Predicting permeability accurately is crucial for effective hydrocarbon extraction, but the intricate pore structures of tight carbonates, resulting from sedimentation, diagenesis, and tectonic activity, present significant challenges. Based on borehole electrical image logging and fractal theory, we developed a method to calculate the fractal dimension of the porosity spectrum to characterise the complexity of the pore structure of the reservoir. Fractal features of the porosity spectra were studied and fractal parameters were calculated, such as the left ( D f_left), middle ( D f_middle), and right fractal dimension ( D f_right). A permeability prediction model was proposed based on fractal parameters by investigating the linear relationship between fractal parameters and core permeability. The results indicate that D f_left and permeability have a coefficient of determination (R2) of 0.78, whereas R2 between porosity and permeability is only 0.03. D f_middle and D f_right have little correlation with core permeability. The prediction results of the D f_left -based permeability model are in good agreement with the experimental data with Pearson product-moment correlation coefficient of 0.93 in the field applications. Our findings suggest that large pores primarily contribute to the permeability of tight carbonates since D f_left corresponds to the macroporous part of the porosity spectrum. This study enhances our understanding of the factors that influence permeability and provides a useful tool for predicting permeability in tight carbonate reservoirs.
准确预测渗透率对于有效开采碳氢化合物至关重要，但由于沉积、成岩和构造活动，致密碳酸盐岩的孔隙结构错综复杂，这给我们带来了巨大挑战。基于井眼电图像测井和分形理论，我们开发了一种计算孔隙度谱分形维度的方法，以描述储层孔隙结构的复杂性。研究了孔隙度谱的分形特征，并计算了分形参数，如左分形维度（D f_left）、中分形维度（D f_middle）和右分形维度（D f_right）。通过研究分形参数与岩心渗透率之间的线性关系，提出了基于分形参数的渗透率预测模型。结果表明，D f_left 与渗透率的决定系数 (R2) 为 0.78，而孔隙度与渗透率之间的 R2 仅为 0.03。D f_middle 和 D f_right 与岩心渗透率的相关性很小。在现场应用中，基于 D f_left 的渗透率模型的预测结果与实验数据非常吻合，皮尔逊积矩相关系数为 0.93。我们的研究结果表明，大孔隙是致密碳酸盐岩渗透率的主要成因，因为 D f_left 相当于孔隙率谱的大孔隙部分。这项研究加深了我们对影响渗透率因素的理解，为预测致密碳酸盐岩储层的渗透率提供了有用的工具。
{"title":"Developing a novel permeability prediction method for tight carbonate reservoirs using borehole electrical image logging","authors":"Kun Meng, Hongyan Yu, Liyong Fan, Zhanrong Ma, Xiaorong Luo, Binfeng Cao, Yihuai Zhang","doi":"10.1190/geo2023-0609.1","DOIUrl":"https://doi.org/10.1190/geo2023-0609.1","url":null,"abstract":"Predicting permeability accurately is crucial for effective hydrocarbon extraction, but the intricate pore structures of tight carbonates, resulting from sedimentation, diagenesis, and tectonic activity, present significant challenges. Based on borehole electrical image logging and fractal theory, we developed a method to calculate the fractal dimension of the porosity spectrum to characterise the complexity of the pore structure of the reservoir. Fractal features of the porosity spectra were studied and fractal parameters were calculated, such as the left ( D f_left), middle ( D f_middle), and right fractal dimension ( D f_right). A permeability prediction model was proposed based on fractal parameters by investigating the linear relationship between fractal parameters and core permeability. The results indicate that D f_left and permeability have a coefficient of determination (R2) of 0.78, whereas R2 between porosity and permeability is only 0.03. D f_middle and D f_right have little correlation with core permeability. The prediction results of the D f_left -based permeability model are in good agreement with the experimental data with Pearson product-moment correlation coefficient of 0.93 in the field applications. Our findings suggest that large pores primarily contribute to the permeability of tight carbonates since D f_left corresponds to the macroporous part of the porosity spectrum. This study enhances our understanding of the factors that influence permeability and provides a useful tool for predicting permeability in tight carbonate reservoirs.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiang Liu, T. M. Müller, R. Rezaee, Yanli Liu, Danping Cao
Quantitative analysis of the relationship between seismic wave velocities and fluid saturation in porous media is of great significance for any fluid injection and extraction operation in subsurface rock formations. However, seismic velocities are not only dependent on the amount of saturation, but also on the distribution of fluid patches and their size. The patch size variation during changes in saturation is oftentimes ignored in modeling studies, even though it is natural to assume that with increasing saturation, fluid patches will form larger and, at some critical saturation, percolating clusters. To capture the evolution of patch size with saturation implied in the velocity-saturation relations, we are inspired by percolation theory. By incorporating the connectivity of water-filled patches in the continuous random medium model, we develop a critical saturation model. We apply this critical saturation model to examine recently reported experimental measurements, specifically analyzing the patch size changes. For measurements of drainage or imbibition processes in four sandstone samples, we indeed find a clear indication of growing patch size with water saturation. The predictions of the critical saturation model are in reasonable agreement with observations. Our approach improves the accuracy of the interpretation of the velocity-saturation relations in partially saturated rocks and forms a basis for exploring its underlying mechanisms.
