracture detection. Subsequently, the study quantitatively predicts fracture density and orientation by integrating coherence attributes with multi-azimuth amplitude attributes, further refined through ellipse fitting to improve accuracy and reliability in fracture characterization. This innovative approach adeptly captures subtle fracture networks by leveraging the azimuthal seismic response. The application of this methodology in the Hutan work area of Xinjiang has demonstrated its effectiveness in identifying small-scale fractures, with prediction results showing a high degree of consistency with imaging logging interpretations. By enhancing fracture characterization, including precise determination of fracture density and orientation, this approach supports optimized well placement and improved hydrocarbon recovery rates. successful implementation highlights its value in addressing the complexities of fractured reservoirs and advancing efficient hydrocarbon extraction. The research conclusively validates that anisotropic fracture prediction technology based on OVT domain data provides a reliable geophysical foundation for the design of drilling and development plans in fracture type oil and gas reservoirs, offering critical insights for reservoir management and production optimization.
Lithology forms the foundation for evaluating high-quality reservoirs and is crucial for effective hydrocarbon development. Deep tight sandstone reservoirs are characterized by significant burial depth, complex lithology and strong vertical heterogeneity, leading to challenges for traditional well-log lithology identification. This study focuses on the deep tight sandstone reservoirs of the Jurassic Sangonghe Formation within the Taibei Sag, Turpan-Hami Basin. Integrating core, thin section, laboratory analyses, and conventional well logs through core-log calibration, reservoir lithology was classified into siltstone, fine sandstone, medium sandstone, coarse sandstone, and sandy conglomerate based on median grain size. The results demonstrate that the Sangonghe Formation reservoirs exhibit complex and diverse lithologies, dominated by fine, medium, and coarse sandstones. The primary pore type is intragranular dissolution pores. A machine learning-based lithology prediction model was developed using gamma ray (GR), deep resistivity (RD), shallow resistivity (RS), acoustic transit time (DT), bulk density (DEN), and compensated neutron log (CNL) curves as input features, with median grain size as the prediction label. The model achieved a high coefficient of determination (R2) of 0.897 on the test dataset, showing strong agreement with core-measured data and enabling continuous vertical lithology classification in single well. Application to blind wells confirmed the model's generalization capability and prediction reliability, overcoming limitations imposed by scarce core data on reservoir evaluation. Further coupling analysis of lithology and physical properties reveals that lithology significantly controls reservoir quality, with medium and coarse sandstones exhibiting optimal properties and representing the primary lithology for high-quality reservoir development. This study provides theoretical and technical support for sweet spot prediction and efficient gas reservoir development in tight sandstones of the Sangonghe Formation, Taibei Sag.
Seismic first arrivals automatic pickup technology is of great significance in seismic exploration. The traditional manual pickup method has problems such as low efficiency and weak noise immunity, so the industry has been looking for more efficient and automated methods. In recent years, deep learning techniques, especially convolutional neural networks, have attracted much attention in seismic wave first arrivals pickup, and by training a large amount of data, the deep learning methods can automatically identify the features and classify them efficiently, which improves the limitations of the traditional methods. In this study, a deep fully convolutional Segnet semantic segmentation network is used to directly output the probabilistic map of the first arrivals location through semantic segmentation of effective waves and noise based on the advantages of fully convolutional neural networks. The method has the advantages of high efficiency and strong noise resistance, and it has achieved good results in the orthogonal data and real dataset tests.
