eprintid: 11651
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/16/51
datestamp: 2023-11-10 03:26:10
lastmod: 2023-11-10 03:26:10
status_changed: 2023-11-10 01:15:46
type: conference_item
metadata_visibility: show
creators_name: Hamamoto, M.
creators_name: Rahim Md Arshad, A.
creators_name: Prasad Ghosh, D.
title: Full waveform inversion based on genetic local search algorithm with hybrid-grid scheme
ispublished: pub
keywords: Electric arcs; Frequency estimation; Genetic algorithms; Global optimization; Industrial electronics; Learning algorithms; Local search (optimization); Seismology; Waveform analysis, Full-waveform inversion; Genetic local search; Genetic local search algorithm; Global optimization method; Gradient-based optimization; High resolution velocity; Stochastic optimizations; Velocity model, Iterative methods
note: cited By 0; Conference of 9th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2019 ; Conference Date: 27 April 2019 Through 28 April 2019; Conference Code:149055
abstract: Seismic full waveform inversion (FWI) is a technique to build a high-resolution velocity model of the subsurface by iteratively minimizing the misfit between recorded and synthesized seismic data. However, classical FWI driven by gradient-based local optimization is vulnerable to local minima caused by lack of low-frequency components and an accurate initial model. Although global optimization methods such as genetic algorithm (GA) are less affected by the presence of local minima, those methods are affected by "curse of dimensionality." This results in low-resolution model less than optimum solution. Therefore, we propose an FWI method based on genetic local search algorithm with hybrid-grid scheme (HGLS-FWI). This method combines GA with coarse grid as a global search and gradient-based optimization with fine grid as a local search to directly deliver high-resolution model, while reducing the risk to be trapped in a local minimum. Our experimental results demonstrated that the proposed method reduced the average velocity estimation error by 62 compared with a classical gradient-based FWI on the condition that neither low-frequency components nor an accurate initial model was available. © 2019 IEEE.
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069169448&doi=10.1109%2fISCAIE.2019.8743763&partnerID=40&md5=f948336bac384cdb144f60f5f14b2b74
id_number: 10.1109/ISCAIE.2019.8743763
full_text_status: none
publication: ISCAIE 2019 - 2019 IEEE Symposium on Computer Applications and Industrial Electronics
pagerange: 1-5
refereed: TRUE
isbn: 9781538685464
citation:   Hamamoto, M. and Rahim Md Arshad, A. and Prasad Ghosh, D.  (2019) Full waveform inversion based on genetic local search algorithm with hybrid-grid scheme.  In: UNSPECIFIED.