This paper describes the concept used to develop a methodology and an integrated approach based on the ambient vibration horizontal-to-vertical spectral ratio (HVSR) combined with geotechnical analysis for assessing soil deformations observed in Djen-Djen port located near Jijel-City (north-east Algeria).
140 ambient vibrations recording were carried out to generate a spatial distribution map of the HVSR curves, seismic vulnerability index (Kg) and the ground shear strain GSS-(γ) value distribution maps. The spatial distribution of Kg and GSS-(γ) values estimated correlates well with both the geological units and soil deformations in the study area. In addition, the mapping of the spatial distribution of the HVSR curves delineates six distinct zones, thus reflecting the sensitivity of the HVSR peak amplification factor with the compactness and technical properties of the soil.
The qualitative and quantitative analysis developed in this study made it possible to characterize the embankments, settlement, and liquefaction observed at the port of Djen-Djen in eastern Algeria. This paper shows that the HVSR method is a useful and promising technique for studying soil settlement and liquefaction. The Kg and GSS maps can be used as a guide to implementation of geotechnical tests before any conventional study and as well to identify sites that are vulnerable to deformation for seismic hazard reduction.
{"title":"Site response measurements and implications for soil deformation using geophysical and geotechnical characterization of Djen-Djen Port, Jijel, Northeast Algeria","authors":"Sarra Zeroual , Assia Bouchelouh , Fares Kessasra , El Hadi Oubaiche , Rabah Bensalem , Abdelhak Hattab , Djamel Machane","doi":"10.1016/j.jappgeo.2024.105568","DOIUrl":"10.1016/j.jappgeo.2024.105568","url":null,"abstract":"<div><div>This paper describes the concept used to develop a methodology and an integrated approach based on the ambient vibration horizontal-to-vertical spectral ratio (HVSR) combined with geotechnical analysis for assessing soil deformations observed in Djen-Djen port located near Jijel-City (north-east Algeria).</div><div>140 ambient vibrations recording were carried out to generate a spatial distribution map of the HVSR curves, seismic vulnerability index (Kg) and the ground shear strain GSS-(γ) value distribution maps. The spatial distribution of Kg and GSS-(γ) values estimated correlates well with both the geological units and soil deformations in the study area. In addition, the mapping of the spatial distribution of the HVSR curves delineates six distinct zones, thus reflecting the sensitivity of the HVSR peak amplification factor with the compactness and technical properties of the soil.</div><div>The qualitative and quantitative analysis developed in this study made it possible to characterize the embankments, settlement, and liquefaction observed at the port of Djen-Djen in eastern Algeria. This paper shows that the HVSR method is a useful and promising technique for studying soil settlement and liquefaction. The Kg and GSS maps can be used as a guide to implementation of geotechnical tests before any conventional study and as well to identify sites that are vulnerable to deformation for seismic hazard reduction.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"232 ","pages":"Article 105568"},"PeriodicalIF":2.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705975","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}
Ground Penetrating Radar (GPR) is widely used for detecting buried utilities, but data interpretation remains challenging due to noise and clutter. Although various methods exist for processing GPR data, the Kalman Filter (KF) has been underutilised despite its strength as an estimator. Traditional KF-based algorithms in GPR studies often rely on chi-squared hypothesis testing, which requires expert-defined thresholds and can lead to biased or uncertain outcomes. This paper introduces a novel KF-based framework that addresses these limitations. The framework employs Kalman Filters for noise reduction, with an optimisation algorithm based on a genetic algorithm to fine-tune KF input parameters. A Normalised Innovation Squared (NIS) parameter is used to generate an NIS signal function for identifying anomalies. Additionally, discrete wavelet transforms are applied to the NIS signal function for anomaly detection and localisation, using varying decomposition levels and vanishing moments. Results demonstrate a proportional relationship between wavelet decomposition levels, selected wavelets, and the detection rates of true and false positives. Statistical analysis using receiver operating characteristic curves shows that the optimal detection rate for all tested wavelets occurs at decomposition levels 5 and 6. This framework enhances GPR data interpretation with minimal user interaction, representing a step forward toward autonomy in GPR data processing and interpretation.
