Automatic pick-up of seismic P-wave first arrivals via fuzzy C-means method constrained by super-virtual interferometry
TAN Jiawei1, LI Jing1, LI Feida2, ZENG Zhaofa1
1. College of Geo-Exploration Science and Techno-logy, Jilin University, Changchun, Jilin 130021, China; 2. Geophysical Exploration Institute of Jilin Pro-vince, Changchun, Jilin 130062, China
Abstract：In seismic data processing，such as static correction and seismic tomography，accurate and quick pick-up of first arrivals is the basic premise of velocity structure imaging and comprehensive interpretation of seismic data.It is difficult to finish big data through manual picking,and manual method may cause man-made errors.For seismic data with low signal-to-noise ratio (SNR),conventional automatic picking methods,such as correlation method，energy ratio method (STA/LTA)，and fractal method,should keep adjusting parameters to achieve desired accuracy,therefore resulting in poor stability.This paper proposes a method to automatically pick up first arrivals based on fuzzy C-means and super-virtual interferometry.Fuzzy C-means (FCM) clustering analysis is an unsupervised machine learning method.It only depends on data themselves for classification,so it can be more flexibly and conveniently applied for picking actual seismic first arrivals.For data with low SNR,to get accurate and stable first arrivals,first super-virtual interferometry (SVI) is used to enhance the energy of weak first arrival signals,such as far offsets,and improve the SNR of the seismic data.Tests on theoretical model data and actual land seismic data show that the method provides a stable and efficient technical means for automatically picking up first arrivals from big seismic data.
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