{"title":"Effect of fluid patch clustering on the P-wave velocity-saturation relation: a critical saturation model","authors":"Qiang Liu, T. M. Müller, R. Rezaee, Yanli Liu, Danping Cao","doi":"10.1190/geo2023-0768.1","DOIUrl":"https://doi.org/10.1190/geo2023-0768.1","url":null,"abstract":"Quantitative analysis of the relationship between seismic wave velocities and fluid saturation in porous media is of great significance for any fluid injection and extraction operation in subsurface rock formations. However, seismic velocities are not only dependent on the amount of saturation, but also on the distribution of fluid patches and their size. The patch size variation during changes in saturation is oftentimes ignored in modeling studies, even though it is natural to assume that with increasing saturation, fluid patches will form larger and, at some critical saturation, percolating clusters. To capture the evolution of patch size with saturation implied in the velocity-saturation relations, we are inspired by percolation theory. By incorporating the connectivity of water-filled patches in the continuous random medium model, we develop a critical saturation model. We apply this critical saturation model to examine recently reported experimental measurements, specifically analyzing the patch size changes. For measurements of drainage or imbibition processes in four sandstone samples, we indeed find a clear indication of growing patch size with water saturation. The predictions of the critical saturation model are in reasonable agreement with observations. Our approach improves the accuracy of the interpretation of the velocity-saturation relations in partially saturated rocks and forms a basis for exploring its underlying mechanisms.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding the orientation of geological structures is crucial for analyzing the complexity of the Earths' subsurface. For instance, information about geological structure orientation can be incorporated into local anisotropic regularization methods as a valuable tool to stabilize the solution of inverse problems and produce geologically plausible solutions. We introduce a new variational method that employs the alternating direction method of multipliers within an alternating minimization scheme to jointly estimate orientation and model parameters in both 2D and 3D inverse problems. Specifically, the proposed approach adaptively integrates recovered information about structural orientation, enhancing the effectiveness of anisotropic Tikhonov#xD;regularization in recovering geophysical parameters. The paper also discusses the automatic tuning of algorithmic parameters to maximize the new method's performance. The proposed algorithm is tested across diverse 2D and 3D examples, including structure-oriented denoising and trace interpolation. The results show that the algorithm is robust in solving the considered large and challenging problems, alongside efficiently estimating the associated tilt field in 2D cases and the dip, strike, and tilt fields in 3D cases. Synthetic and field examples show that the proposed anisotropic regularization method produces a model with enhanced resolution and provides a more accurate representation of the true structures.
