Pub Date : 2025-12-13DOI: 10.1016/j.jappgeo.2025.106067
Wei Zhu , Yulong Chen , Haitao Li , Hongyu Zhai , Xu Chang , Chi Liu , Yuhua Hou
This paper develops an algorithm capable of characterizing the wave velocity in hydraulic fracturing experiments to accurately analyze the fracturing process of rock. A coupled system of linear equations of acoustic emission (AE) source parameters and wave velocity structure is derived via first-order Taylor expansion, and a decoupling method from natural seismology is introduced to decouple the system of equations. A joint inversion algorithm of AE location and imaging is proposed. Numerical simulation and physical experiment of hydraulic fracturing are carried out to demonstrate this joint inversion algorithm. The proposed joint inversion method of AE location and imaging demonstrates that the AE density imaging effectively visualizes the fracture locations and propagation paths, while wave velocity tomography accurately identifies fluid-infiltrated zones by capturing P-wave velocity anomalies induced by fluid saturation during hydraulic fracturing. These two imaging approaches complement and validate each other to provide a comprehensive view of rock fracture evolution from initiation and propagation to nucleation.
{"title":"Joint inversion method of acoustic emission location and imaging in hydraulic fracturing experiment","authors":"Wei Zhu , Yulong Chen , Haitao Li , Hongyu Zhai , Xu Chang , Chi Liu , Yuhua Hou","doi":"10.1016/j.jappgeo.2025.106067","DOIUrl":"10.1016/j.jappgeo.2025.106067","url":null,"abstract":"<div><div>This paper develops an algorithm capable of characterizing the wave velocity in hydraulic fracturing experiments to accurately analyze the fracturing process of rock. A coupled system of linear equations of acoustic emission (AE) source parameters and wave velocity structure is derived via first-order Taylor expansion, and a decoupling method from natural seismology is introduced to decouple the system of equations. A joint inversion algorithm of AE location and imaging is proposed. Numerical simulation and physical experiment of hydraulic fracturing are carried out to demonstrate this joint inversion algorithm. The proposed joint inversion method of AE location and imaging demonstrates that the AE density imaging effectively visualizes the fracture locations and propagation paths, while wave velocity tomography accurately identifies fluid-infiltrated zones by capturing P-wave velocity anomalies induced by fluid saturation during hydraulic fracturing. These two imaging approaches complement and validate each other to provide a comprehensive view of rock fracture evolution from initiation and propagation to nucleation.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106067"},"PeriodicalIF":2.1,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790709","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}
Pub Date : 2025-12-11DOI: 10.1016/j.jappgeo.2025.106062
Tuan Nguyen-Sy , Thi Loan Bui , Bao Viet Tran
This study introduces an optimized Physics-Informed Neural Networks (PINNs) for modeling thermal diffusion and resulted thermal stress around a wellbore, with applications in CO2 injection, geothermal energy, and black oil production. A semi-surrogate PINNs approach is developed by integrating synthetic data from closed-form solutions for short-term diffusion, significantly improving model accuracy in early diffusion regimes. The methodology employs advanced training techniques with Adam and L-BFGS optimizers to balance accuracy and efficiency. The parameterized PINNs model further extends the framework to accommodate varying diffusion coefficients, time scales, and nonlinear thermal behaviors. Validation against numerical methods demonstrates superior performance, particularly in long-term diffusion scenarios. This study provides a computationally efficient framework that is readily extendable to complex multi-physics scenarios, making it valuable for real-time applications in CO2 injection, geothermal energy, and related fields.
