Earthquakes emit two basic types of waves, fast travelling but less energetic P waves (pressure waves, i.e. acoustic), and slower travelling, more energetic S waves (shear-waves, i.e. transverse) and surface waves (also mostly supported by shear motion). P waves arrive first, but all the shaking damage is caused by the later arriving shear- and surface waves. Early warning works by recording the P-waves, ideally close to the source, locating the earthquake and determining its magnitude based on these, and thus warn of the impending damaging S waves up to 10-20 s before they arrive (depending on the geological situation). This works quite well for earthquakes with magnitude up to M~6.5, and algorithms based on a very small number of waveform features do quite well to quickly estimate earthquake size. For larger earthquakes the total rupture time of the earthquake becomes comparable or longer than the typical warning times, meaning that the first damaging waves arrive, while the earthquake itself is still progressing, making it very difficult to set the proper alarm level. In addition, some earthquakes have a slow start before suddenly growing large.
The research question studied in this project is whether the ultimate size (rupture duration) of an earthquake can be predicted based on the initial few seconds of the P wave and by taking into account supplementary data about the environment in which the earthquake happens, and where the station is located. A related and also important research goal concerns a fundamental question of earthquakes physics: Is the whole fault interface in a preparatory condition before a great earthquake, e.g., due to an accelerating creeping motion (nucleation model), or is the growth of a small earthquake into a large earthquake ultimately a stochastic phenomenon (cascade model)? Both questions can be studied based on data available openly or at the GFZ. Earthquakes with M>~6 have a sufficient duration that it is meaningful to discuss the progression of the rupture. Such earthquakes occur globally approximately every 3 days, with data from dense global networks available for the last 15 years at least. Where borehole sensors are available, much smaller events can be examined.
- L. Weber, J. Münchmeyer, T. Rocktäschel, M. Habibi and U. Leser (2019) HUNER: Improving Biomedical NER with Pretraining. Bioinformatics, 36(1), 295-302. 10.1093/bioinformatics/btz528
L. Weber, P. Minervini, J. Münchmeyer, U. Leser and T. Rocktäschel (2019) NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 6151-6161. 10.18653/v1/P19-1618
J. Münchmeyer, D. Bindi, C. Sippl, U. Leser and F. Tilmann (2019) Low uncertainty multi-feature magnitude estimation with 3D corrections and boosting tree regression: application to North Chile. Geophysical Journal International, 220(1), 142-159. doi.org/10.1093/gji/ggz416
J. Münchmeyer, D. Bindi, U. Leser and F. Tilmann (2020) The transformer earthquake alerting model: A new versatile approach to earthquake early warning. Geophysical Journal International, ggaa609. doi.org/10.1093/gji/ggaa609
L. Weber, M. Sänger, J. Münchmeyer, M. Habibi, U. Leser, and A. Akbik (2021) HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition.Bioinformatics, in print
- J. Münchmeyer, D. Bindi, C. Sippl, and F. Tilmann. Increasing magnitude scale consistency by combining multiple waveform features through machine learning. Oral presentation at EGU General Assembly, Vienna, 7-12 April 2019.
- J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. Convolutional event embeddings for fast probabilistic earthquake assessment. Poster presentation at AGU Fall Meeting, San Francisco, 9-13 December 2019.
J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. End-to-end PGA estimation for earthquake early warning using transformer networks. Oral presentation at EGU virtual conference, 4-8 May 2020.
J. Münchmeyer, D. Bindi, U. Leser and F. Tilmann (2021). Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network.https://arxiv.org/abs/2101.02010