Journal of Applied Geophysics最新文献

{"title":"Waveform features and automatic discrimination of deep and shallow microearthquakes in the Changning shale gas field, Southern Sichuan Basin, China","authors":"Jianfeng Liu ,&nbsp;Fujun Xue ,&nbsp;Jingjing Dai ,&nbsp;Jianxiong Yang ,&nbsp;Lei Wang ,&nbsp;Xiangchao Shi ,&nbsp;Shigui Dai ,&nbsp;Jun Hu ,&nbsp;Changwu Liu","doi":"10.1016/j.jappgeo.2025.105850","DOIUrl":"10.1016/j.jappgeo.2025.105850","url":null,"abstract":"<div><div>Identification of microearthquakes at source depth holds significant importance in the field of microearthquake monitoring. Taking 256 microearthquake events (1.5 &lt; <em>M</em>_L &lt; 4) in Changning Shale gas exploration area in the south of Sichuan Basin as the engineering background, this paper introduced a method of extracting six feature sets and 6 × 24 feature parameters, which are derived from microearthquake waveform in time and frequency domains based on Empirical Mode Decomposition and Hilbert Transform. The feature importance ranking and 22 key feature parameters closely related to source depth information were obtained using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms. In addition, principal component analysis (PCA) was used to reduce dimensionality and reconstruct the feature space. The classification performance of multiple algorithms, including XGBoost, Support vector machine (SVM), Logistic Regression (LR), K-Nearest (KN), RF, and Decision Tree (DT) models, was compared. The results show that both the 22-dimensional feature parameters and the feature space reconstructed by PCA can effectively distinguish shallow events with source depths less than 1 km from deep events with source depths greater than 6 km. Using the evaluation indicators of receiver operating characteristic, sensitivity, and specificity, it is believed that XGBoost, SVM, and RF classifiers outperform LR, KN, and DT in identifying source depth. Among them, XGBoost classifiers are the least affected by random sampling and changes in sample proportion. The machine learning technology used in this study can effectively perform automatic source depth classification on seismic signals.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105850"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Helium-3 NMR as a valid probe to study pore distribution of clay-based rocks","authors":"Alexander Bogaychuk ,&nbsp;Kajum Safiullin ,&nbsp;Vyacheslav Kuzmin ,&nbsp;Ekaterina Boltenkova ,&nbsp;Alexander Rodionov ,&nbsp;Eduard Korolev ,&nbsp;Marat Gafurov ,&nbsp;Alexander Klochkov","doi":"10.1016/j.jappgeo.2025.105828","DOIUrl":"10.1016/j.jappgeo.2025.105828","url":null,"abstract":"<div><div>Two probes for nuclear magnetic resonance (NMR) technique are compared and discussed in evaluation terms for basic properties of porous media. The first and conventional one is ¹H NMR of water, the second one is based on ³He NMR. Both approaches allow to estimate the porosity and pore size distribution (PSD) by measurements of the transverse and longitudinal magnetization relaxation times of the probe nuclei, as well as a <span><math><mrow><msub><mrow><mi>T</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>−</mo><msub><mrow><mi>T</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> correlation map between relaxation times. The validity of ³He approach is shown by the comparison of both probes applied to sandstones with a relatively small clay abundance. The advantages of ³He NMR technique are demonstrated on the clay-rich samples that get destroyed by water. The obtained PSD-relevant results show that the ³He method is suitable for characterization of various clay-rich formations and can be used to study unconventional extra-heavy oil reservoirs, tight shales, etc., where a conventional water NMR is inapplicable.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105828"},"PeriodicalIF":2.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-dimensional discrete magnetic inversion using graph theory to determine the framework of isolated sources","authors":"Rômulo Rodrigues de Oliveira ,&nbsp;Rodrigo Bijani ,&nbsp;Nelson Ribeiro-Filho ,&nbsp;Edson Alonso Falla-Luza ,&nbsp;Cosme F. Ponte-Neto","doi":"10.1016/j.jappgeo.2025.105825","DOIUrl":"10.1016/j.jappgeo.2025.105825","url":null,"abstract":"<div><div>Applied geophysics is an important discipline for a prolific description of the subsurface. Commonly, the geophysical data is acquired, treated and inverted to produce interpreted geologic models. In this case, models can be composed by physical-property distributions and/or geometry of causative sources. In magnetic inverse problems, a challenging aspect is to reliably estimate both the geometry and magnetization of causative sources, once these parameters are estimated with tremendous amounts of uncertainties if no stabilization is considered. So, this study revisits graph-theory concepts applied to the equidistance function to stabilize the three-dimensional discrete magnetic inversion of the total-field anomaly using an ensemble of identical dipoles, which simultaneously estimates the magnetic framework and the magnetization direction of homogeneous sources. Our method consists of a Hierarchical Genetic Algorithm (HGA) to generate a population of candidate estimates. Each one comprises a set of identical magnetic dipoles, whose Cartesian coordinates and a single magnetization direction are estimated throughout the HGA generations. To produce unique and stable estimates, our HGA minimizes a single-objective function and a L-curve