Pub Date : 2026-02-01Epub Date: 2025-11-20DOI: 10.1016/j.jappgeo.2025.106034
Yao Wang , Hai Liu , Junhong Chen , Ruoyu Chen , Pei Wang , Qifang Liu , Yanliang Du
Voids behind tunnel linings can lead to leakage and stress concentration, posing significant risks to tunnel integrity. Non-destructive testing methods, particularly ground-penetrating radar (GPR), are commonly employed in screening of such anomalies. However, our experimental results indicate that GPR is less effective in identifying air-filled voids, primarily due to their long wavelength and low radar cross section. In addition, scattering and attenuation of electromagnetic signals caused by reinforcing bars (rebars) also make it difficult to accurately detect air-filled voids. To enhance accurate imaging of void defects behind concrete linings, this paper introduced shear horizontal (SH) wave ultrasonic testing as a complementary approach to GPR. SH-ultrasonic testing, utilizing multi-offset array acquisition, partially mitigates the scattering and attenuation effects of rebars. Moreover, since elastic shear waves cannot propagate through water or air, voids filled with both materials exhibit significant impedance contrasts with the surrounding medium, resulting in strong reflection signals in ultrasonic data. Additionally, ultrasonic methods can delineate grouting layer thickness with high resolution, providing complementary data to GPR imaging. These advantages are demonstrated by model experiments conducted on two test platforms constructed with local metro shield tunnel lining segments. The results substantiate the potential of the ultrasonic-assisted GPR imaging method in effectively detecting voids behind concrete linings/walls.
{"title":"Void detection behind tunnel concrete lining by an SH-Ultrasonic-assisted GPR method","authors":"Yao Wang , Hai Liu , Junhong Chen , Ruoyu Chen , Pei Wang , Qifang Liu , Yanliang Du","doi":"10.1016/j.jappgeo.2025.106034","DOIUrl":"10.1016/j.jappgeo.2025.106034","url":null,"abstract":"<div><div>Voids behind tunnel linings can lead to leakage and stress concentration, posing significant risks to tunnel integrity. Non-destructive testing methods, particularly ground-penetrating radar (GPR), are commonly employed in screening of such anomalies. However, our experimental results indicate that GPR is less effective in identifying air-filled voids, primarily due to their long wavelength and low radar cross section. In addition, scattering and attenuation of electromagnetic signals caused by reinforcing bars (rebars) also make it difficult to accurately detect air-filled voids. To enhance accurate imaging of void defects behind concrete linings, this paper introduced shear horizontal (SH) wave ultrasonic testing as a complementary approach to GPR. SH-ultrasonic testing, utilizing multi-offset array acquisition, partially mitigates the scattering and attenuation effects of rebars. Moreover, since elastic shear waves cannot propagate through water or air, voids filled with both materials exhibit significant impedance contrasts with the surrounding medium, resulting in strong reflection signals in ultrasonic data. Additionally, ultrasonic methods can delineate grouting layer thickness with high resolution, providing complementary data to GPR imaging. These advantages are demonstrated by model experiments conducted on two test platforms constructed with local metro shield tunnel lining segments. The results substantiate the potential of the ultrasonic-assisted GPR imaging method in effectively detecting voids behind concrete linings/walls.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106034"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625391","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 : 2026-02-01Epub Date: 2025-11-27DOI: 10.1016/j.jappgeo.2025.106038
Ramy Eid , Mohamed El-Anbaawy , Adel El-Tehiwy
This study introduces an unsupervised neural network classification approach utilizing multiple seismic attributes to enhance reservoir characterization in the slope channel systems of the Simian Field, offshore Nile Delta. Given the complex nature of these reservoirs marked by significant heterogeneity and anisotropy affecting porosity and permeability, advanced analytical techniques are essential. Principal Component Analysis (PCA) was employed to reduce dimensionality and identify the most influential seismic attributes, including acoustic impedance, Root Mean Square (RMS) amplitude, and variance. The classification revealed two distinct seismic facies patterns, providing insights into subsurface heterogeneity. Furthermore, probability occurrence and zonation maps derived from the classification results enabled the identification of promising drilling targets in the eastern sector of the field. This integrated methodology offers a novel and efficient framework for reservoir forecasting in the geologically complex settings.
