Hongzhu Cai, Ruijin Kong, Ziang He, Xinyu Wang, Shuang Liu, Sining Huang, M. A. Kass, Xiangyun Hu
Inverting potential field data presents a significant challenge due to its ill-posed nature, often leading to non-unique model solutions. Addressing this, our work focuses on developing a robust joint inversion method for potential field data, aiming to achieve more accurate density and magnetic susceptibility distributions. Unlike most previous work that utilizes regular meshes, our approach adopts an adaptive unstructured tetrahedral mesh, offering enhanced capabilities in handling the inverse problem of potential field methods. During inversion, the tetrahedral mesh is refined in response to the model update rate. We integrate a Gramian constraint into the objective function, allowing enforcement of model similarity in terms of either model parameters or their spatial gradients on an unstructured mesh. Additionally, we employ the moving least-squares method for gradient operator computation, essential for model regularization. Our model studies indicate that this method effectively inverts potential field data, yielding reliable subsurface density and magnetic susceptibility distributions. The joint inversion approach, compared to individual dataset inversion, produces coherent geophysical models with enhanced correlations. Notably, it significantly mitigates the non-uniqueness problem, with the recovered anomaly locations aligning more closely with actual ground truths. Applying our methodology and algorithm to field data from the Ring of Fire area in Canada, the joint inversion process has generated comprehensive geophysical models with robust correlations, offering potential benefits for mineral exploration in the region.
{"title":"Joint Inversion of Potential Field Data with Adaptive Unstructured Tetrahedral Mesh","authors":"Hongzhu Cai, Ruijin Kong, Ziang He, Xinyu Wang, Shuang Liu, Sining Huang, M. A. Kass, Xiangyun Hu","doi":"10.1190/geo2023-0280.1","DOIUrl":"https://doi.org/10.1190/geo2023-0280.1","url":null,"abstract":"Inverting potential field data presents a significant challenge due to its ill-posed nature, often leading to non-unique model solutions. Addressing this, our work focuses on developing a robust joint inversion method for potential field data, aiming to achieve more accurate density and magnetic susceptibility distributions. Unlike most previous work that utilizes regular meshes, our approach adopts an adaptive unstructured tetrahedral mesh, offering enhanced capabilities in handling the inverse problem of potential field methods. During inversion, the tetrahedral mesh is refined in response to the model update rate. We integrate a Gramian constraint into the objective function, allowing enforcement of model similarity in terms of either model parameters or their spatial gradients on an unstructured mesh. Additionally, we employ the moving least-squares method for gradient operator computation, essential for model regularization. Our model studies indicate that this method effectively inverts potential field data, yielding reliable subsurface density and magnetic susceptibility distributions. The joint inversion approach, compared to individual dataset inversion, produces coherent geophysical models with enhanced correlations. Notably, it significantly mitigates the non-uniqueness problem, with the recovered anomaly locations aligning more closely with actual ground truths. Applying our methodology and algorithm to field data from the Ring of Fire area in Canada, the joint inversion process has generated comprehensive geophysical models with robust correlations, offering potential benefits for mineral exploration in the region.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859012","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}
Hongzhu Cai, Ruijin Kong, Ziang He, Xinyu Wang, Shuang Liu, Sining Huang, M. A. Kass, Xiangyun Hu
Inverting potential field data presents a significant challenge due to its ill-posed nature, often leading to non-unique model solutions. Addressing this, our work focuses on developing a robust joint inversion method for potential field data, aiming to achieve more accurate density and magnetic susceptibility distributions. Unlike most previous work that utilizes regular meshes, our approach adopts an adaptive unstructured tetrahedral mesh, offering enhanced capabilities in handling the inverse problem of potential field methods. During inversion, the tetrahedral mesh is refined in response to the model update rate. We integrate a Gramian constraint into the objective function, allowing enforcement of model similarity in terms of either model parameters or their spatial gradients on an unstructured mesh. Additionally, we employ the moving least-squares method for gradient operator computation, essential for model regularization. Our model studies indicate that this method effectively inverts potential field data, yielding reliable subsurface density and magnetic susceptibility distributions. The joint inversion approach, compared to individual dataset inversion, produces coherent geophysical models with enhanced correlations. Notably, it significantly mitigates the non-uniqueness problem, with the recovered anomaly locations aligning more closely with actual ground truths. Applying our methodology and algorithm to field data from the Ring of Fire area in Canada, the joint inversion process has generated comprehensive geophysical models with robust correlations, offering potential benefits for mineral exploration in the region.
