In general, velocity anisotropy in shale media has been widely observed in lab and field work, which means that disregarding this characteristic can lead to inaccurate imaging locations when data are imaged with reverse time migration (RTM). Wavefields simulated with the conventional coupled pseudo-acoustic wave equation may introduce shear wave noise and this equation is only valid in transversely isotropic media (TI, [Formula: see text]). Certain decoupled qP-wave equations require the use of the pseudo-spectral method, which makes them computationally inefficient. To address these issues, we propose a new pure qP acoustic wave equation based on the acoustic assumption, which can be solved more efficiently using the finite difference method. This equation can also be used in the forward modeling process of RTM in tilted transverse isotropic (TTI) media. First, we perform a Taylor expansion of the root term in the pure qP-wave dispersion relation. This leads to an anisotropic dispersion relation that is decomposed into an elliptical anisotropic background factor and a circular correction factor. Second, we obtain the pure qP-wave equation in TTI media without a pseudo-differential operator. The new equation can be efficiently solved using finite difference methods and can be applied to RTM in TTI media with strong anisotropy. The proposed method shows greater tolerance to numerical errors and is better suited for strong anisotropy, as compared to previously published methods. Numerical examples show the high kinematic and phase accuracy of the proposed pure qP-wave equation along with its stability in TTI media characterized by ([Formula: see text]). By utilizing a sag model and an overthrust TTI model, we demonstrate the efficiency and accuracy of the proposed TTI RTM.
{"title":"Efficient reverse time migration method in TTI media based on a pure pseudo-acoustic wave equation","authors":"Jiale Han, Jianping Huang, Yi Shen, Jidong Yang, X. Mu, Liang Chen","doi":"10.1190/geo2023-0302.1","DOIUrl":"https://doi.org/10.1190/geo2023-0302.1","url":null,"abstract":"In general, velocity anisotropy in shale media has been widely observed in lab and field work, which means that disregarding this characteristic can lead to inaccurate imaging locations when data are imaged with reverse time migration (RTM). Wavefields simulated with the conventional coupled pseudo-acoustic wave equation may introduce shear wave noise and this equation is only valid in transversely isotropic media (TI, [Formula: see text]). Certain decoupled qP-wave equations require the use of the pseudo-spectral method, which makes them computationally inefficient. To address these issues, we propose a new pure qP acoustic wave equation based on the acoustic assumption, which can be solved more efficiently using the finite difference method. This equation can also be used in the forward modeling process of RTM in tilted transverse isotropic (TTI) media. First, we perform a Taylor expansion of the root term in the pure qP-wave dispersion relation. This leads to an anisotropic dispersion relation that is decomposed into an elliptical anisotropic background factor and a circular correction factor. Second, we obtain the pure qP-wave equation in TTI media without a pseudo-differential operator. The new equation can be efficiently solved using finite difference methods and can be applied to RTM in TTI media with strong anisotropy. The proposed method shows greater tolerance to numerical errors and is better suited for strong anisotropy, as compared to previously published methods. Numerical examples show the high kinematic and phase accuracy of the proposed pure qP-wave equation along with its stability in TTI media characterized by ([Formula: see text]). By utilizing a sag model and an overthrust TTI model, we demonstrate the efficiency and accuracy of the proposed TTI RTM.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139006028","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}
Full-waveform inversion (FWI) builds subsurface parameter models by minimizing the residuals between the modeled and observed data. Accounting for the effects of anisotropy is critical for high-resolution imaging of complex structures. We develop an acoustic anisotropic FWI method based on a pure quasi P-wave (qP-wave) equation. The equation coefficients and their derivatives with respect to the Thomsen’s anisotropy parameters ([Formula: see text] and [Formula: see text]) are estimated by least-squares (LS) optimization. We derive the functional gradients and analyze the radiation patterns for six parameter classes: the velocity along the symmetry axis [Formula: see text], [Formula: see text] and [Formula: see text], the normal moveout velocity [Formula: see text], anisotropy parameters [Formula: see text] and [Formula: see text], the horizontal velocity [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], and [Formula: see text], [Formula: see text] and [Formula: see text]. The parameterization [Formula: see text] has significant tradeoff between [Formula: see text] and [Formula: see text] at the intermediate and wide scattering angles. The anisotropy parameters [Formula: see text] and [Formula: see text] are resolvable at the short scattering angles for the parameterizations [Formula: see text] and [Formula: see text], respectively. The parameter crosstalk for the parameterizations [Formula: see text] and [Formula: see text] is more serious than that for other types of parameterizations. We perform FWI of pure qP-waves in vertical and titled transversely isotropic (VTI and TTI) media. Inversion results on the Overthrust VTI model and the modified BP TTI model show that the velocity, anisotropy parameters and tilt angle can be individually reconstructed when other parameters are sufficiently accurate. The multi-parameter FWI cannot obtain reliable tilt angles for each type of parameterization. The inversion with the parameterization [Formula: see text] produces [Formula: see text], [Formula: see text] and [Formula: see text] models with modest accuracy, whereas the parameterization [Formula: see text] helps to improve the accuracy of [Formula: see text] and [Formula: see text] models.
