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Automatic first-arrival picking based on clustering selection
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AI
DOI：10.1007/s11770-026-1432-z.png)
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Abstract
En 中文
First-arrival picking is an indispensable step in seismic data processing. Existing methods have not always gotten accurate results under the interference of background noises and complex near-surface conditions. This paper proposes the automatic first-arrival picking based on clustering selection (FPCS) algorithm. FPCS consists of four stages: data preprocessing, clustering picking, result selecting and outlier handling. First, range detection and threshold filtering techniques are used to preprocess the data. Then, fuzzy c-means, k-means and DBSCAN are used for clustering picking to obtain three results. Next, an optimization model is designed to select the most suitable first arrivals from the three results. Finally, outlier handling is used to identify and correct outliers. FPCS was compared with MCM, APF, and FPSF on four seismic data sets. The results show that FPCS is more accurate. The robustness of FPCS makes it particularly promising for first-arrival picking in challenging field seismic surveys.
Keywords:
First-arrival picking
Fuzzy c-means
K-means
DBSCAN
clustering selection
Journal
A
IF:
0.8
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181
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Cited Papers
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