A new practical and effective source-independent full-waveform inversion with a velocity-distribution supported deep image prior: Applications to two real datasets
Journal:
arXiv
Published Date:
Mar 1, 2025
Abstract
Full-waveform inversion (FWI) is an advanced technique for reconstructing
high-resolution subsurface physical parameters by progressively minimizing the
discrepancy between observed and predicted seismic data. However, conventional
FWI encounters challenges in real data applications, primarily due to its
conventional objective of direct measurements of the data misfit. Accurate
estimation of the source wavelet is essential for effective data fitting,
alongside the need for low-frequency data and a reasonable initial model to
prevent cycle skipping. Additionally, wave equation solvers often struggle to
accurately simulate the amplitude of observed data in real applications. To
address these challenges, we introduce a correlation-based source-independent
objective function for FWI that aims to mitigate source uncertainty and
amplitude dependency, which effectively enhances its practicality for real data
applications. We develop a deep-learning framework constrained by this new
objective function with a velocity-distribution supported deep image prior,
which reparameterizes velocity inversion into trainable parameters within an
autoencoder, thereby reducing the nonlinearity in the conventional FWI's
objective function. We demonstrate the superiority of our proposed method using
synthetic data from benchmark velocity models and, more importantly, two real
datasets. These examples highlight its effectiveness and practicality even
under challenging conditions, such as missing low frequencies, a crude initial
velocity model, and an incorrect source wavelet.
