seisbench-model-api

SeisBench Model API

Installing SeisBench

The recommended way is installation through pip. Simply run:

pip install seisbench

Overview

SeisBench offers the abstract class WaveformModel that every SeisBench model should subclass. 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 SeisBenchModel 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.

Loading Pretrained Models

For annotating waveforms in a meaningful way, trained model weights are required. SeisBench offers a range of pretrained model weights through a common interface. Model weights are downloaded on the first use and cached locally afterwards. For some model weights, multiple versions are available. For details on accessing these, check the documentation at from_pretrained.