Journal of Applied Geophysics最新文献

{"title":"Anisotropic effect of surrounding rocks using the resistivity method in tunnel prediction","authors":"Daiming Hu ,&nbsp;Bülent Tezkan ,&nbsp;Jianhua Cai ,&nbsp;Xiaoping Wu","doi":"10.1016/j.jappgeo.2025.105924","DOIUrl":"10.1016/j.jappgeo.2025.105924","url":null,"abstract":"<div><div>In predicting water-bearing disaster structures in tunnels, complex geological conditions can pose significant risks to underground construction. To enhance data interpretation accuracy, this study presents a tunnel prediction method that accounts for the anisotropic effect of surrounding rocks using an unstructured finite element resistivity approach. A classic whole-space model has validated the effectiveness of the algorithm. We conducted simulations of a fault structure under the anisotropic surrounding rocks. The results indicated that increasing the anisotropy coefficient and Euler angle resulted in a greater offset distance of the minimum apparent resistivity. Compared with the isotropic model, the predicted distance of the anisotropic model was found to be greater. To address the adverse effects of tunnel and rock anisotropy, a ratio-based method was proposed in data processing, which has been proven to be effective. Next, we simulated a large number of models using the Monte Carlo method and found that anisotropic Euler angles caused significant deviations. Furthermore, our findings revealed that the prediction results were more reliable than those in previous literature. Finally, we applied this method to a real-world case of tunnel prediction, and the predicted results closely matched the true distance. This study will provide important insights for the practical application of tunnel prediction in complex geological environments.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105924"},"PeriodicalIF":2.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144931945","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":"Developing a multi-scale adaptive generalized morphological filtering algorithm for denoising ocean wave-induced interference in marine electromagnetic data","authors":"Jianxin Pei ,&nbsp;Yanyan Lu ,&nbsp;Zhijun Du ,&nbsp;Ming Luo ,&nbsp;Gongyi Sun ,&nbsp;Jiaqi Ge","doi":"10.1016/j.jappgeo.2025.105922","DOIUrl":"10.1016/j.jappgeo.2025.105922","url":null,"abstract":"<div><div>As one of the marine electromagnetic detection method, marine magnetotellurics(MT) is an important geophysical exploration method for detecting underground electrical structures. However, since the nature magnetotelluric field is used as the field source, MT data is easily contaminated by various noise, especially the magnetic field generated by the motion of seawater waves, which leads to alterations in both apparent resistivity and phase characteristics within the frequency range impacted by the interference. Building on the principle of mathematical morphology, we propose an innovative approach that integrates multi-scale morphological filtering with adaptive generalized morphological filtering, and applied it to the marine electromagnetic field for the first time. Multi-scale morphological filtering considers the variation characteristics of noise at different time scales, and adaptive morphological filtering considers the non-stationary characteristics of wave-induced interference and ground electromagnetic signals. In addition, the size of structural elements is selected by introducing the evaluation index of mutual relation number, which realize the adaptive selection of structural elements. This approach can effectively suppress the wave-induced magnetic noise in marine MT signals. The results derived from applications of the method to both simulated datasets with varying levels of ocean wave-induced disturbances and field datasets demonstrate its ability to effectively mitigate magnetic noise stemming from wave-induced effects. As a result, the observed apparent resistivity and phase trends display a high degree of coherence and continuity. This provides new insights into noise suppression in the marine electromagnetic field.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105922"},"PeriodicalIF":2.