{"title":"An optimized physics-informed neural networks for modeling thermal stress around an open wellbore","authors":"Tuan Nguyen-Sy , Thi Loan Bui , Bao Viet Tran","doi":"10.1016/j.jappgeo.2025.106062","DOIUrl":"10.1016/j.jappgeo.2025.106062","url":null,"abstract":"<div><div>This study introduces an optimized Physics-Informed Neural Networks (PINNs) for modeling thermal diffusion and resulted thermal stress around a wellbore, with applications in CO2 injection, geothermal energy, and black oil production. A semi-surrogate PINNs approach is developed by integrating synthetic data from closed-form solutions for short-term diffusion, significantly improving model accuracy in early diffusion regimes. The methodology employs advanced training techniques with Adam and L-BFGS optimizers to balance accuracy and efficiency. The parameterized PINNs model further extends the framework to accommodate varying diffusion coefficients, time scales, and nonlinear thermal behaviors. Validation against numerical methods demonstrates superior performance, particularly in long-term diffusion scenarios. This study provides a computationally efficient framework that is readily extendable to complex multi-physics scenarios, making it valuable for real-time applications in CO2 injection, geothermal energy, and related fields.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106062"},"PeriodicalIF":2.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790707","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}
Pub Date : 2025-12-11DOI: 10.1016/j.jappgeo.2025.106064
Yiliang Luo , Gulan Zhang , Shiyun Ran , Xiangwen Li , Jing Duan , Chenxi Liang , Qihong Zhong , Jiawei Zhang , Caijun Cao
The popular seismic facies-guided high-precision geological anomaly identification method (FHGI) can minimize the impacts of the complexity of seismic data, the accuracy of horizon times (or depths) of the target horizons, and the space-variant seismic wavelet, thereby resulting in high-precision geological anomaly identification results; however, it still requires the horizon time information and has limitations in computational efficiency. In this paper, to achieve high-efficiency and high-precision geological anomaly identification without the horizon time information, we propose a deep learning high-precision geological anomaly identification method (HGIM). HGIM is composed of the flowchart of HGIM, the FHGI-based high-precision geological anomaly identification label automatic generation (FLG), the deep learning high-precision geological anomaly identification network (HGIN), and the loss function of HGIM. FLG aims to use the FHGI results and data augmentation to generate sufficient training data for HGIN; HGIN takes three-dimensional (3D) seismic data as its inputs, the corresponding geological anomaly labels obtained by FLG as its labels, and uses the 3D convolution kernel for high-precision geological anomaly identification; The loss function of HGIM aims to calculate the loss function which focuses on the geological anomalies. An actual 3D seismic data example demonstrates that HGIM has great potential as a technique for high-efficiency and high-precision geological anomaly identification.
{"title":"Deep learning high-precision geological anomaly identification method and application","authors":"Yiliang Luo , Gulan Zhang , Shiyun Ran , Xiangwen Li , Jing Duan , Chenxi Liang , Qihong Zhong , Jiawei Zhang , Caijun Cao","doi":"10.1016/j.jappgeo.2025.106064","DOIUrl":"10.1016/j.jappgeo.2025.106064","url":null,"abstract":"<div><div>The popular seismic facies-guided high-precision geological anomaly identification method (FHGI) can minimize the impacts of the complexity of seismic data, the accuracy of horizon times (or depths) of the target horizons, and the space-variant seismic wavelet, thereby resulting in high-precision geological anomaly identification results; however, it still requires the horizon time information and has limitations in computational efficiency. In this paper, to achieve high-efficiency and high-precision geological anomaly identification without the horizon time information, we propose a deep learning high-precision geological anomaly identification method (HGIM). HGIM is composed of the flowchart of HGIM, the FHGI-based high-precision geological anomaly identification label automatic generation (FLG), the deep learning high-precision geological anomaly identification network (HGIN), and the loss function of HGIM. FLG aims to use the FHGI results and data augmentation to generate sufficient training data for HGIN; HGIN takes three-dimensional (3D) seismic data as its inputs, the corresponding geological anomaly labels obtained by FLG as its labels, and uses the 3D convolution kernel for high-precision geological anomaly identification; The loss function of HGIM aims to calculate the loss function which focuses on the geological anomalies. An actual 3D seismic data example demonstrates that HGIM has great potential as a technique for high-efficiency and high-precision geological anomaly identification.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106064"},"PeriodicalIF":2.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790706","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}
Pub Date : 2025-12-05DOI: 10.1016/j.jappgeo.2025.106060
Xuefeng Gao , Weiping Cao , Ranran Yang , Xuri Huang , Wensheng Duan , Zhongbo Xu
First arrival picking is an important step in seismic data processing, as its accuracy and efficiency directly impact the quality and turnaround time of near-surface velocity models and even the overall seismic processing result. This step can be very challenging for seismic data acquired in regions with complex near-surface structures, such as foothills and desert, where seismic data exhibit low signal-to-noise ratios (SNR) and first arrival picking is critical for effective subsurface exploration. To address these challenges, we propose an automated first arrival picking method that integrates supervirtual interferometry (SVI) with deep learning (DL) to achieve robust picking under low-SNR conditions. Our two-stage framework first employs SVI to enhance the first arrival signals in low-SNR seismic traces, thereby recovering the first arrival signals in low-SNR regions. Subsequently, to correct the impact of the pre-arrival artifacts introduced by SVI, an improved U-Net neural network architecture is properly trained with labels containing these pre-arrival artifacts to achieve accurate first arrival picking for SVI output. Tests on synthetic seismic traces and field low-SNR data from complex near-surface geologic condition demonstrate that this method achieves reliable results under low SNR conditions without human intervention, and verify this approach as a viable tool for automatic picking of first arrival times for low SNR seismic data.
