{"title":"利用多目标灰狼优化法联合反演直流电阻率和 MT 数据","authors":"Rohan Sharma, Divakar Vashisth, Kuldeep Sarkar, Upendra Kumar Singh","doi":"arxiv-2408.02414","DOIUrl":null,"url":null,"abstract":"Joint inversion of geophysical datasets is instrumental in subsurface\ncharacterization and has garnered significant popularity, leveraging\ninformation from multiple geophysical methods. In this study, we implemented\nthe joint inversion of DC resistivity with MT data using the Multi-Objective\nGrey Wolf Optimization (MOGWO) algorithm. As an extension of the widely-used\nGrey Wolf Optimization algorithm, MOGWO offers a suite of pareto optimal\nnon-dominated solutions, eliminating the need for weighting parameters in the\nobjective functions. This set of non-dominated predictions also facilitates the\nunderstanding of uncertainty in the predicted model parameters. Through a field\ncase study in the region around Broken Hill in South Central Australia, the\npaper showcases MOGWO's capabilities in joint inversion, providing confident\nestimates of the model parameters (resistivity profiles), as indicated by a\nnarrow spread in the suite of solutions. The obtained results are comparable to\nwell established methodologies and highlight the efficacy of MOGWO as a\nreliable tool in geophysical exploration.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"88 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Joint Inversion of DC Resistivity and MT Data using Multi-Objective Grey Wolf Optimization\",\"authors\":\"Rohan Sharma, Divakar Vashisth, Kuldeep Sarkar, Upendra Kumar Singh\",\"doi\":\"arxiv-2408.02414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Joint inversion of geophysical datasets is instrumental in subsurface\\ncharacterization and has garnered significant popularity, leveraging\\ninformation from multiple geophysical methods. In this study, we implemented\\nthe joint inversion of DC resistivity with MT data using the Multi-Objective\\nGrey Wolf Optimization (MOGWO) algorithm. As an extension of the widely-used\\nGrey Wolf Optimization algorithm, MOGWO offers a suite of pareto optimal\\nnon-dominated solutions, eliminating the need for weighting parameters in the\\nobjective functions. This set of non-dominated predictions also facilitates the\\nunderstanding of uncertainty in the predicted model parameters. Through a field\\ncase study in the region around Broken Hill in South Central Australia, the\\npaper showcases MOGWO's capabilities in joint inversion, providing confident\\nestimates of the model parameters (resistivity profiles), as indicated by a\\nnarrow spread in the suite of solutions. The obtained results are comparable to\\nwell established methodologies and highlight the efficacy of MOGWO as a\\nreliable tool in geophysical exploration.\",\"PeriodicalId\":501270,\"journal\":{\"name\":\"arXiv - PHYS - Geophysics\",\"volume\":\"88 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Geophysics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.02414\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Geophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.02414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Joint Inversion of DC Resistivity and MT Data using Multi-Objective Grey Wolf Optimization
Joint inversion of geophysical datasets is instrumental in subsurface
characterization and has garnered significant popularity, leveraging
information from multiple geophysical methods. In this study, we implemented
the joint inversion of DC resistivity with MT data using the Multi-Objective
Grey Wolf Optimization (MOGWO) algorithm. As an extension of the widely-used
Grey Wolf Optimization algorithm, MOGWO offers a suite of pareto optimal
non-dominated solutions, eliminating the need for weighting parameters in the
objective functions. This set of non-dominated predictions also facilitates the
understanding of uncertainty in the predicted model parameters. Through a field
case study in the region around Broken Hill in South Central Australia, the
paper showcases MOGWO's capabilities in joint inversion, providing confident
estimates of the model parameters (resistivity profiles), as indicated by a
narrow spread in the suite of solutions. The obtained results are comparable to
well established methodologies and highlight the efficacy of MOGWO as a
reliable tool in geophysical exploration.