SeisBench Waveform Models

Overview

SeisBench offers the abstract class WaveformModel that every SeisBench model working on classical waveforms should inherit from. This class offers two core functions, annotate and classify. Both of the functions are automatically generated based on configurations and submethods implemented in the specific model. The WaveformModel bridges the gap between the pytorch interface of the models and the obspy interface common in seismology. It automatically assembles obspy streams into pytorch tensors and reassembles the results into streams. It also takes care of batch processing. Computations can be run on GPU by simply moving the model to GPU.

The annotate function takes an obspy stream object as input and returns annotations as stream again. For example, for picking models the output would be the characteristic functions, i.e., the pick probabilities over time.

stream = obspy.read("my_waveforms.mseed")
annotations = model.annotate(stream)  # Returns obspy stream object with annotations

The classify function also takes an obspy stream as input, but in contrast to the annotate function returns discrete results. The structure of these results might be model dependent. For example, a pure picking model will return a list of picks, while a picking and detection model might return a list of picks and a list of detections.

stream = obspy.read("my_waveforms.mseed")
outputs = model.classify(stream)  # Returns a list of picks
print(outputs)

Both annotate and classify can be supplied with waveforms from multiple stations at once and will automatically handle the correct grouping of the traces. For details on how to build your own model with SeisBench, check the documentation of WaveformModel. For details on how to apply models, check out the Examples.

Models integrated into SeisBench

You don’t have to build models from scratch if you don’t want to. SeisBench integrates the following notable models from the literature for you to use. Again, as they inherit from the common SeisBench model interface, all these deep learning models are constructed through PyTorch. Where possible, the original trained weights are imported and made available. These can be accessed via the from_pretrained method. For a more in-depth explanation, see the Examples.

Integrated models	Task
`BasicPhaseAE`	Phase Picking
`CRED`	Earthquake Detection
`DPP`	Phase Picking
`DepthPhaseNet`	Depth estimation from depth phases
`DepthPhaseTEAM`	Depth estimation from depth phases
`DeepDenoiser`	Denoising
`SeisDAE`	Denoising
`EQTransformer`	Earthquake Detection/Phase Picking
`EQTP`	Phase Picking/Polarity Picking
`GPD`	Phase Picking
`LFEDetect`	Phase Picking (Low-frequency earthquakes)
`OBSTransformer`	Earthquake Detection/Phase Picking
`PhaseNet`	Phase Picking
`PhaseNetLight`	Phase Picking
`PickBlue`	Earthquake Detection/Phase Picking
`Skynet`	Phase Picking
`VariableLengthPhaseNet`	Phase Picking

Currently integrated models are capable of earthquake detection and phase picking, waveform denoising, depth estimation, and low-frequency earthquake phase picking. Furthermore, with SeisBench you can build ML models to perform general seismic tasks such as magnitude and source parameter estimation, hypocentre determination etc.