{"title":"Joint Inversion of Potential Field Data with Adaptive Unstructured Tetrahedral Mesh","authors":"Hongzhu Cai, Ruijin Kong, Ziang He, Xinyu Wang, Shuang Liu, Sining Huang, M. A. Kass, Xiangyun Hu","doi":"10.1190/geo2023-0280.1","DOIUrl":"https://doi.org/10.1190/geo2023-0280.1","url":null,"abstract":"Inverting potential field data presents a significant challenge due to its ill-posed nature, often leading to non-unique model solutions. Addressing this, our work focuses on developing a robust joint inversion method for potential field data, aiming to achieve more accurate density and magnetic susceptibility distributions. Unlike most previous work that utilizes regular meshes, our approach adopts an adaptive unstructured tetrahedral mesh, offering enhanced capabilities in handling the inverse problem of potential field methods. During inversion, the tetrahedral mesh is refined in response to the model update rate. We integrate a Gramian constraint into the objective function, allowing enforcement of model similarity in terms of either model parameters or their spatial gradients on an unstructured mesh. Additionally, we employ the moving least-squares method for gradient operator computation, essential for model regularization. Our model studies indicate that this method effectively inverts potential field data, yielding reliable subsurface density and magnetic susceptibility distributions. The joint inversion approach, compared to individual dataset inversion, produces coherent geophysical models with enhanced correlations. Notably, it significantly mitigates the non-uniqueness problem, with the recovered anomaly locations aligning more closely with actual ground truths. Applying our methodology and algorithm to field data from the Ring of Fire area in Canada, the joint inversion process has generated comprehensive geophysical models with robust correlations, offering potential benefits for mineral exploration in the region.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799206","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}
When using forward modeling to estimate model parameters, such as the dip, it is also important to estimate the corresponding uncertainty in the model parameters. For gravity data, these uncertainties are dependent on the uncertainty in the Bouguer corrected data. The uncertainty in the gravity meter reading and the height used in the free-air and Bouguer corrections are amongst the most important factors influencing the uncertainty in the Bouguer-corrected data. We used two methods for estimating the uncertainty in the Bouguer corrected data, which give similar answers (0.121 and 0.109 mGal). The uncertainty in the model parameters can be estimated by perturbing the corrected data multiple times by amounts consistent with the estimated uncertainty in the corrected gravity. The standard deviation of the model parameters derived from each perturbed dataset gives an estimate of their uncertainty. Using this procedure for Bouguer gravity profiles that cross the Porcupine Destor fault (a fault that is prospective for gold in the Timmins camp of Ontario, Canada), we found the uncertainty in the dip was one or two degrees, assuming a planar or linear fault. If the uncertainty in the corrected data had been 1 mGal (a value typical of regional surveys, instead of 0.1 mGal for a local survey), then the uncertainty in the dip is 41 degrees for the same model. Knowing the uncertainties in the corrected data is thus very important for estimating the uncertainty in model parameters. Conversely, if a model parameter is known to be required to a specific precision, the survey can be planned so that the corrected gravity has an uncertainty appropriate to achieve that precision.
