A Novel SVM-Based Method for Seismic First-Arrival Detecting

Abstract:

Article Preview

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 et al., "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

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.