{"title":"Unsupervised learning for forecasting deep water slope reservoirs in the Offshore Nile Delta: A novel classification model","authors":"Ramy Eid , Mohamed El-Anbaawy , Adel El-Tehiwy","doi":"10.1016/j.jappgeo.2025.106038","DOIUrl":"10.1016/j.jappgeo.2025.106038","url":null,"abstract":"<div><div>This study introduces an unsupervised neural network classification approach utilizing multiple seismic attributes to enhance reservoir characterization in the slope channel systems of the Simian Field, offshore Nile Delta. Given the complex nature of these reservoirs marked by significant heterogeneity and anisotropy affecting porosity and permeability, advanced analytical techniques are essential. Principal Component Analysis (PCA) was employed to reduce dimensionality and identify the most influential seismic attributes, including acoustic impedance, Root Mean Square (RMS) amplitude, and variance. The classification revealed two distinct seismic facies patterns, providing insights into subsurface heterogeneity. Furthermore, probability occurrence and zonation maps derived from the classification results enabled the identification of promising drilling targets in the eastern sector of the field. This integrated methodology offers a novel and efficient framework for reservoir forecasting in the geologically complex settings.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106038"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625392","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 : 2026-02-01Epub Date: 2025-11-21DOI: 10.1016/j.jappgeo.2025.106030
Lifeng Fan, Mingzhu Ye, Qihao Yang
This paper proposes an approach for determining the mechanical properties of joints exhibiting nonlinear deformation, based on the propagation characteristics of stress waves in rock masses. Firstly, a series of theoretical analyses on wave propagation through nonlinear joints was conducted. The relationship between stress waves and the mechanical parameters of nonlinear joints was established using a characteristic method in conjunction with the BB model. Secondly, an approach was proposed that involves analyzing the initial joint stiffness and maximum allowable closure through the reflected wave. The approach was subsequently refined using the Newton iteration method to enable efficient iterative computation. Finally, the proposed approach was validated by comparing the predicted initial joint stiffness and maximum allowable closure with their theoretical values. The results indicate that the proposed approach can predict the mechanical parameters, such as initial joint stiffness and maximum allowable closure, of a nonlinear joint based on the reflected waves. Moreover, the relative errors for the predictions of initial joint stiffness and maximum allowable closure in the present study are less than 8.6 % and 3.2 %, respectively. The proposed approach has the potential to predict the mechanical properties of nonlinear joints with an acceptable margin of error.
{"title":"An approach for determining the mechanical properties of joints with nonlinear deformation","authors":"Lifeng Fan, Mingzhu Ye, Qihao Yang","doi":"10.1016/j.jappgeo.2025.106030","DOIUrl":"10.1016/j.jappgeo.2025.106030","url":null,"abstract":"<div><div>This paper proposes an approach for determining the mechanical properties of joints exhibiting nonlinear deformation, based on the propagation characteristics of stress waves in rock masses. Firstly, a series of theoretical analyses on wave propagation through nonlinear joints was conducted. The relationship between stress waves and the mechanical parameters of nonlinear joints was established using a characteristic method in conjunction with the B<img>B model. Secondly, an approach was proposed that involves analyzing the initial joint stiffness and maximum allowable closure through the reflected wave. The approach was subsequently refined using the Newton iteration method to enable efficient iterative computation. Finally, the proposed approach was validated by comparing the predicted initial joint stiffness and maximum allowable closure with their theoretical values. The results indicate that the proposed approach can predict the mechanical parameters, such as initial joint stiffness and maximum allowable closure, of a nonlinear joint based on the reflected waves. Moreover, the relative errors for the predictions of initial joint stiffness and maximum allowable closure in the present study are less than 8.6 % and 3.2 %, respectively. The proposed approach has the potential to predict the mechanical properties of nonlinear joints with an acceptable margin of error.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106030"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145594781","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 : 2026-02-01Epub Date: 2025-12-25DOI: 10.1016/j.jappgeo.2025.106079
J. Ortega-Ramirez , M. Bano , J.L. Salas-Corrales , R. Junco Sánchez , L.A. Villa-Alvarado
Fort San Diego in Acapulco, Mexico, is an iconic monument, deeply linked to the history of the continent and a vital source of cultural identity for its community and future generations. Given its immense value as a cultural asset, it is essential to understand its architectural evolution, especially as historical records indicate significant alterations due to seismic activity and changes of use over time.