{"title":"Optimising Ground Penetrating Radar data interpretation: A hybrid approach with AI-assisted Kalman Filter and Wavelet Transform for detecting and locating buried utilities","authors":"Arasti Afrasiabi , Asaad Faramarzi , David Chapman , Alireza Keshavarzi","doi":"10.1016/j.jappgeo.2024.105567","DOIUrl":"10.1016/j.jappgeo.2024.105567","url":null,"abstract":"<div><div>Ground Penetrating Radar (GPR) is widely used for detecting buried utilities, but data interpretation remains challenging due to noise and clutter. Although various methods exist for processing GPR data, the Kalman Filter (KF) has been underutilised despite its strength as an estimator. Traditional KF-based algorithms in GPR studies often rely on chi-squared hypothesis testing, which requires expert-defined thresholds and can lead to biased or uncertain outcomes. This paper introduces a novel KF-based framework that addresses these limitations. The framework employs Kalman Filters for noise reduction, with an optimisation algorithm based on a genetic algorithm to fine-tune KF input parameters. A Normalised Innovation Squared (NIS) parameter is used to generate an NIS signal function for identifying anomalies. Additionally, discrete wavelet transforms are applied to the NIS signal function for anomaly detection and localisation, using varying decomposition levels and vanishing moments. Results demonstrate a proportional relationship between wavelet decomposition levels, selected wavelets, and the detection rates of true and false positives. Statistical analysis using receiver operating characteristic curves shows that the optimal detection rate for all tested wavelets occurs at decomposition levels 5 and 6. This framework enhances GPR data interpretation with minimal user interaction, representing a step forward toward autonomy in GPR data processing and interpretation.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"232 ","pages":"Article 105567"},"PeriodicalIF":2.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/j.jappgeo.2024.105553
Wang Yong-Hong , Huang Yi-Heng , Liang Wei-Qiang
Magnetic techniques have been widely used in recent decades to determine heavy metal pollution in sediments due to their high sensitivity to magnetic particles and considerable measurement convenience. Beaches are usually greatly influenced by human activities, but pollution problems such as heavy metal pollution due to sewage discharge, nearby factories, and garbage disposal have reduced the tourism value and ecological environmental quality of beaches. In this study, three beaches in Qingdao city were chosen as examples, and a magnetic diagnostic model for heavy metal pollution in beach sediments was established using statistical methods. The results showed that beach No. 1 in Qingdao was not polluted, while the pollution level of beach No. 2 was lower than that of beach No. 3. Beach No. 2 exhibited slight Cr and Zn pollution and slight Fe enrichment, while beach No. 3 exhibited slight to severe Cr, Ni, and Zn pollution and severe Fe enrichment. The statistical model results indicated that χ, saturation isothermal remanent magnetization (SIRM), SOFT, and χARM are more suitable for establishing magnetic diagnostic models, and the pollution level, pollution source and diffusion range of heavy metal elements could be detected with this model. The main causes of pollution are sewage outlets and the disposal of artificial coal ash. When the magnetic susceptibility value of the 0.063–0.125 mm particle size fraction of Qingdao beach sediments is greater than 6000 × 10−8 m3kg−1, attention should be given to possible contamination by heavy metals. In this study, we revealed that environmental magnetic methods can be employed to effectively determine the pollution level, source, and diffusion of heavy metals in beach sediments, which can facilitate the management of heavy metals and other pollutants in beach sediments and ecological environmental protection.