{"title":"Automatic first arrival picking for low signal-to-noise ratio data based on supervirtual interferometry and deep learning","authors":"Xuefeng Gao , Weiping Cao , Ranran Yang , Xuri Huang , Wensheng Duan , Zhongbo Xu","doi":"10.1016/j.jappgeo.2025.106060","DOIUrl":"10.1016/j.jappgeo.2025.106060","url":null,"abstract":"<div><div>First arrival picking is an important step in seismic data processing, as its accuracy and efficiency directly impact the quality and turnaround time of near-surface velocity models and even the overall seismic processing result. This step can be very challenging for seismic data acquired in regions with complex near-surface structures, such as foothills and desert, where seismic data exhibit low signal-to-noise ratios (SNR) and first arrival picking is critical for effective subsurface exploration. To address these challenges, we propose an automated first arrival picking method that integrates supervirtual interferometry (SVI) with deep learning (DL) to achieve robust picking under low-SNR conditions. Our two-stage framework first employs SVI to enhance the first arrival signals in low-SNR seismic traces, thereby recovering the first arrival signals in low-SNR regions. Subsequently, to correct the impact of the pre-arrival artifacts introduced by SVI, an improved U-Net neural network architecture is properly trained with labels containing these pre-arrival artifacts to achieve accurate first arrival picking for SVI output. Tests on synthetic seismic traces and field low-SNR data from complex near-surface geologic condition demonstrate that this method achieves reliable results under low SNR conditions without human intervention, and verify this approach as a viable tool for automatic picking of first arrival times for low SNR seismic data.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106060"},"PeriodicalIF":2.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840191","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}
Pub Date : 2025-12-05DOI: 10.1016/j.jappgeo.2025.106055
Hao Xu , Shengquan He , Feng Shen , Dazhao Song , Xueqiu He , Zhenlei Li , Majid Khan , Fanxiang Zhao
Under static-dynamic stress coupling in close-distance multi-seam mining, gob-side roadway surrounding rock and adjacent coal pillar are subjected to intense mine pressure. This study investigates coal-rock failure under coupled longitudinal-transverse wave and stress conditions. Microseismic monitoring, numerical simulations, and field measurements were conducted to show that microseismic events mainly cluster near the excavated coal seam, as well as adjacent roof and floor strata. The surrounding rock of gob-side roadway and the adjacent coal pillar (8 m wide) exhibit a higher microseismic event density compared to other areas. Under static loading, tensile failure initiates at the mid-height of the coal pillar. The roadway exhibits pronounced asymmetric deformation, with lateral displacement reaching 42 mm on the gob side and 10 mm on the coal side. Severe fragmentation occurs on the coal pillar side contributes to this asymmetric deformation. Under dynamic loading of longitudinal-transverse waves, the gob and fracture zones exhibit significantly higher attenuation than other strata. Meanwhile, surrounding rock masses and coal pillar structures show elevated dynamic responses compared to adjacent areas. The kinetic energy reaches its maximum during the longitudinal-transverse wave coupling stage, with the horizontal component exceeding the vertical component. Wave coupling intensifies asymmetric damage, leading to over 70 % of failure volume in the coal pillar. The pillar stress state transitions from compressive to tensile, with 95.8 % of the stored elastic energy released. Borehole imaging shows 7.79 m fracture depth on the pillar side and minimal damage on the coal side. The field observations confirm the reliability of numerical simulations. The analysis indicates that a remaining coal pillar above the studied coal seam causes stress concentration at the working face, with peak stress reaching 50 MPa. The combination effect of high static stress and dynamic disturbances generated by key stratum rupture serves as the main mechanism contributing to strong mine pressure behavior. This mechanism results in asymmetric roadway deformation and coal pillar instability. The findings provide a theoretical basis for optimizing support design and mitigating dynamic hazards in gob-side roadways under similar geological conditions.