The casing pulse neutron logging technology has demonstrated significant effectiveness in the dynamic monitoring of water-flooded layers,supplementing logging data from open-hole wells,and evaluating remaining oil,among other aspects of reservoir development and dynamic monitoring. It serves as a core method for evaluating remaining oil during the middle and later stages of oilfield development. This paper systematically reviews the principles and main interpretation methods of pulsed neutron logging and provides a comparative table of parameters and applications of nine mainstream domestic and international instruments (PNST,PSSL,PNN,RMT,RST,RPM,PNX,CRE,PND-S). The appendix summarizes their technical principles,structural characteristics,advantages,and limitations. Currently,mainstream foreign instruments have improved resolution through multi-probe integration and the adoption of new detector technologies,while domestic developments have achieved breakthroughs in independently developed instruments and dynamic correction algorithms. Although existing technologies are widely applied in the evaluation of remaining oil,identification of water-flooded layers,and monitoring of complex well completions,they still face challenges such as insufficient accuracy in low-permeability formations and interference from multiple factors during data acquisition and transmission. Future development trends focus on the integration of fiber optic technology and artificial intelligence with big data analytics to enhance data accuracy,timeliness,and the precision of interpretation models. Additionally,the application of this technology in unconventional oil and gas resources (e.g.,shale oil,tight gas) has contributed to more accurate exploration and development outcomes.
Water saturation is a critical parameter determining reservoir fluid properties and productivity, and its accurate assessment is of paramount importance. Current methods for predicting water saturation based on seismic data exhibit strong dependence on petrophysical models, rendering them unsuitable for complex reservoirs. Traditional neural network-based approaches, while independent of petrophysical models, require substantial sample data and struggle to yield satisfactory results in areas with limited well data. To address this issue, we propose a water saturation prediction method based on Generative Adversarial Networks (GAN) built upon precise Zoeppritz inversion using particle filtering. This method integrates Convolutional Neural Networks (CNN) and BP neural networks to design an adversarial neural network suitable for small sample data. Application in the AY gas field demonstrates that, compared to conventional neural networks, the adversarial neural network significantly enhances prediction accuracy for water saturation with limited samples, providing reliable parameters for reservoir evaluation.
Reservoir fluid identification is of paramount importance in petroleum exploration and development, directly impacting reserve assessment accuracy and drilling decision-making. However, conventional interpretation methods face persistent challenges including intrinsic non-uniqueness, strong dependency on prior knowledge, and limited resolution when dealing with complex reservoir systems. To overcome these limitations, this research develops an objective and efficient fluid identification methodology through the integration of multiple mud logging data types with advanced machine learning algorithms. The study was conducted in the Shahejie Formation of Qikou Sag, Bohai Bay Basin. We established a comprehensive dataset comprising 1539 validated samples with expert-verified fluid type labels (including oil, gas, water, dry, oil-water, and oil-gas zones). The research methodology comprised three main phases: First, we systematically analyzed the distribution patterns of three key mud logging data categories-gas logging (measuring C1-C5 hydrocarbons), quantitative fluorescence (characterizing aromatic components), and pyrolysis data (indicating free and bound hydrocarbons)-across different fluid types. Second, we implemented a systematic combinatorial optimization approach, generating seven input configurations from the three data categories and evaluating them with four distinct machine learning classifiers (K-Nearest Neighbors, Random Forest, Artificial Neural Network, and Support Vector Classifier). This resulted in 28 input-model combinations, whose hyperparameters were rigorously optimized using Bayesian optimization with Macro F1-score maximization as the objective function. Third, we employed the SHAP (SHapley Additive exPlanations) interpretability framework to elucidate the decision-making mechanism of the optimal model. The experimental results demonstrated that the Random Forest model with combined quantitative fluorescence and pyrolysis data achieved superior performance (Macro F1-score: 94.15%). SHAP analysis revealed that heavy hydrocarbon components (i-C5, n-C5) were the most discriminative features for oil-bearing zones, while also identifying complex, non-linear relationships between certain features (e.g., oil index) and different fluid types. The developed methodology provides a rapid, robust, and objective approach for reservoir fluid identification, with the complete workflow offering valuable insights for feature selection and model construction in similar petrophysical studies.