{"title":"Magnetic diagnosis model for heavy metal pollution in beach sediments of Qingdao, China","authors":"Wang Yong-Hong , Huang Yi-Heng , Liang Wei-Qiang","doi":"10.1016/j.jappgeo.2024.105553","DOIUrl":"10.1016/j.jappgeo.2024.105553","url":null,"abstract":"<div><div>Magnetic techniques have been widely used in recent decades to determine heavy metal pollution in sediments due to their high sensitivity to magnetic particles and considerable measurement convenience. Beaches are usually greatly influenced by human activities, but pollution problems such as heavy metal pollution due to sewage discharge, nearby factories, and garbage disposal have reduced the tourism value and ecological environmental quality of beaches. In this study, three beaches in Qingdao city were chosen as examples, and a magnetic diagnostic model for heavy metal pollution in beach sediments was established using statistical methods. The results showed that beach No. 1 in Qingdao was not polluted, while the pollution level of beach No. 2 was lower than that of beach No. 3. Beach No. 2 exhibited slight Cr and Zn pollution and slight Fe enrichment, while beach No. 3 exhibited slight to severe Cr, Ni, and Zn pollution and severe Fe enrichment. The statistical model results indicated that χ, saturation isothermal remanent magnetization (SIRM), SOFT, and χ<sub>ARM</sub> are more suitable for establishing magnetic diagnostic models, and the pollution level, pollution source and diffusion range of heavy metal elements could be detected with this model. The main causes of pollution are sewage outlets and the disposal of artificial coal ash. When the magnetic susceptibility value of the 0.063–0.125 mm particle size fraction of Qingdao beach sediments is greater than 6000 × 10<sup>−8</sup> m<sup>3</sup>kg<sup>−1</sup>, attention should be given to possible contamination by heavy metals. In this study, we revealed that environmental magnetic methods can be employed to effectively determine the pollution level, source, and diffusion of heavy metals in beach sediments, which can facilitate the management of heavy metals and other pollutants in beach sediments and ecological environmental protection.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105553"},"PeriodicalIF":2.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657862","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 : 2024-11-14DOI: 10.1016/j.jappgeo.2024.105571
Muhammad Abid , Jing Ba , Uti Ikitsombika Markus , Zeeshan Tariq , Syed Haroon Ali
Porosity is a critical petrophysical parameter that governs storage capacity in reservoirs. Despite the introduction of various techniques to assess pore structure, the complexity of rock components and the wide range of pore types have led to limitations in accurately evaluating porosity, particularly in clay-dominant reservoirs. Discrepancies and inconsistencies remain among different analytical calculation methods. Determining porosity using neutron and density logs is especially challenging in the presence of clay minerals and hydrocarbon saturation, particularly gas. Gas saturation reduces rock density, while in clay-dominant formations, neutron logs often indicate excessively high porosity due to the water content in clays. The impact of clay-bound water on rock porosity is still not fully accounted for. This study proposes a modified method for estimating porosity in both conventional and unconventional reservoirs, addressing the effect of clay-bound water on porosity calculations. The proposed method incorporates the rock's composition through its response observed in the neutron and density logs. Analytical equations are formulated to account for the influence of clay-bound water on these logs, and porosity is estimated. To validate the methodology, it was applied to two wells in organic shale reservoirs and one well in a conventional reservoir. The proposed porosity estimation method produced results that closely aligned with previously established methods, demonstrating consistency across all three wells with minimal deviations. This method offers broad applicability for exploration and exploitation in both conventional and unconventional reservoirs.
{"title":"Modified approach to estimate effective porosity using density and neutron logging data in conventional and unconventional reservoirs","authors":"Muhammad Abid , Jing Ba , Uti Ikitsombika Markus , Zeeshan Tariq , Syed Haroon Ali","doi":"10.1016/j.jappgeo.2024.105571","DOIUrl":"10.1016/j.jappgeo.2024.105571","url":null,"abstract":"<div><div>Porosity is a critical petrophysical parameter that governs storage capacity in reservoirs. Despite the introduction of various techniques to assess pore structure, the complexity of rock components and the wide range of pore types have led to limitations in accurately evaluating porosity, particularly in clay-dominant reservoirs. Discrepancies and inconsistencies remain among different analytical calculation methods. Determining porosity using neutron and density logs is especially challenging in the presence of clay minerals and hydrocarbon saturation, particularly gas. Gas saturation reduces rock density, while in clay-dominant formations, neutron logs often indicate excessively high porosity due to the water content in clays. The impact of clay-bound water on rock porosity is still not fully accounted for. This study proposes a modified method for estimating porosity in both conventional and unconventional reservoirs, addressing the effect of clay-bound water on porosity calculations. The proposed method incorporates the rock's composition through its response observed in the neutron and density logs. Analytical equations are formulated to account for the influence of clay-bound water on these logs, and porosity is estimated. To validate the methodology, it was applied to two wells in organic shale reservoirs and one well in a conventional reservoir. The proposed porosity estimation method produced results that closely aligned with previously established methods, demonstrating consistency across all three wells with minimal deviations. This method offers broad applicability for exploration and exploitation in both conventional and unconventional reservoirs.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105571"},"PeriodicalIF":2.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744929","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 : 2024-11-13DOI: 10.1016/j.jappgeo.2024.105569
Meng Wang , Junlu Wang , Jianhua Li , Yuanman Zheng , Pinrong Lin
The recordings of controlled source audio electromagnetic (CSAMT) are invariably contaminated with powerline noise, which seriously impedes the application of CSAMT in densely populated areas. Based on the integer-period cancellation, a powerline noise suppression scheme is described for CSAMT data acquisition and processing. The essence of this scheme is to choose reasonable transmitting frequencies and window lengths of spectrum estimation. According to the specified power transmission standard, a set of CSAMT transmitting-receiving frequencies and sampling lengths can be designed. The effective amplitude and phase can be estimated through dividing the pre-filtered soundings into specified segments for spectrum estimation and Robust stacking. Without involving the magnetic field that is more sensitive to noise, the electric field component is directly converted into the full-field apparent resistivity directly to obtain geoelectric feature. Synthetic and field examples indicate that the nonstandard powerline noise can be effectively suppressed. This scheme can be easily embedded in most of the modern instrumentations, and extend application conditions to high cultural noise areas.
