arXiv - PHYS - Geophysics最新文献

{"title":"Heterogeneous Seismic Waves Pattern Recognition in Oil Exploration with Spectrum Imaging","authors":"Yuyang Wang","doi":"arxiv-2407.14522","DOIUrl":"https://doi.org/arxiv-2407.14522","url":null,"abstract":"The use of seismic waves to explore the subsurface underlying the ground is a\u0000widely used method in the oil industry, since different kinds of the rocks and\u0000mediums have different reflection rate of the seismic waves, so the amplitude\u0000of the reflected waves can unraveling the geological structure and lithologic\u0000character of a certain area under the ground, but the management and processing\u0000of seismic wave data often affects the efficiency of oil exploration and\u0000development. Different kinds of the seismic data bulk are always mixed and hard\u0000to be classified manually. This paper presents a classification model for four\u0000main types of seismic data, and proposed a classification method based on\u0000Mel-spectrum. An accuracy of 98.32% was achieved using pre-trained ResNet34\u0000with transfer learning method. The accuracy is further improved compared with\u0000the pure fourier transformation method widely used in previous studies.\u0000Meanwhile, the transfer learning method and fine-tune strategy to train the\u0000neural network by training the first N-1 layers of the network separately and\u0000then train the fully connected layers further improves the training efficiency.\u0000Our model can also be seen as an efficient data quality control scheme for oil\u0000exploration and development. Meanwhile, our method is future-proofed, for\u0000further improvement of the seismic data processing quality control system,\u0000according to the spectrum characteristics, this model can be further extended\u0000into a error data classification model, reduces the workload of the bulk data\u0000management.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776510","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}

{"title":"A Method for Calculating Attenuation in Creeping Materials","authors":"Ron Maor, Nir Z. Badt, Hugo N. Ulloa, David L. Goldsby","doi":"arxiv-2407.03533","DOIUrl":"https://doi.org/arxiv-2407.03533","url":null,"abstract":"The phase lag between an applied forcing and a response to that forcing is a\u0000fundamen tal parameter in geophysical signal processing. For solid deforming\u0000materials, the phase lag between an oscillatory applied stress and the\u0000resulting strain response encapsulates information about the dynamical behavior\u0000of materials and attenuation. The phase lag is not directly measured and must\u0000be extracted through multiple steps by carefully comparing two time-series\u0000signals. The extracted value of the phase lag is highly sensitive to the\u0000analysis method, and often there are no comparable values to increase\u0000confidence in the calculated results. In this study, we propose a method for\u0000extracting the phase lag between two signals when either one or both include an\u0000underlying nonlinear trend, which is very common when measuring attenuation in\u0000creeping materials. We demonstrate the robustness of the method by analyzing\u0000artificial signals with known phases and quantifying their absolute and\u0000relative errors. We apply the method to two experimental datasets and compare\u0000our results with those of previous studies","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"141 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567348","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}

{"title":"Absorbing boundary conditions in material point method adopting perfectly matched layer theory","authors":"Jun Kurima, Bodhinanda Chandra, Kenichi Soga","doi":"arxiv-2407.02790","DOIUrl":"https://doi.org/arxiv-2407.02790","url":null,"abstract":"This study focuses on solving the numerical challenges of imposing absorbing\u0000boundary conditions for dynamic simulations in the material point method (MPM).\u0000To attenuate elastic waves leaving the computational domain, the current work\u0000integrates the Perfectly Matched Layer (PML) theory into the implicit MPM\u0000framework. The proposed approach introduces absorbing particles surrounding the\u0000computational domain that efficiently absorb outgoing waves and reduce\u0000reflections, allowing for accurate modeling of wave propagation and its further\u0000impact on geotechnical slope stability analysis. The study also includes\u0000several benchmark tests to validate the effectiveness of the proposed method,\u0000such as several types of impulse loading and symmetric and asymmetric base\u0000shaking. The conducted numerical tests also demonstrate the ability to handle\u0000large deformation problems, including the failure of elasto-plastic soils under\u0000gravity and dynamic excitations. The findings extend the capability of MPM in\u0000simulating continuous analysis of earthquake-induced landslides, from shaking\u0000to failure.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553229","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}