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050756","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":"Exploring ancient underground remains using the electrical resistivity method: A case study in Shangqiu, China","authors":"Tao Tao ,&nbsp;Peng Han ,&nbsp;Zhentao Yang ,&nbsp;Kaiyan Hu ,&nbsp;Qiang Zu ,&nbsp;Yihua Zhang ,&nbsp;Shuangshuang Li ,&nbsp;Yufan Hu ,&nbsp;Wen Zhong ,&nbsp;Bingbing Han ,&nbsp;Ruidong Li ,&nbsp;Zhanxiang He","doi":"10.1016/j.jappgeo.2025.105921","DOIUrl":"10.1016/j.jappgeo.2025.105921","url":null,"abstract":"<div><div>Shangqiu, regarded as the center of predynastic and dynastic Shang cultures, possesses a rich historical legacy and attracts significant attention in China. Excavating ancient underground remains in Shangqiu offers archaeologists valuable insights into ancient Chinese history and culture. In this study, electrical resistivity surveys were conducted to investigate potential ancient remains around an archaeological excavation pit in Shangqiu. Due to site constraints, only two approximately perpendicular 2-D survey lines were acquired around the archaeological excavation pit. To improve the reliability of interpretation, we compared 2-D inversion, 3-D inversion using the limited memory quasi-Newton (L-BFGS) method, and 3-D inversion based on broad learning (BL). The 2-D inversion results characterize the ancient city wall but fail to depict the ancient building components. In addition, false anomalies are produced in the deeper regions due to the influence of the archaeological excavation pit. The 3-D inversion results obtained using the L-BFGS method or the BL network depict the ancient city wall and building components beneath the survey lines. Compared to the 3-D inversion results obtained using the L-BFGS method, the BL inversion results reveal a broader anomaly range in the area of the ancient building components. Archaeological excavations confirm that the BL inversion results more closely reflect the actual subsurface conditions. Additionally, the area of the rammed-earth ancient city wall corresponds to lower radon gas values. This study provides valuable insights for decision-makers in archaeological excavations, and the proposed processing technology is helpful for the practical application of the electrical resistivity method in archaeological exploration.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105921"},"PeriodicalIF":2.1,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922973","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":"A new method to predict rock fracture based on Gradient Boosting Decision Tree via multidirectional crack vibration monitoring","authors":"Yihong Zhao ,&nbsp;Xianghui Tian ,&nbsp;Dazhao Song ,&nbsp;Majid Khan ,&nbsp;Huaijun Ji ,&nbsp;Zhenlei Li ,&nbsp;Wuyi Cheng","doi":"10.1016/j.jappgeo.2025.105920","DOIUrl":"10.1016/j.jappgeo.2025.105920","url":null,"abstract":"<div><div>Precise prediction of rock fracture is essential for the accurate monitoring and early warning of dynamic disasters in underground engineering. To achieve this, a multidirectional crack vibration (MDCV) monitoring experiment was conducted during the uniaxial compression fracture of rock using vibration acceleration sensors with directional sensing capabilities. The crack vibration statistics in all rock fracture directions were calculated using rolling windows, and the Autoregressive Integrated Moving Average (ARIMA) model was applied for parameter extraction, yielding rolling statistical features. The results indicate that these features exhibit a clear response to rock fracture. The amplitude of rolling standard deviation (<em>R</em>_std) and rolling mean (<em>R</em>_m) exhibit a sudden amplitude increase preceding fractures. Additionally, the density of high-amplitude points for rolling skewness (<em>R</em>_sk) and rolling kurtosis (<em>R</em>_k) significantly increases before the fractures; Meanwhile, the rolling quantile 25 (<em>R</em>_25%) and rolling quantile 75 (<em>R</em>_75%) demonstrate greater sensitivity to the initial fracture stage. In addition, the characteristics and importance of rolling statistic features differs across different fracture directions, highlighting the necessity of MDCV monitoring for the rock's instability assessment and fracture dynamics. Ultimately, a rock fracture prediction model was established via Gradient Boosting Decision Tree (GBDT) method and validated through MDCV monitoring experiments on rocks subjected to different loading speeds. The results demonstrate that the proposed model effectively predicts fracture events with high warning accuracy and strong generalization ability, remaining unaffected by variations in loading speeds. This research offers a robust and scalable approach for early warning of dynamic disasters in underground engineering worldwide.