{"title":"A powerline noise suppression scheme for the acquisition and processing of CSAMT data","authors":"Meng Wang , Junlu Wang , Jianhua Li , Yuanman Zheng , Pinrong Lin","doi":"10.1016/j.jappgeo.2024.105569","DOIUrl":"10.1016/j.jappgeo.2024.105569","url":null,"abstract":"<div><div>The recordings of controlled source audio electromagnetic (CSAMT) are invariably contaminated with powerline noise, which seriously impedes the application of CSAMT in densely populated areas. Based on the integer-period cancellation, a powerline noise suppression scheme is described for CSAMT data acquisition and processing. The essence of this scheme is to choose reasonable transmitting frequencies and window lengths of spectrum estimation. According to the specified power transmission standard, a set of CSAMT transmitting-receiving frequencies and sampling lengths can be designed. The effective amplitude and phase can be estimated through dividing the pre-filtered soundings into specified segments for spectrum estimation and Robust stacking. Without involving the magnetic field that is more sensitive to noise, the electric field component is directly converted into the full-field apparent resistivity directly to obtain geoelectric feature. Synthetic and field examples indicate that the nonstandard powerline noise can be effectively suppressed. This scheme can be easily embedded in most of the modern instrumentations, and extend application conditions to high cultural noise areas.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105569"},"PeriodicalIF":2.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1016/j.jappgeo.2024.105570
Yangyang Xu, Jianguo Sun, Huachao Sun
The calculation of Green's function is the core of seismic forward and inverse methods based on integral operators. When the Lippmann-Schwinger (L-S) equation is used to calculate Green's function in strongly scattering media, both the Born scattering series and the numerical iterative method encounter issues of slow convergence or divergence. Although the renormalization method derived from quantum mechanics can effectively address the convergence problem of Born scattering series in strong scattering problems, it is acknowledgeed that the convergence conditions and rates of convergence of different reformulation series may vary, and no universal convergence reformulation scattering series exists. Numerical methods for solving integral equations tend to be more general and mathematically robust. In this work, we focus on the numerical solution method of L-S equations. By using a wavelet-domain preconditioner to a reformulated or equivalent Lippmann-Schwinger (L-S) equation, we present an iterative method for numerically solving the equivalent L-S equation aimed at improving the rate of convergence and iteration efficiency in strongly inhomogeneous media. Following Jakobsen et al. (2020), we first introduce a small imaginary component into the background wave number，then rewrite the L-S equation to derive the equivalent complex wave number L-S equation. This reformulation ensures that the coefficient matrix exhibits a banded structure after numerical discretization, allowing the wavelet coefficient matrix to maintain good sparsity. We employ a multi-level fill-in incomplete LU (ILU) factorization method along with a block ILU-based algebraic recursive multilevel solve (ARMS) method in the wavelet domain to generate sparse approximate inverses as preconditioning operators, thereby accelerating the convergence of the generalized successive over-relaxation (GSOR) iterative method. This method is applied to compute numerical Green's functions in strongly inhomogeneous media. Numerical results demonstrate that our method yields simulation outcomes consistent with those obtained from the direct method for solving the original real wave number L-S equation. By testing various preconditioners, we find that the ARMS preconditioner offers significant advantages in operator generation efficiency and non-zero element filling ratio, effectively accelerating the convergence of the GSOR iterative method while achieving higher computational efficiency.
