| Abstrak/Abstract |
The real-time detection of earthquake
occurrences in a seismic wave, drawn by seismograph, is crucial
for disaster mitigation. The earlier the earthquake warning, the
more lives can be saved. One approach that can monitor and
detect earthquake occurrence is binary classification between
earthquake and noise signals. The use of deep learning models
such as CNN (Convolutional Neural Network), is considered
quite accurate to perform an image classification of
seismograph signals. Nevertheless, the tendency to use the large
CNN model is rated to have better accuracy than smaller
models. In fact, the disadvantage of utilizing large model is the
inference time and the deployment of a large model to obtain
real-time inference is more costly than the smaller model. This
paper aims to reduce the size of a CNN model (Resnet50) by
pruning the unnecessary filters and neuron on the model
architecture without sacrificing the accuracy. The task of the
model was to classify two classes (earthquake and noise) of
spectrogram images, the dataset is STEAD (Stanford
Earthquake Dataset). To prioritize which filter or neuron to be
eliminated, L2-norm was calculated on each filter or neuron
weights. We assumed that a filter or neuron with the lowest L2-
norm had the least significant role in the model. By pruning
90% of the filter and neuron of the model and retraining the
pruned model, the inference time was improved from 22.45ms
to 3.6ms (on NVIDIA GTX 1050) per image with the accuracy
of 99.405%. |