{"title":"Using gravity-data uncertainties in forward modeling to estimate uncertainties in model parameters: a case history in estimating the dip and the dip uncertainty of the Porcupine Destor Fault","authors":"Fabiano Della Justina, Richard S. Smith","doi":"10.1190/geo2023-0202.1","DOIUrl":"https://doi.org/10.1190/geo2023-0202.1","url":null,"abstract":"When using forward modeling to estimate model parameters, such as the dip, it is also important to estimate the corresponding uncertainty in the model parameters. For gravity data, these uncertainties are dependent on the uncertainty in the Bouguer corrected data. The uncertainty in the gravity meter reading and the height used in the free-air and Bouguer corrections are amongst the most important factors influencing the uncertainty in the Bouguer-corrected data. We used two methods for estimating the uncertainty in the Bouguer corrected data, which give similar answers (0.121 and 0.109 mGal). The uncertainty in the model parameters can be estimated by perturbing the corrected data multiple times by amounts consistent with the estimated uncertainty in the corrected gravity. The standard deviation of the model parameters derived from each perturbed dataset gives an estimate of their uncertainty. Using this procedure for Bouguer gravity profiles that cross the Porcupine Destor fault (a fault that is prospective for gold in the Timmins camp of Ontario, Canada), we found the uncertainty in the dip was one or two degrees, assuming a planar or linear fault. If the uncertainty in the corrected data had been 1 mGal (a value typical of regional surveys, instead of 0.1 mGal for a local survey), then the uncertainty in the dip is 41 degrees for the same model. Knowing the uncertainties in the corrected data is thus very important for estimating the uncertainty in model parameters. Conversely, if a model parameter is known to be required to a specific precision, the survey can be planned so that the corrected gravity has an uncertainty appropriate to achieve that precision.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139811204","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}
Picking the first arrival of prestack gathers is an indispensable step in seismic data processing. To enhance the efficiency of seismic data processing, some deep-learning-based methods for first arrival picking have been proposed. However, when applying currently trained models to data that significantly differs from the training set, the results are often suboptimal. We refer to this predictive scenario as cross-survey picking. Therefore, further improving model generalization for accurate cross-survey picking has become an urgent problem. To overcome the problem, we propose a multi-stage picking method named Multi-Stage Segmentation-Picking Network (MSSPN), which breaks down the complex picking task into four stages. In the first stage, we propose a Coarse Segmentation Network (CSN) to recognize a rough trend of first arrivals. Second, a robust trend estimation method is proposed in the second stage to further obtain a tighter range of first arrivals. Third, a Refined Segmentation Network (RSN) is conducted in the third stage to pick high-precision first arrivals. Finally, we propose a velocity constraint-based post-processing strategy to remove the outliers of network pickings. Extensive experiments show that MSSPN outperforms current state-of-the-art methods under the cross-survey test situation in terms of the metrics of accuracy and stability. Particularly, MSSPN achieves 94.64% and 89.74% accuracy under the cross-survey field cases of the median and low signal-noise ratio (SNR) data, respectively.
{"title":"MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation-Picking Network","authors":"Hongtao Wang, Jiangshe Zhang, Xiaoli Wei, Chunxia Zhang, Lihong Long, Zhenbo Guo","doi":"10.1190/geo2023-0110.1","DOIUrl":"https://doi.org/10.1190/geo2023-0110.1","url":null,"abstract":"Picking the first arrival of prestack gathers is an indispensable step in seismic data processing. To enhance the efficiency of seismic data processing, some deep-learning-based methods for first arrival picking have been proposed. However, when applying currently trained models to data that significantly differs from the training set, the results are often suboptimal. We refer to this predictive scenario as cross-survey picking. Therefore, further improving model generalization for accurate cross-survey picking has become an urgent problem. To overcome the problem, we propose a multi-stage picking method named Multi-Stage Segmentation-Picking Network (MSSPN), which breaks down the complex picking task into