{"title":"Lippmann-Schwinger equation representation of Green's function and its preconditioned generalized over-relaxation iterative solution in wavelet domain","authors":"Yangyang Xu, Jianguo Sun, Huachao Sun","doi":"10.1016/j.jappgeo.2024.105570","DOIUrl":"10.1016/j.jappgeo.2024.105570","url":null,"abstract":"<div><div>The calculation of Green's function is the core of seismic forward and inverse methods based on integral operators. When the Lippmann-Schwinger (L-S) equation is used to calculate Green's function in strongly scattering media, both the Born scattering series and the numerical iterative method encounter issues of slow convergence or divergence. Although the renormalization method derived from quantum mechanics can effectively address the convergence problem of Born scattering series in strong scattering problems, it is acknowledgeed that the convergence conditions and rates of convergence of different reformulation series may vary, and no universal convergence reformulation scattering series exists. Numerical methods for solving integral equations tend to be more general and mathematically robust. In this work, we focus on the numerical solution method of L-S equations. By using a wavelet-domain preconditioner to a reformulated or equivalent Lippmann-Schwinger (L-S) equation, we present an iterative method for numerically solving the equivalent L-S equation aimed at improving the rate of convergence and iteration efficiency in strongly inhomogeneous media. Following Jakobsen et al. (2020), we first introduce a small imaginary component into the background wave number，then rewrite the L-S equation to derive the equivalent complex wave number L-S equation. This reformulation ensures that the coefficient matrix exhibits a banded structure after numerical discretization, allowing the wavelet coefficient matrix to maintain good sparsity. We employ a multi-level fill-in incomplete LU (ILU) factorization method along with a block ILU-based algebraic recursive multilevel solve (ARMS) method in the wavelet domain to generate sparse approximate inverses as preconditioning operators, thereby accelerating the convergence of the generalized successive over-relaxation (GSOR) iterative method. This method is applied to compute numerical Green's functions in strongly inhomogeneous media. Numerical results demonstrate that our method yields simulation outcomes consistent with those obtained from the direct method for solving the original real wave number L-S equation. By testing various preconditioners, we find that the ARMS preconditioner offers significant advantages in operator generation efficiency and non-zero element filling ratio, effectively accelerating the convergence of the GSOR iterative method while achieving higher computational efficiency.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"232 ","pages":"Article 105570"},"PeriodicalIF":2.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705899","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}
Joint inversion stands as a critical technique for the precise determination of subsurface structures by mitigating the ill-posedness inherent in separate geophysical inversion procedures. Recently, the integration of deep learning (DL) into joint inversion has shown promise in achieving more precise interpretations. However, existing DL-based joint inversion approaches face challenges, particularly when survey configurations between training and test datasets vary, and are prone to overfitting towards specific types of data. In response to these limitations, we introduce the Partial Channel Drop (PCD) method applied to DL joint inversion, resulting in a DL-PCD joint inversion model. Our study utilizes gravity, magnetic, and direct current resistivity data as the multiple geophysical data sources and employs 3D U-Net for the DL joint inversion model. The PCD method is implemented during the DL joint inversion training process, yielding a robust and versatile DL-based joint inversion model that can adapt to differing data configurations and manage scenarios with missing data while preventing overfitting and consequent bias in inversion results. Our proposed approach demonstrates superior generalization performance and robustness compared to separate inversion and DL joint inversion without the PCD method, exhibiting resilience even when faced with added noise. The results validate the effectiveness of the PCD method in bolstering the generalization performance of DL joint inversion, laying the groundwork for transformative possibilities in future 3D joint inversion research.
