Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications
This work addresses feature extraction challenges in seismology, but it is incremental as it evaluates existing autoencoder methods under specific constraints.
The paper investigated using deep convolutional autoencoders as generic feature extractors for seismological applications like event discrimination and phase picking, finding they only perform well under specific conditions such as when target problems align with encoded features and with small training datasets.
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event discrimination (i.e., earthquake vs. noise waveforms, earthquake vs. explosion waveforms, and phase picking). These tests involve training an autoencoder, either undercomplete or overcomplete, on a large amount of earthquake waveforms, and then using the trained encoder as a feature extractor with subsequent application layers (either a fully connected layer, or a convolutional layer plus a fully connected layer) to make the decision. By comparing the performance of these newly designed models against the baseline models trained from scratch, we conclude that the autoencoder feature extractor approach may only perform well under certain conditions such as when the target problems require features to be similar to the autoencoder encoded features, when a relatively small amount of training data is available, and when certain model structures and training strategies are utilized. The model structure that works best in all these tests is an overcomplete autoencoder with a convolutional layer and a fully connected layer to make the estimation.