全波形反演（FWI）通过最小化建模数据与观测数据之间的残差来建立地下参数模型。考虑各向异性的影响对于复杂结构的高分辨率成像至关重要。我们开发了一种基于纯准 P 波（qP 波）方程的声学各向异性 FWI 方法。方程系数及其与汤姆森各向异性参数（[公式：见正文]和[公式：见正文]）的导数是通过最小二乘（LS）优化估算的。我们推导出了函数梯度，并分析了六类参数的辐射模式：沿对称轴的速度[式：见正文]、[式：见正文]和[式：见正文]，法向移动速度[式：见正文]，各向异性参数[式：见正文]和[式：见正文]，水平速度[式：见正文]和[式：见正文]：见正文]、水平速度[式：见正文]、[式：见正文]和[式：见正文]、[式：见正文]、[式：见正文]和[式：见正文]、[式：见正文]和[式：见正文]、[式：见正文]和[式：见正文]、[式：见正文]和[式：见正文]。参数化[式：见正文]在中间角和宽散射角时在[式：见正文]和[式：见正文]之间有明显的折衷。各向异性参数[式：见正文]和[式：见正文]在短散射角时分别可通过参数化[式：见正文]和[式：见正文]解决。公式：见正文]和[公式：见正文]的参数串扰比其他类型的参数串扰更为严重。我们对垂直和倾斜横向各向同性（VTI 和 TTI）介质中的纯 qP 波进行了全波反演。Overthrust VTI 模型和改进的 BP TTI 模型的反演结果表明，当其他参数足够精确时，可以单独重建速度、各向异性参数和倾斜角。多参数 FWI 无法为每种参数化类型获得可靠的倾斜角。用参数化[公式：见正文]反演得到的[公式：见正文]、[公式：见正文]和[公式：见正文]模型精度不高，而参数化[公式：见正文]有助于提高[公式：见正文]和[公式：见正文]模型的精度。
{"title":"Full-waveform inversion of pure quasi-P waves in titled transversely isotropic media based on different parameterization","authors":"Zhiming Ren, Xue Dai, Lei Wang","doi":"10.1190/geo2023-0367.1","DOIUrl":"https://doi.org/10.1190/geo2023-0367.1","url":null,"abstract":"Full-waveform inversion (FWI) builds subsurface parameter models by minimizing the residuals between the modeled and observed data. Accounting for the effects of anisotropy is critical for high-resolution imaging of complex structures. We develop an acoustic anisotropic FWI method based on a pure quasi P-wave (qP-wave) equation. The equation coefficients and their derivatives with respect to the Thomsen’s anisotropy parameters ([Formula: see text] and [Formula: see text]) are estimated by least-squares (LS) optimization. We derive the functional gradients and analyze the radiation patterns for six parameter classes: the velocity along the symmetry axis [Formula: see text], [Formula: see text] and [Formula: see text], the normal moveout velocity [Formula: see text], anisotropy parameters [Formula: see text] and [Formula: see text], the horizontal velocity [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], and [Formula: see text], [Formula: see text] and [Formula: see text]. The parameterization [Formula: see text] has significant tradeoff between [Formula: see text] and [Formula: see text] at the intermediate and wide scattering angles. The anisotropy parameters [Formula: see text] and [Formula: see text] are resolvable at the short scattering angles for the parameterizations [Formula: see text] and [Formula: see text], respectively. The parameter crosstalk for the parameterizations [Formula: see text] and [Formula: see text] is more serious than that for other types of parameterizations. We perform FWI of pure qP-waves in vertical and titled transversely isotropic (VTI and TTI) media. Inversion results on the Overthrust VTI model and the modified BP TTI model show that the velocity, anisotropy parameters and tilt angle can be individually reconstructed when other parameters are sufficiently accurate. The multi-parameter FWI cannot obtain reliable tilt angles for each type of parameterization. The inversion with the parameterization [Formula: see text] produces [Formula: see text], [Formula: see text] and [Formula: see text] models with modest accuracy, whereas the parameterization [Formula: see text] helps to improve the accuracy of [Formula: see text] and [Formula: see text] models.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138983211","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 increasing use of sparse acquisitions in seismic data acquisition offers advantages in cost and time savings. However, it results in irregularly sampled seismic data, adversely impacting the quality of the final images. In this paper, we propose the ResFFT-CAE network, a convolutional neural network with residual blocks based on the Fourier transform. Incorporating residual blocks allows the network to extract both high- and low-frequency features from the seismic data. The high-frequency