Neural Networks for Seismic Pattern Analysis

• Main Authors: Yu-Hsiung Wang, 王宇雄
• Other Authors: Kou-Yuan Huang
• Format: Others
• Language: en_US
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/17752525926655659936

碩士 === 國立交通大學 === 資訊科學系 === 90 === This thesis contains three major chapters, Chapter 2. Self-Organizing Neural Network for Seismic Horizon Linking. Chapter 3. Node Growing of Perceptrons By Sequential Classification Technique. Chapter 4. Neural Network for Robust Recognition of Seismic Patterns. In Chapter 2, we design an algorithm based on self-organizing feature maps to link the seismic horizon in the seismogram. The topology of the neurons is a linear pattern that forms a horizon. The weighting coefficients of the neurons are self-organized by the peaks of the seismogram. One peak catches one neuron. Moment is used in the determination of new neuron creation position. We have applied the algorithm on simulated and real seismograms and the results are quite well. In Chapter 3, combining the important property of the approximating a posteriori probability functions of the classes in the outputs of the trained multilayer perceptron and sequential classification technique, we can get the minimum number of nodes in perceptron and two-layer perceptron. We apply the technique to the typical exclusive OR problem and seismic pattern recognition. The reduction rate of nodes is quite good. In Chapter 4, the multilayer perceptron neural network is trained as a classifier and is applied to the recognition of seismic patterns. Seven moments that are invariant to translation, rotation, and scale, are employed for feature generation of each seismic pattern. In the system, there are training and testing pattern sets. The testing pattern set includes different noise level. The multilayer perceptron is initially trained with the training set of noise-free seismic patterns. After convergence of the training, the network is applied to the classification of the testing set of noisy seismic patterns. Some misclassified patterns with higher noise level are added to the training set for retraining. The training and classification process is repeated through several stages. The converged network at each training stage is applied to the real seismic data at Mississippi Canyon, the bright spot pattern can be detected when the stage is using higher level noisy patterns in the training.