four stages. In the first stage, we propose a Coarse Segmentation Network (CSN) to recognize a rough trend of first arrivals. Second, a robust trend estimation method is proposed in the second stage to further obtain a tighter range of first arrivals. Third, a Refined Segmentation Network (RSN) is conducted in the third stage to pick high-precision first arrivals. Finally, we propose a velocity constraint-based post-processing strategy to remove the outliers of network pickings. Extensive experiments show that MSSPN outperforms current state-of-the-art methods under the cross-survey test situation in terms of the metrics of accuracy and stability. Particularly, MSSPN achieves 94.64% and 89.74% accuracy under the cross-survey field cases of the median and low signal-noise ratio (SNR) data, respectively.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139871266","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}
Fractures are widely distributed underground. The stiffness matrix of fractured rocks has been extensively investigated in a fluid-saturated porous background medium. However, the existing stiffness models only incorporated the attenuation mechanism of wave-induced fluid flow (WIFF). For macroscopic fractures, the elastic scattering (ES) of fractures cannot be ignored. To alleviate this issue, a frequency-dependent stiffness matrix model was developed, including the mesoscopic wave-induced fluid flow between fractures and background (FB-WIFF), the microscopic squirt flow, and the macroscopic ES from the fractures. By combining the far-field scattered wavefields of normal incident P and SV waves with the linear slip theory, the dynamic full-stiffness matrices for fracture-induced effective VTI rocks in a fluid-saturated porous and microcracked background were derived. Then, the P, SV, and SH wave velocities and attenuation can be obtained through the Kelvin-Christoffel equation. The results indicate that the FB-WIFF mechanism significantly affects the velocities and attenuation of the P and SV waves, but has nearly no effect on the SH wave, while the squirt flow and ES mechanisms affect the velocities and attenuation of both the P, SV, and SH waves. For validation, the model was compared with existing models and previous experimental ultrasonic data.
断裂广泛分布于地下。在流体饱和的多孔背景介质中，人们对断裂岩石的刚度矩阵进行了广泛研究。然而，现有的刚度模型只包含了波诱导流体流动（WIFF）的衰减机制。对于宏观裂缝，裂缝的弹性散射（ES）不容忽视。为了缓解这一问题，我们开发了一种频率相关的刚度矩阵模型，其中包括裂缝与背景之间的中观波致流体流（FB-WIFF）、微观喷射流以及来自裂缝的宏观 ES。通过将法向入射 P 波和 SV 波的远场散射波场与线性滑移理论相结合，得出了在流体饱和的多孔微裂缝背景中裂缝诱导有效 VTI 岩石的动态全刚度矩阵。然后，通过开尔文-克里斯托弗方程可以得到 P 波、SV 波和 SH 波的速度和衰减。结果表明，FB-WIFF 机制对 P 波和 SV 波的速度和衰减有很大影响，但对 SH 波几乎没有影响，而喷射流和 ES 机制则对 P 波、SV 波和 SH 波的速度和衰减都有影响。为了进行验证，将该模型与现有模型和以前的超声波实验数据进行了比较。
{"title":"Frequency-dependent elastic properties of fracture-induced VTI rocks in a fluid-saturated porous and microcracked background","authors":"Wenhao Wang, Shengqing Li, Junxin Guo, Chengsen Zhang, Wenxing Duan, Yuanda Su, Xiao-Ming Tang","doi":"10.1190/geo2023-0229.1","DOIUrl":"https://doi.org/10.1190/geo2023-0229.1","url":null,"abstract":"Fractures are widely distributed underground. The stiffness matrix of fractured rocks has been extensively investigated in a fluid-saturated porous background medium. However, the existing stiffness models only incorporated the attenuation mechanism of wave-induced fluid flow (WIFF). For macroscopic fractures, the elastic scattering (ES) of fractures cannot be ignored. To alleviate this issue, a frequency-dependent stiffness matrix model was developed, including the mesoscopic wave-induced fluid flow between fractures and background (FB-WIFF), the microscopic squirt flow, and the macroscopic ES from the fractures. By combining the far-field scattered wavefields of normal incident P and SV waves with the linear slip theory, the dynamic full-stiffness matrices for fracture-induced effective VTI rocks in a fluid-saturated porous and microcracked background were derived. Then, the P, SV, and SH wave velocities and attenuation can be obtained through the Kelvin-Christoffel equation. The results indicate that the FB-WIFF mechanism significantly affects the velocities and attenuation of the P and SV waves, but has nearly no effect on the SH wave, while the squirt flow and ES mechanisms affect the velocities and attenuation of both the P, SV, and SH waves. For validation, the model was compared with existing models and previous experimental ultrasonic data.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139809307","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}