features capture detailed information, while the low-frequency features integrate the overall data structure, facilitating superior recovery of irregularly sampled seismic data in the trace and shot domains. We evaluated the performance of the ResFFT-CAE network on both synthetic and field data. On synthetic data, we compared the ResFFT-CAE network with the compressive sensing (CS) method utilizing the curvelet transform. For field data, we conducted comparisons with other neural networks, including the convolutional autoencoder (CAE) and U-Net. The results demonstrated that the ResFFT-CAE network consistently outperformed other approaches in all scenarios. It produced images of superior quality, characterized by lower residuals and reduced distortions. Furthermore, when evaluating model generalization, tests using models trained on synthetic data also exhibited promising results. In conclusion, the ResFFT-CAE network shows great promise as a highly efficient tool for the regularizing irregularly sampled seismic data. Its excellent performance suggests potential applications in the preconditioning of seismic data analysis and processing flows.
{"title":"Sparse seismic data regularization in both shot and trace domains using a residual block autoencoder based on the fast Fourier transform","authors":"Alexandre L. Campi, R. Misságia","doi":"10.1190/geo2023-0097.1","DOIUrl":"https://doi.org/10.1190/geo2023-0097.1","url":null,"abstract":"The increasing use of sparse acquisitions in seismic data acquisition offers advantages in cost and time savings. However, it results in irregularly sampled seismic data, adversely impacting the quality of the final images. In this paper, we propose the ResFFT-CAE network, a convolutional neural network with residual blocks based on the Fourier transform. Incorporating residual blocks allows the network to extract both high- and low-frequency features from the seismic data. The high-frequency features capture detailed information, while the low-frequency features integrate the overall data structure, facilitating superior recovery of irregularly sampled seismic data in the trace and shot domains. We evaluated the performance of the ResFFT-CAE network on both synthetic and field data. On synthetic data, we compared the ResFFT-CAE network with the compressive sensing (CS) method utilizing the curvelet transform. For field data, we conducted comparisons with other neural networks, including the convolutional autoencoder (CAE) and U-Net. The results demonstrated that the ResFFT-CAE network consistently outperformed other approaches in all scenarios. It produced images of superior quality, characterized by lower residuals and reduced distortions. Furthermore, when evaluating model generalization, tests using models trained on synthetic data also exhibited promising results. In conclusion, the ResFFT-CAE network shows great promise as a highly efficient tool for the regularizing irregularly sampled seismic data. Its excellent performance suggests potential applications in the preconditioning of seismic data analysis and processing flows.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585851","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}
Seismic processing on cross-spread gathers is a valuable but underexploited strategy. To do it properly, sources from a single source line and receivers from a single receiver line must be ordered in a physically sensible way, so that adjacent sources or receivers on a surface diagram are physically near each other. But determining such an ordering is a challenge on irregularly acquired land data. I propose novel automatic ordering algorithms using tools from graph theory that minimize large gaps and preserve the sequential patterns found even in highly irregular acquisition. Java source code is available.
