{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/9187"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/9187","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"Seismic site characterization through joint modeling of complementary data functionals, with applications to Santo Domingo, Dominican Republic.","abstract":"We present a strategy for determining seismic "site characterization" through joint modeling of horizontal to vertical spectral ratios (HVSR) and surface wave dispersion, performed via refraction microtremor (ReMi). Fitting of data functionals by synthetics is driven by global optimization. The products of this approach are shear wave velocity profiles for the subsurface, accompanied by marginal posterior probability distributions (PPD) and parameter correlation matrices. Jointly fitting dispersion curves and HVSR functionals via global optimization allows us to assess model reliability, identify parts of the "best-fit" model that are poorly constrained, and guide us toward new data that might improve model constraints. Tools such as the PPD and the parameter correlation matrix assess the relative contribution of each type of data to model constraints. The joint modeling technique is applied to data acquired in an NSF-funded PASI in Santo Domingo, Dominican Republic, as a proof-of-concept survey in a highly built-up urban environment.","abstract_html":"We present a strategy for determining seismic "site characterization" through joint modeling of horizontal to vertical spectral ratios (HVSR) and surface wave dispersion, performed via refraction microtremor (ReMi). Fitting of data functionals by synthetics is driven by global optimization. The products of this approach are shear wave velocity profiles for the subsurface, accompanied by marginal posterior probability distributions (PPD) and parameter correlation matrices. Jointly fitting dispersion curves and HVSR functionals via global optimization allows us to assess model reliability, identify parts of the "best-fit" model that are poorly constrained, and guide us toward new data that might improve model constraints. Tools such as the PPD and the parameter correlation matrix assess the relative contribution of each type of data to model constraints. 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Fitting of data functionals by synthetics is driven by global optimization. The products of this approach are shear wave velocity profiles for the subsurface, accompanied by marginal posterior probability distributions (PPD) and parameter correlation matrices. Jointly fitting dispersion curves and HVSR functionals via global optimization allows us to assess model reliability, identify parts of the "best-fit" model that are poorly constrained, and guide us toward new data that might improve model constraints. Tools such as the PPD and the parameter correlation matrix assess the relative contribution of each type of data to model constraints. 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