strategy guides for an ideal balance between data-misfit and equidistance functions. Thus, a compact ensemble of magnetic dipoles is obtained when the statistical variance of the distances of an minimum spanning tree is minimized jointly with the data-misfit function during the iterations of the HGA. Hence, the 3D compact distribution of dipoles can be associated with the skeleton of magnetic sources. An innovative searching for promising estimates is supplied by other geophysical methods applied to the magnetic data, such as Euler deconvolution and crosscorrelation. With these, the search spaces for each model parameter are defined in a semi-automatic basis, avoiding a strictly guess-and-check approach. The methodology is validated on two noise-corrupted synthetic data composed by vertical and dipping dike models. Results confirm the decent capability of the method in estimating a stable and compact ensemble of magnetic dipoles. Additionally, the magnetic properties are also recovered in a reliable way. A real test comprising the Morro do Forno magnetic anomaly show that the methodology works adequately in recovering the framework and orientation (i.e., Southwest–Northeast) of the main magnetic structures of the area. Nothing specific about the alkaline dykes of the region could be assertively mentioned, once a more refined study is required.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105825"},"PeriodicalIF":2.2,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid modeling of deep neural networks and unsupervised machine learning algorithms for missing well log prediction based on geological lithofacies similarities","authors":"Wakeel Hussain ,&nbsp;Miao Luo ,&nbsp;Muhammad Ali ,&nbsp;Izhar Sadiq ,&nbsp;Erasto E. Kasala ,&nbsp;Tariq Aziz ,&nbsp;Zuriyat Batool","doi":"10.1016/j.jappgeo.2025.105846","DOIUrl":"10.1016/j.jappgeo.2025.105846","url":null,"abstract":"<div><div>Well logging plays a vital role in formation characterization and resource evaluation in oil and gas exploration. However, acquiring well logging data through conventional field methods is often costly and time-consuming, highlighting the need for accurate and cost-effective predictive solutions. To address these challenges, this study introduces a novel hybrid modeling framework that integrates Self-Organizing Maps (SOM), Multilayer Perceptron (MLP), and Social Ski-Driver (SSD) optimization. SOM is employed for unsupervised lithofacies classification based on similarities in well log responses, and these lithofacies, along with other well log inputs, serve as key features for the supervised MLP model. The SSD algorithm optimizes the MLP's weights and biases, further enhancing its performance. The results demonstrate that the hybrid SOM-MLP-SSD model significantly improves the accuracy of missing log predictions, particularly in lithologically complex and hydrocarbon-bearing zones where traditional methods, such as the Greenberg-Castagna equation, fall short. To benchmark this approach, a SOM-SVM model was also tested to evaluate how another established machine learning algorithm performs with the same facies-guided structure. While SOM-SVM produced reasonable results, the SOM-MLP model consistently achieved more reliable and accurate predictions. The model also incorporates uncertainty quantification using least-squares estimation, increasing prediction robustness. This methodology offers a significant advancement in subsurface characterization by combining geological insights with advanced machine learning and optimization techniques. The hybrid approach enhances prediction accuracy, providing valuable insights for geomechanical analysis, reservoir evaluation, and decision-making in hydrocarbon exploration. The proposed model represents a promising step forward in improving the accuracy of missing log predictions and optimizing resource extraction strategies in complex reservoirs, facilitating more efficient and cost-effective exploration and development.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105846"},"PeriodicalIF":2.2,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of towed TEM potential for rapid characterization of levee foundations","authors":"Kolawole Arowoogun,&nbsp;Katherine Grote,&nbsp;Jeremy Maurer","doi":"10.1016/j.jappgeo.2025.105849","DOIUrl":"10.1016/j.jappgeo.2025.105849","url":null,"abstract":"<div><div>Assessing the geologic conditions of levee foundation soils is a challenging task owing to the extensive length of most levees and the heterogeneity of many alluvial deposits. Traditional investigation techniques (such as boring and cone penetrometer testing [CPT]) are expensive, invasive, and provide spatially-limited information. As a result, they are restricted to pre-identified problematic zones in the levee. To overcome these challenges, geophysical instruments capable of better spatial coverage are proposed for rapid geoelectrical characterization of levees. In this study, we presented a field-based application of the towed time-domain electromagnetic method (tTEM) system in characterizing the subsurface geology adjacent to a vulnerable levee that has experienced underseepage and repeated sand boil occurrences. The tTEM data was acquired along the landward side of the levee, and the results were compared with concurrent electrical resistivity tomography (ERT) profiles and pre-existing CPT and borehole data. The tTEM and ERT results reveal a coarse-grained sand substratum layer overlain by a fine-grained clay blanket of varying thickness. The results also show areas with a very thin or missing clay blanket and a higher degree of heterogeneity; these areas are prone to sand boil formation. A comparison of tTEM and ERT results suggests that the tTEM reliably mapped the clay layer blanket and sand substratum thickness across the study area. While ERT provides better resolution of the substratum heterogeneity, this case study demonstrates the potential of tTEM as a valuable tool to rapidly assess subsurface geology and identify problematic zones in levees over large areas, which could help in mitigating future levee failures.