{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97438"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97438","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Evolutionary data assimilation at Long Valley Caldera, CA","abstract":"\"Despite advancements in volcanic modeling, the time-dependent evolution of volcanoes is still poorly understood. Of particular need are methods for combining extensive monitoring data sets with dynamic models. Sequential data assimilation has been shown to be powerful approach for linking models and data to improve the use of both. One such approach, Evolutionary Data Assimilation (EDA), previously used in hydrological predictions [Dumedah, 2012], is adapted. EDA provides a \"\"snaphot\"\" of parameters such as location and volume change at each timestep, allowing users to update a dynamic model’s trajectory as new observations become available. To test the application of EDA to volcano monitoring, we first develop a series of synthetic numerical experiments to track the ability of the EDA to back out chosen model parameters. Specifically, synthetic GPS and interferometric synthetic aperture radar (InSAR, a satellite based measurement of deformation) data are created from an analytical model with prescribed values for geometry and volume change. We find that EDA performs well in synthetic tests using GPS and InSAR data. After establishing the EDA method with synthetic tests, the EDA is applied to investigate the recent unrest observed at Long Valley Caldera in California using GPS from 1995-2015 and InSAR data from 2012-2014. EDA performed reasonably well at finding the location of the chamber and estimating volume changes. However, due to the analytical Mogi model used and the inherent nonuniqueness of parameters such as depth vs pressure vs radius, EDA was not able to resolve the depth or radius of the chamber. With more robust models, EDA is a powerful method that could be used to track evolution of volcanoes.\"","abstract_html":""Despite advancements in volcanic modeling, the time-dependent evolution of volcanoes is still poorly understood. Of particular need are methods for combining extensive monitoring data sets with dynamic models. Sequential data assimilation has been shown to be powerful approach for linking models and data to improve the use of both. One such approach, Evolutionary Data Assimilation (EDA), previously used in hydrological predictions [Dumedah, 2012], is adapted. EDA provides a ""snaphot"" of parameters such as location and volume change at each timestep, allowing users to update a dynamic model’s trajectory as new observations become available. To test the application of EDA to volcano monitoring, we first develop a series of synthetic numerical experiments to track the ability of the EDA to back out chosen model parameters. Specifically, synthetic GPS and interferometric synthetic aperture radar (InSAR, a satellite based measurement of deformation) data are created from an analytical model with prescribed values for geometry and volume change. We find that EDA performs well in synthetic tests using GPS and InSAR data. After establishing the EDA method with synthetic tests, the EDA is applied to investigate the recent unrest observed at Long Valley Caldera in California using GPS from 1995-2015 and InSAR data from 2012-2014. EDA performed reasonably well at finding the location of the chamber and estimating volume changes. However, due to the analytical Mogi model used and the inherent nonuniqueness of parameters such as depth vs pressure vs radius, EDA was not able to resolve the depth or radius of the chamber. With more robust models, EDA is a powerful method that could be used to track evolution of volcanoes."","abstract_has_math":false,"creators":["Monical, Therese"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Geology","degree_department":null,"school":null,"contributors":["Gregg, Patricia"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:15:44Z","date_published":"2017-08-10T19:15:44Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Data assimilation","Long Valley"],"languages":["en"],"rights":["Copyright 2017 Therese Monical"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97438","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gregg, Patricia"]},{"key":"dc:creator","label":"Author","values":["Monical, Therese"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:15:44Z","2017-04-26","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Geology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data assimilation","Long Valley"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Therese Monical"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97438"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"Despite advancements in volcanic modeling, the time-dependent evolution of volcanoes is still poorly understood. Of particular need are methods for combining extensive monitoring data sets with dynamic models. Sequential data assimilation has been shown to be powerful approach for linking models and data to improve the use of both. One such approach, Evolutionary Data Assimilation (EDA), previously used in hydrological predictions [Dumedah, 2012], is adapted. EDA provides a \"\"snaphot\"\" of parameters such as location and volume change at each timestep, allowing users to update a dynamic model’s trajectory as new observations become available. To test the application of EDA to volcano monitoring, we first develop a series of synthetic numerical experiments to track the ability of the EDA to back out chosen model parameters. Specifically, synthetic GPS and interferometric synthetic aperture radar (InSAR, a satellite based measurement of deformation) data are created from an analytical model with prescribed values for geometry and volume change. We find that EDA performs well in synthetic tests using GPS and InSAR data. After establishing the EDA method with synthetic tests, the EDA is applied to investigate the recent unrest observed at Long Valley Caldera in California using GPS from 1995-2015 and InSAR data from 2012-2014. EDA performed reasonably well at finding the location of the chamber and estimating volume changes. However, due to the analytical Mogi model used and the inherent nonuniqueness of parameters such as depth vs pressure vs radius, EDA was not able to resolve the depth or radius of the chamber. With more robust models, EDA is a powerful method that could be used to track evolution of volcanoes.\"","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Therese Monical, accepted the attached license on 2017-04-25 at 13:42.","The student, Therese Monical, submitted this Thesis for approval on 2017-04-25 at 13:53.","This Thesis was approved for publication on 2017-04-26 at 08:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10981 on 2017-08-10 at 13:43:45","Made available in DSpace on 2017-08-10T19:15:44Z (GMT). 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One such approach, Evolutionary Data Assimilation (EDA), previously used in hydrological predictions [Dumedah, 2012], is adapted. EDA provides a \"\"snaphot\"\" of parameters such as location and volume change at each timestep, allowing users to update a dynamic model’s trajectory as new observations become available. To test the application of EDA to volcano monitoring, we first develop a series of synthetic numerical experiments to track the ability of the EDA to back out chosen model parameters. Specifically, synthetic GPS and interferometric synthetic aperture radar (InSAR, a satellite based measurement of deformation) data are created from an analytical model with prescribed values for geometry and volume change. We find that EDA performs well in synthetic tests using GPS and InSAR data. After establishing the EDA method with synthetic tests, the EDA is applied to investigate the recent unrest observed at Long Valley Caldera in California using GPS from 1995-2015 and InSAR data from 2012-2014. EDA performed reasonably well at finding the location of the chamber and estimating volume changes. However, due to the analytical Mogi model used and the inherent nonuniqueness of parameters such as depth vs pressure vs radius, EDA was not able to resolve the depth or radius of the chamber. With more robust models, EDA is a powerful method that could be used to track evolution of volcanoes.\"","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Therese Monical, accepted the attached license on 2017-04-25 at 13:42.","The student, Therese Monical, submitted this Thesis for approval on 2017-04-25 at 13:53.","This Thesis was approved for publication on 2017-04-26 at 08:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10981 on 2017-08-10 at 13:43:45","Made available in DSpace on 2017-08-10T19:15:44Z (GMT). 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