The article presents a geophysical study with the objective of locating and documenting the hidden architectural remains of the fort constructed in the 17th century. Given the paucity of documentation on the fort's modifications, we used non-destructive methods such as georadar (GPR) and electrical resistivity tomography (ERT). Both techniques identified a large anomaly measuring 3 by 6 m beneath the surface of the fort. This anomaly, characterized by multiple GPR diffractions and high electrical resistivity values, was then validated by a small archaeological excavation. The excavation confirmed that the anomaly corresponded to an ancient architectural foundation, visible from a depth of 30 cm down to at least 2.0 m. We hypothesize that this structure represents the remains of a drawbridge that served as the main entrance to the fort before the devastating earthquake of 1776, supporting the theory that the main gate was located on the opposite side to the current one. The study highlights the effectiveness and versatility of geophysical methods as essential tools for the investigation and conservation of cultural heritage, revealing crucial details about the hidden history of the fort.
{"title":"Geophysical survey methods (GPR and ERT) to find architectural remains from the 17th century at the Fort of San Diego in Acapulco, Mexico. A case study.","authors":"J. Ortega-Ramirez , M. Bano , J.L. Salas-Corrales , R. Junco Sánchez , L.A. Villa-Alvarado","doi":"10.1016/j.jappgeo.2025.106079","DOIUrl":"10.1016/j.jappgeo.2025.106079","url":null,"abstract":"<div><div>Fort San Diego in Acapulco, Mexico, is an iconic monument, deeply linked to the history of the continent and a vital source of cultural identity for its community and future generations. Given its immense value as a cultural asset, it is essential to understand its architectural evolution, especially as historical records indicate significant alterations due to seismic activity and changes of use over time.</div><div>The article presents a geophysical study with the objective of locating and documenting the hidden architectural remains of the fort constructed in the 17th century. Given the paucity of documentation on the fort's modifications, we used non-destructive methods such as georadar (GPR) and electrical resistivity tomography (ERT). Both techniques identified a large anomaly measuring 3 by 6 m beneath the surface of the fort. This anomaly, characterized by multiple GPR diffractions and high electrical resistivity values, was then validated by a small archaeological excavation. The excavation confirmed that the anomaly corresponded to an ancient architectural foundation, visible from a depth of 30 cm down to at least 2.0 m. We hypothesize that this structure represents the remains of a drawbridge that served as the main entrance to the fort before the devastating earthquake of 1776, supporting the theory that the main gate was located on the opposite side to the current one. The study highlights the effectiveness and versatility of geophysical methods as essential tools for the investigation and conservation of cultural heritage, revealing crucial details about the hidden history of the fort.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106079"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884270","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 : 2026-02-01Epub Date: 2025-11-17DOI: 10.1016/j.jappgeo.2025.106025
A. Brindisi , S. D'Amico , L. Beranzoli , D. Embriaco , A. Giuntini , D. Albarello
Ocean bottom measurements of ambient vibrations at a gas emitting area in the Marmara region are analyzed. The overall stability of average Horizontal to Vertical Spectral Ratios (HVSR) of ambient vibrations values above 0.2 Hz obtained in different sea conditions suggests that the relevant pattern is weakly affected by oceanic disturbances and can be considered informative about the subsoil structure. A procedure based on the removal of the water column effect from sea floor HVSR data is illustrated which allows the application to off-shore data of inversion tools developed for inland measurements. On this basis, sea floor HVSR measurements are used to tentatively constrain the local seismostratigraphical configuration in terms of Vs and Vp profiles. On this basis three main seismic impedance contrasts have been identified (respectively around 10, 100 and 500 m below the sea floor) in good correspondence with geological unconformities revealed by seismic reflection data. Moreover, the interpretation of the body wave profiles and, in particular, of the Vs/Vp, ratios suggest the presence of unconsolidated material down to a depth of about 500 m below the sea level with an estimated porosity of the order of 30 %. Based on the Biot-Gassmann model, the body wave profile has been used for a preliminary estimate of the degree of gas saturation which reaches 70 % in the depth range 150–500 m of depths below the sea floor. Beyond these figures, results obtained suggest that a methodology base on the interpretation of HVSR data at sea bottom may represent a new important tool for the characterization of the sea bottom subsoil structure in correspondence of gas reservoirs.
