A blind nonstationary deconvolution method for multichannel seismic data

Abstract

Deconvolution is essential for high-resolution seismic data processing. Conventional deconvolution methods are either based on a stationary convolution model or under the assumption that Q factor and the source wavelet are known. However, in reality, seismic wavelet is usually unknown and time-varying during propagation due to attenuation. Thus, we propose a blind nonstationary deconvolution (BND) method which does not require advance Q factor and source wavelet as inputs and takes into account the lateral continuity of deconvolution results. Firstly, we develop an improved nonstationary convolution model consisting of the time-varying wavelet and reflectivity, which enables us to obtain reflectivity without attenuation estimation. To accommodate the changing frequency spectrum of seismic data, we present a time-varying wavelet estimation method using the frequency spectrum at every sample point and the generalised seismic wavelet function. By incorporating the extracted time-varying wavelet into the improved convolution model, we propose to formulate the objective function for reflectivity inversion as a joint low-rank and sparse inversion convex optimisation problem. It helps deconvolution results keep the sparsity in the vertical direction while maintaining the continuity in the horizontal direction. The performance of BND is evaluated through synthetic examples and a field data example.

KEYWORDS:

• Deconvolution
• attenuation
• processing

Acknowledgments

The research was supported by the National Nature Science Foundation of China #1 under Grant [No. 41674128], Petro China Innovation Foundation #2 under Grant [No. SQ2017YFGX030021] and the Program of China Scholarships Council #3 under Grant [No. csc201906440083]. The authors gratefully acknowledge this financial support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research was supported by the National Nature Science Foundation of China under grant number 41674128; the National Key Research and Development Program of China under grant number SQ2017YFGX030021 and the Program of China Scholarships Council under grant number csc201906440083.