{"title":"Robust Estimation of Structural Orientation Parameters and 2D/3D Local Anisotropic Tikhonov Regularization","authors":"Ali Gholami, S. Gazzola","doi":"10.1190/geo2023-0632.1","DOIUrl":"https://doi.org/10.1190/geo2023-0632.1","url":null,"abstract":"Understanding the orientation of geological structures is crucial for analyzing the complexity of the Earths' subsurface. For instance, information about geological structure orientation can be incorporated into local anisotropic regularization methods as a valuable tool to stabilize the solution of inverse problems and produce geologically plausible solutions. We introduce a new variational method that employs the alternating direction method of multipliers within an alternating minimization scheme to jointly estimate orientation and model parameters in both 2D and 3D inverse problems. Specifically, the proposed approach adaptively integrates recovered information about structural orientation, enhancing the effectiveness of anisotropic Tikhonov#xD;regularization in recovering geophysical parameters. The paper also discusses the automatic tuning of algorithmic parameters to maximize the new method's performance. The proposed algorithm is tested across diverse 2D and 3D examples, including structure-oriented denoising and trace interpolation. The results show that the algorithm is robust in solving the considered large and challenging problems, alongside efficiently estimating the associated tilt field in 2D cases and the dip, strike, and tilt fields in 3D cases. Synthetic and field examples show that the proposed anisotropic regularization method produces a model with enhanced resolution and provides a more accurate representation of the true structures.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a Python code that implements a novel threshold-free polarization strategy for removing random noise from three-component (3C) linearly polarized seismic data. The code, which we refer to as poldw (polarization denoising through windowing), uses closed-form formulas along sliding windows that span the data to determine the optimal rotation angles that allow the transfer of most of the signal energy to a given component. The denoised 3C data is obtained after canceling out the other two components, which are assumed to contain predominantly noise, and rotating back. The method is simple and efficient because it only requires setting the sliding window length. Synthetic and microseismic field data examples show the method’s effectiveness, which significantly improves the signal-to-noise ratio without the need for threshold-based polarization filters. Even so, these filters can be pipelined in the rotation-based strategy for additional noise removal if necessary. When the dataset contains non-linearly polarized data or significant non-random noise, the method is likely to fail. For robustness against non-Gaussian noise and outliers, poldw allows for the use of alternative norms like the L1- or L p-norms instead of the energy. In addition to the code, we provide a Jupyter notebook to illustrate the method step by step and reproduce the results of the field data example.
{"title":"Poldw: a Python code to denoise 3C seismic data with a new threshold-free polarization technique#xD;","authors":"D. Velis, Julián L. Gómez","doi":"10.1190/geo2023-0684.1","DOIUrl":"https://doi.org/10.1190/geo2023-0684.1","url":null,"abstract":"We present a Python code that implements a novel threshold-free polarization strategy for removing random noise from three-component (3C) linearly polarized seismic data. The code, which we refer to as poldw (polarization denoising through windowing), uses closed-form formulas along sliding windows that span the data to determine the optimal rotation angles that allow the transfer of most of the signal energy to a given component. The denoised 3C data is obtained after canceling out the other two components, which are assumed to contain predominantly noise, and rotating back. The method is simple and efficient because it only requires setting the sliding window length. Synthetic and microseismic field data examples show the method’s effectiveness, which significantly improves the signal-to-noise ratio without the need for threshold-based polarization filters. Even so, these filters can be pipelined in the rotation-based strategy for additional noise removal if necessary. When the dataset contains non-linearly polarized data or significant non-random noise, the method is likely to fail. For robustness against non-Gaussian noise and outliers, poldw allows for the use of alternative norms like the L1- or L p-norms instead of the energy. In addition to the code, we provide a Jupyter notebook to illustrate the method step by step and reproduce the results of the field data example.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bo Zhang, Kelin Qu, C. Yin, Yinfeng Wang, Yunhe Liu, Xiuyan Ren, Yang Su
Many mineral deposits demonstrate low-resistivity characteristics. This property makes the electromagnetic (EM) method a very useful tool for mineral exploration. In the past decades, the application of EM exploration technologies has been reviewed in many case studies. However, most reviews focused on EM exploration methods, the development of equipment, or their applications. The three-dimensional (3D) forward modeling and inversions are high-accuracy EM interpretation techniques that have made great progress in recent years in mineral explorations. In this paper, we make a review on the development of EM technology for mineral exploration with focus on 3D EM forward modeling, inversion technology, and its applications. We will first briefly introduce the EM methods for mineral explorations from the methodology. After that, we will give a comprehensive review of 3D EM forward modeling, inversions, and data interpretations, with special attention paid to the development of EM theory and applications in mineral explorations. We hope this review can promote the application of 3D EM numerical simulation and inversion in mineral explorations.
