Abstract
First-arrival picking is a critical step in seismic data processing. This paper proposes the first-arrival picking through sliding windows and fuzzy c-means (FPSF) algorithm with two stages. The first stage detects a range using sliding windows on vertical and horizontal directions. The second stage obtains the first-arrival travel times from the range using fuzzy c-means coupled with particle swarm optimization. Results on both noisy and preprocessed field data show that the FPSF algorithm is more accurate than classical methods.
Highlights
Seismic refraction data analysis is one of the principal methods for near-surface modeling [1,2,3,4].A critical step of the method is first-arrival picking for direct and head waves
We propose the first-arrival picking through sliding windows and fuzzy c-means (FPSF) algorithm
The data is divided into 10 classes and one of the classes is the result of first-arrival picking
Summary
Seismic refraction data analysis is one of the principal methods for near-surface modeling [1,2,3,4]. A critical step of the method is first-arrival picking for direct and head waves. It influences the effectiveness of many steps such as static correction [5,6] and velocity modeling [7]. Misidentifications of these arrival times may have significant effects on the hypocenters [8]. The raw seismic traces are always contaminated by strong background noise with complex near-surface conditions [7]. According to Akram and Eaton [8], there is an urgent need for automatic picking methods as the scale of seismic data continues to grow
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