{"title":"Interpretation of corrected sea floor HVSR data on a gas emitting structure in the Sea of Marmara","authors":"A. Brindisi , S. D'Amico , L. Beranzoli , D. Embriaco , A. Giuntini , D. Albarello","doi":"10.1016/j.jappgeo.2025.106025","DOIUrl":"10.1016/j.jappgeo.2025.106025","url":null,"abstract":"<div><div>Ocean bottom measurements of ambient vibrations at a gas emitting area in the Marmara region are analyzed. The overall stability of average Horizontal to Vertical Spectral Ratios (HVSR) of ambient vibrations values above 0.2 Hz obtained in different sea conditions suggests that the relevant pattern is weakly affected by oceanic disturbances and can be considered informative about the subsoil structure. A procedure based on the removal of the water column effect from sea floor HVSR data is illustrated which allows the application to off-shore data of inversion tools developed for inland measurements. On this basis, sea floor HVSR measurements are used to tentatively constrain the local seismostratigraphical configuration in terms of V<sub>s</sub> and V<sub><em>p</em></sub> profiles. On this basis three main seismic impedance contrasts have been identified (respectively around 10, 100 and 500 m below the sea floor) in good correspondence with geological unconformities revealed by seismic reflection data. Moreover, the interpretation of the body wave profiles and, in particular, of the V<sub>s</sub>/V<sub><em>p</em></sub>, ratios suggest the presence of unconsolidated material down to a depth of about 500 m below the sea level with an estimated porosity of the order of 30 %. Based on the Biot-Gassmann model, the body wave profile has been used for a preliminary estimate of the degree of gas saturation which reaches 70 % in the depth range 150–500 m of depths below the sea floor. Beyond these figures, results obtained suggest that a methodology base on the interpretation of HVSR data at sea bottom may represent a new important tool for the characterization of the sea bottom subsoil structure in correspondence of gas reservoirs.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106025"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694473","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 : 2026-02-01Epub Date: 2025-12-15DOI: 10.1016/j.jappgeo.2025.106069
Sepideh Vafaei Shoushtari, Bernard Giroux, Erwan Gloaguen, Maher Nasr
The underground extraction of mineral resources is often closely linked to induced microseismic events. The use of a seismic network to continuously monitor mining-induced seismicity to reduce risks and improve operational safety is common. For this monitoring to be effective, a comprehensive catalog of microseismic events, containing low-to high-magnitude events, is essential to evaluate the response of the rock mass to mining activities. However, detecting low-magnitude events based on manual picking or automated conventional approaches has been challenging in mining environments owing to the inherent noise level. Recent advancements in deep learning and data-driven methods, particularly Convolutional Neural Networks (CNNs) trained on extensive seismic datasets, have shown improved capabilities in automated event detection and arrival phase picking on seismic data recorded by regional seismic networks. In this study, we assessed the performance of PhaseNet, a deep learning arrival-time picking method, in detecting the P- and S-wave arrivals of mining-induced microseismic events at different noise levels. As access to high-quality, labeled microseismic datasets for such mining applications is rare, a realistic three-component synthetic dataset was generated using full-waveform modeling. This simulation accounted for the geological conditions and network geometry specific to a mine in Ontario, Canada. The mine, which integrates copper and nickel operations, experiences considerable mining-induced earthquakes annually, posing risks to miners and infrastructure. The simulation includes a variety of source mechanisms with different magnitudes and offers more than 270,000 labeled seismograms. The results from the PhaseNet-trained model, which utilized the simulated dataset, demonstrated its effectiveness in managing noisy waveforms. This capability allows the detection of low-magnitude events within the mine environment, which may be overlooked by traditional methods. Furthermore, the model shows high accuracy in picking both the P- and S-wave arrival times, achieving precision rates exceeding 0.9. Tests on real data were performed in three different scenarios. The first scenario involves training the model exclusively using real data. The second scenario combines synthetic and real data to retrain the model previously trained with synthetic data only. Finally, the third scenario focuses on retraining the pre-trained model using only synthetic data. All these trained models were used to evaluate the performance on the real test dataset. The results indicate that the model retrained with synthetic and real seismograms yielded the best arrival time predictions for the mine dataset.
