Data and model dual-driven seismic deconvolution via error-constrained joint sparse representation

What is it about?

Seismic inversion plays an important role in seismic signal processing. As an important technical means to improve the resolution of seismic data, the research on seismic deconvolution has received high attention from scholars at home and abroad. We apply the technology of joint dictionary learning to seismic deconvolution. Compared to traditional deconvolution methods, we mine the mapping relationship between the desired data and known data to make the solution of seismic reflection coefficients more stable and have a wider range of applications. Compared to the classic sparse-spike deconvolution method, the accuracy of the deconvolution results in this paper is improved by more than 25%.

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Photo by Jan Brennenstuhl on Unsplash

Photo by Jan Brennenstuhl on Unsplash