Mikroseismicita v subdukčních zónách: detekce jevů a asociace fází založená na strojovém učení
| Název práce v češtině: | Mikroseismicita v subdukčních zónách: detekce jevů a asociace fází založená na strojovém učení |
|---|---|
| Název v anglickém jazyce: | Microseismicity in subduction zones: machine-learning based event detection and phase association |
| Klíčová slova anglicky: | seismicity|subduction zone|local earthquakes|earthquake catalog|event detection|phase association |
| Akademický rok vypsání: | 2021/2022 |
| Typ práce: | disertační práce |
| Jazyk práce: | čeština |
| Ústav: | Geofyzikální ústav AV ČR, v.v.i. (32-GFUAV) |
| Vedoucí / školitel: | Christian Sippl |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 28.07.2021 |
| Datum zadání: | 28.07.2021 |
| Datum potvrzení stud. oddělením: | 23.09.2021 |
| Zásady pro vypracování |
| Detailed analysis of the distribution of microseismicity in subduction zones can lead to insights into the state and evolution of different parts of the subduction system (e.g. plate interface, downgoing plate, ...). The use of locally recorded seismic data from dense permanent or temporary networks allows determining more precise locations of many more smaller earthquakes compared to what is available from global catalogs, thus significantly enhancing resolution. However, the sheer amount of data to be processed has so far prevented large-scale studies based on local data. With the advent of machine-learning-based earthquake detection and picking systems, this is currently about to change. This is one of two Ph.D. projects aimed at constructing a fully automatized microseismicity detection, autopicking, and event location workflow and its application to large datasets from subduction zone environments.
The student will perform the following tasks: 1) Implementation and performance checking of currently available event detection and phase association routines (e.g., HEX, PhaseLink, scanloc), 2) Design of a novel such method and its implementation into an automatic workflow of earthquake catalog generation from raw data (together with the other Ph.D. project), 3) Application of this workflow to large datasets from different subduction zones (others than the other Ph.D. project). |
| Seznam odborné literatury |
| The topic is rather novel and thus not covered in any textbook. The following papers should provide a good introduction by example:
McBrearty, I., et al. (2019). Earthquake Arrival Association with Backprojection and Graph Theory, Bulletin of the Seismological Society of America, 109 (6), 2510–2531. Woollam, J., et al. (2020). HEX: Hyperbolic Event eXtractor, a Seismic Phase Associator for Highly Active Seismic Regions, Seismological Research Letters, 91 (5), 2769–2778. Ross, Z. E., et al. (2019). PhaseLink: A deep learning approach to seismic phase association, Journal of Geophysical Research, 124, 856–869. |