{"title":"Deep learning-based geophysical joint inversion using partial channel drop method","authors":"Jongchan Oh , Shinhye Kong , Daeung Yoon , Seungwook Shin","doi":"10.1016/j.jappgeo.2024.105554","DOIUrl":"10.1016/j.jappgeo.2024.105554","url":null,"abstract":"<div><div>Joint inversion stands as a critical technique for the precise determination of subsurface structures by mitigating the ill-posedness inherent in separate geophysical inversion procedures. Recently, the integration of deep learning (DL) into joint inversion has shown promise in achieving more precise interpretations. However, existing DL-based joint inversion approaches face challenges, particularly when survey configurations between training and test datasets vary, and are prone to overfitting towards specific types of data. In response to these limitations, we introduce the Partial Channel Drop (PCD) method applied to DL joint inversion, resulting in a DL-PCD joint inversion model. Our study utilizes gravity, magnetic, and direct current resistivity data as the multiple geophysical data sources and employs 3D U-Net for the DL joint inversion model. The PCD method is implemented during the DL joint inversion training process, yielding a robust and versatile DL-based joint inversion model that can adapt to differing data configurations and manage scenarios with missing data while preventing overfitting and consequent bias in inversion results. Our proposed approach demonstrates superior generalization performance and robustness compared to separate inversion and DL joint inversion without the PCD method, exhibiting resilience even when faced with added noise. The results validate the effectiveness of the PCD method in bolstering the generalization performance of DL joint inversion, laying the groundwork for transformative possibilities in future 3D joint inversion research.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105554"},"PeriodicalIF":2.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658463","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 : 2024-11-08DOI: 10.1016/j.jappgeo.2024.105555
Lewen Qiu , Jingtian Tang , Zhengguang Liu
We developed a novel adaptive finite element method (FEM) to address the problem of 3-D direct current (DC) resistivity forward modeling with complex surface topography and arbitrary conductivity anisotropy. The tetrahedra-based FEM and secondary virtual potential algorithm are first used to handle arbitrary complex geo-models. Then, to ensure the accuracy of the simulation solution, an improved goal-oriented adaptive mesh refinement (AMR) algorithm is proposed to realize an optimized mesh density distribution. To avoid the drawback of the traditional goal-oriented AMR algorithm for the DC forward modeling problem, we incorporate a volume-based weighting factor into the posterior error estimation procedure to further optimize the density distribution of the forward modeling grid. In addition, instead of traditional open source mesh generation software, we propose using the longest-edge bisection (LEB) algorithm to perform the mesh refinement process, which can preserve the topological structure between different-level meshes. Finally, the comprehensive test using a two-layered model and two complex 3-D models demonstrate the capability of our newly developed code to obtain highly accurate solutions even on relatively coarse initial grids. By incorporating the volume factor, our novel AMR algorithm achieves a more uniform and reasonable mesh density distribution during these experiments. The LEB refinement technique can generate a series of nested tetrahedral elements and provide fewer tetrahedral elements compared to the traditional Delaunay-based AMR method. The proposed 3-D DC forward modeling method has been implemented into an open source C++ code, which will contribute to the advancement of the 3-D DC resistivity imaging field.
我们开发了一种新颖的自适应有限元法（FEM），以解决具有复杂表面地形和任意电导率各向异性的三维直流（DC）电阻率正演建模问题。首先使用基于四面体的有限元法和二次虚拟电位算法来处理任意复杂的地质模型。然后，为确保仿真解的精度，提出了一种改进的目标导向自适应网格细化（AMR）算法，以实现优化的网格密度分布。为避免传统面向目标的自适应网格细化算法在直流前向建模问题上的缺陷，我们在后向误差估计过程中加入了基于体积的加权因子，以进一步优化前向建模网格的密度分布。此外，我们建议使用最长边分割（LEB）算法代替传统的开源网格生成软件来执行网格细化过程，这样可以保留不同层次网格之间的拓扑结构。最后，使用一个两层模型和两个复杂的三维模型进行的综合测试表明，我们新开发的代码即使在相对较粗的初始网格上也能获得高精度的解。通过加入体积因子，我们的新型 AMR 算法在这些实验中实现了更均匀、更合理的网格密度分布。与传统的基于 Delaunay 的 AMR 方法相比，LEB 细分技术可以生成一系列嵌套的四面体元素，并提供更少的四面体元素。所提出的三维直流正演建模方法已被应用到开源的 C++ 代码中，这将有助于推动三维直流电阻率成像领域的发展。
