ASEG Webinar: Preconditioned Compressive Sensing for Wavefield Reconstruction

Title: Preconditioned Compressive Sensing for Wavefield Reconstruction, Applications to tomography, Helmholtz-Hodge decomposition and Distributed Acoustic Sensing

Presenter: Jack Muir

Date/Time: Sep 2, 2021 1300 (AEST)

Registration: https://us02web.zoom.us/webinar/register/WN_Sr40IBw9SmiEnyMh5dOY4w

Abstract: The proliferation of large seismic arrays have opened many new avenues of geophysical research; however most techniques still fundamentally treat regional and global scale seismic networks as a collection of individual time series rather than as a single unified data product. Wavefield reconstruction allows us to turn a collection of individual records into a single structured form that treats the seismic wavefield as a coherent 3D or 4D entity. We propose a split processing scheme based on a wavelet transform in time and Laplacian preconditioned curvelet based compressive sensing in space to create a sparse representation of the continuous seismic wavefield with smooth second order derivatives. Using this representation, we will illustrate three applications that require accurate access to the full wavefield including spatial gradients - 

Bio: Jack Muir

Jack is a 6th year graduate student in geophysics at the California Institute of Technology Seismological Laboratory (Caltech Seismolab) –- he will take up a Marie Skłodowska-Curie Fellowship at the Oxford University Department of Earth Sciences in late 2021 / early 2022, and is currently a visiting researcher at the Australian National University. He is passionate about inverse problems — some of the projects he is working on now are: imaging the Earth from near surface to the core; improving data captured at seismic arrays; and answering difficult questions about historical data sets.