The inversion of DC resistivity data is a widely employed method for near-surface characterization. Recently, deep learning-based inversion techniques have garnered significant attention due to their capability to elucidate intricate non-linear relationships between geophysical data and model parameters. Nevertheless, these methods face challenges such as limited training data availability and the generation of geologically inconsistent solutions. These concerns can be mitigated through the integration of a physics-informed approach. Moreover, the quantification of prediction uncertainty is crucial yet often overlooked in deep learning-based inversion methodologies. In this study, we utilized Convolutional Neural Networks (CNNs) based Physics-Informed Neural Networks (PINNs) to invert both synthetic and field Schlumberger sounding data while also estimating prediction uncertainty via Monte Carlo dropout. For both synthetic and field case studies, the median profile estimated by PINNs is comparable to the results from existing literature, while also providing uncertainty estimates. Therefore, PINNs demonstrate significant potential for broader applications in near-surface characterization.
{"title":"Inversion of DC Resistivity Data using Physics-Informed Neural Networks","authors":"Rohan Sharma, Divakar Vashisth, Kuldeep Sarkar, Upendra Kumar Singh","doi":"arxiv-2408.02420","DOIUrl":"https://doi.org/arxiv-2408.02420","url":null,"abstract":"The inversion of DC resistivity data is a widely employed method for\u0000near-surface characterization. Recently, deep learning-based inversion\u0000techniques have garnered significant attention due to their capability to\u0000elucidate intricate non-linear relationships between geophysical data and model\u0000parameters. Nevertheless, these methods face challenges such as limited\u0000training data availability and the generation of geologically inconsistent\u0000solutions. These concerns can be mitigated through the integration of a\u0000physics-informed approach. Moreover, the quantification of prediction\u0000uncertainty is crucial yet often overlooked in deep learning-based inversion\u0000methodologies. In this study, we utilized Convolutional Neural Networks (CNNs)\u0000based Physics-Informed Neural Networks (PINNs) to invert both synthetic and\u0000field Schlumberger sounding data while also estimating prediction uncertainty\u0000via Monte Carlo dropout. For both synthetic and field case studies, the median\u0000profile estimated by PINNs is comparable to the results from existing\u0000literature, while also providing uncertainty estimates. Therefore, PINNs\u0000demonstrate significant potential for broader applications in near-surface\u0000characterization.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949415","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}
Earthquake occurrence is notoriously difficult to predict. While some aspects of their spatiotemporal statistics can be relatively well captured by point-process models, very little is known regarding the magnitude of future events, and it is deeply debated whether it is possible to predict the magnitude of an earthquake before it starts. This is due both to the lack of information about fault conditions and to the inherent complexity of rupture dynamics. Consequently, even state of the art forecasting models typically assume no knowledge about the magnitude of future events besides the time-independent Gutenberg Richter (GR) distribution, which describes the marginal distribution over large regions and long times. This approach implicitly assumes that earthquake magnitudes are independent of previous seismicity and are identically distributed. In this work we challenge this view by showing that information about the magnitude of an upcoming earthquake can be directly extracted from the seismic history. We present MAGNET - MAGnitude Neural EsTimation model, an open-source, geophysically-inspired neural-network model for probabilistic forecasting of future magnitudes from cataloged properties: hypocenter locations, occurrence times and magnitudes of past earthquakes. Our history-dependent model outperforms stationary and quasi-stationary state of the art GR-based benchmarks, in real catalogs in Southern California, Japan and New-Zealand. This demonstrates that earthquake catalogs contain information about the magnitude of future earthquakes, prior to their occurrence. We conclude by proposing methods to apply the model in characterization of the preparatory phase of earthquakes, and in operational hazard alert and earthquake forecasting systems.
