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In a seismogram, there exist many kinds of wavelets. The reflected wavelet from the gas sand zone has a different shape with other layers. Usually, the information of each wavelet is weak and unknown, and the unsupervised classification method is applied to the clustering of the wavelets. Using the shape structure of the wavelet, syntactic pattern recognition is applied to the clustering. The extracted wavelets can be represented as strings of symbols. Levenshtein distance is used to calculate the distance between the two strings. Bottom-up and top-down hierarchical clustering methods are used in the construction of the dendrogram. The top-down hierarchical clustering by the recursive method is proposed. A new pseudo F-statistics is proposed to decide the optimal number of clusters. From the experimental results in simulated and real seismograms, the wavelets on the gas sand zone can be detected successfully. It can improve the seismic interpretation.

Sequential pattern detection with simulated annealing (SA) is adopted to estimate parameters and detect lines, ellipses, hyperbolas type by type, and patterns by patterns in each type. The motivation of the sequential detection method is to deal with multiple patterns. The parameters of a pattern are formed as a vector and used as a state, and adjusted in SA. A sequential detection algorithm using SA to detect patterns is proposed. It detects one or a small number of patterns at each step. SA has the capability of the global minimization. The six parameters of patterns are adjusted sequentially step by step. The computation can converge efficiently. In the experiment, the result of sequential detection is better than that of synchronous detection in detecting a large number of patterns. In sequential detection, detection of one pattern at each step can have less computation time and good convergence in total detection than using two or more pattern detections. In simulated seismic data, SA is applied to detect the hyperbolas in the common depth point (CDP) gather. In real one-shot seismogram, SA is applied to detect lines of direct wave and hyperbolas of reflection wave. The results can show that the proposed method is feasible.

Multilayer perceptron (MLP) with back-propagation learning rule is adopted to predict the winning rates of two teams according to their official statistical data of 2006 World Cup Football Game at the previous stages. There are training samples from three classes: win, draw, and loss. At the new stage, new training samples are selected from the previous stages and are added to the training samples, then we retrain the neural network. It is a type of on-line learning. The 8 features are selected with ad hoc choice. We use the theorem of Mirchandani and Cao to determine the number of hidden nodes. And after the testing in the learning convergence, the MLP is determined as 8-2-3 model. The learning rate and momentum coefficient are determined in the cross-learning. The prediction accuracy achieves 75% if the draw games are excluded.

The neural network, using an unsupervised generalized Hebbian algorithm (GHA), is adopted to find the principal eigenvectors of a covariance matrix in different kinds of seismograms. The authors have shown that the extensive computer results of the principal components analysis (PCA) using the neural net of GHA can extract the information of seismic reflection layers and uniform neighboring traces. The analyzed seismic data are the seismic traces with 20-, 25-, and 30-Hz Ricker wavelets, the fault, the reflection and diffraction patterns after normal moveout (NMO) correction, the bright spot pattern, and the real seismogram at Mississippi Canyon. The properties of high amplitude, low frequency, and polarity reversal can be shown from the projections on the principal eigenvectors. For PCA, a theorem is proposed, which states that adding an extra point along the direction of the existing eigenvector can enhance that eigenvector. The theorem is applied to the interpretation of a fault seismogram and the uniform property of other seismograms. The PCA also provides a significant seismic data compression.

采用浸渍法制备了Li-ZnO/La2O3催化剂并考察了其低温催化甲烷氧化偶联反应性能.反应条件下,在考察的w(Li)=2%和w(ZnO)=20%的Li-ZnO/La2O3在680℃得到了甲烷转化率为27.3%,C2选择性为65.2%,C2收率为17.8%的结果;在700℃,C2收率达到21.8%.Raman和XPS表征结果表明,催化剂低温催化性能与表面的活性吸附氧物种含量相关;La2O2CO3物种可能是提高催化剂的C2选择性的关键.

Well log data inversion is important for the inversion of true formation. There exists a nonlinear mapping between the measured apparent conductivity (C a ) and the true formation conductivity (C t ). We adopt the multilayer perceptron (MLP) to approximate the nonlinear input-output mapping and propose the use of particle swarm optimization with mutation (MPSO) algorithm to adjust the weights in MLP. In the supervised training step, the input of the network is the measured C a and the desired output is the C t . MLP with optimal size 10-9-10 is chosen as the model. We have experiment in simulation and real data application. In simulation, there are 31 sets of simulated well log data, where 25 sets are used for training, and 6 sets are used for testing. After training the MLP network, input Ca, then C t ' can be inverted in testing process. Also we apply it to the inversion of real field well log data. The result is acceptable. It shows that the proposed MPSO algorithm in MLP weight adjustments can work on the well log data inversion.

The neural network using an unsupervised generalized Hebbian algorithm (GHA) is adopted to find the principal eigenvectors of a covariance matrix in different kinds of seismograms. We have shown that the extensive computer results of the principal components analysis (PCA) using neural net of GHA can extract the information of seismic reflection layers and uniform neighboring traces. The analyzed seismic data are the seismic traces with 20, 25, and 30 Hz Ricker wavelets, the fault, the reflection and diffraction patterns after NMO correction, the bright spot pattern, and the real seismogram at Mississippi Canyon. The properties of high amplitude, low frequency, and polarity reversal can be shown from the projections on the principal eigenvectors. For PCA, a theorem is proposed that adding extra point along the direction of the existing eigenvector can enhance that eigenvector. The theorem is applied to the interpretation of a fault seismogram and the uniform property of other seismograms. The PCA also provides a significant seismic data compression.

Due to the working characteristics of radar system, i.e. it has to ensure the emission and acceptance of its own radar wave, frequency selective surface (FSS) radome is the most effective stealthy way to give radome the function of frequency-selective. Combining the FSS technique and the radome together, FSS radome provides bandpass transmission properties in the operating frequency of radar, and changes the RCS characteristics of the antenna compartment when it is beyond the antennas operating frequency range, thus it is a realization of out-of-band stealth. This paper introduces the basic design of FSS radome technology and summarizes the research status of FSS radome in the structure design and the technology. The muti-band FSS radome, intelligent FSS radome, thick-screen FSS radome, miniaturized element FSS radome and the technologies to construct radome are predicted to be the perspective of FSS radome.

Decision-theoretic pattern recognition methods are applied to classifying Ricker wavelets and to detecting waveform anomalies in seismograms. The methods include Bayes decision rule and linear and quadratic classifications. Envelope and instantaneous frequency are extracted as the two features of a seismic trace used as input into the classification schemes. A modified fixed-increment training procedure is employed to solve the decision boundary. The classification schemes successfully distinguish zero-phase Ricker wavelets of different peak frequencies from each other and from random noise.