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arXiv · 2201.03926

Deconvolutional double-difference misfit measurements and the application for full-waveform inversion

Fuqiang Chen·Daniel Peter
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Abstract

It is challenging for full-waveform inversion to determine geologically informative models from field data. An inaccurate wavelet can make it more complicated. We develop a novel misfit function, entitled deconvolutional double-difference misfit measurement to cancel the influence of wavelet inaccuracy on inversion results. Unlike the popular double-difference misfit measurement in which the first difference is evaluated by cross-correlation, the proposed one employs deconvolution to do this step. Numerical examples demonstrate that full-waveform inversion with the new misfit function is resilient to the wavelet inaccuracy. It can also converge to plausible local minima even from rough initial models.

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Fuqiang Chen, Daniel Peter. 2022-01-11. Deconvolutional double-difference misfit measurements and the application for full-waveform inversion. https://arxiv.org/abs/2201.03926

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