{"title":"Review on 3D electromagnetic modeling and inversion for Mineral Exploration","authors":"Bo Zhang, Kelin Qu, C. Yin, Yinfeng Wang, Yunhe Liu, Xiuyan Ren, Yang Su","doi":"10.1190/geo2024-0132.1","DOIUrl":"https://doi.org/10.1190/geo2024-0132.1","url":null,"abstract":"Many mineral deposits demonstrate low-resistivity characteristics. This property makes the electromagnetic (EM) method a very useful tool for mineral exploration. In the past decades, the application of EM exploration technologies has been reviewed in many case studies. However, most reviews focused on EM exploration methods, the development of equipment, or their applications. The three-dimensional (3D) forward modeling and inversions are high-accuracy EM interpretation techniques that have made great progress in recent years in mineral explorations. In this paper, we make a review on the development of EM technology for mineral exploration with focus on 3D EM forward modeling, inversion technology, and its applications. We will first briefly introduce the EM methods for mineral explorations from the methodology. After that, we will give a comprehensive review of 3D EM forward modeling, inversions, and data interpretations, with special attention paid to the development of EM theory and applications in mineral explorations. We hope this review can promote the application of 3D EM numerical simulation and inversion in mineral explorations.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. H. Rammay, S. Alyaev, David Larsen, R. Bratvold, S. Alyaev
Continuous integration of real-time logging-while-drilling data into a subsurface model with relevant geological uncertainties enables strategic geosteering: a field-level optimization of the well-placement strategy. Model errors arising from oversimplified conceptual geological models and imperfect simulation of measurements result in unreliable subsurface-model updates. The model errors are particularly pronounced when synthetic measurements are approximated with a fast but imperfect model, such as a deep neural network (DNN).#xD;We present a practical data-assimilation workflow consisting of offline and online phases. The offline phase involves DNN training and building an uncertain prior near-well geo-model. The online phase utilizes the flexible iterative ensemble smoother (FlexIES) to perform real-time assimilation of extra-deep electromagnetic data while accounting for the model errors in the approximate DNN model. We demonstrate the proposed workflow on historic well-log data from the Goliat Field (Barents Sea). #xD;The median of our probabilistic estimation is on par with proprietary inversion, regardless of the number of layers in the chosen prior or the approximate DNN model. By estimating model errors, FlexIES automatically quantifies the uncertainty in the boundaries and resistivities of layers, which is not standard in proprietary inversion. #xD;This capability allows us to capture uncertainties more efficiently, thus providing input for future quantitative decision support methods. We demonstrate the potential of quantitive decision support by visually estimating the ahead-of-bit risk of reservoir exit that has occurred during the considered operation.
{"title":"Strategic Geosteering Workflow with Uncertainty Quantification and Deep Learning: Initial Test on the Goliat Field Data","authors":"M. H. Rammay, S. Alyaev, David Larsen, R. Bratvold, S. Alyaev","doi":"10.1190/geo2023-0576.1","DOIUrl":"https://doi.org/10.1190/geo2023-0576.1","url":null,"abstract":"Continuous integration of real-time logging-while-drilling data into a subsurface model with relevant geological uncertainties enables strategic geosteering: a field-level optimization of the well-placement strategy. Model errors arising from oversimplified conceptual geological models and imperfect simulation of measurements result in unreliable subsurface-model updates. The model errors are particularly pronounced when synthetic measurements are approximated with a fast but imperfect model, such as a deep neural network (DNN).#xD;We present a practical data-assimilation workflow consisting of offline and online phases. The offline phase involves DNN training and building an uncertain prior near-well geo-model. The online phase utilizes the flexible iterative ensemble smoother (FlexIES) to perform real-time assimilation of extra-deep electromagnetic data while accounting for the model errors in the approximate DNN model. We demonstrate the proposed workflow on historic well-log data from the Goliat Field (Barents Sea). #xD;The median of our probabilistic estimation is on par with proprietary inversion, regardless of the number of layers in the chosen prior or the approximate DNN model. By estimating model errors, FlexIES automatically quantifies the uncertainty in the boundaries and resistivities of layers, which is not standard in proprietary inversion. #xD;This capability allows us to capture uncertainties more efficiently, thus providing input for future quantitative decision support methods. We demonstrate the potential of quantitive decision support by visually estimating the ahead-of-bit risk of reservoir exit that has occurred during the considered operation.