Picking the first arrival of prestack gathers is an indispensable step in seismic data processing. To enhance the efficiency of seismic data processing, some deep-learning-based methods for first arrival picking have been proposed. However, when applying currently trained models to data that significantly differs from the training set, the results are often suboptimal. We refer to this predictive scenario as cross-survey picking. Therefore, further improving model generalization for accurate cross-survey picking has become an urgent problem. To overcome the problem, we propose a multi-stage picking method named Multi-Stage Segmentation-Picking Network (MSSPN), which breaks down the complex picking task into four stages. In the first stage, we propose a Coarse Segmentation Network (CSN) to recognize a rough trend of first arrivals. Second, a robust trend estimation method is proposed in the second stage to further obtain a tighter range of first arrivals. Third, a Refined Segmentation Network (RSN) is conducted in the third stage to pick high-precision first arrivals. Finally, we propose a velocity constraint-based post-processing strategy to remove the outliers of network pickings. Extensive experiments show that MSSPN outperforms current state-of-the-art methods under the cross-survey test situation in terms of the metrics of accuracy and stability. Particularly, MSSPN achieves 94.64% and 89.74% accuracy under the cross-survey field cases of the median and low signal-noise ratio (SNR) data, respectively.
{"title":"MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation-Picking Network","authors":"Hongtao Wang, Jiangshe Zhang, Xiaoli Wei, Chunxia Zhang, Lihong Long, Zhenbo Guo","doi":"10.1190/geo2023-0110.1","DOIUrl":"https://doi.org/10.1190/geo2023-0110.1","url":null,"abstract":"Picking the first arrival of prestack gathers is an indispensable step in seismic data processing. To enhance the efficiency of seismic data processing, some deep-learning-based methods for first arrival picking have been proposed. However, when applying currently trained models to data that significantly differs from the training set, the results are often suboptimal. We refer to this predictive scenario as cross-survey picking. Therefore, further improving model generalization for accurate cross-survey picking has become an urgent problem. To overcome the problem, we propose a multi-stage picking method named Multi-Stage Segmentation-Picking Network (MSSPN), which breaks down the complex picking task into four stages. In the first stage, we propose a Coarse Segmentation Network (CSN) to recognize a rough trend of first arrivals. Second, a robust trend estimation method is proposed in the second stage to further obtain a tighter range of first arrivals. Third, a Refined Segmentation Network (RSN) is conducted in the third stage to pick high-precision first arrivals. Finally, we propose a velocity constraint-based post-processing strategy to remove the outliers of network pickings. Extensive experiments show that MSSPN outperforms current state-of-the-art methods under the cross-survey test situation in terms of the metrics of accuracy and stability. Particularly, MSSPN achieves 94.64% and 89.74% accuracy under the cross-survey field cases of the median and low signal-noise ratio (SNR) data, respectively.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139811408","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}
Fractures are widely distributed underground. The stiffness matrix of fractured rocks has been extensively investigated in a fluid-saturated porous background medium. However, the existing stiffness models only incorporated the attenuation mechanism of wave-induced fluid flow (WIFF). For macroscopic fractures, the elastic scattering (ES) of fractures cannot be ignored. To alleviate this issue, a frequency-dependent stiffness matrix model was developed, including the mesoscopic wave-induced fluid flow between fractures and background (FB-WIFF), the microscopic squirt flow, and the macroscopic ES from the fractures. By combining the far-field scattered wavefields of normal incident P and SV waves with the linear slip theory, the dynamic full-stiffness matrices for fracture-induced effective VTI rocks in a fluid-saturated porous and microcracked background were derived. Then, the P, SV, and SH wave velocities and attenuation can be obtained through the Kelvin-Christoffel equation. The results indicate that the FB-WIFF mechanism significantly affects the velocities and attenuation of the P and SV waves, but has nearly no effect on the SH wave, while the squirt flow and ES mechanisms affect the velocities and attenuation of both the P, SV, and SH waves. For validation, the model was compared with existing models and previous experimental ultrasonic data.
