Efficient forward modeling of electromagnetic (EM) fields is the basis of interpretation and inversion of the EM data, which plays an important role in practical geophysical exploration. A novel extrapolation cascadic multigird (EXCMG) method is developed to solve large linear systems encountered in geophysical EM modelling. The original curl-curl equation is regularized by including the gradient of a scaled divergence correction term, and linear edge element method is used to discrete the equation. And for the sake of generality, arbitrary anisotropic conductivity is considered.#xD;We propose a novel approach to address the issue of edge unknowns in 3-D edge element discretizations on nonuniform rectilinear grids. Inspired by the original EXCMG method for nodal elements, we introduce a new prolongation operator that treats edge unknowns as defined on the midpoints of edges. This operator aims to provide an accurate approximation of the finite element solution on the refined grid. Utilizing the good initial guess significantly reduces the number of iterations required by the preconditioned BiCGStab, which is employed as smoother for the EXCMG algorithm. Numerical experiments are carried out to validate the accuracy and efficiency of the proposed EXCMG method, including problems with analytical solutions, problems from magnetotellurics (MT) and controlled-source electromagnetic modelling (CSEM). Results indicate that EXCMG is more efficient than traditional Krylov-subspace iterative solvers, the algebraic multigrid solver AGMG and those depend on the auxiliary-space Maxwell solver (AMS), especially for large-scale problems where the number of unknowns exceeds 10 million. The EXCMG method demonstrates good robustness for a wide range of frequencies and complex geo-electric structures.
{"title":"An Efficient Cascadic Multigrid Method with Regularization Technique for 3-D Electromagnetic Finite-Element Anisotropic Modelling","authors":"Kejia Pan, Jinxuan Wang, Zhengguang Liu, Ziyi Ou, Rongwen Guo, Zhengyong Ren","doi":"10.1190/geo2023-0702.1","DOIUrl":"https://doi.org/10.1190/geo2023-0702.1","url":null,"abstract":"Efficient forward modeling of electromagnetic (EM) fields is the basis of interpretation and inversion of the EM data, which plays an important role in practical geophysical exploration. A novel extrapolation cascadic multigird (EXCMG) method is developed to solve large linear systems encountered in geophysical EM modelling. The original curl-curl equation is regularized by including the gradient of a scaled divergence correction term, and linear edge element method is used to discrete the equation. And for the sake of generality, arbitrary anisotropic conductivity is considered.#xD;We propose a novel approach to address the issue of edge unknowns in 3-D edge element discretizations on nonuniform rectilinear grids. Inspired by the original EXCMG method for nodal elements, we introduce a new prolongation operator that treats edge unknowns as defined on the midpoints of edges. This operator aims to provide an accurate approximation of the finite element solution on the refined grid. Utilizing the good initial guess significantly reduces the number of iterations required by the preconditioned BiCGStab, which is employed as smoother for the EXCMG algorithm. Numerical experiments are carried out to validate the accuracy and efficiency of the proposed EXCMG method, including problems with analytical solutions, problems from magnetotellurics (MT) and controlled-source electromagnetic modelling (CSEM). Results indicate that EXCMG is more efficient than traditional Krylov-subspace iterative solvers, the algebraic multigrid solver AGMG and those depend on the auxiliary-space Maxwell solver (AMS), especially for large-scale problems where the number of unknowns exceeds 10 million. The EXCMG method demonstrates good robustness for a wide range of frequencies and complex geo-electric structures.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thiago Dias dos Santos, Alexandre Olender, Daiane I. Dolci, Bruno Souza Carmo
In geophysical numerical models using the finite-element method or its variant, the spectral-element method, to solve seismic wave equations, a mesh is employed to discretize the domain. Generating or adapting a mesh to complex geological properties is a challenging task. To tackle this challenge, we develop an r-adaptivity method to generate or adapt a two-dimensional mesh to a seismic velocity field. Our scheme relies on the optimal transport theory to perform vertices relocation, which generates good-shaped meshes and prevents tangled elements. The mesh adaptation can delineate different regions of interest, like sharp interfaces, salt bodies, and discontinuities. The algorithm has a few user-defined parameters that control the mesh density. With typical seismic velocity examples (e.g., Camembert, SEAM Phase, Marmousi-2), mesh adaptation capability is illustrated within meshes with triangular and quadrilateral elements, commonly employed in seismic codes. Besides its potential use in mesh generation, the method developed can be embedded in seismic inversion workflows like multiscale full waveform inversion to adapt the mesh to the field being inverted without incurring the I/O cost of re-meshing and load rebalancing in parallel computations. The method can be extended to three-dimensional meshes.