Seismic datasets in many domestic hydrocarbon fields typically were acquired from multiple adjacent blocks and different years. These datasets exhibit inconsistencies in phase, time, and amplitude, which were affected by various factors such as the observation systems and the geophones and so on. The problem of merging these seismic datasets must be solved to fully utilize the seismic and achieve unified fine prediction of reservoirs in the entire area and the integrated deployment of exploration and development. This paper introduced the velocity-type geophone transform into the consistency processing of multi-blocks. The geophone responses of different blocks were unified to the geophone frequency of the reference block combined with differential equations under the condition that parameters related to velocity-type geophone in each block were known. The deterministic differences were eliminated in phase and time caused by variations in the natural frequency of geophones. Wavelet-phase and time discrepancies between adjacent blocks were quantitatively analyzed by the cross-correlation wavelet analysis technology. Moreover, the technology can also conduct quality control on each stage of phase and time consistency processing. The proposed method was applied to dataset from six adjacent blocks in the southwestern Daniudi gas field. And the minimum slice of the target strata was extracted. It was demonstrated that our proposed method yields superior results to the conventional processing approach by comparative analysis. The minimum slice of the target strata by the method proposed in this paper was clearer and could reflect the characteristics of the river channel. The effectiveness of the proposed method is verified.
To address the limitations of conventional post-stack seismic attribute methods (such as coherence and curvature), including insufficient use of single attributes, reliance on manual interpretation, and difficulty in effectively integrating OBN multicomponent data, this study proposes an unsupervised fracture prediction method based on a multi-branch Variational Autoencoder (VAE) for OBN seismic data. The method fully leverages the multivariate attributes acquired by Ocean-Bottom Nodes (OBN), including PP-wave and PS-wave data as well as their corresponding coherence and curvature attributes, and constructs a multi-branch deep learning architecture for intelligent fracture system identification.A variational autoencoder framework is adopted, in which a four-branch encoder is designed to extract deep features from PP waves, PS waves, coherence, and curvature attributes, while multi-scale convolutional blocks and the CBAM attention mechanism are incorporated to enhance feature representation. Considering the geological continuity inherent in seismic data, a geology-aware smoothing loss is introduced and combined with the reconstruction loss and KL divergence, enabling data-driven unsupervised learning. A 128-dimensional latent space is used in the variational bottleneck, and fracture-related anomalies are identified through reconstruction-error analysis. Experimental results demonstrate that the proposed method effectively integrates multicomponent seismic data and seismic attribute information, achieving fracture prediction without the need for manual labels. Compared with traditional single-attribute approaches, the method successfully suppresses ring-shaped artifacts commonly observed in curvature attributes, reduces the excessive boundary responses in coherence attributes, and produces results with improved spatial continuity and geological plausibility, providing a new technical solution for fracture prediction under complex geological conditions.
Microgravity surveys focus on localized small-scale subsurface targets and require higher measurement precision and enhanced data-processing resolution, making them suitable for detailed, engineering-grade investigations such as urban underground-space exploration. Microgravity anomaly inversion exhibits a distinctive capability for fine-scale delineation. This study investigates intelligent identification of microgravity anomalies using a twin-tunnel section of Chengdu Metro Line 9 as a representative underground-space case. A training dataset was first generated through forward modeling based on known tunnel parameters, and a fully connected neural network was developed to establish an end-to-end mapping from microgravity anomaly curves to tunnel geometric parameters. Synthetic experiments demonstrate that the model can accurately recover key geometric attributes—including tunnel depth, radius, spacing, and horizontal position—even under noisy conditions. For real-data preprocessing, the terrain-correction range was determined using gravity-response amplitudes associated with buildings of different scales within the urban area, while the stripping procedure for shallow-interface anomalies was formulated based on gravity-response characteristics induced by bedrock undulations beneath the surficial cover. After applying Bouguer correction and shallow-interface anomaly stripping to the raw gravity data, intelligent identification of tunnel geometry was performed, and the inversion results show strong consistency with engineering geological information, confirming the reliability and applicability of the proposed model. Overall, the method achieves high-precision characterization of shallow underground-space geometry, provides excellent inversion accuracy and strong engineering applicability, and establishes a "scientific testbed" for microgravity detection of small subsurface targets, offering a practical paradigm for the optimization and innovation of related geophysical exploration techniques.
