Paper Title:
A Novel SVM-Based Method for Seismic First-Arrival Detecting
  Abstract

First arrivals detecting on seismic record is important at all times. A novel support vector machine (SVM)-based method for seismic first-arrival pickup is proposed in this research. Firstly, the multi-resolution wavelet decomposition is used to de-noise the seismic record. And then, feature vectors are extracted from the denoise data. Finally, both SVM and artificial neural network (ANN) models are employed to train and predict the feature vectors. Experimental results demonstrate that the SVM model gives better accuracy than the ANN model. It is promising that the novel method is very prospective.

  Info
Periodical
Edited by
Honghua Tan
Pages
973-978
DOI
10.4028/www.scientific.net/AMM.29-32.973
Citation
M. Chen, Y. Li, J. Xie, "A Novel SVM-Based Method for Seismic First-Arrival Detecting", Applied Mechanics and Materials, Vols. 29-32, pp. 973-978, 2010
Online since
August 2010
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