地震的发生是出了名的难以预测。虽然点过程模型可以较好地捕捉到地震时空统计的某些方面，但对未来事件的震级却知之甚少，而且在地震发生之前预测其震级是否可能还存在很大争议。这既是由于缺乏有关断层条件的信息，也是由于破裂动力学本身的复杂性。因此，即使是最先进的预测模型，除了与时间无关的古腾堡-里克特（GR）分布（该分布描述了地震在大区域和长时间内的边际分布）之外，通常也不会对未来事件的震级有所了解。这种方法隐含地假定地震震级与之前的地震活动无关，并且是同分布的。在这项研究中，我们挑战了这一观点，证明可以直接从地震历史中提取即将发生地震的震级信息。我们提出了 MAGNET - MAGnitudeNeural EsTimation 模型，这是一个开源的、受地球物理启发的神经网络模型，用于从编目属性（即过去地震的震中位置、发生时间和震级）对未来震级进行概率预测。在南加州、日本和新西兰的实际地震目录中，我们的历史依赖模型优于基于 GR 的静态和准静态基准。这表明地震目录包含了未来地震发生前的震级信息。最后，我们提出了将该模型应用于地震准备阶段的特征描述以及实用危险警报和地震预报系统的方法。
{"title":"Do earthquakes \"know\" how big they will be? a neural-net aided study","authors":"Neri Berman, Oleg Zlydenko, Oren Gilon, Yossi Matias, Yohai Bar-Sinai","doi":"arxiv-2408.02129","DOIUrl":"https://doi.org/arxiv-2408.02129","url":null,"abstract":"Earthquake occurrence is notoriously difficult to predict. While some aspects\u0000of their spatiotemporal statistics can be relatively well captured by\u0000point-process models, very little is known regarding the magnitude of future\u0000events, and it is deeply debated whether it is possible to predict the\u0000magnitude of an earthquake before it starts. This is due both to the lack of\u0000information about fault conditions and to the inherent complexity of rupture\u0000dynamics. Consequently, even state of the art forecasting models typically\u0000assume no knowledge about the magnitude of future events besides the\u0000time-independent Gutenberg Richter (GR) distribution, which describes the\u0000marginal distribution over large regions and long times. This approach\u0000implicitly assumes that earthquake magnitudes are independent of previous\u0000seismicity and are identically distributed. In this work we challenge this view\u0000by showing that information about the magnitude of an upcoming earthquake can\u0000be directly extracted from the seismic history. We present MAGNET - MAGnitude\u0000Neural EsTimation model, an open-source, geophysically-inspired neural-network\u0000model for probabilistic forecasting of future magnitudes from cataloged\u0000properties: hypocenter locations, occurrence times and magnitudes of past\u0000earthquakes. Our history-dependent model outperforms stationary and\u0000quasi-stationary state of the art GR-based benchmarks, in real catalogs in\u0000Southern California, Japan and New-Zealand. This demonstrates that earthquake\u0000catalogs contain information about the magnitude of future earthquakes, prior\u0000to their occurrence. We conclude by proposing methods to apply the model in\u0000characterization of the preparatory phase of earthquakes, and in operational\u0000hazard alert and earthquake forecasting systems.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949316","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}
Ziye Yu, Yuqi Cai, Weitao Wang, Yanru An, Lu Li, Yueyang Xia, Yunpeng Zhang
We introduce a new seismic wave representation model called PRIME-DP, which stands for Pre-trained Integrated Model for Earthquake Data Processing. Unlike most of the models, which are designed to specifically a singular problem, PRIME-DP is used for multi-task single station seismic waveform processing. PRIME-DP can be used to Pg/Sg/Pn/Sn phase picking, P polarization classification. And can be fine-tunned to wide range of tasks, such as event classification, without architecture modifications. PRIME-DP can achieve over 85% recall on Pg and Sg phases, when picking continuous waveform and achieves over 80% accuracy in P polarization classification. By fine-tunning classification decoder with NeiMeng dataset, PRIME-DP can achieve 95.1% accuracy on event.