{"title":"Instability and failure characteristics of surrounding rock and coal pillar of gob-side roadways under coupled longitudinal-transverse wave and stress fields during close-distance multi-seam mining","authors":"Hao Xu , Shengquan He , Feng Shen , Dazhao Song , Xueqiu He , Zhenlei Li , Majid Khan , Fanxiang Zhao","doi":"10.1016/j.jappgeo.2025.106055","DOIUrl":"10.1016/j.jappgeo.2025.106055","url":null,"abstract":"<div><div>Under static-dynamic stress coupling in close-distance multi-seam mining, gob-side roadway surrounding rock and adjacent coal pillar are subjected to intense mine pressure. This study investigates coal-rock failure under coupled longitudinal-transverse wave and stress conditions. Microseismic monitoring, numerical simulations, and field measurements were conducted to show that microseismic events mainly cluster near the excavated coal seam, as well as adjacent roof and floor strata. The surrounding rock of gob-side roadway and the adjacent coal pillar (8 m wide) exhibit a higher microseismic event density compared to other areas. Under static loading, tensile failure initiates at the mid-height of the coal pillar. The roadway exhibits pronounced asymmetric deformation, with lateral displacement reaching 42 mm on the gob side and 10 mm on the coal side. Severe fragmentation occurs on the coal pillar side contributes to this asymmetric deformation. Under dynamic loading of longitudinal-transverse waves, the gob and fracture zones exhibit significantly higher attenuation than other strata. Meanwhile, surrounding rock masses and coal pillar structures show elevated dynamic responses compared to adjacent areas. The kinetic energy reaches its maximum during the longitudinal-transverse wave coupling stage, with the horizontal component exceeding the vertical component. Wave coupling intensifies asymmetric damage, leading to over 70 % of failure volume in the coal pillar. The pillar stress state transitions from compressive to tensile, with 95.8 % of the stored elastic energy released. Borehole imaging shows 7.79 m fracture depth on the pillar side and minimal damage on the coal side. The field observations confirm the reliability of numerical simulations. The analysis indicates that a remaining coal pillar above the studied coal seam causes stress concentration at the working face, with peak stress reaching 50 MPa. The combination effect of high static stress and dynamic disturbances generated by key stratum rupture serves as the main mechanism contributing to strong mine pressure behavior. This mechanism results in asymmetric roadway deformation and coal pillar instability. The findings provide a theoretical basis for optimizing support design and mitigating dynamic hazards in gob-side roadways under similar geological conditions.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106055"},"PeriodicalIF":2.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737523","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}
Pub Date : 2025-12-05DOI: 10.1016/j.jappgeo.2025.106061
Botao Wang , Jiacheng Zhang , Luoluo Song , Zhiheng Wang , Rentai Liu , Xiuhao Li , Shihao Luo , Mengjun Chen , Jiwen Bai
This study develops a rapid, non-contact grouting evaluation method based on the magnetic detectability of magnetized cement slurry (MCS) containing Fe3O4 particles. The mechanical, rheological, and magnetic properties of MCS were systematically analyzed to construct a quantitative framework for magnetic signal-based grout detection. Results show that Fe3O4 incorporation significantly enhances magnetization without impairing flowability or strength; at W/C = 0.7 and 10 wt% Fe3O4, MCS achieved 8969.14 A/m saturation magnetization, 1.10 relative permeability, and compressive strength improvement from 6.83 MPa to 13.86 MPa. Magnetic field mapping and modeling based on the Maxwell-Garnett and surface magnetic charge theories revealed power-law attenuation consistent with experiments, with <3 % error in permeability estimation. Simulations showed that a 1m3 MCS body produced a detectable anomaly (>0.1μT) at 7.9 m, confirming strong remote sensing capability. A practical grouting detection scheme was further developed and validated through curtain grouting simulations for coal mine water control. This work establishes the fundamental mechanisms and quantitative criteria for magnetically traceable grout design and detection, offering a new pathway toward efficient, high-resolution, and non-destructive grouting evaluation in underground engineering.
