2D inversion of seismic first-arrival traveltime based on FCM clustering model constraint
LIU Jiacheng1, ZHANG Zhiyong1, ZHOU Qinyuan2, LI Man1, LI Hongli3
1. School of Geophysics and Measurement-control Technology, East China University of Technology, Nanchang, Jiangxi 330013, China; 2. Hunan Transportation Planning, Survey and Design Institute Co., LTD., Changsha, Hunan 410008, China; 3. Guangxi University of Science and Technology, Liuzhou, Guangxi 545006, China
Abstract：The 2D inversion of seismic first-arrival traveltime with minimum structure model constraint regularization suffers from the issue of insufficient delineation in model boundaries, particularly when dealing with sparse ray distribution within geological structures, leading to unsatisfactory inversion results. To address this challenge, this paper introduces a model constraint function based on fuzzy C-means (FCM) clustering, so as to improve the accuracy of the inversion results in delineating the model boundaries. This constraint incorporates prior information as reference cluster centers and employs an iterative process to repeatedly modify the cluster centers and the membership degrees of each grid cell to the cluster centers, enabling automatic classification of velocities. On this basis, a multi-grid inversion strategy guided by model sensitivity information is adopted to enhance the stability and effectiveness of the inversion. Parameter selection schemes for FCM clustering model constraint weights and weights of the prior information guidance term are discussed using simplified models. A comparison is made between the inversion results of unsupervised learning and prior information-supervised learning approaches. The latter approach successfully addresses the issue of blurring in depicting the boundaries of the velocity model during inversion, effectively improving the resolution of the inversion results. Finally, the inversion of real measured data verifies the applicability and effectiveness of the approach in practical applications.
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