我们引入了一种新的地震波表示模型，称为 PRIME-DP，即地震数据处理预训练综合模型（Pre-trained Integrated Model for Earthquake Data Processing）。PRIME-DP 可用于 Pg/Sg/Pn/Sn 相位选取、P 极化分类。PRIME-DP 可用于 Pg/Sg/Pn/Sn 相位拾取、P 偏振分类，并可在不修改架构的情况下对事件分类等多种任务进行微调。在挑选连续波形时，PRIME-DP 对 Pg 和 Sg 相位的召回率超过 85%，对 P 极化分类的准确率超过 80%。通过使用内蒙古数据集对分类解码器进行微调，PRIME-DP 在事件分类上的准确率达到 95.1%。
{"title":"PRIME-DP: Pre-trained Integrated Model for Earthquake Data Processing","authors":"Ziye Yu, Yuqi Cai, Weitao Wang, Yanru An, Lu Li, Yueyang Xia, Yunpeng Zhang","doi":"arxiv-2408.01919","DOIUrl":"https://doi.org/arxiv-2408.01919","url":null,"abstract":"We introduce a new seismic wave representation model called PRIME-DP, which\u0000stands for Pre-trained Integrated Model for Earthquake Data Processing. Unlike\u0000most of the models, which are designed to specifically a singular problem,\u0000PRIME-DP is used for multi-task single station seismic waveform processing.\u0000PRIME-DP can be used to Pg/Sg/Pn/Sn phase picking, P polarization\u0000classification. And can be fine-tunned to wide range of tasks, such as event\u0000classification, without architecture modifications. PRIME-DP can achieve over\u000085% recall on Pg and Sg phases, when picking continuous waveform and achieves\u0000over 80% accuracy in P polarization classification. By fine-tunning\u0000classification decoder with NeiMeng dataset, PRIME-DP can achieve 95.1%\u0000accuracy on event.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949315","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}
Shiqi Dong, Xintong Dong, Kaiyuan Zheng, Ming Cheng, Tie Zhong, Hongzhou Wang
Seismic images obtained by stacking or migration are usually characterized as low signal-to-noise ratio (SNR), low dominant frequency and sparse sampling both in depth (or time) and offset dimensions. For improving the resolution of seismic images, we proposed a deep learning-based method to achieve super-resolution (SR) in only one step, which means performing the denoising, interpolation and frequency extrapolation at the same time. We design a seismic image super-resolution Transformer (SIST) to extract and fuse local and global features, which focuses more on the energy and extension shapes of effective events (horizons, folds and faults, etc.) from noisy seismic images. We extract the edge images of input images by Canny algorithm as masks to generate the input data with double channels, which improves the amplitude preservation and reduces the interference of noises. The residual groups containing Swin-Transformer blocks and residual connections consist of the backbone of SIST, which extract the global features in a window with preset size and decrease computational cost meanwhile. The pixel shuffle layers are used to up-sample the output feature maps from the backbone to improve the edges, meanwhile up-sampling the input data through a skip connection to enhance the amplitude preservation of the final images especially for clarifying weak events. 3-dimensional synthetic seismic volumes with complex geological structures are created, and the amplitudes of half of the volumes are mixtures of strong and weak, then select 2-dimensional slices randomly to generate training datasets which fits field data well to perform supervised learning. Both numerical tests on synthetic and field data in different exploration regions demonstrate the feasibility of our method.