Affected by factors such as instrument failure, mud invasion, borehole collapse, and incomplete logging data acquisition during secondary development of new layers in mature fields, neutron logging curves in some depth intervals are often distorted or missing, which directly affects the accuracy of reservoir evaluation. To address the difficulty of traditional methods in capturing the correlation features among multiple well logging curves, this paper proposes a neutron logging curve reconstruction method that integrates a Graph Convolutional Neural Network (GCN), a Bidirectional Gated Recurrent Unit (BiGRU), and a Multi-Head Attention (MHA) mechanism. In the proposed method, the GCN is employed to characterize the correlations among multiple well logging curves, the BiGRU is used to model the bidirectional sequential features of logging data along the depth direction, and the MHA mechanism is introduced to enhance the model's focus on important features, thereby improving the reconstruction accuracy of neutron logging curves. Lithofacies-constrained experiments and comparative model evaluations are conducted using multi-well logging data, which verify that the incorporation of formation lithofacies indicators effectively enhances the reconstruction capability of the proposed model. The proposed model is compared with several baseline models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (BiGRU), and the GCN-BiGRU model. The evaluation results demonstrate that the proposed method significantly outperforms the comparison models across multiple performance metrics. Practical applications of reconstructed logging curves further confirm that the proposed method achieves high accuracy and strong applicability in neutron logging curve reconstruction.
With the increasing depth of seismic exploration, the accuracy of middle-deep velocity modeling is low due to the complex geological structure and drastic lateral velocity variation in the subsurface, which affects the quality of migration imaging. At present, among the depth domain velocity inversion methods, ray travel time tomography has high computational efficiency, but it is prone to problems such as shadow region and caustics. However, the full waveform inversion has high accuracy, but it has some shortcomings such as large calculation. Therefore, a velocity modeling method that takes into account the computational efficiency of ray tomography and the accuracy of full waveform inversion is needed. Gaussian beam tomography is an intermediate method between ray theory and wave theory, which improves the stability of tomography method by using kernel functions to reduce the pathologies of the sensitivity matrix. In this paper, on the basis of using Gaussian beam to extract the Angle domain common imaging gather, a data-driven seismic DNA algorithm is used to automatically fit the residual curvature of the gather and extract the depth residual, and the depth residual is converted into the travel time residual in the inversion equation, and then the cumulative error carried by the downward propagation of seismic waves is eliminated by a reverse recursion method. Further improve the accuracy of the migration velocity. The accuracy, effectiveness and adaptability of the method are verified by the model and practical data.
Fractures of different scales are developed in the tight sandstone reservoirs of the Upper Triassic Yanchang Formation in the Ordos Basin, which are complexly distributed and difficult to predict. In this paper, taking the tight sandstone reservoirs of the Upper Triassic Chang 6 Oil Group in the Wuqi area of Northern Shaanxi Province as an example, based on the data of conventional logging, imaging logging, core observation, and cast thin sections, an improved variable scale fractal method combined with second derivative of lg(R/S) and grey relational analysis was used to calculate the fracture development coefficient Q established on five types of conventional logs. By integrating this with imaging logging data, we determined the lower limit value of Q indicative of fracture development and quantitatively evaluated the fracture development in the Chang 6 tight sandstone reservoirs of the study area. The results show that the reservoirs in the Chang 6 Oil Group of the Yanchang Formation in the Wuqi area mainly develop vertical fractures and high-angle fractures, and the lower limit of the value of the fracture development coefficient Q for this set of reservoirs is 0.8, and the locations of fracture development. assessed by this method show a high degree of agreement with the fractures interpreted from the imaging logs. Additionally, the fracture development coefficient Q evaluated by this method is positively correlated with fracture line density, with a high degree of correlation. The improved variable scale fractal method, compared to the conventional variable scale fractal method, has higher evaluation accuracy in identifying fractures for the tight sandstone reservoirs. It is widely recognized that the improved variable scale fractal method for quantitative evaluation of fractures in tight sandstone reservoirs has better reliability compared to conventional methods. So, this study can provide a new approach for predicting natural fractures in tight sandstone oil reservoirs.