{"title":"Rapid grouting evaluation with magnetic detection: Methods and mechanisms","authors":"Botao Wang , Jiacheng Zhang , Luoluo Song , Zhiheng Wang , Rentai Liu , Xiuhao Li , Shihao Luo , Mengjun Chen , Jiwen Bai","doi":"10.1016/j.jappgeo.2025.106061","DOIUrl":"10.1016/j.jappgeo.2025.106061","url":null,"abstract":"<div><div>This study develops a rapid, non-contact grouting evaluation method based on the magnetic detectability of magnetized cement slurry (MCS) containing Fe<sub>3</sub>O<sub>4</sub> particles. The mechanical, rheological, and magnetic properties of MCS were systematically analyzed to construct a quantitative framework for magnetic signal-based grout detection. Results show that Fe<sub>3</sub>O<sub>4</sub> incorporation significantly enhances magnetization without impairing flowability or strength; at W/C = 0.7 and 10 wt% Fe<sub>3</sub>O<sub>4</sub>, MCS achieved 8969.14 A/m saturation magnetization, 1.10 relative permeability, and compressive strength improvement from 6.83 MPa to 13.86 MPa. Magnetic field mapping and modeling based on the Maxwell-Garnett and surface magnetic charge theories revealed power-law attenuation consistent with experiments, with <3 % error in permeability estimation. Simulations showed that a 1m<sup>3</sup> MCS body produced a detectable anomaly (>0.1μT) at 7.9 m, confirming strong remote sensing capability. A practical grouting detection scheme was further developed and validated through curtain grouting simulations for coal mine water control. This work establishes the fundamental mechanisms and quantitative criteria for magnetically traceable grout design and detection, offering a new pathway toward efficient, high-resolution, and non-destructive grouting evaluation in underground engineering.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106061"},"PeriodicalIF":2.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737520","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}
Pub Date : 2025-12-03DOI: 10.1016/j.jappgeo.2025.106053
Pengyu Wang, Xiaofeng Yi, Shumin Wang
Water inrush of goaf floor is one of the most important factors threatening the safety production of coal mines, which often causes great economic losses and casualties. After the goaf floor is filled with water, the apparent resistivity value decreases significantly. Therefore, the electrical resistivity tomography (ERT), which is sensitive to low-resistivity anomalous bodies such as water, has a unique advantage in the detection of water in goaf floor. At present, the main method for advanced detection of goaf floor is ERT three-point-source method, but this method can only realize one-dimensional positioning of the water-bearing body in goaf floor, which is easy to misjudge the location of the water-bearing body in practical application. To solve this problem, the random forest algorithm is used to process the advanced detection data, and then the apparent resistivity contour map of the goaf floor is predicted, which simplifies the measurement process and realizes two-dimensional positioning of the water-bearing body in goaf floor. Its effectiveness has been proved by the verification experiments, and the prediction accuracy reaches 98.86 %. This method is used to detect the goaf floor in Ji 17–33,200 coal mining face, and the location of the suspected water-bearing body has been determined.