{"title":"Transformer for seismic image super-resolution","authors":"Shiqi Dong, Xintong Dong, Kaiyuan Zheng, Ming Cheng, Tie Zhong, Hongzhou Wang","doi":"arxiv-2408.01695","DOIUrl":"https://doi.org/arxiv-2408.01695","url":null,"abstract":"Seismic images obtained by stacking or migration are usually characterized as\u0000low signal-to-noise ratio (SNR), low dominant frequency and sparse sampling\u0000both in depth (or time) and offset dimensions. For improving the resolution of\u0000seismic images, we proposed a deep learning-based method to achieve\u0000super-resolution (SR) in only one step, which means performing the denoising,\u0000interpolation and frequency extrapolation at the same time. We design a seismic\u0000image super-resolution Transformer (SIST) to extract and fuse local and global\u0000features, which focuses more on the energy and extension shapes of effective\u0000events (horizons, folds and faults, etc.) from noisy seismic images. We extract\u0000the edge images of input images by Canny algorithm as masks to generate the\u0000input data with double channels, which improves the amplitude preservation and\u0000reduces the interference of noises. The residual groups containing\u0000Swin-Transformer blocks and residual connections consist of the backbone of\u0000SIST, which extract the global features in a window with preset size and\u0000decrease computational cost meanwhile. The pixel shuffle layers are used to\u0000up-sample the output feature maps from the backbone to improve the edges,\u0000meanwhile up-sampling the input data through a skip connection to enhance the\u0000amplitude preservation of the final images especially for clarifying weak\u0000events. 3-dimensional synthetic seismic volumes with complex geological\u0000structures are created, and the amplitudes of half of the volumes are mixtures\u0000of strong and weak, then select 2-dimensional slices randomly to generate\u0000training datasets which fits field data well to perform supervised learning.\u0000Both numerical tests on synthetic and field data in different exploration\u0000regions demonstrate the feasibility of our method.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949327","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}
In the field of exploration geophysics, seismic vibrator is one of the widely used seismic sources to acquire seismic data, which is usually named vibroseis. "Ringing effect" is a common problem in vibroseis data processing due to the limited frequency bandwidth of the vibrator, which degrades the performance of first-break picking. In this paper, we proposed a novel deringing model for vibroseis data using deep convolutional neural network (CNN). In this model we use end-to-end training strategy to obtain the deringed data directly, and skip connections to improve model training process and preserve the details of vibroseis data. For real vibroseis deringing task we synthesize training data and corresponding labels from real vibroseis data and utilize them to train the deep CNN model. Experiments are conducted both on synthetic data and real vibroseis data. The experiment results show that deep CNN model can attenuate the ringing effect effectively and expand the bandwidth of vibroseis data. The STA/LTA ratio method for first-break picking also shows improvement on deringed vibroseis data using deep CNN model.
{"title":"A Deep CNN Model for Ringing Effect Attenuation of Vibroseis Data","authors":"Zhuang Jia, Wenkai Lu","doi":"arxiv-2408.01831","DOIUrl":"https://doi.org/arxiv-2408.01831","url":null,"abstract":"In the field of exploration geophysics, seismic vibrator is one of the widely\u0000used seismic sources to acquire seismic data, which is usually named vibroseis.\u0000\"Ringing effect\" is a common problem in vibroseis data processing due to the\u0000limited frequency bandwidth of the vibrator, which degrades the performance of\u0000first-break picking. In this paper, we proposed a novel deringing model for\u0000vibroseis data using deep convolutional neural network (CNN). In this model we\u0000use end-to-end training strategy to obtain the deringed data directly, and skip\u0000connections to improve model training process and preserve the details of\u0000vibroseis data. For real vibroseis deringing task we synthesize training data\u0000and corresponding labels from real vibroseis data and utilize them to train the\u0000deep CNN model. Experiments are conducted both on synthetic data and real\u0000vibroseis data. The experiment results show that deep CNN model can attenuate\u0000the ringing effect effectively and expand the bandwidth of vibroseis data. The\u0000STA/LTA ratio method for first-break picking also shows improvement on deringed\u0000vibroseis data using deep CNN model.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"186 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949317","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 mapping of the top of the inner core indicates two distinct areas of high P-wave velocity, the stronger one located beneath Asia, and the other located beneath the Atlantic. This two-fold pattern supports the idea that a lower-mantle heterogeneity can be transmitted to the inner core through outer core convection. In this study, a two-component convective dynamo model, where thermal convection is near critical and compositional convection is strongly supercritical, produces a substantial inner core heterogeneity in the rapidly rotating strongly driven regime of Earth's core. While the temperature profile that models secular cooling ensures that the mantle heterogeneity propagates as far as the inner core boundary (ICB), the distribution of heat flux at the ICB is determined by the strength of compositional buoyancy. A large heat flux variation $q^*$ of $O(10)$ at the core-mantle boundary (CMB), where $q^*$ is the ratio of the maximum heat flux difference to the mean heat flux at the CMB, produces a core flow regime of long-lived convection in the east and time-varying convection in the west. Here, the P-wave velocity estimated from the ICB heat flux in the dynamo is higher in the east than in the west, with the hemispherical difference of the same order as the observed lower bound, 0.5%. Additional observational constraints are satisfied in this regime -- the variability of high-latitude magnetic flux in the east is lower than that in the west; and the stratified F-layer at the base of the outer core, which is fed by the mass flux from regional melting of the inner core and magnetically damped, attains a steady-state height of $sim$ 200 km.