Karst caves play a vital role in hydrocarbon accumulation and reservoir formation, and accurately delineating cave features is of great significance for the exploration and development of carbonate reservoirs. Traditional seismic P-wave velocity inversion identifies karst caves primarily based on bead-like anomalous reflections, but its accuracy is limited by seismic data quality and structural discontinuities. Although recent deep learning approaches have improved automatic identification capabilities, they often suffer from blurred boundaries and inadequate resolution. To address these issues, we propose a method for predicting cave-related attribute parameters by integrating deep learning with seismic facies-controlled chaotic inversion. This approach combines the cave feature extraction capability of deep learning with the high-resolution advantage of chaotic inversion guided by seismic facies. It effectively overcomes the limitations of deep learning in delineating fine-scale cave geometries and reduces the sensitivity of inversion results to seismic data quality. Furthermore, we establish a porosity calibration model by linking seismic rock physics forward modeling with measured data, enabling the conversion of cave velocities into porosity estimates. This yields high-precision quantitative descriptions of cave attributes. Both theoretical modeling and field application results demonstrate that the proposed method can clearly delineate cave positions and morphologies, enhance vertical and lateral resolution, and provide reliable porosity distribution within caves. These capabilities offer strong technical support for the efficient exploration and development of carbonate reservoirs.
The equivalent source method is widely used in magnetic field data processing and conversion. In the field of magnetic method, the current common equivalent source method research is based on the total magnetic field data, and the research on the reconstruction of equivalent source data using different component magnetic data is relatively few. Based on this, this paper discusses the influence of single component magnetic data and different component magnetic data combination on the reconstruction accuracy of total magnetic field data by using equivalent source method, and carries out equivalent source model test of single component data and different component magnetic data combination. The results show that the single component magnetic data can reconstruct the total magnetic field data by using the equivalent source method, and the vertical component data can reconstruct the total magnetic field data by using the equivalent source method compared with the horizontal component data. The accuracy of the results is relatively high, and the reconstruction accuracy of the total magnetic field data will be further improved by using the more comprehensive component combination data. This method is applied to the reconstruction of equivalent source data in the Galinge iron mine area of Qinghai Province, China, and the expected results are achieved, which provides a reference for the subsequent research of equivalent source method.
Three-dimensional viscoelastic forward modeling is a crucial tool for understanding seismic wave propagation in regions covered by thick loess, red clay and saturated sandy soils. It also serves as a fundamental component in the theoretical study of three-dimensional passive source surface wave detection. The fractional-order wave equation based on the Kjartansson constant-Q model is widely used for two-dimensional viscoelastic forward modeling, reverse time migration and full waveform inversion, owing to its ability to decouple amplitude attenuation from velocity dispersion. However, its application to three-dimensional viscoelastic media faces significant challenges, including high memory requirements, substantial computational costs, and difficulties in accurately implementing the Viscoelastic Stress Image method (VSI) for free boundary conditions. To overcome these limitations, this study proposes a three-dimensional viscoelastic non-fractional wave equation forward modeling approach based on the Generalized Standard Linear Solid (GSLS) model integrated with VSI. The proposed method utilizes a high-order finite-difference scheme on a staggered grid, combined with Multiaxial Convolution Perfectly Matched Layer (MC-PML) absorbing boundary conditions. This approach efficiently yields numerical solutions to wave equations on structured grids, demonstrating strong practicality in exploration seismology. The method enables the successful implementation of VSI, providing high accuracy in simulating surface wave travel times and amplitudes, while maintaining numerical stability in media with high Poisson's ratios. The validity of the proposed method is verified through comparisons between numerical solutions and analytical solutions for both uniform half-space and layered models. Additionally, forward modeling tests on complex three-dimensional viscoelastic models confirm the method's adaptability to intricate geological conditions. The results indicate that the proposed method accurately captures phase distortion, amplitude attenuation, physical dispersion, wave conversion and energy redistribution in viscoelastic media. Furthermore, the approach demonstrates notable advantages in computational efficiency and memory usage, underscoring its potential for application in seismic exploration.