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105920"},"PeriodicalIF":2.1,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916560","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":"Edge-detection-driven first-arrival picking method for borehole radial velocity imaging","authors":"Peng Li ,&nbsp;Zhilong Fang ,&nbsp;Hua Wang","doi":"10.1016/j.jappgeo.2025.105919","DOIUrl":"10.1016/j.jappgeo.2025.105919","url":null,"abstract":"<div><div>Accurately determining the radial velocity structure of formations near the borehole is essential for evaluating borehole stability, detecting mud invasion, and optimizing reservoir production. Currently, the most widely used and reliable approach involves calculating the radial velocity of near-borehole formations using first-arrivals from monopole acoustic logging. However, the accuracy of this method is constrained by errors in first-arrival picking, which limits the precision of near-borehole formation velocity imaging. To address this limitation, this study introduces a first-arrival picking method based on image edge detection, aiming to enhance the accuracy of radial velocity imaging near the borehole. Our method improves the accuracy of first-arrival picking through three steps: (1) wavelet transformation, which extracts the signal's time-frequency characteristics; (2) mathematical morphology, which removes noise and enhances image edges; and (3) edge detection techniques, which accurately pick the first-arrivals of seismic signals. Numerical experiments validate the accuracy of the proposed first-arrival picking algorithm under varying signal-to-noise ratio (SNR) conditions for synthetic waveforms, significantly outperforming the conventional short-term average/long-term average (STA/LTA) algorithm. At an SNR of 5 dB, the algorithm reduces the average picking error from 0.43 to 0.07 and the relative error of near-borehole velocity inversion results from 3.843 to 0.131. Field data validation further demonstrates the algorithm's reliability, with imaging results aligning closely with gamma-ray lithology analysis. These findings provide strong technical support for hydraulic fracturing optimization and borehole completion engineering.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105919"},"PeriodicalIF":2.1,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924942","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":"Tunnel drilling seismic record of drilling jumbo reconstruction gain method and its engineering application","authors":"Weidong Guo ,&nbsp;Ronghui Bai ,&nbsp;Shaoshuai Shi ,&nbsp;Zhenyan Tian ,&nbsp;Ruijie Zhao","doi":"10.1016/j.jappgeo.2025.105918","DOIUrl":"10.1016/j.jappgeo.2025.105918","url":null,"abstract":"<div><div>In view of the complex frequency components of the drilling source of the drilling jumbo, some false events will be introduced during the deconvolution and cross-correlation processing, which will lead to the reduction of the quality of the reconstructed seismic record. In this paper, the research on the reconstruction gain method of the drilling source seismic record of the drilling jumbo based on VMD-spectral whitening is carried out. Taking the single-arm borehole source signal of rock drilling jumbo as the research object, combining the variational mode decomposition (VMD) and Hilbert spectrum whitening technology. Then, targeted increase of simulated drilling source signals, which are synthesized by multiple sine waves with different amplitudes and frequencies. Next, the reconstruction of borehole source seismic record is realized by least square deconvolution and cross-correlation interference technology, which weakens the influence of false events and improves the signal-to noise ratio of borehole source reconstruction seismic record. The processing effect is analyzed by forward simulation, and the method is applied to practical engineering. The detection results are basically consistent with the actual excavation situation, which verifies the effectiveness and feasibility of the method.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105918"},"PeriodicalIF":2.1,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924943","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":"Contribution of geophysical methods in mining exploration: Case study of El Haouaria-Oued Brache-Northern Tunisian Atlas","authors":"Manel Toumi ,&nbsp;Anis Barhoumi ,&nbsp;Wajdi Belkhiria ,&nbsp;Imen Hamdi Nasr ,&nbsp;Ahmed Ezzine ,&nbsp;Jamel Ayari","doi":"10.1016/j.jappgeo.2025.105916","DOIUrl":"10.1016/j.jappgeo.2025.105916","url":null,"abstract":"<div><div>The Northern Tunisian Atlas hosts numerous lead‑zinc ore deposits. While most of the shallow resources have been depleted, relatively deep structures remain underexplored and the use of geophysics is very limited. The El Haouaria-Oued Barche, located in Oued Zarga (northern Tunisia) represents one of these under-explored structures. This mine was in