SeisBench - A toolbox for machine learning in seismology
annotate/classify
backend has been substantially revamped. Large chunks of pre- and postprocessing are now conducted as vectorized operations in PyTorch. This leads to at least 20 % speed ups on CPU and more than 50 % speed ups on GPU. For DeepDenoiser, speed ups can be as high as 20x. Picking one day of 100 Hz three-component waveforms on GPU now takes below 1 s. For further details and more benchmark results, see #282Thanks to everyone how contributed to this release with feedback, issues, and especially with PRs.
Full Changelog: https://github.com/seisbench/seisbench/compare/v0.6.0...v0.7.0
Thanks to everyone how contributed to this release with feedback, issues, and especially with PRs.
Full Changelog: https://github.com/seisbench/seisbench/compare/v0.5.0...v0.6.0
model.classify(stream).picks
. Similarly, EQTransformer or CRED detections can be accessed at model.classify(stream).detections
. For convenience, picks and detections can now also be filtered through station and confidence criteria. For details see #230.Thanks to everyone how contributed to this release with feedback, issues, and especially with PRs.
Full Changelog: https://github.com/seisbench/seisbench/compare/v0.4.0...v0.5.0
Full Changelog: https://github.com/seisbench/seisbench/compare/v0.3.0...v0.4.0
grouping
argument for datasets and the GroupGenerator
annotate
and classify
have been cleaned up. They now are documented automatically, have clear default values and are validated to avoid invalid arguments.Thanks to everyone how contributed to this release with feedback, issues, and especially with PRs.
Full Changelog: https://github.com/seisbench/seisbench/compare/v0.2.1...v0.3.0
DeepDenoiser.from_pretrained("urban")
(#83).ProbabilisticLabeller
now supports different label shapes, i.e., Gaussian, Triangular and Box (#67).save
and load
functions. The functions ensure that not only model weights can be saved and loaded but also further model parameters, such as the component order or the sampling rate #69, #71, #86).WaveformModel
annotate
/classify
functions now support multiprocessing for annotating large datasets. Simply set the parallelism
parameter in the call to these functions. The previous API based on asyncio is maintained for processing small stream objects, as multiprocessing has a higher overhead due to latency (#64, #68, #81).from_pretrained
API to load pretrained models has been completely rewritten. It now supports model versioning, coming with the new function list_versions
and the version_str
parameter for from_pretrained
. In addition, more control and transparency were added whether the function queries the SeisBench remote repository or only the local cache (#76, #77, #86).get_idx_from_trace_name
to avoid trace name collisions for chunked data sets and multi-part datasets (#78, #84)Thanks to everyone contributing to this release through issues, PRs and commits!
Full Changelog: https://github.com/seisbench/seisbench/compare/v0.1.9...v0.1.10
Full Changelog: https://github.com/seisbench/seisbench/compare/v0.1.7...v0.1.8
Full Changelog: https://github.com/seisbench/seisbench/compare/v0.1.6...v0.1.7
This release adds the Iquique dataset, a dataset consisting of events around the Mw=8.1 Iquique earthquake in Chile 2014. The dataset is relatively small, providing a good starting point for model development on low resources. As we are still in v0.1, the dataset is added using a patch version, rather than a new minor version.