Seismologist Jannes Münchmeyer has received the Helmholtz Doctoral Award for his outstanding doctoral thesis on earthquake early warning, following the Adlershof Dissertation Award. He did his phd in section 2.4 "Seismology". He is now a post-doctoral researcher at the Université Grenoble.
The Helmholtz Doctoral Prize supports young scientists with a one-time award of 5000 euros. In addition, research stays abroad of up to six months are funded with 2000 euros per month. In this context, Jannes Münchmeyer is now back at the GFZ for a guest stay until the end of September.
In the interview, he tells us in more detail what his research focus is.
What is your research about?
Jannes Münchmeyer: I have developed deep learning methods for earthquake early warning and real-time estimation of earthquake magnitude and location. Early warning and risk assessment can prevent major damage. Valuable protective measures can often still be taken in the few seconds between the onset of an earthquake and the strong shaking. I have investigated how early such warnings are possible and where the limits lie. Using a probabilistic framework, I have tested the predictability of the progression of earthquake ruptures and have been able to show with my research that earthquake ruptures can indeed not be accurately assessed during their initial growth phase and thus early warning is not possible during this phase.
What were the biggest challenges?
J.M.: For me, there were two major challenges during the PhD. The first challenge was the interdisciplinary work. Before the PhD, I studied mathematics and computer science; thus doing my doctorate at the German Research Center for Geosciences was a big leap. I had to familiarise myself with many geoscientific topics first. At the same time, this interdisciplinary work allowed me to apply new machine learning methods to very current issues in seismology. The second challenge was scientific in nature. Machine learning works best when large amounts of data are available. Large earthquakes, on the other hand, rarely occur. So my research was a constantly walking the tightrope between deriving as many and as significant results as possible while respecting the limited data generated by large earthquakes.
What are your plans for the future?
J.M.: In my PhD, I looked at the phase right at the onset of an earthquake. Now, at the Université Grenoble Alpes, where I am currently continuing my research as a postdoc, I am investigating possible phases that systematically precede an earthquake. For this, I am developing machine learning methods to detect so-called 'low-frequency earthquakes'. These particular earthquakes are showing slow deformations that may indicate coming earthquakes. However, many aspects of this correlation are still unclear. Eventually, it is hoped, my research will contribute to a better understanding of these so-called 'preparatory phases' of earthquakes. In order to carry out this research project, I have received a Marie Skłodowska-Curie Individual Fellowship from the European Union. As part of this fellowship, I will also conduct a six-month research stay at the Massachusetts Institute of Technology (MIT) in Boston.
Thank you for the interview!