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.
- Leon Weber, Jannes Münchmeyer, Tim Rocktäschel, Maryam Habibi and Ulf Leser (2019). HUNER: Improving Biomedical NER with Pretraining. Bioinformatics 10.1093/bioinformatics/btz528
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser and Tim 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
Jannes Münchmeyer, Dino Bindi, Christian Sippl, Ulf Leser and Frederik Tilmann (2019). Low uncertainty multi-feature magnitude estimation with 3D corrections and boosting tree regression: application to North Chile. Geophysical Journal International doi.org/10.1093/gji/ggz416
- Jannes Münchmeyer, Dino Bindi, Christian Sippl, and Frederik Tilmann. Increasing magnitude scale consistency by combining multiple waveform features through machine learning. Oral presentation at EGU General Assembly, Vienna, 7-12 April 2019.
- Jannes Münchmeyer, Dino Bindi, Ulf Leser, and Frederik Tilmann. Convolutional event embeddings for fast probabilistic earthquake assessment. Poster presentation at AGU Fall Meeting, San Francisco, 9-13 December 2019.
Jannes Münchmeyer, Dino Bindi, Ulf Leser, and Frederik Tilmann. End-to-end PGA estimation for earthquake early warning using transformer networks. Oral presentation at EGU virtual conference, 4-8 May 2020.
Leon Weber, Mario Sänger, Jannes Münchmeyer, Maryam Habibi, Ulf Leser, and Alan Akbik (2020). HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition arXiv:2008.07347.
Jannes Münchmeyer, Dino Bindi, Ulf Leser and Frederik Tilmann (2020). The transformer earthquake alerting model: A new versatile approach to earthquake early warning arXiv:2009.06316