Nonlinear principal component analysis for seismic data compression
Nonlinear principal component analysis for seismic data compression
复制标题
地震数据压缩的非线性主成分分析
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
S. Gangashetty
中科院分区：
文献类型：
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作者：
T. A. Reddy;K. R. Devi;S. Gangashetty
用于解释地下特征的地震数据处理既是计算密集型的，也是数据密集型的。有必要保持数据的维数尽可能小，以便从有限的数据中进行良好的泛化。因此，压缩地震数据大小的方法值得探索。在本文中，我们考虑了用于地震信号处理的数据压缩的线性和非线性主成分分析（PCA）方法。主成分分析 (PCA) 可以改善地震解释。线性压缩是通过 Karhunen-Loeve 变换 (KLT) 和三层自关联神经网络 (AANN) 模型实现的。探讨了五层 AANN 模型的分布捕获能力，用于地震数据压缩的非线性主成分分析。
Seismic data processing to interpret subsurface features is both computationally and data intensive. It is necessary to keep the dimensionality of data as small as possible, for good generalization from limited data. Therefore it is worthwhile exploring methods to compress the size of seismic data. In this paper, we consider approaches for linear and nonlinear principal component analysis (PCA) methods for compression of data for seismic signal processing. Principal component analysis (PCA) can improve seismic interpretations. Linear compression is realized by Karhunen-Loeve transform (KLT) and also by three layer autoassociative neural network (AANN) models. The distribution capturing ability of five layer AANN model is explored for nonlinear principal component analysis for compression of seismic data.