The Lle Algorithm In The Seismic Attributes Drop Dimension

There are some characteristics in the geophysical and geochemical non-seismic exploration of oil and gas. For example, there are abundant method parameters and information. The structure of integrating anomaly is complex. According as the characteristics mentioned above, we base the need and requirement in the practice work and use an associative method of mathematics and practical experience. We have explored the high-dimensional data reduction method and its application in the seismic attribute of a certain area in Beijing.The data of real world is usually of high-dimensional data, which is difficulty to understand, present and process for its high dimensions. So it is faced with two puzzles. The first one is the curses of dimensionality which has challenged the pattern recognition and discovering formulas on high-dimensional data. The second is the blessings of dimensionality which shows that the abundance information of the high-dimensional data set means the new feasibility. How to express the high-dimensional data in the low-dimensional space and discover the intrinsic structure is the pivotal problem of high-dimensional information processing. Whether at home or abroad, the dimensionality reduction method in the geophysical and geochemical exploration of oil and gas is developing and exploring continuously. The multifarious former methods have produced an important promote effect, but have some different restriction. So this kind of development and exploration is necessary.The locally linear embedding(LLE) algorithm proposed by L.Saul and S.Roweis has recently emerged as a technique for nonlinear dimensionality reduction of high-dimensional data. It has a number of attractive features: it does not require an iterative algorithm, and just a few parameters need to be set, what's more, it perform very well on high-dimensional data of face data sets. However, the algorithm is sensitive to three parameters that should be set artificially, which is seldom researched., especially to get reliable estimators of embedding dimension still remains as a open problem. For that, we propose a hierarchical method for automatic selection of an optimal parameter value. Our approach is experimentally verified on the seismic attribute of a certain area in Beijing, which widen the applied area of the algorithm. Above all, our work enrich the LLE algorithm theory system proposed by L.Saul.