Jannes Münchmeyer

Shi, P., Meier, M.-A., Villiger, L., Tuinstra, K., Selvadurai, P., Lanza, F., Yuan, S., Obermann, A., Mesimeri, M., Münchmeyer, J., Bianchi, P., Wiemer, S. (2024). From Labquakes to Megathrusts: Scaling Deep Learning Based Pickers Over 15 Orders of Magnitude. JGR: Machine Learning. [paper]

Liu, T., Münchmeyer, J., Laurenti, L., Marone, C., V de Hoop, M., Dokmanić, I. (2024). SeisLM: a Foundation Model for Seismic Waveforms. NeurIPS: Foundation Models for Science workshop. [paper]

Münchmeyer, J., Giffard-Roisin, S., Malfante, M., Frank, W., Poli, P., Marsan, D., Socquet A. (2024). Deep learning detects uncataloged low-frequency earthquakes across regions. Seismica. [paper]

Münchmeyer, J. (2024). PyOcto: A high-throughput seismic phase associator. Seismica. [paper] [code]

Münchmeyer, J., Saul, J., Tilmann, F. (2023). Learning the Deep and the Shallow: Deep‐Learning‐Based Depth Phase Picking and Earthquake Depth Estimation. Seismological Research Letters. [paper]

González-Vidal, D., Moreno, M., Sippl, C., Baez, J. C., Ortega-Culaciati, F., Lange, D., Tilmann, F., Socquet, A., Bolte, J., Hormazabal, J., Langlais, M., Morales-Yáñez, C., Melnick, D., Benavente, R., Münchmeyer, J., Araya, R., Heit, B. (2023). Relation Between Oceanic Plate Structure, Patterns of Interplate Locking and Microseismicity in the 1922 Atacama Seismic Gap. Geophysical Research Letters. [paper]

Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock, A., Barcheck, G., Grevemeyer, I., Tilmann, F. (2023). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning. Earth and Space Science. [paper]

Sippl, C., Schurr, B., Münchmeyer, J., Barrientos, S., Oncken, O. (2023). The Northern Chile forearc constrained by 15 years of permanent seismic monitoring. Journal of South American Earth Sciences. [paper]

Foster, W., Allen, B., Kitzmann, N., Münchmeyer, J., Rettelbach, T., Witts, J., Whittle, R., Larina, E., Clapham, M. & Dunhill, A. (2023). How predictable are mass extinction events? Royal Society Open Science. [paper]

Münchmeyer, J. (2022). Machine learning for fast and accurate assessment of earthquake source parameters: Implications for rupture predictability and early warning. PhD thesis, Humbolt-Universität zu Berlin. [thesis]

Münchmeyer, J., Leser, U., & Tilmann, F. (2022). A probabilistic view on rupture predictability: all earthquakes evolve similarly. Geophysical Research Letters. [paper]

Singh, K., Münchmeyer, J., Weber, L., Leser, U., & Bande, A. (2022). Graph Neural Networks for Learning Molecular Excitation Spectra. Journal of Chemical Theory and Computation. [paper]

Münchmeyer, J.*, Woollam, J.*, Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J. & Soto, H. (2022). Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. Journal of Geophysical Research: Solid Earth. ^{*: Equal contribution} [paper] [code]

Woollam, J.*, Münchmeyer, J.*, Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J. & Soto, H. (2022). SeisBench - A Toolbox for Machine Learning in Seismology. Seismological Research Letters. ^{*: Equal contribution} [paper] [code]

Foster, W., Ayzel, G., Münchmeyer, J., Rettelbach, T., Kitzmann, N., Isson, T., Mutti, M. & Aberhan, M. (2022). Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction. Paleobiology. [paper]

Weber, L., Garda, S., Münchmeyer, J. & Leser, U. (2021). Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling. EMNLP 2021, Punta Cana, Dominican Republic. [paper] [code]

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network. Geophysical Journal International. [paper] [code]

Weber, L., Sänger, M., Münchmeyer, J., Habibi, M., Leser, U., & Akbik, A. (2021). HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition. Bioinformatics. [paper] [code]

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2020). The transformer earthquake alerting model: A new versatile approach to earthquake early warning. Geophysical Journal International. [paper] [code]

