Work

High-Resolution Imaging: An Approach by Compensating Absorption and Dispersion in Prestack Time Migration With Effective Q Estimation and Fresnel Zone Identification Based on Deep Learning

Year: 2022

Type: article

Abstract: We have developed a migration scheme that can compensate absorption and dispersion with effective Q estimation and Fresnel zone identification based on deep learning. We use the U-Net neural network technology in deep learning to automatically identify Fresnel zones from compensated migrated dip-angle gathers and obtain the optimal aperture for migration, avoiding the tedious task of manually modifying the boundaries of Fresnel zones. Instead of the interval Q factor, we used an effective Q parameter to compensate absorption and dispersion. The effective Q is estimated using VSP well data and surface seismic velocity data. The proposed scheme can be incorporated into conventional seismic data processing workflow. A field data set was employed to validate the proposed scheme. Higher resolution imaging results with low noise levels are obtained.

Source: Frontiers in Earth Science

DOI: 10.3389/feart.2021.771570

Authors: Jizhong Wu, Ying Shi, Aihua Guo, Pengfei Lu, Qianqian Yang

Institutions: Northeast Petroleum University, East China University of Technology

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: $2,950

Funders: National Natural Science Foundation of China, China Postdoctoral Science Foundation