The high‐resolution seismic deconvolution method based on joint sparse representation using logging–seismic data
Yaojun Wang, Guiqian Zhang, Haishan Li, Wuyang Yang, Wanli Wang
Abstract
ABSTRACT Seismic high‐resolution processing is an essential part of seismic processing. Sparse‐spike deconvolution is a widely used method for improving the resolution of seismic data. However, the stratigraphic reflection coefficients do not fully satisfy the hypothesis of sparse‐spike deconvolution, and this method does not make full use of prior information, such as well‐logging data. In this paper, we have developed a high‐resolution processing method based on joint sparse representation using logging and seismic data. This method can extract stratigraphic information from well‐logging reflection coefficients and observational seismic data at the same location by joint dictionary learning. Through joint sparse representation, the relationship between observed seismic data and the reflection coefficient is established. Under the framework of joint sparse representation, the deconvolution of seismic data is realized. The synthetic data and field data tests show that our method can reveal thin layers and can invert reflection coefficients from strongly noisy seismic data accurately. Moreover, the deconvolution results of our method match well with the well‐logging data. The tests demonstrate that the improvement of accuracy of deconvolution results with our method, compared to sparse‐spike deconvolution.