{"title":"Ordering cross-spread gathers","authors":"Stewart Trickett","doi":"10.1190/geo2023-0161.1","DOIUrl":"https://doi.org/10.1190/geo2023-0161.1","url":null,"abstract":"Seismic processing on cross-spread gathers is a valuable but underexploited strategy. To do it properly, sources from a single source line and receivers from a single receiver line must be ordered in a physically sensible way, so that adjacent sources or receivers on a surface diagram are physically near each other. But determining such an ordering is a challenge on irregularly acquired land data. I propose novel automatic ordering algorithms using tools from graph theory that minimize large gaps and preserve the sequential patterns found even in highly irregular acquisition. Java source code is available.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139011090","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}
Rayleigh wave dispersion energy spectra have been widely used to extract dispersion curves and invert for underground shear-wave velocity structures for engineering geophysics and seismology. We propose a frequency-Hankel (F-H) transform method to extract high-quality multimodal Rayleigh wave dispersion energy spectra from active and passive source Rayleigh wave data. The F-H transform method is inspired by the frequency-Bessel (F-J) transform method and considers the physical meaning of Greens functions for Rayleigh wave dispersion analysis. The F-H transform method can naturally avoid crossed artefacts caused by converging waves on F-J spectrograms and obtains more multimodal dispersion spectra of the same quality with fewer Rayleigh wave data than the F-J transform method. Both synthetic and field Rayleigh wave data from active and passive sources for near-surface exploration and ambient noise tomography are used to demonstrate the validity, accuracy and applicability of the F-H transform method. The F-H transform method unifies the F-J transform method and its modifications for active and passive sources Rayleigh wave data. The F-H transform method is a robust and efficient multimodal Rayleigh wave dispersion analysis method for active and passive source Rayleigh wave data.
{"title":"A frequency-Hankel transform method to extract multimodal Rayleigh wave dispersion spectra from active and passive source surface wave data","authors":"Zhentao Yang, Yao-Chong Sun, Dazhou Zhang, Peng Han, Xiaofei Chen","doi":"10.1190/geo2023-0189.1","DOIUrl":"https://doi.org/10.1190/geo2023-0189.1","url":null,"abstract":"Rayleigh wave dispersion energy spectra have been widely used to extract dispersion curves and invert for underground shear-wave velocity structures for engineering geophysics and seismology. We propose a frequency-Hankel (F-H) transform method to extract high-quality multimodal Rayleigh wave dispersion energy spectra from active and passive source Rayleigh wave data. The F-H transform method is inspired by the frequency-Bessel (F-J) transform method and considers the physical meaning of Greens functions for Rayleigh wave dispersion analysis. The F-H transform method can naturally avoid crossed artefacts caused by converging waves on F-J spectrograms and obtains more multimodal dispersion spectra of the same quality with fewer Rayleigh wave data than the F-J transform method. Both synthetic and field Rayleigh wave data from active and passive sources for near-surface exploration and ambient noise tomography are used to demonstrate the validity, accuracy and applicability of the F-H transform method. The F-H transform method unifies the F-J transform method and its modifications for active and passive sources Rayleigh wave data. The F-H transform method is a robust and efficient multimodal Rayleigh wave dispersion analysis method for active and passive source Rayleigh wave data.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139011080","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 complex geological settings and in the presence of sparse acquisition systems, seismic migration images manifest as non-stationary blurred versions of the unknown subsurface model. Thus, image-domain deblurring is an important step to produce interpretable and high-resolution models of the subsurface. Most deblurring methods focus on inverting seismic images for their underlying reflectivity by iterative least-squares inversion of a local Hessian approximation; this is obtained by either direct modeling of the so-called point spread functions or by a migration-demigration process. In this work, we adopt a novel deep learning framework, based on invertible Recurrent Inference Machines (i-RIMs), which allows approaching any inverse problem as a supervised learning task informed by the known modeling operator (convolution with point-spread functions in our case): the proposed algorithm can directly invert migrated images for impedance perturbation models, assisted with the prior information of a smooth velocity model and the modeling operator. Because i-RIMs are constrained by the forward operator, they implicitly learn to shape/regularise output models in a training-data-driven fashion. As such, the resulting deblurred images show great robustness to noise in the data and spectral deficiencies (e.g., due to limited acquisition). The key role played by the i-RIM network design and the inclusion of the forward operator in the training process is supported by several synthetic examples. Finally, using field data, we show that i-RIM-based deblurring has great potential in yielding robust, high-quality relative impedance estimates from migrated seismic images. Our approach could be of importance towards future Deep-Learning-based quantitative reservoir characterization and monitoring.
