Work

The construction of a space-variant point spread function for enhancing deep reflector imaging

Year: 2025

Type: article

Abstract: Least-squares migration (LSM) is a commonly used method for high-precision subsurface imaging. In recent years, image-domain LSM based on the approximated Hessian matrix has attracted widespread attention. However, the wavelet in the Hessian matrix is typically assumed to be fixed, which limits the imaging resolution. In this study, we analyze the imaging resolution affected by wavelet at different depths, and develop an image-domain LSM that constructs a space-variant point spread function (PSF) based on time-varying wavelets. First, a polynomial spectral simulation method is introduced to extract time-varying wavelets from each segmented seismic record in the frequency domain. Then, the space-variant PSF is constructed using the extracted time-varying wavelets at different depths, thereby effectively approximating the Hessian matrix. Finally, a non-iterative image-domain LSM is applied based on the constructed space-variant PSF to enhance the imaging resolution of deep reflectors. Numerical tests on both synthetic and field data demonstrate the effectiveness and practical potential of the proposed method for high-resolution imaging of complex geological structures.

Source: Journal of Geophysics and Engineering

DOI: 10.1093/jge/gxaf062

Authors: Chao Zhang, Shaoyong Liu, Wenjun Ni, Aminou Issoufou, Minao Sun

Institutions: China University of Geosciences, SINOPEC Geophysical Research Institute (China)

Language: English

Topic: Seismic Imaging and Inversion Techniques

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,550

Funders: National Natural Science Foundation of China, China University of Geosciences