Earthquakes count among the largest natural threats to humans. The current state of research suggests that it will not be possible to predict earthquakes reliably anytime in the near future. What is possible, on the other hand, is to provide reliable early warning in the context of ongoing earthquakes. The goal is to provide warnings a few seconds before strong shaking occurs. Such warnings can trigger automatic reactions, like stopping trains, or alert humans early enough to allow them to seek cover.
Usually, early warnings are based on recording the early, relatively weak shaking caused by an earthquake, inferring its size, and then predicting the level of shaking to follow. However, a look at the physics behind earthquakes reveals a crucial issue in attempting to make such predictions. An earthquake emits seismic waves from a rapidly growing rupture between two tectonic plates. These ruptures can traverse distances of tens or even hundreds of kilometers, and consequently, even when a rupture grows quickly, it might take tens of seconds or even minutes for the full rupture to occur.
There is no scientific consensus on how accurately the size of an earthquake can be assessed at what time during an ongoing rupture. There are two basic positions among experts: one holds that the size of an earthquake can be accurately predicted from its onset or during the first few seconds, and the other that accurate assessment is impossible until the rupture is largely finished. Which of these positions is correct will have a profound impact on the potential of early warning methods: if the earthquake's size can only be determined at the end of the rupture, then only short warning times – if any at all – will be possible.
In this PhD project, I am taking a novel, data-driven approach to the question of predictability. Using machine learning, I will build real-time assessment systems to predict the size of an event during an ongoing rupture. If we can design a model that can accurately assess the size of an earthquake from its first seconds, this will be a demonstration that ruptures can be feasibly predicted. A further step will be to integrate our real-time assessment model into earthquake early warning systems, to improve their performance with our state-of-the-art estimation methodology.
- 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, btab042, https://doi.org/10.1093/bioinformatics/btab042
J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann (2021). Earthquake magnitude and location estimation from real time seismic waveforms with a Transformer Network. Geophysical Journal International, 226(2), 1086-1104. https://doi.org/10.1093/gji/ggab139
- J. Münchmeyer, D. Bindi, C. Sippl, and F. Tilmann. Increasing magnitude scale consistency by combining multiple waveform features through machine learning. (Oral presentation), 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), 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), EGU General Assembly, Online, 4-8 May 2020.
- J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. The Transformer Earthquake Alerting Model: Improving Earthquake Early Warning with Deep Learning. (Oral presentation), AGU Fall Meeting, Online, 13-17 December 2020.
- J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. Insights into deep learning for earthquake magnitude and location estimation. (PICO presentation), EGU General Assembly, Online, 19-30 April 2021. doi.org/10.5194/egusphere-egu21-4718
- J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. The Transformer Earthquake Alerting Model: A Data Driven Approach to Early Warning. (Oral presentation), Seismological Society of America (SSA) Annual Meeting, Online, 19-23 April 2021.
- J. Münchmeyer, J. Woollam, D. Jozinovic, J. Saul, A. Michelini, C. Giunchi, T. Diehl, F. Haslinger, D. Lange, A. Rietbrock, and F. Tilmann. SeisBench: A framework for machine learning in seismology. (Oral presentation), 37th General Assembly of the European Seismological Commission, Online, 19-24 September 2021.