{"title":"An improved goal-oriented adaptive finite-element method for 3-D direct current resistivity anisotropic forward modeling using nested tetrahedra","authors":"Lewen Qiu , Jingtian Tang , Zhengguang Liu","doi":"10.1016/j.jappgeo.2024.105555","DOIUrl":"10.1016/j.jappgeo.2024.105555","url":null,"abstract":"<div><div>We developed a novel adaptive finite element method (FEM) to address the problem of 3-D direct current (DC) resistivity forward modeling with complex surface topography and arbitrary conductivity anisotropy. The tetrahedra-based FEM and secondary virtual potential algorithm are first used to handle arbitrary complex geo-models. Then, to ensure the accuracy of the simulation solution, an improved goal-oriented adaptive mesh refinement (AMR) algorithm is proposed to realize an optimized mesh density distribution. To avoid the drawback of the traditional goal-oriented AMR algorithm for the DC forward modeling problem, we incorporate a volume-based weighting factor into the posterior error estimation procedure to further optimize the density distribution of the forward modeling grid. In addition, instead of traditional open source mesh generation software, we propose using the longest-edge bisection (LEB) algorithm to perform the mesh refinement process, which can preserve the topological structure between different-level meshes. Finally, the comprehensive test using a two-layered model and two complex 3-D models demonstrate the capability of our newly developed code to obtain highly accurate solutions even on relatively coarse initial grids. By incorporating the volume factor, our novel AMR algorithm achieves a more uniform and reasonable mesh density distribution during these experiments. The LEB refinement technique can generate a series of nested tetrahedral elements and provide fewer tetrahedral elements compared to the traditional Delaunay-based AMR method. The proposed 3-D DC forward modeling method has been implemented into an open source C++ code, which will contribute to the advancement of the 3-D DC resistivity imaging field.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105555"},"PeriodicalIF":2.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658353","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 : 2024-11-06DOI: 10.1016/j.jappgeo.2024.105557
Soumitra Kumar Kundu , Ashim Kanti Dey , Sanjog Chhetri Sapkota , Prasenjit Debnath , Prasenjit Saha , Arunava Ray , Manoj Khandelwal
Electrical Resistivity (ER) is one of the best geophysical methods for subsurface investigation, especially for geotechnical and geo-environmental studies. Being non-invasive, economical and rapid, this method is highly preferable to geotechnical engineers for continuous evaluation of soil properties along the resistivity profile. Numerous studies have been conducted to correlate the subsurface properties with the ER. However, most of the studies consider a single input variable, which is correlated with the resistivity values using some conventional regression analyses. Very few studies have been conducted to obtain the resistivity value with multiple input parameters, like unit weight, temperature, porosity, moisture content, etc. Since, the soil parameters have a combined effect on resistivity, hence, correlations between the resistivity and the multiple input parameters are urgently required for a better and more reliable result. Moreover, the non-linear properties of soil make the task more complicated. To fill up this research gap, in the present study, 2772 ER tests were conducted using seven different types of soil with different combinations of temperature, density, and water content. Using this database, a Support Vector Regression (SVR), Artificial Neural Network (ANN) model and Extreme Gradient Boosting (XGB) were developed for prediction of ER. It has been understood that all the models are acknowledged as trustworthy data modelling tools. However, the XGB model performs better with an R2 of 0.99 during the training and testing phase. Further, a parametric study was also done to determine, how each input parameter affects the ER. An error analysis was also performed to see the consistent discrepancy between the experimental and projected values of ER. The outcomes validate the robustness of the XGB model, indicating that it can serve as a substitute method for ER prediction.
电阻率（ER）是进行地下勘测，特别是岩土工程和地质环境研究的最佳地球物理方法之一。这种方法具有非侵入性、经济性和快速性等特点，非常适合岩土工程师沿电阻率剖面对土壤特性进行连续评估。为了将地下属性与电阻率相关联，已经进行了大量研究。然而，大多数研究考虑的是单一输入变量，并通过一些传统的回归分析将其与电阻率值相关联。很少有研究利用单位重量、温度、孔隙度、含水量等多个输入参数来获取电阻率值。由于土壤参数会对电阻率产生综合影响，因此迫切需要将电阻率与多个输入参数相关联，以获得更好、更可靠的结果。此外，土壤的非线性特性使这项工作变得更加复杂。为了填补这一研究空白，本研究使用七种不同类型的土壤，以不同的温度、密度和含水量组合进行了 2772 次 ER 试验。利用该数据库，开发了支持向量回归（SVR）、人工神经网络（ANN）模型和极梯度提升（XGB）模型，用于预测 ER。据了解，所有模型都被认为是值得信赖的数据建模工具。不过，在训练和测试阶段，XGB 模型的 R2 值为 0.99，表现更佳。此外，还进行了参数研究，以确定每个输入参数对 ER 的影响。还进行了误差分析，以了解 ER 的实验值和预测值之间的一致差异。结果验证了 XGB 模型的稳健性，表明它可以作为 ER 预测的替代方法。
