A novel SVM-based method for seismic first-arrival detecting

被引：2

作者：

Chen, Ming ^{[1
]}

Li, Yong ^{[1
]}

Xie, Jun ^{[2
]}

机构：

[1] China Univ Petr, Dept Comp Sci & Technol, Bei Jing, Peoples R China

[2] Capital Normal Univ, Informat Engn Coll, Beijing, Peoples R China

来源：

APPLIED MECHANICS AND MECHANICAL ENGINEERING, PTS 1-3 | 2010年 / 29-32卷

关键词：

Wavelet transform; feature extraction; first-arrival detecting; artificial neural network; support vector machine;

D O I：

10.4028/www.scientific.net/AMM.29-32.973

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

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.

引用

页码：973 / +

页数：2