Data weighted full-waveform inversion with adaptive moment estimation for near-surface seismic refraction data

Full-waveform inversion (FWI) is a technique for high resolution subsurface velocity estimation. Normally a least-squares error between observed and calculated waveform is used as a penalty function, which is sensitive to outliers in the data. We propose a noise-resistant inverse strategy by estimating and incorporating the data covariance matrix into time-domain seismic waveform inversion, which is termed Data weighted full-waveform inversion (DWFWI). The data covariance matrix is built from waveform reciprocal errors or from pre-first-arrival signal-to-noise ratio. There are two modifications in DWFWI compared to conventional FWI: (1) Waveform gradient and misfit function are weighted by data covariance matrix to suppress artifacts from data with low signal-to-noise ratio and to determine stopping criteria for the inversion; (2) FWI penalty function optimization using adaptive moment estimation method. Synthetic examples show the estimated DWFWI models are more accurate than the corresponding FWI models, at the same level of misfit. In addition, Adam outperforms the conjugate gradient method in reaching an ideal fit of the data, resulting in better amplitude recovery. We present preliminary results of applying DWFWI to a 2D near-surface seismic refraction data.

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