Seismographs are highly sensitive and broadband instrument designed for detecting and recording seismic waves. In recent years, seismographs have been widely applied in the field of traffic monitoring, demonstrating significant potential, particularly for monitoring low-altitude aircraft. Radar, the traditional tool for aircraft detection, provides accurate and real-time capabilities in locating aircraft position, speed, and flight direction. However, it has limitations in detecting low-altitude aircraft, flexibility, susceptibility to terrain masking, and accuracy under extreme weather conditions. To overcome these limitations, researchers have explored various alternative methods for low-altitude aircraft detection, including infrared, imaging, and acoustic detection. Nevertheless, these methods also face challenges related to cost, weather susceptibility, detection range, and environmental noise.To address these challenges, this paper proposes a real-time recognition method for low-altitude aircraft, based on seismographs and deep learning technology. Leveraging the high precision and anti-interference capability of seismographs and integrating deep learning techniques, the method employs a lightweight MobileNetV3 network model for training, achieving automated identification of low-altitude aircraft signals. Furthermore, the vibrational frequency characteristics of the observed aircraft signals, inverted over time, yielded key flight parameters. The accuracy of these derived parameters was validated through comparison with flight trajectory data.The proposed method exhibits real-time performance and high recognition accuracy for low-altitude aircraft identification. Simultaneously, benefiting from the portability and ease of deployment of seismographs, the method is adaptable to complex environmental application requirements. It offers a feasible technical solution for the field of air traffic monitoring.
Underground silt is commonly found in southern cities with well-developed water systems in China. It poses safety and quality risks to urban infrastructure construction due to its complex composition and poor engineering properties. It is of great importance to identify the spatial distribution characteristics of underground silt before engineering construction. This study takes the investigation of the underground silt of Ningbo Rail Transit Line 7 as an example, employing the high-density resistivity method and microtremor survey for comprehensive detection of the spatial distribution characteristics of underground silt. The comprehensive geophysical prospecting results are used to guide the geological drilling implementation, and their reliability is further verified based on the geological drilling results. Traditional microtremor data processing methods struggle to achieve high lateral resolution for detecting local subsurface heterogeneities. This study employs Multiscale Window Analysis of Surface Waves (MWASW), overcoming the limitations of traditional methods in terms of lateral resolution and enabling accurate delineation of the undulating morphology of underground silt basal boundaries. The high-density resistivity method has good resolution in both horizontal and vertical directions, but the identification of anomalies involves uncertainties or nonuniqueness merely based on resistivity inversion. The two geophysical prospecting methods are mutually complementary and verified quite well, effectively identifying the spatial distribution characteristics of the underground silt, with less than 8% depth error compared to drilling-revealed basal boundaries. It is concluded that the integrated geophysical prospecting technology combining microtremor survey and high-density resistivity method, supplemented with limited geological drillings, achieves both detection accuracy and operational efficiency in underground silt exploration. This successful application can provide a reference for geophysical prospecting in similar projects.
According to the hydrogeological conditions, the Jinniu Lake area in Nanjing exhibits significant geothermal resource potential. To investigate the characteristics and spatial distribution of tectonic fractures in this region and identify potential deep geothermal drilling targets, an integrated geophysical exploration approach was adopted. The study first employed the Controlled Source Audio-Frequency Magnetotelluric (CSAMT) method to delineate the positions of fault structures and basement morphology within the exploration area. Subsequently, survey lines were deployed at favorable fracture locations, and the Time-Frequency Electromagnetic (TFEM) method—known for its strong anti-interference capability and large detection depth—was applied for further verification. Based on inversion and interpretation results, promising geothermal drilling well locations were proposed. Finally, microtremor surveys were conducted along the proposed well sites to refine the target locations by integrating low-velocity anomalies from microtremor data and low-resistivity features from electromagnetic methods. Drilling verification confirmed the presence of a fracture at 1, 600 m, with the final borehole depth reaching 2, 800 m. Post-drilling pumping tests revealed a wellhead water temperature of 51 ℃, indicating high-quality deep geothermal resources.By progressively narrowing the exploration scope from regional to linear and finally to point scales, and through mutual validation of multiple geophysical methods, optimal well locations were successfully determined. This integrated geophysical approach provides valuable geothermal exploration experience for similar study.