{"title":"Velocity model-based adapted meshes using optimal transport","authors":"Thiago Dias dos Santos, Alexandre Olender, Daiane I. Dolci, Bruno Souza Carmo","doi":"10.1190/geo2023-0581.1","DOIUrl":"https://doi.org/10.1190/geo2023-0581.1","url":null,"abstract":"In geophysical numerical models using the finite-element method or its variant, the spectral-element method, to solve seismic wave equations, a mesh is employed to discretize the domain. Generating or adapting a mesh to complex geological properties is a challenging task. To tackle this challenge, we develop an r-adaptivity method to generate or adapt a two-dimensional mesh to a seismic velocity field. Our scheme relies on the optimal transport theory to perform vertices relocation, which generates good-shaped meshes and prevents tangled elements. The mesh adaptation can delineate different regions of interest, like sharp interfaces, salt bodies, and discontinuities. The algorithm has a few user-defined parameters that control the mesh density. With typical seismic velocity examples (e.g., Camembert, SEAM Phase, Marmousi-2), mesh adaptation capability is illustrated within meshes with triangular and quadrilateral elements, commonly employed in seismic codes. Besides its potential use in mesh generation, the method developed can be embedded in seismic inversion workflows like multiscale full waveform inversion to adapt the mesh to the field being inverted without incurring the I/O cost of re-meshing and load rebalancing in parallel computations. The method can be extended to three-dimensional meshes.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Distributed Acoustic Sensing (DAS) is a promising technology introducing a new paradigm in the acquisition of high-resolution seismic data. However, DAS data often show weak signals compared to the background noise, especially in tough installation environments. In this study, we propose a new approach to denoise DAS data that leverages an unsupervised deep learning (DL) model, eliminating the need for labeled training data. The DL model aims to reconstruct the DAS signal while simultaneously attenuating DAS noise. The input DAS data undergo band-pass filtering to eliminate high-frequency content. Subsequently, a continuous wavelet transform (CWT) is performed, and the finest scale is used to guide the DL model in reconstructing the DAS signal. First, we extract 2D patches from both the band-pass filtered data and the CWT scale of the data. Then, these patches are converted using an unrolling mechanism into 1D vectors to form the input of the DL model. The architecture of the proposed DL network is composed of several fully-connected layers. A self-attention layer is further included in each layer to extract the spatial relation between the band-pass filtered data and the CWT scale. Through an iterative process, the DL model tunes its parameters to suppress DAS noise, with the band-pass filtered data serving as the target for the network. We employ the log cosh as a loss function for the DL model, enhancing its robustness against erratic noise. The denoising performance of the proposed framework is validated using field examples from the San Andreas Fault Observatory at Depth (SAFOD) and Frontier Observatory for Research in Geothermal Energy (FORGE) datasets, where the data are recorded by a fiber-optic cable. Comparative analyses against three benchmark methods reveal the robust denoising performance of the proposed framework.