{"title":"Geophysical Observations of the 24 September 2023 OSIRIS-REx Sample Return Capsule Re-Entry","authors":"Elizabeth A. Silber, Daniel C. Bowman, Chris G. Carr, David P. Eisenberg, Brian R. Elbing, Benjamin Fernando, Milton A. Garcés, Robert Haaser, Siddharth Krishnamoorthy, Charles A. Langston, Yasuhiro Nishikawa, Jeremy Webster, Jacob F. Anderson, Stephen Arrowsmith, Sonia Bazargan, Luke Beardslee, Brant Beck, Jordan W. Bishop, Philip Blom, Grant Bracht, David L. Chichester, Anthony Christe, Kenneth Cummins, James Cutts, Lisa Danielson, Carly Donahue, Kenneth Eack, Michael Fleigle, Douglas Fox, Ashish Goel, David Green, Yuta Hasumi, Chris Hayward, Dan Hicks, Jay Hix, Stephen Horton, Emalee Hough, David P. Huber, Madeline A. Hunt, Jennifer Inman, S. M. Ariful Islam, Jacob Izraelevitz, Jamey D. Jacob, Jacob Clarke, James Johnson, Real J. KC, Attila Komjathy, Eric Lam, Justin LaPierre, Kevin Lewis, Richard D. Lewis, Patrick Liu, Léo Martire, Meaghan McCleary, Elisa A. McGhee, Ipsita Mitra, Amitabh Nag, Luis Ocampo Giraldo, Karen Pearson, Mathieu Plaisir, Sarah K. Popenhagen, Hamid Rassoul, Miro Ronac Giannone, Mirza Samnani, Nicholas Schmerr, Kate Spillman, Girish Srinivas, Samuel K. Takazawa, Alex Tempert, Reagan Turley, Cory Van Beek, Loïc Viens, Owen A. Walsh, Nathan Weinstein, Robert White, Brian Williams, Trevor C. Wilson, Shirin Wyckoff, Masa-yuki Yamamoto, Zachary Yap, Tyler Yoshiyama, Cleat Zeiler","doi":"arxiv-2407.02420","DOIUrl":"https://doi.org/arxiv-2407.02420","url":null,"abstract":"Sample Return Capsules (SRCs) entering Earth's atmosphere at hypervelocity\u0000from interplanetary space are a valuable resource for studying meteor\u0000phenomena. The 24 September 2023 arrival of the OSIRIS-REx (Origins, Spectral\u0000Interpretation, Resource Identification, and Security-Regolith Explorer) SRC\u0000provided an unprecedented chance for geophysical observations of a\u0000well-characterized source with known parameters, including timing and\u0000trajectory. A collaborative effort involving researchers from 16 institutions\u0000executed a carefully planned geophysical observational campaign at\u0000strategically chosen locations, deploying over 400 ground-based sensors\u0000encompassing infrasound, seismic, distributed acoustic sensing (DAS), and GPS\u0000technologies. Additionally, balloons equipped with infrasound sensors were\u0000launched to capture signals at higher altitudes. This campaign (the largest of\u0000its kind so far) yielded a wealth of invaluable data anticipated to fuel\u0000scientific inquiry for years to come. The success of the observational campaign\u0000is evidenced by the near-universal detection of signals across instruments,\u0000both proximal and distal. This paper presents a comprehensive overview of the\u0000collective scientific effort, field deployment, and preliminary findings. The\u0000early findings have the potential to inform future space missions and\u0000terrestrial campaigns, contributing to our understanding of meteoroid\u0000interactions with planetary atmospheres. Furthermore, the dataset collected\u0000during this campaign will improve entry and propagation models as well as\u0000augment the study of atmospheric dynamics and shock phenomena generated by\u0000meteoroids and similar sources.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513065","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}

