# Elastic Full-Waveform Inversion

Multiparameter elastic full-waveform inversion estimates several elastic properties at once, and crosstalk between them leaves artifacts in the recovered models. INR-EFWI replaces independent grid-point values with a coordinate neural network that generates the subsurface model continuously, and pairs it with a Vp, P-wave impedance Ip and Vs/Vp parameterization whose radiation patterns overlap less. On the Marmousi and Overthrust synthetic models and the Volve ocean-bottom dataset, it yields lower model error and better structural continuity than automatic-differentiation EFWI, though the authors stress that this parameterization is not universally optimal.

Multiparameter elastic full-waveform inversion estimates several elastic properties at once, and crosstalk between them leaves artifacts in the recovered models. INR-EFWI replaces independent grid-point values with a coordinate neural network that generates the subsurface model continuously, and pairs it with a Vp, P-wave impedance Ip and Vs/Vp parameterization whose radiation patterns overlap less. On the Marmousi and Overthrust synthetic models and the Volve ocean-bottom dataset, it yields lower model error and better structural continuity than automatic-differentiation EFWI, though the authors stress that this parameterization is not universally optimal.