production from 1906 to 1940 before closing due to water ingressions in the underground galleries, which damaged the site infrastructure and posed safety risks. In this study, we use a range of geophysical techniques including gravity, Electrical Resistivity Imaging (ERI), and induced polarization data to understand the structure of El Haouaria-Oued Barche ore deposits and to explore potential subsurface targets for future drilling exploration programs. Geophysical lineament extraction represents one of the most useful techniques successfully successfully applied to mineral exploration. First, gravity data has been analyzed to map the contrasting gravity responses related to changes in density. The main identified lineaments coincide more or less with known faults/contacts and new directions have been identified. The main directions are N<em>E</em>-SW, NW-SE, E-W, and N-S. In a second step, IP was used to study disseminated pyrite associated with lead‑zinc mineralization. Gravity was used to map the structure and directly detect massive sulfide bodies within 200 m of the surface. Compared to Bouguer's anomaly, the obtained residual map brings to light some new features previously masked. The most distinctive features of the residual gravity are High-gravity anomalies positive. This suggests that the size of these deposits is very low. If the mineralization is not associated with pyrite or chalcopyrite, it is unlikely to be identified with IP. The horizontal gradient map has allowed a better delineation of the subsurface structural features of the area and can serve as a background document to guide future mineral exploration within this ore deposit.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105916"},"PeriodicalIF":2.1,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144931944","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":"Multi-modal seismic impedance inversion using a semi-supervised neural network with dual physics constraints","authors":"Ming Li ,&nbsp;Xuesong Yan ,&nbsp;Qinghua Wu","doi":"10.1016/j.jappgeo.2025.105911","DOIUrl":"10.1016/j.jappgeo.2025.105911","url":null,"abstract":"<div><div>Seismic impedance inversion is a crucial process for subsurface characterization in seismic exploration, offering insights into rock properties by converting seismic reflection data into impedance models. Data-driven deep learning methods have been widely applied to seismic data processing since they can provide better performance compared to traditional methods. However, current deep learning techniques also face challenges in powering seismic inversion due to problems such as poor continuity, the image assimilation of seismic data, and the lack of labeled data. To address these limitations, we propose a novel semi-supervised seismic impedance inversion neural network by integrating multi-modal attention mechanisms and dual physics constraints. Our approach leverages multi-modal attention mapping to transform seismic data into multiple domains, enabling the network to capture different features and improve inversion accuracy. By incorporating both seismic reflection data and well-log information, the semi-supervised framework learns effectively from both labeled and unlabeled data. The dual physics constraints, grounded in wave propagation and guided filtering mechanism, further guide the network towards physically consistent solutions and improve the continuity of the predicted impedance models. Experimental results on synthetic and field data demonstrate that the proposed method outperforms traditional deep learning seismic inversion techniques and provides more reliable impedance models. This approach highlights the potential of combining multi-modal attention mechanisms and physics-based constraints in deep learning inversion methods to advance subsurface imaging and resource exploration.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105911"},"PeriodicalIF":2.1,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896043","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":"Inverse distance weighted Random Forest for S-wave velocity prediction","authors":"Mengxuan Cao ,&nbsp;Sanyi Yuan ,&nbsp;Yue Yu ,&nbsp;Junliang Yuan ,&nbsp;Haoyue Liu","doi":"10.1016/j.jappgeo.2025.105908","DOIUrl":"10.1016/j.jappgeo.2025.105908","url":null,"abstract":"<div><div>S-wave velocity (Vs) serves as a crucial link between rock physics and seismic exploration. It plays an essential role in unveiling the complexity of subsurface structures and their dynamic behavior. It plays an essential role in unveiling the complexity of subsurface structures and their dynamic behavior. It is essential for revealing