内核顶部的地震绘图显示出两个不同的高P波速度区域，一个位于亚洲下方，另一个位于大西洋下方。这种双重模式支持了下地幔异质性可以通过外核对流传递到内核的观点。在这项研究中，双成分对流动力模型（热对流接近临界，成分对流为强超临界）在快速旋转的地核强驱动机制中产生了大量的内核异质性。虽然模拟世俗冷却的温度曲线确保地幔异质性最远传播到内核边界（ICB），但内核边界的热通量分布是由成分浮力的强度决定的。地核-地幔边界（CMB）的热通量差$q^*$为$O(10)$，其中$q^*$为地核-地幔边界的最大热通量差与平均热通量之比。在这里，根据动力学中的 ICB 热通量估算出的 P 波速度在东部高于西部，半球差异与观测到的下限（0.5%）相同。在这一机制中，其他观测约束条件也得到了满足--东部高纬度磁通量的可变性低于西部；外核底部的分层F层由内核区域熔化产生的质量通量提供，并受到磁阻尼，其稳态高度为$sim$ 200千米。
{"title":"Inner core heterogeneity induced by a large variation in lower mantle heat flux","authors":"Aditya Varma, Binod Sreenivasan","doi":"arxiv-2408.03158","DOIUrl":"https://doi.org/arxiv-2408.03158","url":null,"abstract":"Seismic mapping of the top of the inner core indicates two distinct areas of\u0000high P-wave velocity, the stronger one located beneath Asia, and the other\u0000located beneath the Atlantic. This two-fold pattern supports the idea that a\u0000lower-mantle heterogeneity can be transmitted to the inner core through outer\u0000core convection. In this study, a two-component convective dynamo model, where\u0000thermal convection is near critical and compositional convection is strongly\u0000supercritical, produces a substantial inner core heterogeneity in the rapidly\u0000rotating strongly driven regime of Earth's core. While the temperature profile\u0000that models secular cooling ensures that the mantle heterogeneity propagates as\u0000far as the inner core boundary (ICB), the distribution of heat flux at the ICB\u0000is determined by the strength of compositional buoyancy. A large heat flux\u0000variation $q^*$ of $O(10)$ at the core-mantle boundary (CMB), where $q^*$ is\u0000the ratio of the maximum heat flux difference to the mean heat flux at the CMB,\u0000produces a core flow regime of long-lived convection in the east and\u0000time-varying convection in the west. Here, the P-wave velocity estimated from\u0000the ICB heat flux in the dynamo is higher in the east than in the west, with\u0000the hemispherical difference of the same order as the observed lower bound,\u00000.5%. Additional observational constraints are satisfied in this regime -- the\u0000variability of high-latitude magnetic flux in the east is lower than that in\u0000the west; and the stratified F-layer at the base of the outer core, which is\u0000fed by the mass flux from regional melting of the inner core and magnetically\u0000damped, attains a steady-state height of $sim$ 200 km.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949412","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}
Polar ice develops anisotropic crystal orientation fabrics under deformation, yet ice is most often modelled as an isotropic fluid. We present three-dimensional simulations of the crystal orientation fabric of Derwael Ice Rise including the surrounding ice shelf using a crystal orientation tensor evolution equation corresponding to a fixed velocity field. We use a semi-Lagrangian numerical method that constrains the degree of crystal orientation evolution to solve the equations in complex flow areas. We perform four simulations based on previous studies, altering the rate of evolution of the crystal anisotropy and its dependence on a combination of the strain rate and deviatoric stress tensors. We provide a framework for comparison with radar observations of the anisotropy field, outlining areas where the assumption of one vertical eigenvector may not hold and provide resulting errors in measured eigenvalues. We recognise the areas of high horizontal divergence at the ends of the flow divide as important areas to make comparisons with observations. Here, poorly constrained model parameters result in the largest difference in fabric type. These results are important in the planning of future campaigns for gathering data to constrain model parameters and as a link between observations and computationally-efficient, simplified models of anisotropy.
