Data Mining and Knowledge Discovery Based on Denoising Algorithms in Geology Exploration

Xue Hao; Lin Ren; Na Li; Zhi Cheng Huang

doi:10.4028/www.scientific.net/AMM.310.640

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 310Data Mining and Knowledge Discovery Based on...

Data Mining and Knowledge Discovery Based on Denoising Algorithms in Geology Exploration

Abstract:

There are mass data in geology exploration, but it is vital to find useful information or knowledge from these data. This paper is concerned with the analysis of the seismic data by the multi-channel wiener filter algorithm and the wavelet denoising method using neighboring coefficients. Known the velocity of reflection event, utilizes the resemblance of reflection signals in each seismic trace, the multi-channel wiener filter algorithm is effective in enhance reflection events and suppress the random noise. But the wavelet denoising methods don’t need any assuming conditions. The computed simulations of these two kinds of algorithms are provided to prove the availability.

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Periodical:

Applied Mechanics and Materials (Volume 310)

Pages:

640-643

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.310.640

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Online since:

February 2013

Authors:

Xue Hao, Lin Ren, Na Li, Zhi Cheng Huang

Keywords:

Data Mining (DM), De-Noising Algorithm, Geology Exploration, Knowledge Discovery, Multi-Channel Wiener Filter, Wavelet Denoising

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