Notebook: SeisBench and DeepSubDAS
Open this Notebook and follow more instructions on the video tutorial starting at 11:09
This notebook provides a comprehensive tutorial on using
SeisBench for DAS (Distributed Acoustic Sensing) phase picking. It
introduces how to install SeisBench with DAS support and import
necessary libraries like seisbench.models and xdas. The tutorial then guides through loading and inspecting example DAS data, which is handled efficiently using xdas's virtual data arrays to manage large datasets.
Two main approaches for phase picking are demonstrated:
- Wrapping 3C models: This section shows how to adapt
models trained on traditional 3-component seismic data for DAS data using
DASWaveformModelWrapper. It covers the process of moving models to preferred devices for acceleration and introduces the concept ofcallbacksfor handling large model outputs without memory overload. - DeepSubDAS | A DAS-native model: This section highlights the advantages of using models specifically designed for DAS data, such as DeepSubDAS, which leverage the spatial coherence of DAS signals. It reiterates the process of loading the model, moving it to a GPU, and using callbacks for processing, demonstrating that DAS-native models yield more coherent and confident picks.
Finally, the notebook addresses larger-than-memory processing capabilities of SeisBench. It explains how xdas handles virtual input data and how WriterCallback can be used to write large probability functions to disk, allowing the processing of datasets that exceed available memory.
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