分布式声学传感（DAS）是一项前景广阔的技术，它为高分辨率地震数据的采集引入了一种新的模式。然而，与背景噪声相比，DAS 数据往往显示出微弱的信号，尤其是在恶劣的安装环境中。在本研究中，我们提出了一种利用无监督深度学习（DL）模型对 DAS 数据进行去噪的新方法，从而消除了对标记训练数据的需求。深度学习模型旨在重建 DAS 信号，同时减弱 DAS 噪音。输入的 DAS 数据经过带通滤波，以消除高频内容。随后，进行连续小波变换（CWT），并使用最小尺度引导 DL 模型重建 DAS 信号。首先，我们从带通滤波数据和数据的 CWT 尺度中提取二维斑块。然后，利用解卷机制将这些斑块转换为一维向量，形成 DL 模型的输入。拟议的 DL 网络结构由多个全连接层组成。每个层中还包括一个自注意层，用于提取带通滤波数据与 CWT 比例之间的空间关系。通过迭代过程，DL 模型调整其参数以抑制 DAS 噪声，并将带通滤波数据作为网络的目标。我们采用 log cosh 作为 DL 模型的损失函数，增强其对不稳定噪声的鲁棒性。我们使用圣安德烈亚斯断层深度观测站（SAFOD）和地热能源研究前沿观测站（FORGE）数据集的现场实例验证了所提框架的去噪性能，这些数据集是由光纤电缆记录的。与三种基准方法的对比分析表明，所提出的框架具有强大的去噪性能。
{"title":"Noise Attenuation in Distributed Acoustic Sensing Data Using a Guided Unsupervised Deep Learning Network","authors":"Omar M. Saad, Matteo Ravasi, T. Alkhalifah","doi":"10.1190/geo2024-0109.1","DOIUrl":"https://doi.org/10.1190/geo2024-0109.1","url":null,"abstract":"Distributed Acoustic Sensing (DAS) is a promising technology introducing a new paradigm in the acquisition of high-resolution seismic data. However, DAS data often show weak signals compared to the background noise, especially in tough installation environments. In this study, we propose a new approach to denoise DAS data that leverages an unsupervised deep learning (DL) model, eliminating the need for labeled training data. The DL model aims to reconstruct the DAS signal while simultaneously attenuating DAS noise. The input DAS data undergo band-pass filtering to eliminate high-frequency content. Subsequently, a continuous wavelet transform (CWT) is performed, and the finest scale is used to guide the DL model in reconstructing the DAS signal. First, we extract 2D patches from both the band-pass filtered data and the CWT scale of the data. Then, these patches are converted using an unrolling mechanism into 1D vectors to form the input of the DL model. The architecture of the proposed DL network is composed of several fully-connected layers. A self-attention layer is further included in each layer to extract the spatial relation between the band-pass filtered data and the CWT scale. Through an iterative process, the DL model tunes its parameters to suppress DAS noise, with the band-pass filtered data serving as the target for the network. We employ the log cosh as a loss function for the DL model, enhancing its robustness against erratic noise. The denoising performance of the proposed framework is validated using field examples from the San Andreas Fault Observatory at Depth (SAFOD) and Frontier Observatory for Research in Geothermal Energy (FORGE) datasets, where the data are recorded by a fiber-optic cable. Comparative analyses against three benchmark methods reveal the robust denoising performance of the proposed framework.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding wave scattering in the Earth is considered fundamental in describing seismic wave propagation and providing information on structural features of the Earths interior. Petrophysical parameters (especially porosity and permeability) affect the reflection coefficients of subsurface interfaces, which can better explain the field data and infer the subsurface structure. However, the numerical solutions to the scattering problem for efficient modeling of wave propagation in poroelastic earth structures have limitations. We develop a numerical algorithm for solving the poroelastic scattering integral equations. Specifically, applying perturbation theory to Biots equations, the solutions are expressed by the Lippman-Schwinger integral equations, which can express the displacement and pressure fields. We derive the contrast source integral equations of the decoupled poroelastic wave equations. We apply a Conjugate Gradient Fast Fourier Transform (CG-FFT) method for fast solutions of the integral equations. We show that despite the complexity of the geological structure, the numerical method enables the modeling of the displacement and pressure fields in both the frequency and time domains. We demonstrate that the wave scattering problem for the Biot model provides a good description to understand the Earths interior.