{"title":"Modelling the three-dimensional, diagnostic anisotropy field of an ice rise","authors":"A. Clara J. Henry, Carlos Martín, Reinhard Drews","doi":"arxiv-2408.01069","DOIUrl":"https://doi.org/arxiv-2408.01069","url":null,"abstract":"Polar ice develops anisotropic crystal orientation fabrics under deformation,\u0000yet ice is most often modelled as an isotropic fluid. We present\u0000three-dimensional simulations of the crystal orientation fabric of Derwael Ice\u0000Rise including the surrounding ice shelf using a crystal orientation tensor\u0000evolution equation corresponding to a fixed velocity field. We use a\u0000semi-Lagrangian numerical method that constrains the degree of crystal\u0000orientation evolution to solve the equations in complex flow areas. We perform\u0000four simulations based on previous studies, altering the rate of evolution of\u0000the crystal anisotropy and its dependence on a combination of the strain rate\u0000and deviatoric stress tensors. We provide a framework for comparison with radar\u0000observations of the anisotropy field, outlining areas where the assumption of\u0000one vertical eigenvector may not hold and provide resulting errors in measured\u0000eigenvalues. We recognise the areas of high horizontal divergence at the ends\u0000of the flow divide as important areas to make comparisons with observations.\u0000Here, poorly constrained model parameters result in the largest difference in\u0000fabric type. These results are important in the planning of future campaigns\u0000for gathering data to constrain model parameters and as a link between\u0000observations and computationally-efficient, simplified models of anisotropy.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949320","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}
Thomas ForbrigerKarlsruhe Institute of Technology, Nasim KaramzadehKarlsruhe Institute of Technologynow at University of Münster Institut für Geophysik, Münster, Germany, Jérôme AzzolaKarlsruhe Institute of Technology, Emmanuel GaucherKarlsruhe Institute of Technology, Rudolf Widmer-SchnidrigInstitute of Geodesy, University of Stuttgart, Stuttgart, Germany, Andreas RietbrockKarlsruhe Institute of Technology
The power of distributed acoustic sensing (DAS) lies in its ability to sample deformation signals along an optical fiber at hundreds of locations with only one interrogation unit (IU). While the IU is calibrated to record 'fiber strain', the properties of the cable and its coupling to the rock control the 'strain transfer rate' and hence how much of 'rock strain' is represented in the recorded signal. We use DAS recordings in an underground installation colocated with an array of strainmeters in order to calibrate the 'strain transfer rate' in situ, using earthquake signals between 0.05 Hz and 0.1 Hz. A tight-buffered cable and a standard loose-tube telecommunication cable (running in parallel) are used, where a section of both cables loaded down by loose sand and sand bags is compared to a section, where cables are just unreeled on the floor. The 'strain transfer rate' varies between 0.13 and 0.53 depending on cable and installation type. The sandbags show no obvious effect and the tight-buffered cable generally provides a larger 'strain transfer rate'. Calibration of the 'strain transfer rate' with respect to the strainmeter does not depend on wave propagation parameters. Hence it is applicable to the large amplitude surface wave signal in a strain component almost perpendicular to the great-circle direction for which a waveform comparison with seismometer data does not work. The noise background for 'rock strain' in the investigated band is found at about an rms-amplitude of 0.1 nstrain in 1/6 decade for the tight-buffered cable. This allows a detection of marine microseisms at times of high microseism amplitude.