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Compressive sensing (CS) is an alternative to regular Shannon sampling that captures similar information from reduced measurements. It relies on randomized sampling patterns and a sparse data representation to reconstruct the regularly sampled object. CS is an important ingredient in afford- able seismic acquisition which can lead to improvements in the near surface mapping and in noise suppression for land data. However, the near surface traps the majority of the source-generated energy, resulting in data that are rich in high-wavenumber content and have amplitudes spanning several orders of magnitude. When dealing with such high dynamic range non-stationary data, the Fourier domain is not optimal for providing a sparse representation - a necessary condition for successful application of CS. In contrast, a discrete complex wavelet transform can localize high energy features, has good directional selectivity, and is near-shift invariant. Combined, these properties allow complex wavelets to represent detail-rich wavefields in a compact form. To leverage these features and achieve good CS reconstructions, we develop a scale- and orientation- dependent iterative soft thresholding scheme (IST) for reconstructing high dynamic range wavefields. Our approach requires little parametrization, is easy to implement, and robust to reconstructed wave- field sampling grid and dynamic range. We test IST on different wavefields with randomly missing traces, and compare the data reconstructions to the spectral projected gradient solver and projection onto convex sets. We quantify the reconstructions by a direct comparison of Fourier coefficients between fully sampled and reconstructed wavefields. Taking log10 of Fourier coefficients prior to computing the quality metric de-emphasizes the importance of magnitude match while highlighting Fourier coefficient support accuracy which usually translates into good structural fidelity of reconstructed data. We find that IST performs consistently among all examples, yielding a good phase match while performing gentle denoising.
{"title":"High dynamic range land wavefield reconstruction from randomized acquisition","authors":"Iga Pawelec, Paul Sava","doi":"10.1190/geo2023-0506.1","DOIUrl":"https://doi.org/10.1190/geo2023-0506.1","url":null,"abstract":"Compressive sensing (CS) is an alternative to regular Shannon sampling that captures similar information from reduced measurements. It relies on randomized sampling patterns and a sparse data representation to reconstruct the regularly sampled object. CS is an important ingredient in afford- able seismic acquisition which can lead to improvements in the near surface mapping and in noise suppression for land data. However, the near surface traps the majority of the source-generated energy, resulting in data that are rich in high-wavenumber content and have amplitudes spanning several orders of magnitude. When dealing with such high dynamic range non-stationary data, the Fourier domain is not optimal for providing a sparse representation - a necessary condition for successful application of CS. In contrast, a discrete complex wavelet transform can localize high energy features, has good directional selectivity, and is near-shift invariant. Combined, these properties allow complex wavelets to represent detail-rich wavefields in a compact form. To leverage these features and achieve good CS reconstructions, we develop a scale- and orientation- dependent iterative soft thresholding scheme (IST) for reconstructing high dynamic range wavefields. Our approach requires little parametrization, is easy to implement, and robust to reconstructed wave- field sampling grid and dynamic range. We test IST on different wavefields with randomly missing traces, and compare the data reconstructions to the spectral projected gradient solver and projection onto convex sets. We quantify the reconstructions by a direct comparison of Fourier coefficients between fully sampled and reconstructed wavefields. Taking log10 of Fourier coefficients prior to computing the quality metric de-emphasizes the importance of magnitude match while highlighting Fourier coefficient support accuracy which usually translates into good structural fidelity of reconstructed data. We find that IST performs consistently among all examples, yielding a good phase match while performing gentle denoising.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seismic data reconstruction in five dimensions (5D) has become a central focus in seismic data processing, addressing challenges posed by irregular sampling due to physical and budgetary constraints. Most traditional high-dimensional reconstruction methods commonly utilize the fast Fourier transform (FFT), requiring regular grids and preliminary 4D binning before 5D interpolation. Discrete Fourier transform and non-equidistant FFT can honour original irregular coordinates. However, when using exact locations, these methods become computationally expensive. This study introduces an unsupervised deep-learning methodology to learn a continuous function across sampling points in seismic data, facilitating reconstruction on both regular and irregular grids. The network comprises a multilayer perceptron (MLP) with linear layers and element-wise periodic activation functions. It excels at mapping input coordinates to corresponding seismic data amplitudes without relying on external training sets. The networks intrinsic low-frequency bias is crucial in prioritizing acquiring self-similar features over high-frequency, incoherent ones during training. This characteristic mitigates incoherent noise in seismic data, including random and erratic components. To assess the robustness of the unsupervised reconstruction technique, we conduct comprehensive evaluations using synthetic data examples sampled both regularly and irregularly, as well as field-data examples with and without binning. The findings demonstrate the efficacy of the proposed deep-learning framework in achieving resilient and accurate seismic data reconstruction across diverse sampling scenarios.