断裂广泛分布于地下。在流体饱和的多孔背景介质中，人们对断裂岩石的刚度矩阵进行了广泛研究。然而，现有的刚度模型只包含了波诱导流体流动（WIFF）的衰减机制。对于宏观裂缝，裂缝的弹性散射（ES）不容忽视。为了缓解这一问题，我们开发了一种频率相关的刚度矩阵模型，其中包括裂缝与背景之间的中观波致流体流（FB-WIFF）、微观喷射流以及来自裂缝的宏观 ES。通过将法向入射 P 波和 SV 波的远场散射波场与线性滑移理论相结合，得出了在流体饱和的多孔微裂缝背景中裂缝诱导有效 VTI 岩石的动态全刚度矩阵。然后，通过开尔文-克里斯托弗方程可以得到 P 波、SV 波和 SH 波的速度和衰减。结果表明，FB-WIFF 机制对 P 波和 SV 波的速度和衰减有很大影响，但对 SH 波几乎没有影响，而喷射流和 ES 机制则对 P 波、SV 波和 SH 波的速度和衰减都有影响。为了进行验证，将该模型与现有模型和以前的超声波实验数据进行了比较。
{"title":"Frequency-dependent elastic properties of fracture-induced VTI rocks in a fluid-saturated porous and microcracked background","authors":"Wenhao Wang, Shengqing Li, Junxin Guo, Chengsen Zhang, Wenxing Duan, Yuanda Su, Xiao-Ming Tang","doi":"10.1190/geo2023-0229.1","DOIUrl":"https://doi.org/10.1190/geo2023-0229.1","url":null,"abstract":"Fractures are widely distributed underground. The stiffness matrix of fractured rocks has been extensively investigated in a fluid-saturated porous background medium. However, the existing stiffness models only incorporated the attenuation mechanism of wave-induced fluid flow (WIFF). For macroscopic fractures, the elastic scattering (ES) of fractures cannot be ignored. To alleviate this issue, a frequency-dependent stiffness matrix model was developed, including the mesoscopic wave-induced fluid flow between fractures and background (FB-WIFF), the microscopic squirt flow, and the macroscopic ES from the fractures. By combining the far-field scattered wavefields of normal incident P and SV waves with the linear slip theory, the dynamic full-stiffness matrices for fracture-induced effective VTI rocks in a fluid-saturated porous and microcracked background were derived. Then, the P, SV, and SH wave velocities and attenuation can be obtained through the Kelvin-Christoffel equation. The results indicate that the FB-WIFF mechanism significantly affects the velocities and attenuation of the P and SV waves, but has nearly no effect on the SH wave, while the squirt flow and ES mechanisms affect the velocities and attenuation of both the P, SV, and SH waves. For validation, the model was compared with existing models and previous experimental ultrasonic data.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139869147","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}
When using forward modeling to estimate model parameters, such as the dip, it is also important to estimate the corresponding uncertainty in the model parameters. For gravity data, these uncertainties are dependent on the uncertainty in the Bouguer corrected data. The uncertainty in the gravity meter reading and the height used in the free-air and Bouguer corrections are amongst the most important factors influencing the uncertainty in the Bouguer-corrected data. We used two methods for estimating the uncertainty in the Bouguer corrected data, which give similar answers (0.121 and 0.109 mGal). The uncertainty in the model parameters can be estimated by perturbing the corrected data multiple times by amounts consistent with the estimated uncertainty in the corrected gravity. The standard deviation of the model parameters derived from each perturbed dataset gives an estimate of their uncertainty. Using this procedure for Bouguer gravity profiles that cross the Porcupine Destor fault (a fault that is prospective for gold in the Timmins camp of Ontario, Canada), we found the uncertainty in the dip was one or two degrees, assuming a planar or linear fault. If the uncertainty in the corrected data had been 1 mGal (a value typical of regional surveys, instead of 0.1 mGal for a local survey), then the uncertainty in the dip is 41 degrees for the same model. Knowing the uncertainties in the corrected data is thus very important for estimating the uncertainty in model parameters. Conversely, if a model parameter is known to be required to a specific precision, the survey can be planned so that the corrected gravity has an uncertainty appropriate to achieve that precision.
