An overview of the core model API of SeisBench, a Python framework for training and applying machine learning algorithms to seismic data. It is useful for an...
If the waveform data happen to be extremely small in scale (<=1e-10), there might be risk of numerical instability. It is acceptable to increase the value first (by multiplying a large number like 1e10) before normalization or passing to the model.
Although the seisbench model API will normalize the waveform for you, it is still highly suggested to apply normalization yourself. Since seisbench's normalization scheme uses an epsilon (waveform - mean(waveform)) / (std(waveform) + epsilon), for extremely small values (such as <=1e-10), their normalization can destroy the signals in the waveform.
The seisbench model API can process a stream of waveform data of arbitrary length. Hence, it is not necessary to segment the data yourself. In addition, you should not assume a stream of waveform can only contain one P-wave and one S-wave. It is the best to treat the stream like what it is: a stream of continuous data.