Accurately detecting the type and distribution of underground targets is a prerequisite and key to ensuring the safety of urban underground space. Ground Penetrating Radar (GPR) is a conventional method for identifying concealed diseases in urban underground spaces. However, its data interpretation primarily relies on the manual work of experienced practitioners, which is time-consuming and labor-intensive, and deep learning-based automatic target recognition methods require a large number of labeled samples, mostly utilizing B-scan data, resulting in low recognition accuracy. To address these challenges, this paper proposes a three-dimensional (3D) GPR target recognition method based on temporal ensembling semi-supervised deep learning. This method combines three slices of 3D GPR data (B-scan, C-scan, and D-scan) into triple-channel GPR images as network inputs. By employing a temporal ensembling semi-supervised deep learning approach, the model is jointly trained using a small amount of labeled data and a large amount of unlabeled data, thereby reducing the demand for labeled data. The temporal ensembling method is a kind of self-ensembling learning method, which utilizes multiple versions (ensembles) of a model during training to enhance performance, robustness, and generalization without requiring much labeled data. Additionally, a triplet attention module is introduced to learn spatial and channel information of 3D data more effectively, thus improving the classification performance. Indoor experimental results demonstrate that when only 10% of the training data is labeled, the proposed method achieves an average accuracy of 96.45% for four types of underground targets (i.e., metal pipe, plastic pipe, void, and background). Comparative experiments indicate that this method improves the average accuracy by 5.28% compared to B-scan-based methods and outperforms supervised methods based on transfer learning and four semi-supervised methods. These findings demonstrate that the proposed method can be effectively applied to intelligent GPR underground target recognition with limited labeled data, facilitating the promotion of deep learning in GPR applications.
Seabed characterization based on multi-beam seabed Backscatter Strength (BS) data classifies the seabed as a certain type in the sampling results, which may lead to misclassification in complex sediment areas (including different sediment mixing areas, transition areas, etc.). Currently, there is a lack of reliable analysis and evaluation methods for interpreting the complex sediment areas based on multi-beam seabed BS data. To solve this problem, this article first references seabed samplers to statistic the backscatter data of typical sediment, and obtained its Probability Density Function (PDF). Based on this, (1) for the sediment mixing zone, an equation is established by using a combination of above-mentioned typical PDF to approximate the PDF of the measured BS data, and the steepest descent method is used to solve the proportion of each typical PDF; (2) A qualitative analysis method is proposed for seabed between two typical seabeds, as well as transition zones between different seabeds, based on the PDF of their BS and its skewness, kurtosis, cumulative distribution function, etc. This paper provides a reliable quantitative inversion and qualitative evaluation method for complex seabed areas, where often with characterization errors using conventional methods. It can also be used as an effective means of analyzing the entire area based on BS data when seabed sampling is insufficient.
The 3D marine seismic technology is the key technology for the deep-water hydrocarbon exploration and development. Compared with conventional 3D towed streamer seismic, which lacks low frequency components, the 3D slant streamer seismic contains abundant low and high frequency components, which can effectively improve the imaging accuracy of deep water turbidite sand. Ghost in slant broadband seismic data are one of the key factors affecting high-resolution imaging of deep-water turbidite sandstones, and In this paper, On the basis of analyzing the ghost notch diversity of 3D slant streamer seismic, we have established a method for generating 3D oblique streamer seismic mirror data, systematically investigated the principle of deghosting by joint deconvolution of a migration and a mirror migration, and optimized the pre-stacked deghosting by multichannel joint deconvolution with the schmidt orthogonal polynomial technique. The pre-stack or post-stack deghosting by joint deconvolution can effectively suppress the slant streamer ghost and improve the resolution of seismic imaging. The slant streamer broadband seismic has achieved good results in deepwater DX reservoir exploration. The slant broadband seismic imaging is the doubled resolution compared with that of conventional marine 3D seismic imaging, and can improves the seismic identification accuracy of turbidite. It can not only identify turbidite sandstone with a thickness of about 8 meters, but also finely characterize the internal characteristics of turbidite sand in deepwater reservoirs. Reservoir characterization based on the slant broadband seismic data improves the accuracy of reservoir geological reserve assessment and reduces the risk of deepwater hydrocarbon exploration and developmen.