{"title":"Using gravity-data uncertainties in forward modeling to estimate uncertainties in model parameters: a case history in estimating the dip and the dip uncertainty of the Porcupine Destor Fault","authors":"Fabiano Della Justina, Richard S. Smith","doi":"10.1190/geo2023-0202.1","DOIUrl":"https://doi.org/10.1190/geo2023-0202.1","url":null,"abstract":"When using forward modeling to estimate model parameters, such as the dip, it is also important to estimate the corresponding uncertainty in the model parameters. For gravity data, these uncertainties are dependent on the uncertainty in the Bouguer corrected data. The uncertainty in the gravity meter reading and the height used in the free-air and Bouguer corrections are amongst the most important factors influencing the uncertainty in the Bouguer-corrected data. We used two methods for estimating the uncertainty in the Bouguer corrected data, which give similar answers (0.121 and 0.109 mGal). The uncertainty in the model parameters can be estimated by perturbing the corrected data multiple times by amounts consistent with the estimated uncertainty in the corrected gravity. The standard deviation of the model parameters derived from each perturbed dataset gives an estimate of their uncertainty. Using this procedure for Bouguer gravity profiles that cross the Porcupine Destor fault (a fault that is prospective for gold in the Timmins camp of Ontario, Canada), we found the uncertainty in the dip was one or two degrees, assuming a planar or linear fault. If the uncertainty in the corrected data had been 1 mGal (a value typical of regional surveys, instead of 0.1 mGal for a local survey), then the uncertainty in the dip is 41 degrees for the same model. Knowing the uncertainties in the corrected data is thus very important for estimating the uncertainty in model parameters. Conversely, if a model parameter is known to be required to a specific precision, the survey can be planned so that the corrected gravity has an uncertainty appropriate to achieve that precision.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139871286","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}
Full waveform inversion (FWI) has been proven as an effective method to estimate subsurface parameters by iteratively reducing the data residual between the predictions and the observations. Nevertheless, FWI is greatly dependent on the initial model and a poor initial model will lead to a wrong solution. Furthermore, owing to the anelasticity of the earth, seismic waves will attenuate during propagation, which results in an attenuated gradient and makes the convergence rate of FWI even worse in viscoacoustic media. To mitigate these problems, we propose an improved method for multiparameter (e.g. velocity and Q) waveform inversion. Benefiting from the theory of Q-compensated wavefield propagation, we formulate a Q-compensated joint multiparameter waveform inversion method to weaken the nonlinearity of the FWI objective function, which enables it to cope with challenges related with attenuation-induced gradient energy loss and cycle skipping simultaneously. We refer to the proposed Q-compensated joint multiparameter FWI scheme as QJMFWI. The main contributions of QJMFWI are: (1) given the difficulty associated with the estimating of velocity and Q simultaneously in viscoacoustic media, QJMFWI provides a straightforward waveform inversion method for velocity and Q model construction, by which we can obtain velocity and Q information with improved accuracy and resolution; (2) compared with conventional FWI methods, QJMFWI relaxes the requirement for good initial velocity and Q model, which can avoid trapping into local minima. Numerical and field data examples demonstrate that QJMFWI is an effective method to invert for accurate subsurface parameters in viscoacoustic media.
