Automatic Wave Group Identification on Deep Seismic Refraction Data Using SMF Clustering

This letter proposes algorithms for the automatic identification of direct (Pg) and head (Pn) wave groups from first break readings of a deep seismic refraction profile. The process employs a split-and-merge fuzzy iterative prototype clustering technique to extract line segments from first arrivals....

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2008-10, Vol.5 (4), p.687-690
Main Authors: de Melo, F.X., Borges, G.A., Soares, J.E.P.
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Data mining
Deep refraction
Fuzzy
fuzzy clustering
Fuzzy logic
Geoscience
Image segmentation
Iterative algorithms
Navigation
phase identification
Prototypes
Refraction
Robotics and automation
Segments
Seismic engineering
Seismic phenomena
split and merge
Surfaces
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Summary:This letter proposes algorithms for the automatic identification of direct (Pg) and head (Pn) wave groups from first break readings of a deep seismic refraction profile. The process employs a split-and-merge fuzzy iterative prototype clustering technique to extract line segments from first arrivals. A constant velocity value is used as a threshold to identify Pg and Pn groups. Synthetic and real data were used to evaluate the algorithm with satisfactory identification results.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2008.2002643