{"title":"Detection of mining-induced microseismicity through a deep convolutional neural network","authors":"Sepideh Vafaei Shoushtari, Bernard Giroux, Erwan Gloaguen, Maher Nasr","doi":"10.1016/j.jappgeo.2025.106069","DOIUrl":"10.1016/j.jappgeo.2025.106069","url":null,"abstract":"<div><div>The underground extraction of mineral resources is often closely linked to induced microseismic events. The use of a seismic network to continuously monitor mining-induced seismicity to reduce risks and improve operational safety is common. For this monitoring to be effective, a comprehensive catalog of microseismic events, containing low-to high-magnitude events, is essential to evaluate the response of the rock mass to mining activities. However, detecting low-magnitude events based on manual picking or automated conventional approaches has been challenging in mining environments owing to the inherent noise level. Recent advancements in deep learning and data-driven methods, particularly Convolutional Neural Networks (CNNs) trained on extensive seismic datasets, have shown improved capabilities in automated event detection and arrival phase picking on seismic data recorded by regional seismic networks. In this study, we assessed the performance of PhaseNet, a deep learning arrival-time picking method, in detecting the P- and S-wave arrivals of mining-induced microseismic events at different noise levels. As access to high-quality, labeled microseismic datasets for such mining applications is rare, a realistic three-component synthetic dataset was generated using full-waveform modeling. This simulation accounted for the geological conditions and network geometry specific to a mine in Ontario, Canada. The mine, which integrates copper and nickel operations, experiences considerable mining-induced earthquakes annually, posing risks to miners and infrastructure. The simulation includes a variety of source mechanisms with different magnitudes and offers more than 270,000 labeled seismograms. The results from the PhaseNet-trained model, which utilized the simulated dataset, demonstrated its effectiveness in managing noisy waveforms. This capability allows the detection of low-magnitude events within the mine environment, which may be overlooked by traditional methods. Furthermore, the model shows high accuracy in picking both the P- and S-wave arrival times, achieving precision rates exceeding 0.9. Tests on real data were performed in three different scenarios. The first scenario involves training the model exclusively using real data. The second scenario combines synthetic and real data to retrain the model previously trained with synthetic data only. Finally, the third scenario focuses on retraining the pre-trained model using only synthetic data. All these trained models were used to evaluate the performance on the real test dataset. The results indicate that the model retrained with synthetic and real seismograms yielded the best arrival time predictions for the mine dataset.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106069"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790708","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 : 2026-02-01Epub Date: 2025-12-16DOI: 10.1016/j.jappgeo.2025.106065
Mohammed Farfour
Seismic inversion plays a pivotal role in reservoir characterization, enabling interpreters to transform seismic data into physical, elastic, and petrophysical properties directly related to reservoir lithology and fluid content. From seismic inversion products (e.g., P-wave and S-wave impedances and density), a wide range of reservoir attributes can be derived. These include Vp/Vs ratios, Poisson's ratio, bulk modulus, porosity, water saturation, effective stress, and pore pressure, among others. Successful seismic inversion relies on high-quality seismic data and a sufficient number of wells with the necessary logging data. However, interpreters often face challenges due to the lack of critical well logs, such as P-wave and S-wave velocity logs. To address this, several approaches, including machine learning, have been developed. In this study, seismic data and well logs from offshore Australia were prepared for seismic inversion to extract various attributes related to reservoir lithology and fluid content. Three reservoirs were identified in the study area using petrophysical logs such as gamma ray, neutron, density porosity, and resistivity. However, P-wave and S-wave logs were available for only two of the reservoirs. To overcome this limitation, machine learning—specifically an artificial neural network (ANN)—was utilized to predict the missing logs for the third reservoir. All available logs were used for training and testing the ANN. The trained ANN model was subsequently validated on wells excluded from the training process and demonstrated high accuracy in predicting the P-wave and S-wave logs. Following this validation, the ANN was applied to generate the missing logs for the target reservoir. Using the complete set of logs, a new seismic inversion was conducted to produce P-wave and S-wave impedance volumes. These impedance volumes were further used to derive additional elastic properties and facilitate comprehensive geophysical reservoir characterization.