{"title":"Image-domain seismic inversion by deblurring with invertible Recurrent Inference Machines","authors":"Haorui Peng, Ivan Vasconcelos, M. Ravasi","doi":"10.1190/geo2022-0780.1","DOIUrl":"https://doi.org/10.1190/geo2022-0780.1","url":null,"abstract":"In complex geological settings and in the presence of sparse acquisition systems, seismic migration images manifest as non-stationary blurred versions of the unknown subsurface model. Thus, image-domain deblurring is an important step to produce interpretable and high-resolution models of the subsurface. Most deblurring methods focus on inverting seismic images for their underlying reflectivity by iterative least-squares inversion of a local Hessian approximation; this is obtained by either direct modeling of the so-called point spread functions or by a migration-demigration process. In this work, we adopt a novel deep learning framework, based on invertible Recurrent Inference Machines (i-RIMs), which allows approaching any inverse problem as a supervised learning task informed by the known modeling operator (convolution with point-spread functions in our case): the proposed algorithm can directly invert migrated images for impedance perturbation models, assisted with the prior information of a smooth velocity model and the modeling operator. Because i-RIMs are constrained by the forward operator, they implicitly learn to shape/regularise output models in a training-data-driven fashion. As such, the resulting deblurred images show great robustness to noise in the data and spectral deficiencies (e.g., due to limited acquisition). The key role played by the i-RIM network design and the inclusion of the forward operator in the training process is supported by several synthetic examples. Finally, using field data, we show that i-RIM-based deblurring has great potential in yielding robust, high-quality relative impedance estimates from migrated seismic images. Our approach could be of importance towards future Deep-Learning-based quantitative reservoir characterization and monitoring.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589283","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}
Bayan Obo ore deposit is the world’s largest rare-earth element (REE) resource, the second largest niobium (Nb) resource, and also a significant iron (Fe) resource in China. Evaluating resource potential for the deposit has become a focus of global interest. Rock physical properties bridge geophysical exploration and geological modeling; variation in these parameters is necessary for successful geophysical application. REE, Nb, iron, and potassium are mainly hosted in dolomite and slate in the Bayan Obo Group, and REE mineralization is genetically associated with carbonatite. Three physical properties (resistivity, polarizability, and magnetic susceptibility (MS)) of iron ore, slate, dolomite, and carbonatite dike outcrop samples at Bayan Obo are measured and statistically analyzed using three-dimensional reconstruction, one-/two-/three-dimensional kernel density estimation, scatterplot matrix, three-dimensional histogram, and Pearson- and maximum-information-coefficient-based correlation analysis. It is evident that iron ore, iron-mineralized fine-grained dolomite, and iron-mineralized slate are mainly of low resistivity, and iron ore and iron-mineralized fine-grained dolomite have high MS. MS favorably distinguishes iron ore from slate; MS and resistivity distinguish between iron-mineralized fine-grained dolomite and carbonatite dikes. The physical properties and whole rock geochemistry (major and trace elements) jointly demonstrate that MS of iron ore, slate, and dolomite is positively correlated with TFe2O3 content, polarizability is correlated with TFe2O3, SiO2 content is correlated with K2O, and resistivity is correlated with MS and polarizability respectively. Resistivity of iron ore and dolomite is negatively correlated with TFe2O3 content. Resistivity of iron ore is negatively correlated with TFe2O3, total rare-earth element (REE), and Nb, respectively, and correlated with thorium. The methods used have intuitive visual expression and reflect the characteristics of the physical properties and their correlation with the mineralogical composition. The results will be beneficial to determining the geometry of ore-hosting rock masses and providing crucial evidence for the resource evaluation.
