The wavelet transform （WT） method has been employed to decompose an original geophysical signal into a series of components containing different information about reservoir features such as pore fluids, lithology, a...The wavelet transform （WT） method has been employed to decompose an original geophysical signal into a series of components containing different information about reservoir features such as pore fluids, lithology, and pore structure. We have developed a new method based on WT energy spectra analysis, by which the signal component reflecting the reservoir fluid property is extracted. We have successfully processed real log data from an oil field in central China using this method. The results of the reservoir fluid identification agree with the results of well tests.展开更多
Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in Radar Automatic Target Recognition (RATR) using Hig...Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in Radar Automatic Target Recognition (RATR) using High-Resolution Range Profiles (HRRPs). Several existing feature reduction methods in pattern recognition are analyzed, and a weighted feature reduction method based on Fisher's Discriminant Ratio (FDR) is proposed in this paper. According to the characteristics of radar HRRP target recognition, this proposed method searches the optimal weight vector for power spectra of HRRPs by means of an iterative algorithm, and thus reduces feature dimensionality. Compared with the method of using raw power spectra and some existing feature reduction methods, the weighted feature reduction method can not only reduce feature dimensionality, but also improve recognition performance with low computation complexity. In the recognition experiments based on measured data, the proposed method is robust to different test data and achieves good recognition results.展开更多
基金This research is sponsored by Nation Natural Science Foundation of China （No.50404001 and No.50374048）.
文摘The wavelet transform （WT） method has been employed to decompose an original geophysical signal into a series of components containing different information about reservoir features such as pore fluids, lithology, and pore structure. We have developed a new method based on WT energy spectra analysis, by which the signal component reflecting the reservoir fluid property is extracted. We have successfully processed real log data from an oil field in central China using this method. The results of the reservoir fluid identification agree with the results of well tests.
基金Partially supported by the National Natural Science Foundation of China (No.60302009)the National Defense Advanced Research Foundation of China (No.413070501).
文摘Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in Radar Automatic Target Recognition (RATR) using High-Resolution Range Profiles (HRRPs). Several existing feature reduction methods in pattern recognition are analyzed, and a weighted feature reduction method based on Fisher's Discriminant Ratio (FDR) is proposed in this paper. According to the characteristics of radar HRRP target recognition, this proposed method searches the optimal weight vector for power spectra of HRRPs by means of an iterative algorithm, and thus reduces feature dimensionality. Compared with the method of using raw power spectra and some existing feature reduction methods, the weighted feature reduction method can not only reduce feature dimensionality, but also improve recognition performance with low computation complexity. In the recognition experiments based on measured data, the proposed method is robust to different test data and achieves good recognition results.
