TY - ELEC
AU - Liliana Borcea
PY - 2019
TI - Quantitative inverse scattering via reduced order modeling
LA - eng
M3 - Moving Image
AB - I will discuss an inverse problem for the wave equation, where a collection (array) of sensors probes an unknown heterogeneous
medium with waves and measures the echoes.The goal is to determine scattering structures in the medium modeled by a reflectivity function. Much of the existing imaging methodology is based on a linear least squares data fit approach. However, the mapping between the reflectivity and the wave measured at the array is nonlinear and the resulting images have artifacts. I will show how to use a reduced order model (ROM) approach to solve the inverse scattering problem. The ROM is data driven i.e., it is constructed from the data, with no knowledge of the medium. It approximates the wave propagator, which is the operator that maps the wave from one time step to the next. I will show how to use the ROM to: (1) Remove the multiple scattering (nonlinear) effects from the data, which can then be used with any linearized inversion algorithm. (2) Obtain a well conditioned quantitative inversion algorithm for estimating the reflectivity.
N2 - I will discuss an inverse problem for the wave equation, where a collection (array) of sensors probes an unknown heterogeneous
medium with waves and measures the echoes.The goal is to determine scattering structures in the medium modeled by a reflectivity function. Much of the existing imaging methodology is based on a linear least squares data fit approach. However, the mapping between the reflectivity and the wave measured at the array is nonlinear and the resulting images have artifacts. I will show how to use a reduced order model (ROM) approach to solve the inverse scattering problem. The ROM is data driven i.e., it is constructed from the data, with no knowledge of the medium. It approximates the wave propagator, which is the operator that maps the wave from one time step to the next. I will show how to use the ROM to: (1) Remove the multiple scattering (nonlinear) effects from the data, which can then be used with any linearized inversion algorithm. (2) Obtain a well conditioned quantitative inversion algorithm for estimating the reflectivity.
UR - https://open.library.ubc.ca/collections/48630/items/1.0386789
ER - End of Reference