{"title":"Research on ERT advanced detection imaging of goaf floor in coal mining face based on random forest algorithm","authors":"Pengyu Wang, Xiaofeng Yi, Shumin Wang","doi":"10.1016/j.jappgeo.2025.106053","DOIUrl":"10.1016/j.jappgeo.2025.106053","url":null,"abstract":"<div><div>Water inrush of goaf floor is one of the most important factors threatening the safety production of coal mines, which often causes great economic losses and casualties. After the goaf floor is filled with water, the apparent resistivity value decreases significantly. Therefore, the electrical resistivity tomography (ERT), which is sensitive to low-resistivity anomalous bodies such as water, has a unique advantage in the detection of water in goaf floor. At present, the main method for advanced detection of goaf floor is ERT three-point-source method, but this method can only realize one-dimensional positioning of the water-bearing body in goaf floor, which is easy to misjudge the location of the water-bearing body in practical application. To solve this problem, the random forest algorithm is used to process the advanced detection data, and then the apparent resistivity contour map of the goaf floor is predicted, which simplifies the measurement process and realizes two-dimensional positioning of the water-bearing body in goaf floor. Its effectiveness has been proved by the verification experiments, and the prediction accuracy reaches 98.86 %. This method is used to detect the goaf floor in Ji 17–33,200 coal mining face, and the location of the suspected water-bearing body has been determined.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106053"},"PeriodicalIF":2.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737522","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}
Pub Date : 2025-12-03DOI: 10.1016/j.jappgeo.2025.106058
Jonatã Barbosa Teixeira , Gabriella Fazio , Silvia Lorena Bejarano Bermudez , Ângela Leão Andrade , Vitor Emmanuel Paes Silveira , Agide Gimenez Marassi , Mariane Candido , Arthur Gustavo de Araújo Ferreira , José Domingos Fabris , Luiz Carlos Bertolino , Marco Antônio Rodrigues de Ceia , Daniel Ribeiro Franco , Tito José Bonagamba , Ricardo Ivan Ferreira Trindade
Magnetic minerals, such as magnetite, can significantly influence 1H Nuclear Magnetic Resonance (NMR) measurements, introducing biases that can affect petrophysical interpretations in reservoir rocks. Understanding these effects is crucial for improving the accuracy of fluid content estimations in subsurface evaluations. In this study, we investigate how nanometric-sized magnetite impacts T₂ relaxation times in synthesized carbonate samples with controlled porosity and magnetite concentrations. Twelve carbonate samples were synthesized with varying magnetite content (0.0 %–0.8 % wt.), ensuring a controlled environment for evaluating NMR responses. These samples underwent petrophysical (bulk volume, pore volume, grain density, and NMR), mineralogical (XRD and SEM-EDS), and magnetic (low-field magnetic susceptibility, hysteresis loop, FORC, and IRM measurements) characterization to ensure the integrity of both the synthesis and the magnetite contamination. Our findings indicate that (1) the synthesis successfully produced samples with consistent properties, showing a decrease in pore volume with increasing cementing fluid and a corresponding enhancement of magnetic properties with higher magnetite contamination; (2) 1H NMR-based porosity estimates were significantly affected by magnetite contamination, displaying a noticeable flattening of T₂ relaxation curves and a reduction in relaxation times, likely due to enhanced diffusional effects; and (3) increasing magnetite concentrations induced nonlinear distortions in porosity ϕNMR, leading to systematic deviations from expected values and, consequently causing porosity underestimation. These results underscore the need to account for magnetic mineral contamination in NMR analyses of carbonate reservoirs and highlight the importance of controlled research into magnetite's impact on petrophysical assessments.
{"title":"Nanometer-sized magnetite and its impact on 1H NMR petrophysical characterization of synthetic carbonates","authors":"Jonatã Barbosa Teixeira , Gabriella Fazio , Silvia Lorena Bejarano Bermudez , Ângela Leão Andrade , Vitor Emmanuel Paes Silveira , Agide Gimenez Marassi , Mariane Candido , Arthur Gustavo de Araújo Ferreira , José Domingos Fabris , Luiz Carlos Bertolino , Marco Antônio Rodrigues de Ceia , Daniel Ribeiro Franco , Tito José Bonagamba , Ricardo Ivan Ferreira Trindade","doi":"10.1016/j.jappgeo.2025.106058","DOIUrl":"10.1016/j.jappgeo.2025.106058","url":null,"abstract":"<div><div>Magnetic minerals, such as magnetite, can significantly influence <sup>1</sup>H Nuclear Magnetic Resonance (NMR) measurements, introducing biases that can affect petrophysical interpretations in reservoir rocks. Understanding these effects is crucial for improving the accuracy of fluid content estimations in subsurface evaluations. In this study, we investigate how nanometric-sized magnetite impacts T₂ relaxation times in synthesized carbonate samples with controlled porosity and magnetite concentrations. Twelve carbonate samples were synthesized with varying magnetite content (0.0 %–0.8 % wt.), ensuring a controlled environment for evaluating NMR responses. These samples underwent petrophysical (bulk volume, pore volume, grain density, and NMR), mineralogical (XRD and SEM-EDS), and magnetic (low-field magnetic susceptibility, hysteresis loop, FORC, and IRM measurements) characterization to ensure the integrity of both the synthesis and the magnetite contamination. Our findings indicate that (1) the synthesis successfully produced samples with consistent properties, showing a decrease in pore volume with increasing cementing fluid and a corresponding enhancement of magnetic properties with higher magnetite contamination; (2) <sup>1</sup>H NMR-based porosity estimates were significantly affected by magnetite contamination, displaying a noticeable flattening of T₂ relaxation curves and a reduction in relaxation times, likely due to enhanced diffusional effects; and (3) increasing magnetite concentrations induced nonlinear distortions in porosity ϕ<sub>NMR</sub>, leading to systematic deviations from expected values and, consequently causing porosity underestimation. These results underscore the need to account for magnetic mineral contamination in NMR analyses of carbonate reservoirs and highlight the importance of controlled research into magnetite's impact on petrophysical assessments.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106058"},"PeriodicalIF":2.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694474","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}
Pub Date : 2025-12-03DOI: 10.1016/j.jappgeo.2025.106057
Senguo Cao , Congde Lu , Xiao Wang , Peng Zhang , Guanglai Jin , Wenlong Cai
Interlayer distress detection in asphalt pavement is critical for highway maintenance, as timely identification of pavement distress can ensure operational safety, reliability, and extended service life. However, the problems of feature information loss and the substantial confusable backgrounds significantly hinder detection accuracy. To address these limitations, we propose an enhanced network specifically designed for automated interlayer distress detection named ME-YOLO. Firstly, we design a Multiscale Adaptive Feature Fusion (MAFF) module, which aggregates more scale information by Adaptive Spatial Feature Fusion (ASFF). This design links all feature scales to make discriminative features in each scale propagate directly to subsequent modules, enriching semantic representations and mitigating the risk of feature loss, while leveraging shallow-layer features to strengthen spatial localization. Furthermore, the Efficient Partial Self-Attention (EPSA) module is introduced to suppress background interference in complex environments. Unlike conventional transformers, EPSA adopts partial self-attention operations with multi-path fusion, which can enable the network to acquire global representation capability with low computational overhead. Extensive experiments indicate that the ME-YOLO network outperforms the given state-of-the-art models, including Faster-RCNN, RT-DETR, YOLOv8s, and YOLOv11s, on the interlayer distress dataset. Compared to YOLOv5s, ME-YOLO achieves improvements of 2.2% in mAP0.5 and 3.5% in mAP0.5:0.95, while maintaining an inference speed of 6.7 ms per image. The source code will be available at https://github.com/caosenguo/ME-YOLO.
沥青路面夹层损伤检测对公路养护至关重要，及时识别路面损伤可以保证路面运行的安全性、可靠性和延长使用寿命。然而，特征信息的丢失和大量的背景混淆问题严重影响了检测的准确性。为了解决这些限制，我们提出了一个专门为层间自动遇险检测设计的增强网络，名为ME-YOLO。首先，设计了多尺度自适应特征融合（MAFF）模块，通过自适应空间特征融合（ASFF）聚合更多尺度信息；本设计将所有特征尺度联系起来，使每个尺度中的判别特征直接传播到后续模块，丰富语义表示，降低特征丢失的风险，同时利用浅层特征加强空间定位。此外，本文还引入了EPSA （Efficient Partial Self-Attention）模块来抑制复杂环境下的背景干扰。与传统的变压器不同，EPSA采用部分自关注的多路径融合运算，使网络能够以较低的计算开销获得全局表示能力。大量实验表明，在层间压力数据集上，ME-YOLO网络优于现有的最先进模型，包括Faster-RCNN、RT-DETR、YOLOv8s和YOLOv11s。与YOLOv5s相比，ME-YOLO在mAP0.5和mAP0.5:0.95中分别提高了2.2%和3.5%，同时保持了6.7 ms /张图像的推理速度。源代码可从https://github.com/caosenguo/ME-YOLO获得。