分布式声学传感技术（DAS）的强大之处在于，它只需一个询问单元（IU），就能在数百个位置对光纤沿线的形变信号进行采样。虽然 IU 经过校准以记录 "光纤应变"，但光缆的特性及其与岩石的耦合控制着 "应变传递率"，从而控制着记录信号中 "岩石应变 "的大小。我们使用 DAS 记录地下装置中的应变计阵列，利用 0.05 Hz 和 0.1 Hz 之间的地震信号就地校准 "应变传递率"。我们使用了一根直管电缆和一根标准的松套管通信电缆（平行运行），将两根电缆上都装有松散沙粒和沙袋的部分与电缆在地面上松开的部分进行比较。应变传递率 "介于 0.13 和 0.53 之间，取决于电缆和安装类型。沙袋没有明显的影响，而密闭缓冲缆索通常提供更大的 "应变传递率"。因此，它适用于几乎垂直于大圆方向的应变分量中的大振幅表面波信号，在这种情况下，与地震仪数据进行波形比较是无效的。在所研究的波段中，发现密闭缓冲电缆的 "岩石应变 "噪声背景的均方根振幅约为 0.1 nstrain in 1/6 decade。这样就可以在微震振幅较高时探测到海洋微震。
{"title":"Calibration of the strain amplitude recorded with DAS using a strainmeter array","authors":"Thomas ForbrigerKarlsruhe Institute of Technology, Nasim KaramzadehKarlsruhe Institute of Technologynow at University of Münster Institut für Geophysik, Münster, Germany, Jérôme AzzolaKarlsruhe Institute of Technology, Emmanuel GaucherKarlsruhe Institute of Technology, Rudolf Widmer-SchnidrigInstitute of Geodesy, University of Stuttgart, Stuttgart, Germany, Andreas RietbrockKarlsruhe Institute of Technology","doi":"arxiv-2408.01151","DOIUrl":"https://doi.org/arxiv-2408.01151","url":null,"abstract":"The power of distributed acoustic sensing (DAS) lies in its ability to sample\u0000deformation signals along an optical fiber at hundreds of locations with only\u0000one interrogation unit (IU). While the IU is calibrated to record 'fiber\u0000strain', the properties of the cable and its coupling to the rock control the\u0000'strain transfer rate' and hence how much of 'rock strain' is represented in\u0000the recorded signal. We use DAS recordings in an underground installation\u0000colocated with an array of strainmeters in order to calibrate the 'strain\u0000transfer rate' in situ, using earthquake signals between 0.05 Hz and 0.1 Hz. A\u0000tight-buffered cable and a standard loose-tube telecommunication cable (running\u0000in parallel) are used, where a section of both cables loaded down by loose sand\u0000and sand bags is compared to a section, where cables are just unreeled on the\u0000floor. The 'strain transfer rate' varies between 0.13 and 0.53 depending on\u0000cable and installation type. The sandbags show no obvious effect and the\u0000tight-buffered cable generally provides a larger 'strain transfer rate'.\u0000Calibration of the 'strain transfer rate' with respect to the strainmeter does\u0000not depend on wave propagation parameters. Hence it is applicable to the large\u0000amplitude surface wave signal in a strain component almost perpendicular to the\u0000great-circle direction for which a waveform comparison with seismometer data\u0000does not work. The noise background for 'rock strain' in the investigated band\u0000is found at about an rms-amplitude of 0.1 nstrain in 1/6 decade for the\u0000tight-buffered cable. This allows a detection of marine microseisms at times of\u0000high microseism amplitude.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949319","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}
This work discusses how to choose performance measures to compare numerical simulations of a flood event with one satellite image, e.g., in a model calibration or validation procedure. A series of criterion are proposed to evaluate the sensitivity of performance measures with respect to the flood extent, satellite characteristics (position, orientation), and measurements/processing errors (satellite raw values or extraction of the flood maps). Their relevance is discussed numerically in the case of one flooding event (on the Garonne River in France in February 2021), using a distribution of water depths simulated from a shallow-water model parameterized by an uncertain friction field. After identifying the performance measures respecting the most criteria, a correlation analysis is carried out to identify how various performance measures are similar. Then, a methodology is proposed to rank performance measures and select the most robust to observation errors. The methodology is shown useful at identifying four performance measures out of 28 in the study case. Note that the various top-ranked performance measures do not lead to the same calibration result as regards the friction field of the shallow-water model. The methodology can be applied to the comparison of any flood model with any flood event.