{"title":"Distributed Instruments for Planetary Surface Science: Scientific Opportunities and Technology Feasibility","authors":"Federico Rossi, Robert C. Anderson, Saptarshi Bandyopadhyay, Erik Brandon, Ashish Goel, Joshua Vander Hook, Michael Mischna, Michaela Villarreal, Mark Wronkiewicz","doi":"arxiv-2407.01757","DOIUrl":"https://doi.org/arxiv-2407.01757","url":null,"abstract":"In this paper, we assess the scientific promise and technology feasibility of\u0000distributed instruments for planetary science. A distributed instrument is an\u0000instrument designed to collect spatially and temporally correlated data from\u0000multiple networked, geographically distributed point sensors. Distributed\u0000instruments are ubiquitous in Earth science, where they are routinely employed\u0000for weather and climate science, seismic studies and resource prospecting, and\u0000detection of industrial emissions. However, to date, their adoption in\u0000planetary surface science has been minimal. It is natural to ask whether this\u0000lack of adoption is driven by low potential to address high-priority questions\u0000in planetary science; immature technology; or both. To address this question,\u0000we survey high-priority planetary science questions that are uniquely\u0000well-suited to distributed instruments. We identify four areas of research\u0000where distributed instruments hold promise to unlock answers that are largely\u0000inaccessible to monolithic sensors, namely, weather and climate studies of\u0000Mars; localization of seismic events on rocky and icy bodies; localization of\u0000trace gas emissions, primarily on Mars; and magnetometry studies of internal\u0000composition. Next, we survey enabling technologies for distributed sensors and\u0000assess their maturity. We identify sensor placement (including descent and\u0000landing on planetary surfaces), power, and instrument autonomy as three key\u0000areas requiring further investment to enable future distributed instruments.\u0000Overall, this work shows that distributed instruments hold great promise for\u0000planetary science, and paves the way for follow-on studies of future\u0000distributed instruments for Solar System in-situ science.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141530522","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}

{"title":"The Nash-MTL-STCN for Prestack Three-Parameter Inversion","authors":"Yingtian Liu, Yong Li, Huating Li, Junheng Peng, Zhangquan Liao, Wen Feng","doi":"arxiv-2407.00684","DOIUrl":"https://doi.org/arxiv-2407.00684","url":null,"abstract":"Deep learning (DL) techniques have been widely used in prestack\u0000three-parameter inversion to address its ill-posed problems. Among these DL\u0000techniques, Multi-task learning (MTL) methods can simultaneously train multiple\u0000tasks, thereby enhancing model generalization and predictive performance.\u0000However, existing MTL methods typically adopt heuristic or non-heuristic\u0000approaches to jointly update the gradient of each task, leading to gradient\u0000conflicts between different tasks and reducing inversion accuracy. To address\u0000this issue, we propose a semi-supervised temporal convolutional network (STCN)\u0000based on Nash equilibrium (Nash-MTL-STCN). Firstly, temporal convolutional\u0000networks (TCN) with non-causal convolution and convolutional neural networks\u0000(CNNs) are used as multi-task layers to extract the shared features from\u0000partial angle stack seismic data, with CNNs serving as the single-task layer.\u0000Subsequently, the feature mechanism is utilized to extract shared features in\u0000the multi-task layer through hierarchical processing, and the gradient\u0000combination of these shared features is treated as a Nash game for strategy\u0000optimization and joint updates. Ultimately, the overall utility of the\u0000three-parameter is maximized, and gradient conflicts are alleviated. In\u0000addition, to enhance the network's generalization and stability, we have\u0000incorporated geophysical forward modeling and low-frequency models into the\u0000network. Experimental results demonstrate that the proposed method overcomes\u0000the gradient conflict issue of the conventional MTL methods with constant\u0000weights (CW) and achieves higher precision than four widely used non-heuristic\u0000MTL methods. Further field data experiments also validate the method's\u0000effectiveness.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508090","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}

{"title":"Features of the intra-mass buoyancy","authors":"Alexander Kochin","doi":"arxiv-2407.00798","DOIUrl":"https://doi.org/arxiv-2407.00798","url":null,"abstract":"The buoyancy force is the cause of ordered vertical movements in the\u0000atmosphere, therefore, the analysis of the causes and conditions of its\u0000formation is important not only for the formation of convective clouds, but\u0000also for understanding all atmospheric transport processes. Due to the absence\u0000of rigid boundaries inside the gas, a horizontal pressure gradient in a static\u0000state cannot exist in the allocated volume. The pressure inside the allocated\u0000volume with a different density is equal to the external pressure and the\u0000intra-mass buoyancy force, according to its definition, is formally zero. The\u0000observed force of intra-mass buoyancy arises due to the difference in vertical\u0000pressure gradients in media with different densities. In this case, the\u0000buoyancy force is volumetric, and its value corresponds to generally accepted\u0000ratios.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513068","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}