{"title":"Modeling of seismic wave scattering in poroelastic media","authors":"Xingguo Huang, Tong Sun, Stewart Greenhalgh","doi":"10.1190/geo2023-0616.1","DOIUrl":"https://doi.org/10.1190/geo2023-0616.1","url":null,"abstract":"Understanding wave scattering in the Earth is considered fundamental in describing seismic wave propagation and providing information on structural features of the Earths interior. Petrophysical parameters (especially porosity and permeability) affect the reflection coefficients of subsurface interfaces, which can better explain the field data and infer the subsurface structure. However, the numerical solutions to the scattering problem for efficient modeling of wave propagation in poroelastic earth structures have limitations. We develop a numerical algorithm for solving the poroelastic scattering integral equations. Specifically, applying perturbation theory to Biots equations, the solutions are expressed by the Lippman-Schwinger integral equations, which can express the displacement and pressure fields. We derive the contrast source integral equations of the decoupled poroelastic wave equations. We apply a Conjugate Gradient Fast Fourier Transform (CG-FFT) method for fast solutions of the integral equations. We show that despite the complexity of the geological structure, the numerical method enables the modeling of the displacement and pressure fields in both the frequency and time domains. We demonstrate that the wave scattering problem for the Biot model provides a good description to understand the Earths interior.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Subsurface reservoirs are compressed by overburden stress resulting from the gravity of overlying masses, and the resulting stress changes significantly affect the seismic reflection responses generated at the reservoir interfaces. Several exact reflection coefficient equations have been well established to delineate the role that in-situ stress plays in altering the energy transition and amplitude of seismic reflection responses. These exact equations, however, cannot be effectively used in practice due to their intricate formulations and the difficult geophysical estimation for the third-order elastic constants (3oECs) embedded in reflection coefficients. Based on the theories of nonlinear elasticity and elastic wave inverse scattering, we derive an approximate seismic reflection coefficient equation for overburden-stressed isotropic media in terms of the P-wave modulus, shear modulus, density and a defined stress-related parameter (SRP). The SRP is a combined quantity of elastic moduli, 3oECs and overburden stress, which can be naturally treated as a dimensionless stress-induced anisotropy parameter. Its inclusion effectively eliminates the need for 3oECs information when using our equation to estimate the desired reservoir properties from seismic observations. By comparing our equation to the exact one, we confirm its validity within the moderate-stress range. Then we introduce a Bayesian inversion approach incorporating the new reflection coefficient equation to estimate four model parameters. In our approach, the Cauchy and Gaussian distribution functions are used for the a priori probability and the likelihood distributions, respectively. The synthetic tests from two well-log datasets and a field example demonstrate that four parameters can be reasonably inverted using our approach with rather smooth initial models, which illustrates the feasibility of our inversion approach.
地下储层受到上覆岩体重力所产生的上覆应力的压缩，由此产生的应力变化对储层界面产生的地震反射响应有很大影响。目前已经建立了几个精确的反射系数方程，用于描述原位应力在改变地震反射响应的能量转换和振幅方面所起的作用。然而，这些精确方程由于其复杂的公式和反射系数中嵌入的三阶弹性常数（3oECs）的地球物理估算困难，无法在实践中有效使用。基于非线性弹性和弹性波反散射理论，我们用 P 波模量、剪切模量、密度和定义的应力相关参数（SRP）推导出了覆盖层应力各向同性介质的近似地震反射系数方程。SRP 是弹性模量、3oECs 和覆盖层应力的综合量，可自然地视为无量纲应力诱导各向异性参数。在使用我们的方程根据地震观测结果估算所需的储层属性时，加入该参数可有效消除对 3oECs 信息的需求。通过将我们的方程与精确方程进行比较，我们确认了它在中等应力范围内的有效性。然后，我们介绍了一种贝叶斯反演方法，该方法结合了新的反射系数方程来估计四个模型参数。在我们的方法中，先验概率分布和似然分布分别使用了考奇分布函数和高斯分布函数。两个井记录数据集和一个野外实例的合成测试表明，在初始模型相当平滑的情况下，使用我们的方法可以合理地反演四个参数，这说明我们的反演方法是可行的。
{"title":"Seismic scattering inversion for multiple parameters of overburden-stressed isotropic media","authors":"Fubin Chen, Zhaoyun Zong, Xingyao Yin","doi":"10.1190/geo2023-0636.1","DOIUrl":"https://doi.org/10.1190/geo2023-0636.1","url":null,"abstract":"Subsurface reservoirs are compressed by overburden stress resulting from the gravity of overlying masses, and the resulting stress changes significantly affect the seismic reflection responses generated at the reservoir interfaces. Several exact reflection coefficient equations have been well established to delineate the role that in-situ stress plays in altering the energy transition and amplitude of seismic reflection responses. These exact equations, however, cannot be effectively used in practice due to their intricate formulations and the difficult geophysical estimation for the third-order elastic constants (3oECs) embedded in reflection