{"id":15359503,"url":"https://github.com/hbueno/eage2016-avo","last_synced_at":"2026-02-03T01:07:01.059Z","repository":{"id":71360468,"uuid":"87469971","full_name":"hbueno/eage2016-avo","owner":"hbueno","description":"Code for \"Global optimization for AVO inversion: a genetic algorithm using a table-based ray-theory algorithm\" presented at the 78th EAGE Conference and Exhibition 2016.","archived":false,"fork":false,"pushed_at":"2017-04-07T18:26:02.000Z","size":1340,"stargazers_count":7,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-05-31T03:34:16.339Z","etag":null,"topics":["avo","density","genetic-algorithm","global-optimization","seismic","velocity"],"latest_commit_sha":null,"homepage":"http://dx.doi.org/10.3997/2214-4609.201600847","language":"PostScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hbueno.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-04-06T20:03:59.000Z","updated_at":"2024-07-01T08:57:54.000Z","dependencies_parsed_at":null,"dependency_job_id":"55f137a0-5717-41a5-8b9d-66099c3e8f22","html_url":"https://github.com/hbueno/eage2016-avo","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hbueno/eage2016-avo","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hbueno%2Feage2016-avo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hbueno%2Feage2016-avo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hbueno%2Feage2016-avo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hbueno%2Feage2016-avo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hbueno","download_url":"https://codeload.github.com/hbueno/eage2016-avo/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hbueno%2Feage2016-avo/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262056606,"owners_count":23251710,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["avo","density","genetic-algorithm","global-optimization","seismic","velocity"],"created_at":"2024-10-01T12:44:54.577Z","updated_at":"2026-02-03T01:07:01.030Z","avatar_url":"https://github.com/hbueno.png","language":"PostScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"Abstract submitted to the [78th EAGE Conference \u0026 Exhibition 2016](http://www.eage.org/event/index.php?eventid=1391).\n\nDates: 30 May - 2 June 2016.\n\nLocation: Reed Messe Wien, Vienna, Austria.\n\nDeadline to submit the expanded abstract: 15 of January 2016.\n\nPublished expanded abstract:\n[DOI: 10.3997/2214-4609.201600847](http://dx.doi.org/10.3997/2214-4609.201600847)\n\nCitation:\n\n\u003e Ferreira, W. C., F. Hilterman, L. A. Diogo, H. B. Santos, J. Schleicher, and A.\n\u003e Novais, 2016, Global optimization for AVO inversion: a genetic algorithm using\n\u003e a table-based ray-theory algorithm: Presented at the 78th EAGE Conference and\n\u003e Exhibition 2016, EAGE Publications, doi: 10.3997/2214-4609.201600847.\n\n\n# Global optimization for AVO inversion: a genetic algorithm using a table-based ray-theory algorithm\n\u003c!--Max. 120 characters--\u003e\n\n**Authors**:\n\u003c!--Wanderson Conceição Ferreira--\u003e\n\u003c!--Fred John Hilterman--\u003e\n\u003c!--Liliana Alcazar Diogo--\u003e\n\u003c!--Henrique Bueno dos Santos--\u003e\n\u003c!--Joerg Dietrich Wilhelm Schleicher--\u003e\n\u003c!--Maria Amélia Novais Schleicher--\u003e\n[Wanderson C. Ferreira](http://lattes.cnpq.br/0795723887788274),\n[Fred Hilterman](http://www.uh.edu/nsm/earth-atmospheric/people/faculty/fred-hilterman/),\n[Liliana A. Diogo](http://lattes.cnpq.br/6272957939885857),\n[Henrique B. Santos](http://lattes.cnpq.br/4062685231290581),\n[Joerg Schleicher](http://lattes.cnpq.br/0373061112091020) and\n[Amélia Novais](http://lattes.cnpq.br/4767998352165705)\n\n\n**keywords:** AVO; inversion problem; P-wave; S-wave; velocity; density;\n\n## Summary\n\u003c!--Word count (max. 200 or in case no paper upload is required max. 400--\u003e\nAmplitude Variation with Offset (AVO) inversion provides estimates of the\nP-wave velocity, S-wave velocity and density of a stratified medium. Global\noptimization is desirable for the inversion to account for the multi-parametric\nbehaviour of the AVO inversion which is strongly affected by the initial\nestimates of the model rock properties. We carried out an analysis to verify\nthe dependency between P-wave, S-wave velocity and density in the recovered\nparameters using empirical relations as constraints. In inversion schemes, the\nforward modelling is often the most time consuming pro- cess. To reduce\ncomputation time, we have implemented a genetic algorithm using a table-based\nray-theory algorithm to allow for a large amount of models in the global\nsearch. Our results show that the genetic algorithm was capable of recovering\nthe physical parameters with good agreement for examples using the empirical\nconstraints. However, it sometimes converged to solutions which were far from\nthe correct answer, but were good models to explain the observed dataset. The\nforward modelling algorithm has shown excellent performance to be used in\nglobal optimization schemes, because it allows the use of a large number of\nmembers in the population of the genetic algorithm.\n\n\n### Topics\nSession: AVO-AVA - Theory I\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhbueno%2Feage2016-avo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhbueno%2Feage2016-avo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhbueno%2Feage2016-avo/lists"}