{"title":"ME-YOLO: A novel real-time detection network for pavement interlayer distress using ground-penetrating radar","authors":"Senguo Cao , Congde Lu , Xiao Wang , Peng Zhang , Guanglai Jin , Wenlong Cai","doi":"10.1016/j.jappgeo.2025.106057","DOIUrl":"10.1016/j.jappgeo.2025.106057","url":null,"abstract":"<div><div>Interlayer distress detection in asphalt pavement is critical for highway maintenance, as timely identification of pavement distress can ensure operational safety, reliability, and extended service life. However, the problems of feature information loss and the substantial confusable backgrounds significantly hinder detection accuracy. To address these limitations, we propose an enhanced network specifically designed for automated interlayer distress detection named ME-YOLO. Firstly, we design a Multiscale Adaptive Feature Fusion (MAFF) module, which aggregates more scale information by Adaptive Spatial Feature Fusion (ASFF). This design links all feature scales to make discriminative features in each scale propagate directly to subsequent modules, enriching semantic representations and mitigating the risk of feature loss, while leveraging shallow-layer features to strengthen spatial localization. Furthermore, the Efficient Partial Self-Attention (EPSA) module is introduced to suppress background interference in complex environments. Unlike conventional transformers, EPSA adopts partial self-attention operations with multi-path fusion, which can enable the network to acquire global representation capability with low computational overhead. Extensive experiments indicate that the ME-YOLO network outperforms the given state-of-the-art models, including Faster-RCNN, RT-DETR, YOLOv8s, and YOLOv11s, on the interlayer distress dataset. Compared to YOLOv5s, ME-YOLO achieves improvements of 2.2% in mAP<sub>0.5</sub> and 3.5% in mAP<sub>0.5:0.95</sub>, while maintaining an inference speed of 6.7 ms per image. The source code will be available at <span><span>https://github.com/caosenguo/ME-YOLO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106057"},"PeriodicalIF":2.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694472","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}
Pub Date : 2025-12-03DOI: 10.1016/j.jappgeo.2025.106056
Feng Gao , Zhangqing Sun , Xiunan Fan , Zihao Li , Fuxing Han , Jipu Lu , Wenpan Cen , Mingchen Liu , Zhenghui Gao , Jiawei Xie
Karst formations near the surface in complex geological settings scatter seismic waves, adversely affecting the signal-to-noise ratio (SNR) of seismic data. Accurately characterizing the formation mechanisms of low SNR seismic data is vital for enhancing the efficacy of seismic exploration. This study introduces a composite multi-scale random medium modeling technique that addresses the characteristics of random heterogeneous media in karst regions. The methodology superimposes various random perturbations of different scales in the same area. The elastic wave spectral element method (SEM) is employed to numerically simulate the seismic wave field in complex karst environments. A case study in Guangxi, China, demonstrates that the composite multi-scale random medium modeling approach effectively captures the characteristics of the medium. The simulated data generated using the elastic wave SEM closely resembling actual data. This paper offers insights into the formation mechanisms of low SNR seismic data in complex karst areas. These insights provide valuable references for advancing seismic data processing techniques.
{"title":"Formation mechanism of low signal-to-noise ratio seismic data in complex Karst areas","authors":"Feng Gao , Zhangqing Sun , Xiunan Fan , Zihao Li , Fuxing Han , Jipu Lu , Wenpan Cen , Mingchen Liu , Zhenghui Gao , Jiawei Xie","doi":"10.1016/j.jappgeo.2025.106056","DOIUrl":"10.1016/j.jappgeo.2025.106056","url":null,"abstract":"<div><div>Karst formations near the surface in complex geological settings scatter seismic waves, adversely affecting the signal-to-noise ratio (SNR) of seismic data. Accurately characterizing the formation mechanisms of low SNR seismic data is vital for enhancing the efficacy of seismic exploration. This study introduces a composite multi-scale random medium modeling technique that addresses the characteristics of random heterogeneous media in karst regions. The methodology superimposes various random perturbations of different scales in the same area. The elastic wave spectral element method (SEM) is employed to numerically simulate the seismic wave field in complex karst environments. A case study in Guangxi, China, demonstrates that the composite multi-scale random medium modeling approach effectively captures the characteristics of the medium. The simulated data generated using the elastic wave SEM closely resembling actual data. This paper offers insights into the formation mechanisms of low SNR seismic data in complex karst areas. These insights provide valuable references for advancing seismic data processing techniques.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106056"},"PeriodicalIF":2.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737524","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}
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