coefficients. Based on the theories of nonlinear elasticity and elastic wave inverse scattering, we derive an approximate seismic reflection coefficient equation for overburden-stressed isotropic media in terms of the P-wave modulus, shear modulus, density and a defined stress-related parameter (SRP). The SRP is a combined quantity of elastic moduli, 3oECs and overburden stress, which can be naturally treated as a dimensionless stress-induced anisotropy parameter. Its inclusion effectively eliminates the need for 3oECs information when using our equation to estimate the desired reservoir properties from seismic observations. By comparing our equation to the exact one, we confirm its validity within the moderate-stress range. Then we introduce a Bayesian inversion approach incorporating the new reflection coefficient equation to estimate four model parameters. In our approach, the Cauchy and Gaussian distribution functions are used for the a priori probability and the likelihood distributions, respectively. The synthetic tests from two well-log datasets and a field example demonstrate that four parameters can be reasonably inverted using our approach with rather smooth initial models, which illustrates the feasibility of our inversion approach.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ocean-bottom node (OBN) is widely used because of its wide azimuth, long offset, and low frequency advantages. However, in the vertical component of the OBN geophone, a significant amount of shear waves induced noise may be recorded. This significantly impacts the quality of the vertical component data and may affect the follow-up merging of dual-sensor data. Some commercially available methods apply normal-movement correction, which necessitates velocity data. We avoid this by adaptive matching method, which relies solely on seismic data. Therefore, we developed a novel adaptive subtraction method for S-wave leakage suppression using the horizontal component as the noise model. Our method effectively handles the non-stationarity of the input seismic data in all time and space directions, mitigating instability caused by manual selection of the smoothing radius. A method is introduced for estimating a global non-stationary smoothing radius using the noise model. Compared to the commercial and stationary smoothing methods, our method can better suppress shear wave noise while balancing residual noise and signal leakage more effectively. Both synthetic and field data examples demonstrate significant improvement of the proposed method.
洋底节点（OBN）因其方位角宽、偏移长、频率低等优点而被广泛使用。然而，在 OBN 地震检波器的垂直分量中，可能会记录到大量剪切波引起的噪声。这会严重影响垂直分量数据的质量，并可能影响双传感器数据的后续合并。一些市场上销售的方法会应用法向移动校正，这就需要速度数据。我们采用自适应匹配方法避免了这一问题，该方法仅依赖于地震数据。因此，我们利用水平分量作为噪声模型，开发了一种新的自适应减法方法来抑制 S 波泄漏。我们的方法能有效处理输入地震数据在所有时间和空间方向上的非平稳性，减轻了人工选择平滑半径造成的不稳定性。介绍了一种利用噪声模型估算全局非稳态平滑半径的方法。与商业和固定平滑方法相比，我们的方法能更好地抑制剪切波噪声，同时更有效地平衡残余噪声和信号泄漏。合成数据和现场数据实例都证明了所提方法的显著改进。
{"title":"Non-stationary adaptive S-wave suppression of ocean bottom node data","authors":"Zhihao Chen, Zhaolin Zhu, Bangyu Wu, Yangkang Chen","doi":"10.1190/geo2023-0779.1","DOIUrl":"https://doi.org/10.1190/geo2023-0779.1","url":null,"abstract":"Ocean-bottom node (OBN) is widely used because of its wide azimuth, long offset, and low frequency advantages. However, in the vertical component of the OBN geophone, a significant amount of shear waves induced noise may be recorded. This significantly impacts the quality of the vertical component data and may affect the follow-up merging of dual-sensor data. Some commercially available methods apply normal-movement correction, which necessitates velocity data. We avoid this by adaptive matching method, which relies solely on seismic data. Therefore, we developed a novel adaptive subtraction method for S-wave leakage suppression using the horizontal component as the noise model. Our method effectively handles the non-stationarity of the input seismic data in all time and space directions, mitigating instability caused by manual selection of the smoothing radius. A method is introduced for estimating a global non-stationary smoothing radius using the noise model. Compared to the commercial and stationary smoothing methods, our method can better suppress shear wave noise while balancing residual noise and signal leakage more effectively. Both synthetic and field data examples demonstrate significant improvement of the proposed method.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Zhen, Zhen Zhao, Xiaoming Zhao, Jiawang Ge, An Zhang, Changcheng Yang