{"title":"Robust joint adaptive multiparameter waveform inversion with attenuation compensation in viscoacoustic media","authors":"Chao Li, Guochang Liu, Fang Li, Zhiyong Wang","doi":"10.1190/geo2022-0663.1","DOIUrl":"https://doi.org/10.1190/geo2022-0663.1","url":null,"abstract":"Full waveform inversion (FWI) has been proven as an effective method to estimate subsurface parameters by iteratively reducing the data residual between the predictions and the observations. Nevertheless, FWI is greatly dependent on the initial model and a poor initial model will lead to a wrong solution. Furthermore, owing to the anelasticity of the earth, seismic waves will attenuate during propagation, which results in an attenuated gradient and makes the convergence rate of FWI even worse in viscoacoustic media. To mitigate these problems, we propose an improved method for multiparameter (e.g. velocity and Q) waveform inversion. Benefiting from the theory of Q-compensated wavefield propagation, we formulate a Q-compensated joint multiparameter waveform inversion method to weaken the nonlinearity of the FWI objective function, which enables it to cope with challenges related with attenuation-induced gradient energy loss and cycle skipping simultaneously. We refer to the proposed Q-compensated joint multiparameter FWI scheme as QJMFWI. The main contributions of QJMFWI are: (1) given the difficulty associated with the estimating of velocity and Q simultaneously in viscoacoustic media, QJMFWI provides a straightforward waveform inversion method for velocity and Q model construction, by which we can obtain velocity and Q information with improved accuracy and resolution; (2) compared with conventional FWI methods, QJMFWI relaxes the requirement for good initial velocity and Q model, which can avoid trapping into local minima. Numerical and field data examples demonstrate that QJMFWI is an effective method to invert for accurate subsurface parameters in viscoacoustic media.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139592923","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}
Yanlong Niu, Gang Fang, Yunyue Elita Li, S. C. Chian, E. Nilot
We propose a new automatic framework for non-destructive multi-channel analysis of surface waves (MASW) that combines multi-mode dispersion spectrum matching and the finite element method (FEM)-based inversion to enhance the accuracy of subsurface profiling in site investigation activities. This framework eliminates the need for manual identification of the Rayleigh wave energy component and multi-mode assignment, reducing the dependence on operator experience and judgment. The dispersion spectrum is generated through a FEM model that simulates 2D seismic wave propagation, taking into account the actual acquisition layout and lateral variations in the subsurface. We introduce the Wasserstein distance (WD) for evaluating the difference between observed and simulated spectra, and incorporate Bayesian optimization for efficiently inverting shear wave velocity profiles. The effectiveness of the proposed framework is demonstrated through synthetic data examples, and the superiority of the WD-based objective function is illustrated by comparing it with the conventional mean square error (MSE)-based objective function. Subsequently, we conduct a field test on a reclaimed landfill to validate the proposed framework. This test confirms the ability of framework to retrieve multi-mode Rayleigh waves and demonstrates its effectiveness in providing high-resolution shear wave profiles of the shallow subsurface.
{"title":"Multi-Mode Rayleigh Wave Dispersion Spectrum Inversion Using Wasserstein Distance Coupled with Bayesian Optimization","authors":"Yanlong Niu, Gang Fang, Yunyue Elita Li, S. C. Chian, E. Nilot","doi":"10.1190/geo2023-0223.1","DOIUrl":"https://doi.org/10.1190/geo2023-0223.1","url":null,"abstract":"We propose a new automatic framework for non-destructive multi-channel analysis of surface waves (MASW) that combines multi-mode dispersion spectrum matching and the finite element method (FEM)-based inversion to enhance the accuracy of subsurface profiling in site investigation activities. This framework eliminates the need for manual identification of the Rayleigh wave energy component and multi-mode assignment, reducing the dependence on operator experience and judgment. The dispersion spectrum is generated through a FEM model that simulates 2D seismic wave propagation, taking into account the actual acquisition layout and lateral variations in the subsurface. We introduce the Wasserstein distance (WD) for evaluating the difference between observed and simulated spectra, and incorporate Bayesian optimization for efficiently inverting shear wave velocity profiles. The effectiveness of the proposed framework is demonstrated through synthetic data examples, and the superiority of the WD-based objective function is illustrated by comparing it with the conventional mean square error (MSE)-based objective function. Subsequently, we conduct a field test on a reclaimed landfill to validate the proposed framework. This test confirms the ability of framework to retrieve multi-mode Rayleigh waves and demonstrates its effectiveness in providing high-resolution shear wave profiles of the shallow subsurface.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139594353","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：