Münchmeyer, J., Bindi, D., Sippl, C., Leser, U., & Tilmann, F. (2019). Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile. Geophysical Journal International, 220(1), 142-159. [paper] [code]

Weber, L.*, Münchmeyer, J.*, Rocktäschel, T., Habibi, M., & Leser, U. (2019). HUNER: Improving Biomedical NER with Pretraining. Bioinformatics. ^{*: Equal contribution} [paper] [code]

Weber, L., Minervini, P., Münchmeyer, J., Leser, U., & Rocktäschel, T. (2019). NLprolog: Reasoning with Weak Unification for Question Answering in Natural Language. ACL 2019, Florence, Italy. [paper] [code]

Trescher, S., Münchmeyer, J., & Leser, U. (2017). Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization. BMC systems biology, 11(1), 41. [paper]

Münchmeyer, J., Giffard-Roisin, S., Malfante, M., Marsan, D. & Socquet, A. (2024). Identifying uncataloged low-frequency earthquake sources with deep learning. EGU General Assembly 2024, Vienna, Austria.

Isken, M., Dahm, T., Heimann, S., Münchmeyer, J., Cesca, S. & Niemz, P. (2024). Advancing Seismic Event Detection: Integrating Machine Learning with Waveform-Stacking Techniques. EGU General Assembly 2024, Vienna, Austria.

Chouli, A., Costes, L., Marsan, D., Münchmeyer, J., Giffard-Roisin, S. & Socquet, A. (2024). Search for repeaters in the central part of the Chilean subduction zone. EGU General Assembly 2024, Vienna, Austria.

Molina, D., Münchmeyer, J., Radiguet, M., Socquet, A. & Doin, M. (2024). Structural control on aseismic and seismic slip interactions during the 2020 SSE in the Atacama region, Chile. EGU General Assembly 2024, Vienna, Austria.

Puente, J., Münchmeyer, J., McBrearty, I. & Sippl, C. (2024). Benchmarking seismic phase associators: Insights from synthetic scenarios. EGU General Assembly 2024, Vienna, Austria.

Münchmeyer, J. (2023). PyOcto: A high-throughput seismic phase associator. AGU Fall Meeting 2023, San Francisco, USA.

Münchmeyer, J., Giffard-Roisin, S., Malfante, M., Marsan, D., & Socquet, A. (2023). Detecting low-frequency earthquakes with deep learning. AGU Fall Meeting 2023, San Francisco, USA. (Poster)

Münchmeyer, J. (2023). Opportunities and limitations of deep learning for earthquake monitoring. National Academies, Commitee of Solid Earth Geophysics, Artificial Intelligence and Machine Learning in Geophysics: Are We Beyond the Black Box? [invited talk] [recording]

Saul, J., Bornstein, T., Tilmann, F., Münchmeyer, J. (2023). Deep-learning-based phase picking in SeisComP using SeisBench. IUGG, Berlin, Germany.

Isken, M., Reiss, M., Cesca, S., Hensch, M., Schmidt, B., Dahm, T., Münchmeyer, J. (2023). Eifel Large-N Experiment: Detection and Localization of Seismic Events using Stacking and Migration Approach combined with Neural Network Phase Characterization. IUGG, Berlin, Germany.

Münchmeyer, J., Tilmann, F., Saul, J. (2023). Constraining earthquake depth at teleseismic distance: Picking depth phases with deep learning. IUGG, Berlin, Germany.

Münchmeyer, J., Giffard-Roisin, S., Malfante, M., Marsan, D., & Socquet, A. (2023). Detecting low-frequency earthquakes with deep learning. EGU General Assembly 2023, Vienna, Austria. (Poster)

Tilmann, F., Bornstein, T., Saul, J., Münchmeyer, J., Beutel, M. (2023). Employing machine learning pickers for routine global earthquake monitoring with SeisComP: What are the benefits and how can we quantify the uncertainty of picks? EGU General Assembly 2023, Vienna, Austria. (Poster)

Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock, A., Barcheck, G., Grevemeyer, I. & Tilmann, F. (2023). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning. EGU General Assembly 2023, Vienna, Austria.

Münchmeyer, J., Bornstein, T., Lange, D., Woollam, J., Rietbrock, A., Barcheck, G., Grevemeyer, I. & Tilmann, F. (2022). Phase Picking on OBS Data with Deep Learning: Bringing SeisBench to the Ocean Bottom. AGU Fall Meeting 2022, Chicago, USA.

