Publication Name: AEGC 2021
Authors: Yuqing Chen, Erdinc Saygin
Date Published: September 2021
Number of Pages: 4
Abstract:
Full waveform inversion (FWI) has been widely used for recovering a high-resolution subsurface structure from the entire content of measured seismic data. However, FWI requires a good starting model which needs to be close to the true model. If it is not, FWI may converge to a local minimum and will yield an incorrect subsurface image. To mitigate this problem, we propose a hybrid machine learning (HML) inversion method that uses a lowdimensional representation of the measured seismic data for inverting subsurface velocity distribution.