How does a denoising autoencoder improve earthquake detection and the estimation of magnitude in seismic networks?

Seismogram records always contain seismic noise from different sources. Previous studies have shown that denoising autoencoders can be used to suppress different types of disturbing noise at seismological stations, even when earthquake signal and noise share common frequency bands. A denoising autoencoder is a convolutional neural network that learns from a large training data set how to separate earthquake signal and noise. To train the denoising autoencoder, we used earthquake signals with high signal-to-noise ratio from the Stanford Earthquake Dataset and noise from single seismological stations. We used 160 seismological stations in Germany and surrounding countries and trained a denoising autoencoder model for each station. Afterwards, one year of continuous recorded data have been denoised.

EQTransformer, a deep-learning model for earthquake detection and phase picking, was then applied to the raw and denoised data of each station. Working with denoised data leads to a massive increase of earthquake detections. First results show that in dense seismic networks more than 100% additional earthquakes can be detected compared to events detected in the raw data set. Moreover, the localization accuracy is increased as more stations can be used.

However, like common filter techniques, denoising autoencoders decrease the waveform amplitude. Since earthquake magnitudes are often determined from these amplitudes, we expect a lower amplitude and thus a lower magnitude when using denoised data instead of raw data. So far, we did not find an empirical relation between the raw and denoised magnitude.