The purpose of this paper is to clarify the spatial spreading characteristics of the channel sand body in the Jurassic Shaximiao Formation reservoir in central Sichuan, and to improve the precision of channel characterization. Aiming at the problems of insufficient machine learning training samples and a lack of continuity of prediction results in the study area, we select the No. 7 sand formation of the second member of Shaximiao Formation as an example and use the method of combining boosted regression tree (BRT) model and virtual points to accurately depict the spatial distribution of the sand body. Starting from the known sand thickness and seismic attribute data, the BRT model is used to fuse the selected attributes to obtain the preliminary prediction results. On this basis, grid division is used to select virtual points to obtain three virtual datasets for sand body prediction. The three predictions are then analyzed using the clustering?topology method to obtain the dominant regions, and the virtual points are selected a second time for the final sand body prediction. The results show that the prediction accuracy of the BRT model is improved compared with other machine learning methods. Meanwhile, to address the insufficient number of samples in the study area, after using the two-stage virtual point generation method proposed in this paper, the R² of the test set in the model training results reaches 0.887. The final prediction results show that the sand body distribution effect is satisfactory, the lack of continuity of the channel can be improved, and the agreement with the well is high.
{"title":"Method and application of sand body thickness prediction based on virtual sample-machine learning","authors":"Yan Zhen, Zhen Zhao, Xiaoming Zhao, Jiawang Ge, An Zhang, Changcheng Yang","doi":"10.1190/geo2023-0709.1","DOIUrl":"https://doi.org/10.1190/geo2023-0709.1","url":null,"abstract":"The purpose of this paper is to clarify the spatial spreading characteristics of the channel sand body in the Jurassic Shaximiao Formation reservoir in central Sichuan, and to improve the precision of channel characterization. Aiming at the problems of insufficient machine learning training samples and a lack of continuity of prediction results in the study area, we select the No. 7 sand formation of the second member of Shaximiao Formation as an example and use the method of combining boosted regression tree (BRT) model and virtual points to accurately depict the spatial distribution of the sand body. Starting from the known sand thickness and seismic attribute data, the BRT model is used to fuse the selected attributes to obtain the preliminary prediction results. On this basis, grid division is used to select virtual points to obtain three virtual datasets for sand body prediction. The three predictions are then analyzed using the clustering?topology method to obtain the dominant regions, and the virtual points are selected a second time for the final sand body prediction. The results show that the prediction accuracy of the BRT model is improved compared with other machine learning methods. Meanwhile, to address the insufficient number of samples in the study area, after using the two-stage virtual point generation method proposed in this paper, the R² of the test set in the model training results reaches 0.887. The final prediction results show that the sand body distribution effect is satisfactory, the lack of continuity of the channel can be improved, and the agreement with the well is high.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seepage is a common hydrogeological hazard in engineering. Determining the seepage paths is vital for de-risking the instability of embankment structures. With the improvement of the acquisition accuracy of magnetic sensors, the magnetometric resistivity method has become an emerging technology for detecting seepage paths through earth-filled dams. This technique is non-destructive and gives prominent signals. However, the resulting magnetic data have seen ambiguity in fully determining the targets. We propose an induced magnetic gradient surveying approach to monitor seepage paths in earth-filled dams. First, we briefly review the electromagnetic theory for the magnetic gradient tensor based on Maxwells equations. To match against the measurements, we present an accurate modeling framework using the third-order finite element method and a novel compact difference scheme. We verify our approach on both semi-analytical 1D and 3D models. Systematic modeling studies are then carried out to investigate the spatial distribution characteristics and sensitivities of the induced magnetic gradient to the seepage in typical dam scenarios. In addition, we conducted two field experiments in the Zhongmou experimental base and Xixiayuan Reservoir in Henan Province, China,respectively. The induced magnetic field vector and its gradient components were both acquired. Cross-validation with a-priori geological information shows that the seepage path can be spatially identified by the induced magnetic gradient components Byy, Byz, Bzy, and Bzz while the field components failed to locate the seepage pathways. This successful application indicates that the proposed approach could be a promising solution for seepage path discrimination in earth-filled dams with high resolution.