{"title":"Machine Learning aids seismic inversion in reservoir characterization: A case study","authors":"Mohammed Farfour","doi":"10.1016/j.jappgeo.2025.106065","DOIUrl":"10.1016/j.jappgeo.2025.106065","url":null,"abstract":"<div><div>Seismic inversion plays a pivotal role in reservoir characterization, enabling interpreters to transform seismic data into physical, elastic, and petrophysical properties directly related to reservoir lithology and fluid content. From seismic inversion products (e.g., P-wave and S-wave impedances and density), a wide range of reservoir attributes can be derived. These include Vp/Vs ratios, Poisson's ratio, bulk modulus, porosity, water saturation, effective stress, and pore pressure, among others. Successful seismic inversion relies on high-quality seismic data and a sufficient number of wells with the necessary logging data. However, interpreters often face challenges due to the lack of critical well logs, such as P-wave and S-wave velocity logs. To address this, several approaches, including machine learning, have been developed. In this study, seismic data and well logs from offshore Australia were prepared for seismic inversion to extract various attributes related to reservoir lithology and fluid content. Three reservoirs were identified in the study area using petrophysical logs such as gamma ray, neutron, density porosity, and resistivity. However, P-wave and S-wave logs were available for only two of the reservoirs. To overcome this limitation, machine learning—specifically an artificial neural network (ANN)—was utilized to predict the missing logs for the third reservoir. All available logs were used for training and testing the ANN. The trained ANN model was subsequently validated on wells excluded from the training process and demonstrated high accuracy in predicting the P-wave and S-wave logs. Following this validation, the ANN was applied to generate the missing logs for the target reservoir. Using the complete set of logs, a new seismic inversion was conducted to produce P-wave and S-wave impedance volumes. These impedance volumes were further used to derive additional elastic properties and facilitate comprehensive geophysical reservoir characterization.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106065"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790735","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 : 2026-02-01Epub Date: 2025-12-17DOI: 10.1016/j.jappgeo.2025.106063
Seunghoon Han , Seokjoon Moon , Hyunggu Jun , Youngseo Kim , Yi Shen , Yongchae Cho
Deconvolution is a crucial data processing step for enhancing the resolution of seismic exploration data, thereby enabling subsurface structures to be accurately interpreted. However, traditional deconvolution methods using an inverse filter of source wavelets provide unique results that do not account for the natural attenuation of wavelets with depth, leading to inherent accuracy limitations and difficulty in evaluating the uncertainty. This paper introduces a novel deconvolution method called Stochastic-Decon, which processes data through stochastic inversion rather than the traditional approach of applying an inverse filter. The method estimates the positions of stratigraphic boundaries from the posterior distribution of interface boundaries obtained through inversion. And it calculates the reflection coefficients from the posterior distribution of the impedance model. To evaluate the proposed stochastic deconvolution algorithm, we created a 1D model and verified the algorithm through application to a synthetic example. The algorithm was subsequently applied to 3D data from the Norne field to assess its applicability to real data. The results with spectral analysis and well-log data demonstrated that the proposed algorithm distinctly delineates stratigraphic boundaries, enhancing data resolution and suppressing source wavelets. These findings are expected to help identify stratigraphic boundaries and physical properties contrasts in future seismic exploration results. This paper also presents discussions and studies on the parameter settings necessary for detecting interlayer boundaries.