巴彦奥博矿床是世界上最大的稀土元素（REE）资源、第二大铌（Nb）资源，也是中国重要的铁（Fe）资源。评估该矿床的资源潜力已成为全球关注的焦点。岩石物理性质是地球物理勘探和地质建模的桥梁；这些参数的变化是地球物理应用取得成功的必要条件。REE 、Nb、铁和钾主要赋存于巴彦奥博组的白云岩和板岩中，REE 矿化在遗传上与碳酸盐岩有关。利用三维重建、一/二/三维核密度估计、散点图矩阵、三维直方图以及基于皮尔逊和最大信息系数的相关分析，对巴彦奥博的铁矿、板岩、白云岩和碳酸盐岩堤露头样品的三种物理性质（电阻率、极化率和磁感应强度（MS））进行了测量和统计分析。结果表明，铁矿石、铁矿化细粒白云岩和铁矿化板岩的电阻率主要较低，铁矿石和铁矿化细粒白云岩的 MS 值较高。MS值可将铁矿石与板岩区分开来；MS值和电阻率可将铁矿化细粒白云岩与碳酸盐岩尖晶石区分开来。物理性质和全岩地球化学（主要元素和微量元素）共同表明，铁矿石、板岩和白云岩的MS与TFe2O3含量成正相关，极化率与TFe2O3成正相关，SiO2含量与K2O成正相关，电阻率分别与MS和极化率成正相关。铁矿石和白云石的电阻率与 TFe2O3 含量呈负相关。铁矿石的电阻率分别与 TFe2O3、稀土元素总量和铌呈负相关，与钍呈正相关。所采用的方法具有直观形象的表达效果，反映了物理性质的特点及其与矿物成分的相关性。研究结果将有助于确定矿床岩体的几何形状，为资源评价提供重要依据。
{"title":"Physical property characterization of rocks in the Bayan Obo REE-Nb-Fe deposit, China","authors":"Lili Zhang, Hongrui Fan, Jian Wang, Liang Zhao, Kuifeng Yang, Ya Xu, Yonggang Zhao, Xingwang Xu, Meizhen Hao, Zhanfeng Yang, Xianhua Li","doi":"10.1190/geo2023-0439.1","DOIUrl":"https://doi.org/10.1190/geo2023-0439.1","url":null,"abstract":"Bayan Obo ore deposit is the world’s largest rare-earth element (REE) resource, the second largest niobium (Nb) resource, and also a significant iron (Fe) resource in China. Evaluating resource potential for the deposit has become a focus of global interest. Rock physical properties bridge geophysical exploration and geological modeling; variation in these parameters is necessary for successful geophysical application. REE, Nb, iron, and potassium are mainly hosted in dolomite and slate in the Bayan Obo Group, and REE mineralization is genetically associated with carbonatite. Three physical properties (resistivity, polarizability, and magnetic susceptibility (MS)) of iron ore, slate, dolomite, and carbonatite dike outcrop samples at Bayan Obo are measured and statistically analyzed using three-dimensional reconstruction, one-/two-/three-dimensional kernel density estimation, scatterplot matrix, three-dimensional histogram, and Pearson- and maximum-information-coefficient-based correlation analysis. It is evident that iron ore, iron-mineralized fine-grained dolomite, and iron-mineralized slate are mainly of low resistivity, and iron ore and iron-mineralized fine-grained dolomite have high MS. MS favorably distinguishes iron ore from slate; MS and resistivity distinguish between iron-mineralized fine-grained dolomite and carbonatite dikes. The physical properties and whole rock geochemistry (major and trace elements) jointly demonstrate that MS of iron ore, slate, and dolomite is positively correlated with TFe2O3 content, polarizability is correlated with TFe2O3, SiO2 content is correlated with K2O, and resistivity is correlated with MS and polarizability respectively. Resistivity of iron ore and dolomite is negatively correlated with TFe2O3 content. Resistivity of iron ore is negatively correlated with TFe2O3, total rare-earth element (REE), and Nb, respectively, and correlated with thorium. The methods used have intuitive visual expression and reflect the characteristics of the physical properties and their correlation with the mineralogical composition. The results will be beneficial to determining the geometry of ore-hosting rock masses and providing crucial evidence for the resource evaluation.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138984006","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}
M. Dossi, E. Forte, B. Cosciotti, S. Lauro, E. Mattei, E. Pettinelli, M. Pipan
We developed an auto-picking algorithm that is designed to automatically detect subsurface diffractors within GPR data sets; to accurately track the hyperbolic diffractions originating from the identified scatterers; and to recover the subsurface EM velocity distribution, among other possible analyses. The proposed procedure presents several advantages with respect to other commonly applied diffraction tracking techniques, since it can be applied with minimal signal pre-processing, thus making it more versatile and adaptable to local conditions; it requires only limited input from the interpreter, in the form of a few thresholds for the tracking parameters, thus making the results more objective; and it does not involve pre-training, as opposed to machine learning algorithms, thus removing the need to gather a large and comprehensive image database of all possible subsurface situations, which would not be necessarily limited to just examples of diffractions. The presented algorithm starts by identifying those signals that are likely to belong to diffraction apexes, which are then used as initial seeds by the auto-tracking process. The horizontal search window used during the auto-tracking process is locally adapted through a rough preliminary estimate of the size of each diffraction. In addition, multiple seeds within the same apex can produce several acceptable hyperbolas tracking the same diffraction phase. The algorithm thus selects the best-fitting ones by assessing several signal attributes, while also removing both redundant hyperbolas and the expected false positives. The algorithm was applied to two glaciological GPR profiles, and it was able to accurately track the vast majority of the recorded diffractions, with very few false positives and negatives. This produced a statistically sound EM velocity distribution, which was used to assess the state of the surveyed alpine glacier.