{"title":"Feasibility of Induced Magnetic Gradient Surveying for Seepage Detection in Earth-filled Dams: Insights from Synthetic and Field Studies","authors":"Shuanggui Hu, Feiyan Wang, Jingtian Tang, Guangyin Lu, Zhihai Jiang, Jiao Zhu, Yusong Guo, Shiming Guo","doi":"10.1190/geo2024-0037.1","DOIUrl":"https://doi.org/10.1190/geo2024-0037.1","url":null,"abstract":"Seepage is a common hydrogeological hazard in engineering. Determining the seepage paths is vital for de-risking the instability of embankment structures. With the improvement of the acquisition accuracy of magnetic sensors, the magnetometric resistivity method has become an emerging technology for detecting seepage paths through earth-filled dams. This technique is non-destructive and gives prominent signals. However, the resulting magnetic data have seen ambiguity in fully determining the targets. We propose an induced magnetic gradient surveying approach to monitor seepage paths in earth-filled dams. First, we briefly review the electromagnetic theory for the magnetic gradient tensor based on Maxwells equations. To match against the measurements, we present an accurate modeling framework using the third-order finite element method and a novel compact difference scheme. We verify our approach on both semi-analytical 1D and 3D models. Systematic modeling studies are then carried out to investigate the spatial distribution characteristics and sensitivities of the induced magnetic gradient to the seepage in typical dam scenarios. In addition, we conducted two field experiments in the Zhongmou experimental base and Xixiayuan Reservoir in Henan Province, China,respectively. The induced magnetic field vector and its gradient components were both acquired. Cross-validation with a-priori geological information shows that the seepage path can be spatially identified by the induced magnetic gradient components Byy, Byz, Bzy, and Bzz while the field components failed to locate the seepage pathways. This successful application indicates that the proposed approach could be a promising solution for seepage path discrimination in earth-filled dams with high resolution.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, machine learning (ML) approaches have gained significant attention in seismic-based subsurface property estimation problems. However, because of the purely data-driven nature, it is challenging to evaluate the quality of the estimated properties in regions without ground truth data. In this article, we discuss evaluating the quality of ML predicted subsurface properties through ML-based seismic data reconstruction. We use a deep learning workflow to reconstruct the poststack seismic data, then use the misfit between the measured data and the reconstructed data as a proxy for the quality of ML predicted subsurface properties. We also use self-supervised learning to improve the model generalization when training the deep learning model for reconstruction. This proposed method is particularly valuable for subsurface properties without direct physical relation to seismic data. We provide both synthetic and field data examples to demonstrate the consistency of the proposed method.
