{"title":"Simulating 2-D magnetotelluric responses using vector-quantized temporal associative memory artificial neural network-based approaches","authors":"Phongphan Mukwachi, Banchar Arnonkijpanich, Weerachai Sarakorn","doi":"10.1186/s40562-024-00328-8","DOIUrl":null,"url":null,"abstract":"In this research, we explore the application of artificial neural networks, specifically the vector-quantized temporal associative memory (VQTAM) and VQTAM coupled with locally linear embedding (VQTAM-LLE) techniques, for simulating 2-D magnetotelluric forward modeling. The study introduces the concepts of VQTAM and VQTAM-LLE in the context of simulating 2-D magnetotelluric responses, outlining their underlying principles. We rigorously evaluate the accuracy and efficiency of both VQTAM variants through extensive numerical experiments conducted on diverse benchmark resistivity and real-terrain models. The results demonstrate the remarkable capability of VQTAM and VQTAM-LLE in accurately and efficiently predicting apparent resistivity and impedance phases, surpassing the performance of traditional numerical methods. This study underscores the potential of VQTAM and VQTAM-LLE as valuable computational alternatives for simulating magnetotelluric responses, offering a viable choice alongside conventional methods.","PeriodicalId":48596,"journal":{"name":"Geoscience Letters","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience Letters","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1186/s40562-024-00328-8","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In this research, we explore the application of artificial neural networks, specifically the vector-quantized temporal associative memory (VQTAM) and VQTAM coupled with locally linear embedding (VQTAM-LLE) techniques, for simulating 2-D magnetotelluric forward modeling. The study introduces the concepts of VQTAM and VQTAM-LLE in the context of simulating 2-D magnetotelluric responses, outlining their underlying principles. We rigorously evaluate the accuracy and efficiency of both VQTAM variants through extensive numerical experiments conducted on diverse benchmark resistivity and real-terrain models. The results demonstrate the remarkable capability of VQTAM and VQTAM-LLE in accurately and efficiently predicting apparent resistivity and impedance phases, surpassing the performance of traditional numerical methods. This study underscores the potential of VQTAM and VQTAM-LLE as valuable computational alternatives for simulating magnetotelluric responses, offering a viable choice alongside conventional methods.
Geoscience LettersEarth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
4.90
自引率
2.50%
发文量
42
审稿时长
25 weeks
期刊介绍:
Geoscience Letters is the official journal of the Asia Oceania Geosciences Society, and a fully open access journal published under the SpringerOpen brand. The journal publishes original, innovative and timely research letter articles and concise reviews on studies of the Earth and its environment, the planetary and space sciences. Contributions reflect the eight scientific sections of the AOGS: Atmospheric Sciences, Biogeosciences, Hydrological Sciences, Interdisciplinary Geosciences, Ocean Sciences, Planetary Sciences, Solar and Terrestrial Sciences, and Solid Earth Sciences. Geoscience Letters focuses on cutting-edge fundamental and applied research in the broad field of the geosciences, including the applications of geoscience research to societal problems. This journal is Open Access, providing rapid electronic publication of high-quality, peer-reviewed scientific contributions.