Rifts and rifted continental margins host significant natural resources, such as groundwater and geothermal energy, but are also sites of strong, infrequent earthquakes posing considerable natural hazard. To understand the occurrence of geohazards and georessources, we need to quantify how strain localises and how fault networks evolve in crustal-scale extensional systems. This project follows an innovative, multidisciplinary approach by using machine learning techniques in order to analyze big data sets from both 3D seismic reflection surveys and 3D numerical geodynamic models.
This project will be embedded in GFZ’s Section 2.5 “Geodynamic Modeling”. The post-doctoral researcher's role will be to develop and employ techniques involving convolutional neural networks and signal processing to analyse large volumes of 3D seismic reflection data and time-dependent 3D numerical forward models. Results will be compared to manual interpretations of both data types and interpreted in terms of their geodynamic context in order to shed new light on the dynamics of extensional fault networks in space and time.
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