{"title":"Robust unsupervised 5D seismic data reconstruction on both regular and irregular grid","authors":"Ji Li, Dawei Liu, Daniel Trad, Mauricio Sacchi","doi":"10.1190/geo2024-0098.1","DOIUrl":"https://doi.org/10.1190/geo2024-0098.1","url":null,"abstract":"Seismic data reconstruction in five dimensions (5D) has become a central focus in seismic data processing, addressing challenges posed by irregular sampling due to physical and budgetary constraints. Most traditional high-dimensional reconstruction methods commonly utilize the fast Fourier transform (FFT), requiring regular grids and preliminary 4D binning before 5D interpolation. Discrete Fourier transform and non-equidistant FFT can honour original irregular coordinates. However, when using exact locations, these methods become computationally expensive. This study introduces an unsupervised deep-learning methodology to learn a continuous function across sampling points in seismic data, facilitating reconstruction on both regular and irregular grids. The network comprises a multilayer perceptron (MLP) with linear layers and element-wise periodic activation functions. It excels at mapping input coordinates to corresponding seismic data amplitudes without relying on external training sets. The networks intrinsic low-frequency bias is crucial in prioritizing acquiring self-similar features over high-frequency, incoherent ones during training. This characteristic mitigates incoherent noise in seismic data, including random and erratic components. To assess the robustness of the unsupervised reconstruction technique, we conduct comprehensive evaluations using synthetic data examples sampled both regularly and irregularly, as well as field-data examples with and without binning. The findings demonstrate the efficacy of the proposed deep-learning framework in achieving resilient and accurate seismic data reconstruction across diverse sampling scenarios.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhen Zhang, Yang Liu, Yicheng Ye, Nan Yao, Nanyan Hu, Binyu Luo, Fei Fu, Xiaobing Luo, Jie Feng
Event classification is important for accurately monitoring and warning against rockburst hazards using microseismic technology. Here, we propose an automatic classification method for microseismic events based on machine vision. The method uses Histogram of Oriented Gradient (HOG) integrated with Support Vector Machine (SVM) as the core model (HOG-SVM, HSVM) to classify microseismic events. First, the method uses as input spectrograms generated from microseismic event signals recorded in the field. Next, the HOG method is used to accurately extract the spectral feature information of the useful signals of microseismic events under the interference of noisy signal. Finally, the extracted feature data is used to train SVM, after the training is completed, the SVM is used to classify the microseismic events. The performance of the method for categorizing microseismic events was tested using multiple independent test sets built from data monitored in the field of a mine in Shandong Province. The results show that the method can effectively extract the spectral feature information of useful signals of microseismic events contaminated with noise, with good classification accuracy and robustness to noise. It classifies microseismic events with high accuracy and efficiency compared to well-performing classification methods based on seismic source parameters and typical depth models. The method can provide technical support for the effective classification of microseismic events in complex construction sites, especially in noisy deep underground construction environments.
