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However, when certain biological\u2010evolution\u2013based optimization algorithms, eg, genetic algorithms, are used to solve this problem, the computation exhibits fast convergence and a strong tendency to be trapped to a local optimum, thereby leading to unsatisfactory inversion results. To address this issue, this paper proposes a swarm\u2010intelligence\u2010based method\u2010Particle Swarm Optimization (PSO) algorithm to handle the elastic parameter inversion problem. Based on the Aki\u2010Richards approximation to the Zoeppritz equations, the improved PSO algorithm adopts a special initialization strategy, which can enhance the smoothness of the initialization parametric curves. Extensive experimental research confirms the superiority of the proposed algorithm. Specifically, the improved PSO algorithm is able to not only markedly enhance inversion precision but also render remarkably high correlation coefficients associated with the elastic parameters.<\/jats:p>","DOI":"10.1002\/cpe.4987","type":"journal-article","created":{"date-parts":[[2018,9,22]],"date-time":"2018-09-22T01:53:57Z","timestamp":1537581237000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem"],"prefix":"10.1002","volume":"31","author":[{"given":"Qinghua","family":"Wu","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Engineering Wuhan Institute of Technology Wuhan 430074 China"}]},{"given":"Zhixin","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences Wuhan 430074 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3333-1466","authenticated-orcid":false,"given":"Xuesong","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences Wuhan 430074 China"}]},{"given":"Wenyin","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences Wuhan 430074 China"}]}],"member":"311","published-online":{"date-parts":[[2018,9,21]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1190\/1.1439241"},{"key":"e_1_2_7_3_1","unstructured":"LiSP.The Study and Application of AVO Seismic Parameter Inversion Methods[master's thesis].Qingdao China:China University of Petroleum;2009."},{"key":"e_1_2_7_4_1","unstructured":"ChenJJ.Study of Three\u2010Term AVO Inversion Method[doctor' thesis].Qingdao China:China University of Petroleum;2007."},{"key":"e_1_2_7_5_1","doi-asserted-by":"crossref","unstructured":"BergE.Simple convergent genetic algorithm for inversion of multiparameter data. 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