基于独立分量分析的地震属性分析方法研究

Conventional seismic attribute analysis is not only time consuming, but also has several possible results. Therefore, seismic attribute optimization and multi-attribute analysis are needed. In this paper, Fuyu oil layer in Daqing oil field is our main studying object. And there is much difference between seismic attributes and well logs. So under this condition, Independent Component Analysis (ICA) and Kohonen neural net are introduced to seismic attribute optimization and multi-attribute analysis. The main contents are as follows: (1) Now the method of seismic attribute compression is mainly principal component analysis (PCA). In this article, independent component analysis (ICA), which is superficially related to PCA, but much more powerful, is used to seismic reservoir characterizeation. The fundamental, algorithms and applications of ICA are surveyed. And comparation of ICA with PCA is stydied. On basis of the ne-entropy measurement of independence, the FastICA algorithm is implemented. (2) Two parts of ICA application are included in this article: First, ICA is used directly to identify sedimentary characters. Combined with geology and well data, ICA results can be used to predict sedimentary characters. Second, ICA treats many attributes as multi-dimension random vectors. Through ICA transform, a few good new attributes can be got from a lot of seismic attributes. Attributes got from ICA optimization are independent. (3) In this paper, Kohonen self-organizing neural network is studied. First, the characteristics of neural network’s structure and algorithm is analyzed in detail, and the traditional algorithm is achieved which has been used in seism. From experimental results, we know that the Kohonen self-organizing neural network converges fast and classifies accurately. Second, the self-organizing feature map algorithm needs to be improved because the result of classification is not very exact, the boundary is not quite clear and the velocity is not fast enough, and so on. Here frequency sensitive principle is introduced. Combine it with the self-organizing feature map algorithm, then get frequency sensitive self-organizing feature map algorithm. Experimental results show that it is really better. (4) Kohonen self-organizing neural network is used to classify seismic attributes. And it can be avoided drawing confusing conclusions because the algorithm’s characteristics integrate many kinds of seismic features. The result can be used in the division of sand group’s seismic faces, and so on. And when attributes are extracted from seismic data, some useful information is lost because of difference and deriveative. But multiattributes can make this lost information compensated in a certain degree.

Electro-mass olfactory multi-sensor (EMMS)

For an olfactory sensor or electronic nose, the task is not only to detect the object concentration, but also to recognize it. It is well known that all the elements can be identified by their charge to mass ratio e(+)/m. We tried to imitate this principle for molecular recognition. Two kinds of sensors are used simultaneously in testing. One is quartz crystal microbalance (QCM) for detecting the change in mass, the other is interdigital electrode (IE) for detecting the change in conduction, as an electro-mass multi-sensor (EMMS). in this paper, the principle and the feasibility of this method are discussed. The preliminary results on the recognition of alcohol by EMMS coated with lipids are presented. Meanwhile, the multi-sensor can also be used as an instrument for research on some physico-chemistry problems. The change in conduction of coated membrane caused by one absorbed molecule is reported. It is found that when a QCM is coated with membrane, it still obeys the relationship Delta F (frequency change of QCM) = K Delta m (mass change of absorbed substance) and the proportional coefficient, K, depends not only on quartz properties but also on membrane characteristics as well. (C) 2000 Elsevier Science S.A. All rights reserved.

Electro-mass multi odor sensor

For an olfactory sensor or electronic nose the task is not only to detect the object concentration, but also to recognize it. It is well known that all the elements can be identified by their charge to mass ratio e+/m. We tried to use this principle for molecular recognition. Two kinds of sensors are used simultaneously in testing. One is Quartz Crystal Microbalance (QCM) for detecting the change in mass, the other is Interdigital Electrode (IE) for detecting the change in conduction. In this paper the principle and the feasibility of this method are reported. The preliminary results on the recognition of alcohols are presented. The multisensor can be used as an instrument for research on material properties and kinetic process as well.

Electro-mass olfactory multi-sensor (EMMS)

For an olfactory sensor or electronic nose, the task is not only to detect the object concentration, but also to recognize it. It is well known that all the elements can be identified by their charge to mass ratio e(+)/m. We tried to imitate this principle for molecular recognition. Two kinds of sensors are used simultaneously in testing. One is quartz crystal microbalance (QCM) for detecting the change in mass, the other is interdigital electrode (IE) for detecting the change in conduction, as an electro-mass multi-sensor (EMMS). in this paper, the principle and the feasibility of this method are discussed. The preliminary results on the recognition of alcohol by EMMS coated with lipids are presented. Meanwhile, the multi-sensor can also be used as an instrument for research on some physico-chemistry problems. The change in conduction of coated membrane caused by one absorbed molecule is reported. It is found that when a QCM is coated with membrane, it still obeys the relationship Delta F (frequency change of QCM) = K Delta m (mass change of absorbed substance) and the proportional coefficient, K, depends not only on quartz properties but also on membrane characteristics as well. (C) 2000 Elsevier Science S.A. All rights reserved.