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105849"},"PeriodicalIF":2.2,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144516895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SATEM: A new drone-based semi-airborne transient electromagnetic receiver system","authors":"Nuoya Zhang ,&nbsp;Huaifeng Sun ,&nbsp;Yuchao Liu ,&nbsp;Ziqiang Zheng ,&nbsp;Rui Liu ,&nbsp;Shangbin Liu ,&nbsp;Xushan Lu","doi":"10.1016/j.jappgeo.2025.105848","DOIUrl":"10.1016/j.jappgeo.2025.105848","url":null,"abstract":"<div><div>Semi-airborne Transient Electromagnetic method (SATEM) stands as a pivotal tool for subsurface mineral resources exploration, geological survey, and groundwater investigation in complex terrains. This study develops a bespoke SATEM receiver system tailored for such explorations, comprising a lightweight recorder and small-size receiving coil. The recorder employs a dual microprogrammed control unit (MCU) hierarchical structure framework, facilitating seamless SATEM data acquisition and storage. Meanwhile, the receiving coil, featuring a shield and multi-turn loop, integrates an embedded signal acquisition module capable of storing electromagnetic signals alongside Global Navigation Satellite System (GNSS), acceleration, and altitude data. Time synchronization between receiver and transmitter relies on the pulse per second (PPS) signal from GNSS, so it can work with any transmitter systems equipped with GNSS based synchronization. The low internal noise of the receiver system is about 0.834 μV and it has a high sampling rate of 256 kbps. We carried out a geophysical survey using the developed SATEM receiver system in Zhaiya Tunnel in Guangxi, China, where the topographical relief is significant and geological data are scarce. The SATEM system detected subsurface karst development area around the tunnel site, providing critical information ahead of tunnel construction. The results are in good agreement with the true karst revealed during the tunnel excavation.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105848"},"PeriodicalIF":2.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SCLRAD: Semi-supervised contrastive learning using random replacement of adjacent depths for lithology identification","authors":"Fengda Zhao ,&nbsp;Haobing Zhai ,&nbsp;Zihan Zhou ,&nbsp;Pengwei Zhang ,&nbsp;Xianshan Li","doi":"10.1016/j.jappgeo.2025.105795","DOIUrl":"10.1016/j.jappgeo.2025.105795","url":null,"abstract":"<div><div>Lithological identification is significant in logging interpretation work, as it is the foundation for evaluating reservoirs and describing reservoirs and basins. In lithological identification, due to the difficulty in obtaining lithological data and the high cost of labeling, there is often a situation of needing labels. How to fully utilize the lithological data with missing labels is a problem that needs to be solved. Therefore, this paper introduces a semi-supervised contrastive learning model, SCLRAD (Semi-supervised contrastive learning using random replacement of adjacent depths), aiming to fully utilize lithology data, extract better feature representations, and improve the accuracy of lithology recognition. Lithological data exhibits a discernible pattern of variation in depth. Sample pairs for contrastive learning are constructed by harnessing depth information to unearth deeper features within the data. The loss function weights for various proxy tasks are fine-tuned through a semi-supervised joint training framework. The combined effects of contrast loss and classification loss act on the encoder, enabling it to learn the intrinsic characteristics of the lithological data and capture its inherent differences and similarities, thus enhancing the classifier’s performance. Accordingly, the experimental approach presented in this study is grounded in blind wells testing. The lithology identification accuracy on the China Daqing Fields datasets and Hugoton and Panoma Fields datasets reached 82.02% and 68.55%, respectively. By manipulating the ratio of unlabeled data on the Hugoton and Panoma Fields dataset, even when the labeling ratio is below 60%, comparing favorably with the 1D-CNN, the accuracy improves by 2.77% to 4.22%.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105795"},"PeriodicalIF":2.