Münchmeyer, J., Woollam, J., Giunchi, C., Jozinovic, D., Diehl, T., Saul, J., Michelini, A., Haslinger, F., Lange, D., Rietbrock, A., & Tilmann, F. (2022). Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. AGU Fall Meeting 2022, Chicago, USA. [invited talk]

Münchmeyer, J. (2022). SeisBench: Benchmarking and applying deep learning based phase pickers. STATSEI12, Cargése, Corsica, France.

Münchmeyer, J. (2022). Accelerating machine learning development and deployment in seismology through standardisation. 3ECEES, Bucharest, Romania. [invited talk]

Münchmeyer, J., Woollam, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J. & Soto, H. (2022). Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. EGU General Assembly 2022, Vienna, Austria. [abstract]

Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J. & Soto, H. (2022). SeisBench - A Toolbox for Machine Learning in Seismology. EGU General Assembly 2022, Vienna, Austria. [abstract]

Münchmeyer, J., Leser, U., & Tilmann, F. (2021). A probabilistic view of earthquake rupture predictability. AGU Fall Meeting 2021, New Orleans, USA. [abstract]

Woollam, J., Münchmeyer, J., Giunchi, C., Jozinovic, D., Diehl, T., Saul, J., Michelini, A., Haslinger, F., Lange, D., Tilmann, F., & Rietbrock, A. (2021). SeisBench: A Python Toolbox for Benchmarking and Deploying ML Models in Seismology. AGU Fall Meeting 2021, New Orleans, USA. [abstract]

Münchmeyer, J., Woollam, J., Giunchi, C., Jozinovic, D., Diehl, T., Saul, J., Michelini, A., Haslinger, F., Lange, D., Rietbrock, A., & Tilmann, F. (2021). SeisBench: A framework for machine learning in seismology. General Assembly of the European Seismological Commission. Virtual conference 2021.

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). Insights into deep learning for earthquake magnitude and location estimation. EGU virtual conference 2021. [abstract]

Woollam, J., Münchmeyer, J., Giunchi, C., Jozinovic, D., Diehl, T., Saul, J., Michelini, A., Haslinger, F., Lange, D., Tilmann, F., & Rietbrock, A. (2021). SeisBench: A toolbox for benchmarking and applying machine learning in seismology. EGU virtual conference 2021. [abstract]

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). The Transformer Earthquake Alerting Model: A Data Driven Approach to Early Warning. SSA virtual conference 2021.

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2020). The Transformer Earthquake Alerting Model: Improving Earthquake Early Warning with Deep Learning. AGU virtual conference 2020. [recorded talk]

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2020). End-to-end PGA estimation for earthquake early warning using transformer networks. EGU virtual conference 2020. [presentation slides]

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. J. (2019). Convolutional event embeddings for fast probabilistic earthquake assessment. AGU Fall Meeting 2019, San Francisco, USA.

Münchmeyer, J., Bindi, D., Sippl, C., & Tilmann, F. (2019). Increasing magnitude scale consistency by combining multiple waveform features through machine learning. EGU General Assembly 2019, Vienna, Austria.

Convener of session "Challenges and Opportunities for Machine Learning in Solid Earth Geophysics" at EGU General Assembly 2024, Vienna, together with Giffard-Roisin, S., Corbi, F. & Marone, C. [link]

Convener of session "Machine Learning–Driven Analysis of Geophysical Signals" at AGU Fall Meeting 2023, San Francisco, together with Zhu., W., McBrearty, I & Li, Y. [link]

Convener of session "Machine-Learning-Based Earthquake Monitoring and Seismic Analysis" at AGU Fall Meeting 2022, Chicago, together with Zhang, M., Zhu., W., & McBrearty, I. [link]

Convener of session "Decoding Geophysical Signatures With Machine Learning: Novel Methods and Results" at AGU Fall Meeting 2021, New Orleans, together with Pyrak-Nolte, L., Chen, T., & Woollam, J. [link]

Convener of session "Data Science and Machine Learning for Natural Hazards and Seismology" at EGU virtual conference 2020 together with Tang, H., Chen, K., Olen, S. & Corbi, F. [link]