Through-casing logging technology has important development prospects in mature fields. A dual-source through-casing acoustic logging tool is designed. The operation principle and the overall design of the tool are carried out, especially the spacing distance between the transmitting transducers. A dual-source transmitting circuit is designed to send out two excitation signals of opposite polarities, and the shape selection of the excitation waves is researched. This tool was logged in casing wells with different cementation qualities in Shengli oilfield, and the logging results showed that the instrument could obtain high-quality formation signals in casing wells with different cementation qualities, which was consistent with the measurement results of open-hole wells.
In order to ensure the reliability of navigation and positioning systems in China's airborne geophysical work, research has been conducted on the Beidou-3 navigation satellite system to ensure high-quality and efficient completion of aerogeophysical work, and to enhance the country's independence and safety in the field of aerogeophysical exploration. Firstly, research was conducted on the Beidou-3 navigation satellite system, designing its positioning and signal acquisition modules and converting the collected data into formats. The developed system was statically tested in Shijiazhuang and Zhongwei, and the test results showed that the convergence time of the Beidou-3 navigation satellite system developed in this paper was 21 minutes, with a static positioning accuracy of 0.061 m, which can well meet the requirements of aerogeophysical survey. Further testing and application of the system were conducted in the Ordos measurement area. The developed Beidou-3 positioning system and GPS system were loaded onto the P-750 aircraft, and completed data collection for 1686 kilometers of survey line. The average difference in positioning error between the two positioning systems was 0.002 m, and the number of measrement lines with Beidou-3 positioning accuracy higher than GPS positioning accuracy accounted for 40%. After track recovery, the airborne/magnetic data collected by GPS and Beidou systems at different measurement times were compared. The results showed that there was no mutual interference between the developed Beidou positioning system and GPS system, and they could work independently of each other. This indicates that the developed Beidou positioning system can effectively complete aerogeophysical survey, with high reference and application value, providing new means and methods for positioning of aerogeophysical survey, and improving the security of exploration information.
Most conventional Reverse Time Migration (RTM) method for Ground Penetrating Radar (GPR) data under undulating surface condition employ Finite Difference Time Domain (FDTD) method for wavefield reverse time extrapolation. The staircase approximation inherent in Yee-grid FDTD discretization of undulating surfaces and complex interfaces generates geometric discretization errors, which consequently degrade the resolution of RTM imaging. To overcome this, this paper proposed a RTM imaging method for GPR data with undulating surfaces that utilizes the Finite Element Time Domain (FETD) with triangular mesh discretization. The FETD with triangular mesh discretization and non-split Complex Frequency Shifted Perfectly Matched Layer (CFS-PML) boundary condition is employed for GPR wavefield simulation and reverse time extrapolation. And the zero time imaging condition is applied to obtain the final imaging results. Numerical experiments of RTM imaging both of synthetic undulating surface data and field data demonstrated that compared with the RTM imaging results of FDTD with Yee mesh, the target and interface beneath the undulating surface in the RTM imaging results of FETD with triangular mesh are more convergent to the true positions, achieves higher imaging resolution, and requires fewer mesh elements and nodes. Furthermore, the proposed RTM method can effectively mitigate the adverse effects of undulating surface on migration quality, generating imaging results that are more reliable for geological interpretation.
ISSN 1004-2903 (Print)
Started from
Published by:
