基于FCM聚类模型约束的二维初至旅行时反演

doi:10.13810/j.cnki.issn.1000-7210.2023.05.008

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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.

Key words： seismic first-arrival traveltime tomography fuzzy C-means (FCM) clustering regularization inversion supervised learning

Received: 17 October 2022

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P631

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	LIU Jiacheng
	ZHANG Zhiyong
	ZHOU Qinyuan
	LI Man
	LI Hongli

Cite this article:

LIU Jiacheng,ZHANG Zhiyong,ZHOU Qinyuan等. 2D inversion of seismic first-arrival traveltime based on FCM clustering model constraint[J]. OGP, 2023, 58(5): 1115-1123.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2023.05.008 OR http://www.ogp-cn.com/EN/Y2023/V58/I5/1115

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