Title :
Neural network approach to seismic signal analysis
Author :
Wei, Foo Say ; Yee, Lh Ming
Author_Institution :
Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
Abstract :
The paper summarises the preliminary findings of a neural network approach to automatic classification of moving objects based on seismic signals detected through a geophone. Four types of moving objects were considered: human beings, motorcycles, cars and buses. A 32-16-4 network structure was used and the data was preprocessed before neural analysis. The results reveal 92% accuracy in classifying human beings from vehicles. However, only 74% accuracy was achieved in classifying the four different types of moving objects
Keywords :
feedforward neural nets; geophysical signal processing; multilayer perceptrons; neural net architecture; pattern classification; seismology; automatic classification; buses; cars; geophone; human beings; motorcycles; moving objects classification; neural analysis; neural network; seismic signal analysis; seismic signals detection; Acoustic sensors; Earth; Humans; Magnetic sensors; Motorcycles; Neural networks; Probes; Signal analysis; Signal detection; Signal generators;
Conference_Titel :
Singapore ICCS '94. Conference Proceedings.
Print_ISBN :
0-7803-2046-8
DOI :
10.1109/ICCS.1994.474072