{"title":"Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data","authors":"Bas Peters, Eldad Haber, Keegan Lensink","doi":"arxiv-2407.00595","DOIUrl":"https://doi.org/arxiv-2407.00595","url":null,"abstract":"The large spatial/temporal/frequency scale of geoscience and remote-sensing\u0000datasets causes memory issues when using convolutional neural networks for\u0000(sub-) surface data segmentation. Recently developed fully reversible or fully\u0000invertible networks can mostly avoid memory limitations by recomputing the\u0000states during the backward pass through the network. This results in a low and\u0000fixed memory requirement for storing network states, as opposed to the typical\u0000linear memory growth with network depth. This work focuses on a fully\u0000invertible network based on the telegraph equation. While reversibility saves\u0000the major amount of memory used in deep networks by the data, the convolutional\u0000kernels can take up most memory if fully invertible networks contain multiple\u0000invertible pooling/coarsening layers. We address the explosion of the number of\u0000convolutional kernels by combining fully invertible networks with layers that\u0000contain the convolutional kernels in a compressed form directly. A second\u0000challenge is that invertible networks output a tensor the same size as its\u0000input. This property prevents the straightforward application of invertible\u0000networks to applications that map between different input-output dimensions,\u0000need to map to outputs with more channels than present in the input data, or\u0000desire outputs that decrease/increase the resolution compared to the input\u0000data. However, we show that by employing invertible networks in a non-standard\u0000fashion, we can still use them for these tasks. Examples in hyperspectral\u0000land-use classification, airborne geophysical surveying, and seismic imaging\u0000illustrate that we can input large data volumes in one chunk and do not need to\u0000work on small patches, use dimensionality reduction, or employ methods that\u0000classify a patch to a single central pixel.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513066","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}

{"title":"Overview and Analysis of Seismic Resilient Structures with Modular Rocking Shear Walls","authors":"Mehrdad Aghagholizadeh","doi":"arxiv-2406.19582","DOIUrl":"https://doi.org/arxiv-2406.19582","url":null,"abstract":"The high occupancy rates in urban multi-story buildings, combined with\u0000present safety concerns, necessarily prompt a reassessment of performance\u0000goals. Given the notable seismic damage and instances of weak-story failures\u0000that have been documented after major earthquakes, this paper studies the use\u0000of modular shear walls that are free to rock above their foundation. This paper\u0000first provides a comprehensive background in analysis of rocking elements such\u0000as columns and shear-walls. Then discusses different configurations of\u0000rocking-shear-walls. Next, the paper provides two numerical case studies on\u00009-story and 20-story moment-resisting frames using OpenSees. The floor\u0000displacement and interstory drifts under various earthquake excitations for\u0000both structures compared for the cases of with and without modular rocking\u0000walls. The result shows that the addition of rocking-shear-wall, makes the\u0000first mode of the frame becomes dominant which enforces a uniform distribution\u0000of interstory drifts that would avoid a soft-story failure.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513067","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}

{"title":"AI for Extreme Event Modeling and Understanding: Methodologies and Challenges","authors":"Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Adrian Höhl, Andrea Castelletti, Aytac Pacal, Claire Robin, Francesco Martinuzzi, Ioannis Papoutsis, Ioannis Prapas, Jorge Pérez-Aracil, Katja Weigel, Maria Gonzalez-Calabuig, Markus Reichstein, Martin Rabel, Matteo Giuliani, Miguel Mahecha, Oana-Iuliana Popescu, Oscar J. Pellicer-Valero, Said Ouala, Sancho Salcedo-Sanz, Sebastian Sippel, Spyros Kondylatos, Tamara Happé, Tristan Williams","doi":"arxiv-2406.20080","DOIUrl":"https://doi.org/arxiv-2406.20080","url":null,"abstract":"In recent years, artificial intelligence (AI) has deeply impacted various\u0000fields, including Earth system sciences. Here, AI improved weather forecasting,\u0000model emulation, parameter estimation, and the prediction of extreme events.\u0000However, the latter comes with specific challenges, such as developing accurate\u0000predictors from noisy, heterogeneous and limited annotated data. This paper\u0000reviews how AI is being used to analyze extreme events (like floods, droughts,\u0000wildfires and heatwaves), highlighting the importance of creating accurate,\u0000transparent, and reliable AI models. We discuss the hurdles of dealing with\u0000limited data, integrating information in real-time, deploying models, and\u0000making them understandable, all crucial for gaining the trust of stakeholders\u0000and meeting regulatory needs. We provide an overview of how AI can help\u0000identify and explain extreme events more effectively, improving disaster\u0000response and communication. We emphasize the need for collaboration across\u0000different fields to create AI solutions that are practical, understandable, and\u0000trustworthy for analyzing and predicting extreme events. Such collaborative\u0000efforts aim to enhance disaster readiness and disaster risk reduction.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513069","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}