{"title":"A stochastic deconvolution via trans-dimensional Markov-chain Monte Carlo","authors":"Seunghoon Han , Seokjoon Moon , Hyunggu Jun , Youngseo Kim , Yi Shen , Yongchae Cho","doi":"10.1016/j.jappgeo.2025.106063","DOIUrl":"10.1016/j.jappgeo.2025.106063","url":null,"abstract":"<div><div>Deconvolution is a crucial data processing step for enhancing the resolution of seismic exploration data, thereby enabling subsurface structures to be accurately interpreted. However, traditional deconvolution methods using an inverse filter of source wavelets provide unique results that do not account for the natural attenuation of wavelets with depth, leading to inherent accuracy limitations and difficulty in evaluating the uncertainty. This paper introduces a novel deconvolution method called Stochastic-Decon, which processes data through stochastic inversion rather than the traditional approach of applying an inverse filter. The method estimates the positions of stratigraphic boundaries from the posterior distribution of interface boundaries obtained through inversion. And it calculates the reflection coefficients from the posterior distribution of the impedance model. To evaluate the proposed stochastic deconvolution algorithm, we created a 1D model and verified the algorithm through application to a synthetic example. The algorithm was subsequently applied to 3D data from the Norne field to assess its applicability to real data. The results with spectral analysis and well-log data demonstrated that the proposed algorithm distinctly delineates stratigraphic boundaries, enhancing data resolution and suppressing source wavelets. These findings are expected to help identify stratigraphic boundaries and physical properties contrasts in future seismic exploration results. This paper also presents discussions and studies on the parameter settings necessary for detecting interlayer boundaries.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106063"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790736","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 : 2026-02-01Epub Date: 2025-12-31DOI: 10.1016/j.jappgeo.2025.106086
Ruotong Zhao , Hemin Yuan , Xin Zhang
The micro-scale physical properties of gas hydrate-bearing sediments (GHBS) play a crucial role in elucidating their macro-scale elastic responses, thereby affecting the effectiveness of seismic exploration. Hydrate may have various morphologies in sediments, casting different influences on the elastic properties of GHBS. Various models have been proposed to simulate the hydrates with different morphologies. However, few of them have addressed the generation environment of the different morphologies. In this work, we characterized the elastic properties of GHBS based on the different generation mechanisms using an improved Iso-Frame (IF) model. Based on laboratory observations, we identified different IF values corresponding to P- and S-wave velocities, respectively, reflecting varying influences of hydrate on GHBS elastic properties. Afterwards, we derived the relation between IFP and IFS by studying laboratory and well log data statistically for excess-water and excess-gas scenarios, revealing the influences of generation mechanism on GHBS elastic properties. Then these relations were applied on the prediction of S-wave velocity, and the results were compared with the predictions of original IF model and commonly-used hydrate models, which demonstrated that the modified model has improved the Vs prediction. This work highlights the different bulk-shear moduli relations based on the hydrate generation mechanism and provides an alternative route of modeling GHBS, which can facilitate the characterization of GHBS elastic properties.
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Pub Date : 2026-02-01Epub 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":"2026-02-01","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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