Marchenko imaging based on self-adaptive traveltime updating
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Marchenko imaging based on self-adaptive traveltime updating* Chen Xiao-Chun1,2, Hu Ye-Zheng1,2, Huang Xu-Ri♦1,2, Zhang Hou-Zhu3, Cao Wei-Ping1,2, Xu Yun-Gui1,2, and Tang Jing1,2 Abstract: Marchenko imaging obtains the subsurface reflectors using one-way Green’s functions, which are retrieved by solving the Marchenko equation. This method generates an image that is free of spurious artifacts due to internal multiples. The Marchenko imaging method is a target-oriented technique; thus, it can image a user specified area. In the traditional Marchenko method, an accurate velocity model is critical for estimating direct waves from imaging points to the surface. An error in the velocity model results in the inaccurate estimation of direct waves. In turn, this leads to errors in computation of one-way Green’s functions, which then affects the final Marchenko images. To solve this problem, in this paper, we propose a self-adaptive traveltime updating technique based on the principle of equal traveltime to improve the Marchenko imaging method. The proposed method calculates the time shift of direct waves caused by the error in the velocity model, and corrects the wrong direct wave according to the time shift and reconstructs the correct Green’s functions. The proposed method improves the results of imaging using an inaccurate velocity model. By comparing the results from traditional Marchenko and the new method using synthetic data experiments, we demonstrated that the adaptive traveltime updating Marchenko imaging method could restore the image of geological structures to their true positions. Keywords: Marchenko imaging, Marchenko equation, Green’s function, principle of equal traveltime, self-adaptive traveltime updating
Introduction The main purpose of seismic imaging is to determine the position and reflectivity of the subsurface reflection interface and image the subsurface structure for determining the location of the oil and gas reservoir
using the reflection data recorded on the surface. Commonly used migration imaging algorithms mainly include Kirchhoff migration (Schneider, 1978; Gray and May, 1994), Gaussian beam migration (Hill, 1990; Gray and Bleistein, 2009), one-way wave equation migration (Claerbout, 1985), and two-way wave equationbased reverse time migration (RTM) (Baysal et al.,
Manuscript received by the Editor July 22, 2019; revised manuscript received January 30, 2020. *This research is supported by the National Natural Science Foundation of China (No. 41874167), the National Science and Technology Major Project of China (No. 2017YFB0202904), and the National Natural Science Foundation of China (No. 41904130). 1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China. 2. School of Geosciences and Technology, Southwest Petroleum University, Chengdu 610500, China. 3. Aramco Services Company, Houston Research Center, Houston, Texas, USA. ♦Corresponding author: Huang Xu-Ri (Email: [email protected]) ©2020 The Editorial Department of A
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