{"title":"Estimation of the subsurface EM velocity distribution from diffraction hyperbolas by means of a novel automated picking procedure: Theory and application to glaciological GPR data sets","authors":"M. Dossi, E. Forte, B. Cosciotti, S. Lauro, E. Mattei, E. Pettinelli, M. Pipan","doi":"10.1190/geo2023-0042.1","DOIUrl":"https://doi.org/10.1190/geo2023-0042.1","url":null,"abstract":"We developed an auto-picking algorithm that is designed to automatically detect subsurface diffractors within GPR data sets; to accurately track the hyperbolic diffractions originating from the identified scatterers; and to recover the subsurface EM velocity distribution, among other possible analyses. The proposed procedure presents several advantages with respect to other commonly applied diffraction tracking techniques, since it can be applied with minimal signal pre-processing, thus making it more versatile and adaptable to local conditions; it requires only limited input from the interpreter, in the form of a few thresholds for the tracking parameters, thus making the results more objective; and it does not involve pre-training, as opposed to machine learning algorithms, thus removing the need to gather a large and comprehensive image database of all possible subsurface situations, which would not be necessarily limited to just examples of diffractions. The presented algorithm starts by identifying those signals that are likely to belong to diffraction apexes, which are then used as initial seeds by the auto-tracking process. The horizontal search window used during the auto-tracking process is locally adapted through a rough preliminary estimate of the size of each diffraction. In addition, multiple seeds within the same apex can produce several acceptable hyperbolas tracking the same diffraction phase. The algorithm thus selects the best-fitting ones by assessing several signal attributes, while also removing both redundant hyperbolas and the expected false positives. The algorithm was applied to two glaciological GPR profiles, and it was able to accurately track the vast majority of the recorded diffractions, with very few false positives and negatives. This produced a statistically sound EM velocity distribution, which was used to assess the state of the surveyed alpine glacier.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138983853","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}
Yuhan Sui, Yue Ma, Lu Liu, Dongliang Zhang, Yubing Li
Multiple removal is a crucial step in seismic data processing prior to velocity model building and imaging. After the prediction, adaptive multiple subtraction is employed to suppress multiples (considered noise) in seismic data, thereby highlighting primaries (considered signal). In practice, conventional adaptive subtraction methods fit the predicted and recorded multiples in the least-squares sense using a sliding window, formulating a localized adaptive matched filter. Subsequently, the filter is applied to the prediction to remove multiples from the recorded data. However, such a strategy runs the risk of over attenuating the useful primaries under the minimization energy constraint. To avoid damage to valuable signals, we propose a novel approach that replaces the conventional matched filter with a structure-oriented version. From the predicted multiples, we extract the structural information to be used in the derivation of the adaptive matched filter. The proposed structure-oriented matched filter emphasizes the structures of predicted multiples which helps to better preserve primaries during the subtraction. Synthetic and field data examples demonstrate the efficacy of the proposed structure-oriented adaptive subtraction approach, highlighting its superior performance in multiple removal and primary preservation compared to conventional methods on 2D regularly sampled data.