近年来，机器学习（ML）方法在基于地震的地下属性估计问题中获得了极大关注。然而，由于其纯数据驱动的性质，在没有地面实况数据的区域评估估计属性的质量具有挑战性。在本文中，我们将讨论通过基于 ML 的地震数据重建来评估 ML 预测的地下属性的质量。我们使用深度学习工作流程重建叠后地震数据，然后使用测量数据与重建数据之间的不匹配度作为 ML 预测地下属性质量的替代指标。在训练用于重建的深度学习模型时，我们还使用自监督学习来提高模型的泛化能力。这种方法对于与地震数据没有直接物理关系的地下属性尤其有价值。我们提供了合成数据和野外数据实例，以证明所提方法的一致性。
{"title":"Evaluating machine learning predicted subsurface properties via seismic data reconstruction","authors":"T. Zhao, Haibin Di, Aria Abubakar","doi":"10.1190/geo2023-0124.1","DOIUrl":"https://doi.org/10.1190/geo2023-0124.1","url":null,"abstract":"In recent years, machine learning (ML) approaches have gained significant attention in seismic-based subsurface property estimation problems. However, because of the purely data-driven nature, it is challenging to evaluate the quality of the estimated properties in regions without ground truth data. In this article, we discuss evaluating the quality of ML predicted subsurface properties through ML-based seismic data reconstruction. We use a deep learning workflow to reconstruct the poststack seismic data, then use the misfit between the measured data and the reconstructed data as a proxy for the quality of ML predicted subsurface properties. We also use self-supervised learning to improve the model generalization when training the deep learning model for reconstruction. This proposed method is particularly valuable for subsurface properties without direct physical relation to seismic data. We provide both synthetic and field data examples to demonstrate the consistency of the proposed method.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The model that is used to explain potential-field data is highly dependent on the constraints applied in the modelling process. Many studies demonstrate the necessity of constraining gravity and magnetic models. However, typically they do not demonstrate the individual enhancements that come as a consequence of integrating each constraint into the geophysical model. In this paper, we show that when there are no constraints, it is possible to find an inverse model that is consistent with gravity data, but the model is unrealistic, as one sedimentary basin is too deep. Adding a depth weighting constraint can ensure the depth is correct, but all other features have the same depth, which is unrealistic. Including densities from a density compilation makes the densities at surface realistic, but the dips are all close to vertical and the thicknesses are similar, which is unrealistic. In this case, the inversion is believed to have found a local minimum close to the starting model. Reflection seismic data was used to constrain a two-dimensional (2D) modeling exercise (on multiple profiles) to determine the geometry of one sedimentary sub-basin. These 2D models were then combined to build a realistic three-dimensional (3D) starting model. An inversion from this model fixed the densities of each lithology, but allowed the thicknesses of the layers to vary. The resulting model was realistic, with the dips and thicknesses away from the seismic constraints being consistent with geological expectations. Although the fit to the data was much better than the previous model, it was poorer than hoped. If the densities were then allowed to vary within a realistic range of values, the fit could be improved so that both the fit to the data and the geologic model are realistic.
{"title":"A case-history tutorial describing the incorporation of geophysical, petrophysical and geological constraints to generate realistic geological models of the Matheson Study Area, Ontario","authors":"F. Della Justina, Richard S. Smith, R. Vayavur","doi":"10.1190/geo2023-0522.1","DOIUrl":"https://doi.org/10.1190/geo2023-0522.1","url":null,"abstract":"The model that is used to explain potential-field data is highly dependent on the constraints applied in the modelling process. Many studies demonstrate the necessity of constraining gravity and magnetic models. However, typically they do not demonstrate the individual enhancements that come as a consequence of integrating each constraint into the geophysical model. In this paper, we show that when there are no constraints, it is possible to find an inverse model that is consistent with gravity data, but the model is unrealistic, as one sedimentary basin is too deep. Adding a depth weighting constraint can ensure the depth is correct, but all other features have the same depth, which is unrealistic. Including densities from a density compilation makes the densities at surface realistic, but the dips are all close to vertical and the thicknesses are similar, which is unrealistic. In this case, the inversion is believed to have found a local minimum close to the starting model. Reflection seismic data was used to constrain a two-dimensional (2D) modeling exercise (on multiple profiles) to determine the geometry of one sedimentary sub-basin. These 2D models were then combined to build a realistic three-dimensional (3D) starting model. An inversion from this model fixed the densities of each lithology, but allowed the thicknesses of the layers to vary. The resulting model was realistic, with the dips and thicknesses away from the seismic constraints being consistent with geological expectations. Although the fit to the data was much better than the previous model, it was poorer than hoped. If the densities were then allowed to vary within a realistic range of values, the fit could be improved so that both the fit to the data and the geologic model are realistic.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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