標題: SEISMIC SIGNAL CLASSIFICATION USING PERCEPTRON WITH DIFFERENT LEARNING RULES
作者: Huang, Kou-Yuan
Abdurrahman, Fajar
資訊工程學系
電機資訊國際碩士學位學程
Department of Computer Science
EECS International Graduate Program-Master
關鍵字: perceptron;learning rule;wavelet;bright spot;seismogram
公開日期: 1-一月-2019
摘要: In a seismogram, there exist many kinds of wavelets. It can be classified into two classes. One class is normal; the other is abnormal. The abnormal may be the bright spot pattern caused from the gas sand zone. It has the properties of high amplitude and low frequency content in the wavelets. Perceptron is adopted to classify these 10 classes. Four different learning rules are used in the training of perceptron. Those are the fixed increment, normalized perceptron, fractional correction, and absolute correction rules. The experiments are in the simulated and real seismograms. For the comparison of four learning rules in the experiment of simulated seismogram, the absolute correction rule can get the fastest convergence. Then, it is applied to the real seismic data. The bright spot pattern can be detected. The result can improve the seismic interpretation.
URI: http://hdl.handle.net/11536/153989
ISBN: 978-1-5386-9154-0
ISSN: 2153-6996
期刊: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
起始頁: 1410
結束頁: 1413
顯示於類別:會議論文