A Novel Filtering Algorithm Based on Least Square Support Vector

Ya Qin Li; Yang Hua Xu

doi:10.4028/www.scientific.net/AMR.532-533.1732

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533A Novel Filtering Algorithm Based on Least Square...

A Novel Filtering Algorithm Based on Least Square Support Vector

Abstract:

In this paper, we proposed a novel filtering algorithm that using the Ricker wavelet kernel to reduce the noise. The algorithm based on Support vector machine (SVM) which is a machine learning method on the base of statistical learning theory. Those parameters of the new algorithm affect the rising edge, the band width and central frequency of passband. The experimental results of synthetic seismic data show that the filter with the Ricker wavelet kernel works better than other methods．

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Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1732-1735

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1732

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Online since:

June 2012

Authors:

Ya Qin Li, Yang Hua Xu

Keywords:

Filtering System, Frequency Response, Random Noise, Support Vector Machine (SVM)

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