P3.5-342 Seismic Signal Classification using Deep

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P3.5-342 Seismic Signal Classification using Deep
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P3.5-342 Seismic Signal Classification using Deep Neural Networks El Hassan Ait Laasri, Driss Agliz and Abderrahman Atmani Faculty of Applied Sciences, Ibn Zohr University Convolution 1 Max-Pooling Convolution 2 Max-Pooling Convolution 3 n1

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P3.5-342 Seismic Signal Classification using Deep Neural Networks
El Hassan Ait Laasri, Driss Agliz and Abderrahman Atmani
Faculty of Applied Sciences, Ibn Zohr University Convolution 1 Max-Pooling Convolution 2 Max-Pooling Convolution 3 n1 channels n2 channels n3 channels Fully-connected NN Flattening Spectrogram Seismic signal RGB image The proposed classifier consists of the following steps:
- Compute the spectrogram
- Convert the spectrogram to an RGB image
- Apply the image to the proposed CNN
The proposed CNN architecture is composed of three layers of convolution, followed by Max-pooling. These layers aim to extract the pertinent features. The latter are then applied to a fully-connected neural network.
To evaluate the proposed classifier, a volcano dataset of four classes is used.<br>