{"title":"Seismic Adaptive Multiple Subtraction Using a Structure-oriented Matched Filter","authors":"Yuhan Sui, Yue Ma, Lu Liu, Dongliang Zhang, Yubing Li","doi":"10.1190/geo2023-0025.1","DOIUrl":"https://doi.org/10.1190/geo2023-0025.1","url":null,"abstract":"Multiple removal is a crucial step in seismic data processing prior to velocity model building and imaging. After the prediction, adaptive multiple subtraction is employed to suppress multiples (considered noise) in seismic data, thereby highlighting primaries (considered signal). In practice, conventional adaptive subtraction methods fit the predicted and recorded multiples in the least-squares sense using a sliding window, formulating a localized adaptive matched filter. Subsequently, the filter is applied to the prediction to remove multiples from the recorded data. However, such a strategy runs the risk of over attenuating the useful primaries under the minimization energy constraint. To avoid damage to valuable signals, we propose a novel approach that replaces the conventional matched filter with a structure-oriented version. From the predicted multiples, we extract the structural information to be used in the derivation of the adaptive matched filter. The proposed structure-oriented matched filter emphasizes the structures of predicted multiples which helps to better preserve primaries during the subtraction. Synthetic and field data examples demonstrate the efficacy of the proposed structure-oriented adaptive subtraction approach, highlighting its superior performance in multiple removal and primary preservation compared to conventional methods on 2D regularly sampled data.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594624","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 application of direction vectors in the generation of reverse time migration (RTM) angle gathers in complex acoustic anisotropic media often encounters three main challenges: not pointing to the phase-velocity direction (PVD) of the Poynting vector, inaccuracy due to overlapping wavefields, and instability due to zero points of the direction vector. In general anisotropic media, the normally used Poynting vector indicates the group-velocity direction (GVD), whereas reflection and transmission phenomena rely on the PVD. Anisotropy introduces discrepancies between the GVD and the PVD. To overcome this issue, we employ the so-called PVD vector to directly calculate the PVD from anisotropic wavefields, eliminating the need of the approxi- mated conversion from the GVD to the PVD. To mitigate the inaccuracy problem, we apply the Hilbert transform based wavefield decomposition method to separate over- lapping wavefields into their up/down components, and then we calculate the PVDs using the separated wavefields. To tackle the instability problem, we incorporate the additionally simulated quadrature wavefield during the wavefield decomposition procedure. By combining the direction vector of the quadrature wavefield with that of the original wavefield, we can eliminate the zero points and thus obtain a stabi- lized PVD vector. With those problems solved or alleviated, we present a scheme for the generation of anisotropic RTM angle gathers in complex areas. Two numerical examples utilizing synthetic data sets demonstrate our methods effectiveness.
{"title":"Reverse time migration angle gathers in acoustic anisotropic media using direction vectors","authors":"Kai Yang, Jianfeng Zhang","doi":"10.1190/geo2023-0328.1","DOIUrl":"https://doi.org/10.1190/geo2023-0328.1","url":null,"abstract":"The application of direction vectors in the generation of reverse time migration (RTM) angle gathers in complex acoustic anisotropic media often encounters three main challenges: not pointing to the phase-velocity direction (PVD) of the Poynting vector, inaccuracy due to overlapping wavefields, and instability due to zero points of the direction vector. In general anisotropic media, the normally used Poynting vector indicates the group-velocity direction (GVD), whereas reflection and transmission phenomena rely on the PVD. Anisotropy introduces discrepancies between the GVD and the PVD. To overcome this issue, we employ the so-called PVD vector to directly calculate the PVD from anisotropic wavefields, eliminating the need of the approxi- mated conversion from the GVD to the PVD. To mitigate the inaccuracy problem, we apply the Hilbert transform based wavefield decomposition method to separate over- lapping wavefields into their up/down components, and then we calculate the PVDs using the separated wavefields. To tackle the instability problem, we incorporate the additionally simulated quadrature wavefield during the wavefield decomposition procedure. By combining the direction vector of the quadrature wavefield with that of the original wavefield, we can eliminate the zero points and thus obtain a stabi- lized PVD vector. With those problems solved or alleviated, we present a scheme for the generation of anisotropic RTM angle gathers in complex areas. Two numerical examples utilizing synthetic data sets demonstrate our methods effectiveness.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138988314","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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