{"title":"Advanced predictive modelling of electrical resistivity for geotechnical and geo-environmental applications using machine learning techniques","authors":"Soumitra Kumar Kundu , Ashim Kanti Dey , Sanjog Chhetri Sapkota , Prasenjit Debnath , Prasenjit Saha , Arunava Ray , Manoj Khandelwal","doi":"10.1016/j.jappgeo.2024.105557","DOIUrl":"10.1016/j.jappgeo.2024.105557","url":null,"abstract":"<div><div>Electrical Resistivity (ER) is one of the best geophysical methods for subsurface investigation, especially for geotechnical and geo-environmental studies. Being non-invasive, economical and rapid, this method is highly preferable to geotechnical engineers for continuous evaluation of soil properties along the resistivity profile. Numerous studies have been conducted to correlate the subsurface properties with the ER. However, most of the studies consider a single input variable, which is correlated with the resistivity values using some conventional regression analyses. Very few studies have been conducted to obtain the resistivity value with multiple input parameters, like unit weight, temperature, porosity, moisture content, etc. Since, the soil parameters have a combined effect on resistivity, hence, correlations between the resistivity and the multiple input parameters are urgently required for a better and more reliable result. Moreover, the non-linear properties of soil make the task more complicated. To fill up this research gap, in the present study, 2772 ER tests were conducted using seven different types of soil with different combinations of temperature, density, and water content. Using this database, a Support Vector Regression (SVR), Artificial Neural Network (ANN) model and Extreme Gradient Boosting (XGB) were developed for prediction of ER. It has been understood that all the models are acknowledged as trustworthy data modelling tools. However, the XGB model performs better with an <em>R</em><sup><em>2</em></sup> of 0.99 during the training and testing phase. Further, a parametric study was also done to determine, how each input parameter affects the ER. An error analysis was also performed to see the consistent discrepancy between the experimental and projected values of ER. The outcomes validate the robustness of the XGB model, indicating that it can serve as a substitute method for ER prediction.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105557"},"PeriodicalIF":2.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1016/j.jappgeo.2024.105556
Alexandro Vera-Arroyo, Heather Bedle
Assessing the presence and quality of reservoir rocks and their sealing capacity is crucial for various applications, including hydrocarbon, geothermal, and CO2 sequestration projects. Typically, exploration geoscientists rely on seismic attributes and borehole logs into interpretation to integrate diverse data for estimating reservoirs and seals. However, for all seismic interpreters, the process is time-consuming.
In this study, we explore the application of Hierarchical Clustering Analysis (HCA), an unsupervised machine learning technique, to streamline the integration of multidisciplinary information. While HCA and similar techniques may occasionally misclassify critical data, we demonstrate how to enhance their accuracy by carefully selecting the number of clusters and their calibration with borehole data.
The novelty of our work is the innovative transformation of HCA clusters into a 3D lithology model, which can significantly facilitate the estimation of reservoir rock and seal-rock juxtaposition risk. Using the HCA clustering hierarchy, five clusters effectively discern the presence and quality of seal and reservoir rock in two different datasets. The classification, in combination with the fault probability, addresses the seal risk offshore the Northern Carnarvon Basin.
{"title":"Seal and reservoir risk evaluation using hierarchical clustering analysis with seismic attributes in Northwestern Australia","authors":"Alexandro Vera-Arroyo, Heather Bedle","doi":"10.1016/j.jappgeo.2024.105556","DOIUrl":"10.1016/j.jappgeo.2024.105556","url":null,"abstract":"<div><div>Assessing the presence and quality of reservoir rocks and their sealing capacity is crucial for various applications, including hydrocarbon, geothermal, and CO<sub>2</sub> sequestration projects. Typically, exploration geoscientists rely on seismic attributes and borehole logs into interpretation to integrate diverse data for estimating reservoirs and seals. However, for all seismic interpreters, the process is time-consuming.</div><div>In this study, we explore the application of Hierarchical Clustering Analysis (HCA), an unsupervised machine learning technique, to streamline the integration of multidisciplinary information. While HCA and similar techniques may occasionally misclassify critical data, we demonstrate how to enhance their accuracy by carefully selecting the number of clusters and their calibration with borehole data.</div><div>The novelty of our work is the innovative transformation of HCA clusters into a 3D lithology model, which can significantly facilitate the estimation of reservoir rock and seal-rock juxtaposition risk. Using the HCA clustering hierarchy, five clusters effectively discern the presence and quality of seal and reservoir rock in two different datasets. The classification, in combination with the fault probability, addresses the seal risk offshore the Northern Carnarvon Basin.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"232 ","pages":"Article 105556"},"PeriodicalIF":2.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705897","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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