the complexity of underground structures and their dynamic characteristics. However, existing artificial intelligence methods typically build Vs prediction models using common sensitive parameters from multiple wells, which overlook the differences of sensitive parameters among wells and lack spatial constraints among wells. These limitations reduce the prediction performance of artificial intelligence models. Additionally, the necessity to select common logging parameters as input may result in the loss of sensitive logging parameters specific to certain wells. To address these issues, we propose an inverse distance weighted Random Forest method for Vs prediction. In the proposed method, decision trees are first employed to rank the logging parameters of each training well based on their sensitivity to Vs, acknowledging that sensitive parameters may vary between wells. Next, individual Vs prediction models are trained using the top-ranked sensitive parameters for each well. Finally, by considering the spatial distance and geological similarity between test and training wells, comprehensive estimates for the test wells are obtained through inverse distance weighted integration of predictions from multiple single-well models. We use three wells as training data and select one blind well in each working area for testing. Datasets from two different working areas demonstrate that the proposed method is effective for Vs prediction. The performance is quantified by comparing the predicted Vs with the true Vs using mean absolute percentage error and coefficient of determination. Compared to support vector regression, extreme gradient boosting, and Random Forest, the testing results show that the proposed method has the highest accuracy and stronger robustness in predicting Vs in blind wells.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105908"},"PeriodicalIF":2.1,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896055","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":"Estimating nuclear magnetic resonance transverse surface relaxivity in pre-salt carbonates of the Santos Basin, Brazil","authors":"Marta H. Jácomo ,&nbsp;Gelvam A. Hartmann ,&nbsp;Everton Lucas-Oliveira ,&nbsp;Daniel Rojas ,&nbsp;Emilson P. Leite","doi":"10.1016/j.jappgeo.2025.105913","DOIUrl":"10.1016/j.jappgeo.2025.105913","url":null,"abstract":"<div><div>This study explores the Nuclear Magnetic Resonance (NMR) characteristics of pre-salt carbonates rocks, focusing on transverse relaxation time (T₂) cut off values and surface relaxivity parameters. We analyzed nine carbonate samples to capture the petrophysical heterogeneity of the Barra Velha and Itapema Formations in the Santos Basin, Brazil. By relating NMR T₂ with the surface-area-to-volume (S/V) ratio, surface relaxivity was determined using Mercury Injection Capillary Pressure (MICP) and X-ray microcomputed tomography (μCT) to enhance pore size estimation. Based on NMR results, samples were grouped into three categories: Group 1 (Samples A, B2, B3, C, D2, D3, E): T₂ peaks above the cut off, dominated by macropores; Group 2 (Sample B1): Bimodal T₂ distribution, indicating a mix of macro- and micropores; Group 3 (Sample D1): T₂ peaks below the cut off, suggestive of micropore dominance and irreducible fluid storage. The study observes that while these patterns are not sufficient for distinguishing facies types, a comparative analysis of MICP pore-throat sizes and NMR T₂ distributions reveals essential insights into pore system variability. Notably, larger dissolution-related pores in Samples A (coquina) and D2 (spherulitic shrubstone) affect permeability estimation. The maximum T₂ cut off for spherulitic shrubstones was 0.65 s, highlighting the significance of pore structure in fluid storage and transport. Additionally, results underscore the vital role of surface relaxivity in converting T₂ to pore size, showing that relaxivity is more influenced by rock morphology and texture processes than solely by iron composition such as presence of coatings or presence of vuggy or cavities. Incorporating relaxivity into NMR models significantly improved permeability estimation accuracy, with the ρ-corrected model (R² = 0.84) outperforming traditional SDR and TIM models. These findings emphasize the necessity of integrating NMR, MICP, and μCT techniques for a comprehensive understanding of pore architecture and fluid flow in heterogeneous carbonate reservoirs.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"242 ","pages":"Article 105913"},"PeriodicalIF":2.1,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924941","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}