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-local self-similarity guided graph attention network for DAS-VSP noise and signal separation","authors":"Guijin Yao,&nbsp;Qing Zhang,&nbsp;Hairong Zhang,&nbsp;Yue Li","doi":"10.1016/j.jappgeo.2025.105835","DOIUrl":"10.1016/j.jappgeo.2025.105835","url":null,"abstract":"<div><div>Fiber-optic distributed acoustic sensing (DAS) technology represents an emerging seismic exploration technique. Due to multicomponent noise contamination, noise suppression and separation have been paramount tasks in DAS. Non-local self-similarity (NSS) serving as an effective prior in seismic data was initially utilized for separating noise and signal. However, the existing deep learning nonlocal methods are pixel-based, they are prone to biases when confronted with the strong noise characteristics of DAS VSP data. To address the aforementioned issues, this paper proposes and validates the effective prior of nonlocal self-similarity based on patch-wise analysis for DAS VSP data. Inspired by the powerful ability of graph attention networks (GATs) for non-local feature aggregation, we introduce a dynamic attention graph learning model. This model utilizes patch-wise graph convolution to capture the long-range correlations in DAS, significantly mitigating the impact of noise levels on the separation of noise and signal. Furthermore, we design the graph-based channel attention module (CE) to dynamically construct the node and edge sets of the graph, generating adaptive thresholds for each node to select neighbors with similarity higher than the threshold as edges for graph connection. This enables the model to focus on key information in seismic signal. In experiments with synthetic DAS data, our method achieved an improvement in signal-to-noise ratio (SNR) of approximately 26.6 dB. In experiments with real DAS data, the separating performance was also superior to other methods. The proposed network can serve as an effective tool for processing DAS VSP data.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105835"},"PeriodicalIF":2.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of fluid saturation on the elastic properties of coal measure rocks under confining pressure","authors":"Fei Gong ,&nbsp;Guangui Zou ,&nbsp;Suping Peng ,&nbsp;Zhaoji Zhang ,&nbsp;Hao Chen ,&nbsp;Yawei Zhao","doi":"10.1016/j.jappgeo.2025.105845","DOIUrl":"10.1016/j.jappgeo.2025.105845","url":null,"abstract":"<div><div>Coal measure gas is a hot spot for gas exploration at present, which mainly includes coalbed methane, shale gas and tight sandstone gas. A good understanding of the effect of water on the shear moduli of coal measure gas reservoir rocks, despite being essential for seismic exploration, has not yet been well studied and understood. Therefore, three major groups of coal measure rock types, including sandstone, mudstone and coal from the Qinshui Basin, are selected to investigate the effects of confining pressure and fluid on the elastic properties of the rocks. The P-wave velocities of dry coal measure rocks are more sensitive to confining pressure than the S-wave velocities, while the contrary situation occurs under saturated condition. The differential Kuster–Toksöz model can simulate the change in shear moduli caused by saturated water and can assume specific pore geometry, suggesting that the pore structure of coal measure rocks is affected by the combined effect of pressure and pore fluid. The change in shear modulus of coal measure rocks is sensitive to the confining pressure, the ratio of saturated to dry shear moduli decreases exponentially with the confining pressure. Further, the change in shear modulus after saturation is a very complex process. As confining pressure increases, coals and mudstone always display shear softening after saturation, while the shear moduli of sandstone could transit from stiffening to softening. Due to their importance in prediction of shear wave velocity, this complex variation pattern should be considered during the fracturing process.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105845"},"PeriodicalIF":2.2,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on 1-D forward modeling and inversion of TEM Considering induced polarization effects","authors":"Zhanbo Yuan ,&nbsp;Muyang Wu ,&nbsp;Nengyi Fu ,&nbsp;Zhengyu Xu ,&nbsp;Zhihong Fu","doi":"10.1016/j.jappgeo.2025.105844","DOIUrl":"10.1016/j.jappgeo.2025.105844","url":null,"abstract":"<div><div>The transient electromagnetic (TEM) method, as an efficient geophysical exploration technique, plays a crucial role in mineral resource exploration, environmental engineering, and geological hazard assessment. TEM forward modeling and inversion sometimes neglect the subsurface medium's induced polarization (IP) effects. This may result in inaccurate interpretation of the subsurface electrical structure. To address this issue, this study conducts TEM forward modeling and inversion incorporating IP effects, aiming to improve TEM data's inversion accuracy and interpretability under geological conditions involving polarizable bodies. First, based on the Cole-Cole complex resistivity model, 1-D TEM forward modeling considering IP effects is implemented, and the accuracy and reliability of the algorithm are validated. Subsequently, nonlinear optimization algorithms are applied to process TEM data during the inversion, with a focus on comparing and analyzing the inversion performance of the particle swarm optimization (PSO) algorithm and the velocity pausing particle swarm optimization (VPPSO) algorithm in some layered medium models. The results demonstrate that although the computational efficiency of the VPPSO algorithm is slightly lower than that of the PSO algorithm, it exhibits significant advantages in convergence speed, global search capability, and noise resistance. Finally, inversion experiments using field data further validate the effectiveness and practicality of the VPPSO algorithm. The research works in this article provide new methods for TEM exploration of subsurface polarizable bodies.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"241 ","pages":"Article 105844"},"PeriodicalIF":2.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}