{"title":"Research on the classification of complex noise-mixed microseismic events based on machine vision","authors":"Zhen Zhang, Yang Liu, Yicheng Ye, Nan Yao, Nanyan Hu, Binyu Luo, Fei Fu, Xiaobing Luo, Jie Feng","doi":"10.1190/geo2023-0395.1","DOIUrl":"https://doi.org/10.1190/geo2023-0395.1","url":null,"abstract":"Event classification is important for accurately monitoring and warning against rockburst hazards using microseismic technology. Here, we propose an automatic classification method for microseismic events based on machine vision. The method uses Histogram of Oriented Gradient (HOG) integrated with Support Vector Machine (SVM) as the core model (HOG-SVM, HSVM) to classify microseismic events. First, the method uses as input spectrograms generated from microseismic event signals recorded in the field. Next, the HOG method is used to accurately extract the spectral feature information of the useful signals of microseismic events under the interference of noisy signal. Finally, the extracted feature data is used to train SVM, after the training is completed, the SVM is used to classify the microseismic events. The performance of the method for categorizing microseismic events was tested using multiple independent test sets built from data monitored in the field of a mine in Shandong Province. The results show that the method can effectively extract the spectral feature information of useful signals of microseismic events contaminated with noise, with good classification accuracy and robustness to noise. It classifies microseismic events with high accuracy and efficiency compared to well-performing classification methods based on seismic source parameters and typical depth models. The method can provide technical support for the effective classification of microseismic events in complex construction sites, especially in noisy deep underground construction environments.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141664165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seismic denoising methods using supervised methods rely on a large number of high-quality paired training datasets to reach satisfactory performances. There are two ways to generate labels for network training: one is to simulate the synthetic data using the wave equation, and the other is to utilize denoised data obtained via conventional methods. However, using these labels will limit the networks' noise attenuation performance compared with using large volumes of noise-free data as labels. Here, we propose a ground-truth-free way for three-dimensional (3-D) seismic data processing. First, we use the 3-D patch scheme to divide the noisy seismic data into many fixed-size blocks and then flatten the obtained 3-D patches to expand the training set and capture more higher-order waveform characteristics from the input noisy data. Next, the obtained training dataset is sent into the proposed deep learning (DL) network, where the encoder blocks compress the feature map to extract the waveform features, and the decoder blocks reconstruct the denoised feature map. Notably, the convolutional bottleneck attention module (CBAM) and efficient channel attention (ECA) module are applied to guide the network to focus on signal fluctuation features with fewer network parameters. In addition, the concatenation mechanism is used to enable deep networks to reuse shallow-layer waveform features and mitigate overfitting during training. Finally, the unpatching scheme is used to reconstruct the denoised 3-D seismic data. Numerical experiments demonstrate that the proposed method outperforms benchmark approaches in terms of signal-to-noise ratio (SNR) improvement and useful signal preservation.
{"title":"Ground-truth-free Deep Learning for 3-D Seismic Denoising and Reconstruction with Channel Attention Mechanism","authors":"Yang Cui, Juan Wu, M. Bai, Yangkang Chen","doi":"10.1190/geo2023-0592.1","DOIUrl":"https://doi.org/10.1190/geo2023-0592.1","url":null,"abstract":"Seismic denoising methods using supervised methods rely on a large number of high-quality paired training datasets to reach satisfactory performances. There are two ways to generate labels for network training: one is to simulate the synthetic data using the wave equation, and the other is to utilize denoised data obtained via conventional methods. However, using these labels will limit the networks' noise attenuation performance compared with using large volumes of noise-free data as labels. Here, we propose a ground-truth-free way for three-dimensional (3-D) seismic data processing. First, we use the 3-D patch scheme to divide the noisy seismic data into many fixed-size blocks and then flatten the obtained 3-D patches to expand the training set and capture more higher-order waveform characteristics from the input noisy data. Next, the obtained training dataset is sent into the proposed deep learning (DL) network, where the encoder blocks compress the feature map to extract the waveform features, and the decoder blocks reconstruct the denoised feature map. Notably, the convolutional bottleneck attention module (CBAM) and efficient channel attention (ECA) module are applied to guide the network to focus on signal fluctuation features with fewer network parameters. In addition, the concatenation mechanism is used to enable deep networks to reuse shallow-layer waveform features and mitigate overfitting during training. Finally, the unpatching scheme is used to reconstruct the denoised 3-D seismic data. Numerical experiments demonstrate that the proposed method outperforms benchmark approaches in terms of signal-to-noise ratio (SNR) improvement and useful signal preservation.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141665880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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