{"title":"Evaluation of Performance Measures for Qualifying Flood Models with Satellite Observations","authors":"Jean-Paul Travert, Sébastien Boyaval, Cédric Goeury, Vito Bacchi, Fabrice Zaoui","doi":"arxiv-2408.00571","DOIUrl":"https://doi.org/arxiv-2408.00571","url":null,"abstract":"This work discusses how to choose performance measures to compare numerical\u0000simulations of a flood event with one satellite image, e.g., in a model\u0000calibration or validation procedure. A series of criterion are proposed to\u0000evaluate the sensitivity of performance measures with respect to the flood\u0000extent, satellite characteristics (position, orientation), and\u0000measurements/processing errors (satellite raw values or extraction of the flood\u0000maps). Their relevance is discussed numerically in the case of one flooding\u0000event (on the Garonne River in France in February 2021), using a distribution\u0000of water depths simulated from a shallow-water model parameterized by an\u0000uncertain friction field. After identifying the performance measures respecting\u0000the most criteria, a correlation analysis is carried out to identify how\u0000various performance measures are similar. Then, a methodology is proposed to\u0000rank performance measures and select the most robust to observation errors. The\u0000methodology is shown useful at identifying four performance measures out of 28\u0000in the study case. Note that the various top-ranked performance measures do not\u0000lead to the same calibration result as regards the friction field of the\u0000shallow-water model. The methodology can be applied to the comparison of any\u0000flood model with any flood event.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"105 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880847","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}
The power law of aftershock evolution was proposed by Hirano in 1924 and introduced by Utsu into seismology in the second half of the last century. The Hirano-Utsu law is widely used in studying the relaxation of earthquake source after the main shock of an earthquake. The prevailing view in the literature is that the Hirano-Utsu law is an improved version of Omori's hyperbolic law, formulated in 1894. The author disagrees with this notion. The paper proposes an axiomatic approach to the study of aftershocks. A phenomenological parameter of the source, called the deactivation coefficient, was introduced. The theory is based on axioms that do not contain any a priori statements regarding the form of the law of aftershock evolution. Formulas for the deactivation coefficient are derived from the axioms, allowing one to experimentally establish the truth or falsity of the Hirano-Utsu and Omori laws. A two-stage mode of source relaxation was discovered. In the first stage, called the Omori epoch, the Omori law is strictly followed. The Omori epoch ends with a bifurcation, after which aftershock activity becomes unpredictable. Omori's law is not fulfilled at the second stage of evolution. The Hirano-Utsu law is not fulfilled either at the first or second stage. Keywords: earthquake source, main shock, relaxation, deactivation coefficient, evolution equation, inverse problem, Omori epoch, bifurcation, two-stage relaxation mode.
{"title":"An axiomatic method for studying the truth or falsity of the Hirano-Utsu law describing aftershocks","authors":"A. V. Guglielmi","doi":"arxiv-2407.21446","DOIUrl":"https://doi.org/arxiv-2407.21446","url":null,"abstract":"The power law of aftershock evolution was proposed by Hirano in 1924 and\u0000introduced by Utsu into seismology in the second half of the last century. The\u0000Hirano-Utsu law is widely used in studying the relaxation of earthquake source\u0000after the main shock of an earthquake. The prevailing view in the literature is\u0000that the Hirano-Utsu law is an improved version of Omori's hyperbolic law,\u0000formulated in 1894. The author disagrees with this notion. The paper proposes\u0000an axiomatic approach to the study of aftershocks. A phenomenological parameter\u0000of the source, called the deactivation coefficient, was introduced. The theory\u0000is based on axioms that do not contain any a priori statements regarding the\u0000form of the law of aftershock evolution. Formulas for the deactivation\u0000coefficient are derived from the axioms, allowing one to experimentally\u0000establish the truth or falsity of the Hirano-Utsu and Omori laws. A two-stage\u0000mode of source relaxation was discovered. In the first stage, called the Omori\u0000epoch, the Omori law is strictly followed. The Omori epoch ends with a\u0000bifurcation, after which aftershock activity becomes unpredictable. Omori's law\u0000is not fulfilled at the second stage of evolution. The Hirano-Utsu law is not\u0000fulfilled either at the first or second stage. Keywords: earthquake source,\u0000main shock, relaxation, deactivation coefficient, evolution equation, inverse\u0000problem, Omori epoch, bifurcation, two-stage relaxation mode.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880843","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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