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SeisBench: A Toolbox for Machine Learning in Seismology

Woollam, Jack 1; Münchmeyer, Jannes; Tilmann, Frederik; Rietbrock, Andreas 1; Lange, Dietrich; Bornstein, Thomas; Diehl, Tobias; Giunchi, Carlo; Haslinger, Florian; Jozinović, Dario; Michelini, Alberto; Saul, Joachim; Soto, Hugo
1 Geophysikalisches Institut (GPI), Karlsruher Institut für Technologie (KIT)


Machine Learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques. With the entire spectrum of seismological tasks, e.g., seismic picking, source property estimation, ground motion prediction, hypocentre determination; among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluate these algorithms, quality controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing these various benchmark datasets for training and implementing the standardization of models is currently a time-consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect "practitioners" seeking to deploy the latest models on seismic data, without having to necessarily learn entirely new ML frameworks to perform this task. ... mehr

Zugehörige Institution(en) am KIT Geophysikalisches Institut (GPI)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 01.11.2021
Sprache Englisch
Identifikator KITopen-ID: 1000146910
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