Postdoctoral Fellow
Research group |?Centre for Studies of the Physics of the Earth (NJORD)
Main supervisor |?Francois Renard
Co-supervisor |?-
Affiliation |?Department of Geosciences, UiO
Contact |?karinlo@uio.no
Short bio
I completed my PhD at the GFZ Helmholtz Centre for Geosciences and the University of Potsdam in Germany in 2025. My thesis, titled "Data-driven testing of site amplification models and alternative proxies for large-scale seismic hazard and risk assessment," focused on seismic hazard and earthquake site amplification. My PhD was part of the URBASIS-EU (New Challenges for Urban Engineering Seismology) project. Following the PhD I worked as a postdoctoral researcher with the Norwegian National Seismic Network at NORSAR.
Currently, I am a postdoctoral fellow at the Njord Center for the Physics of the Earth, associated with the departments of Geoscience and Physics. Through my project; SeisMonAI, I aim to use ambient seismic noise to study groundwater dynamics in Norway and Sweden.
Research interests and hobbies
My main research interests are seismology, geophysics, and natural hazards. During my PhD, I studied how local geology affects earthquake shaking and how to incorporate this effect into seismic hazard and risk modeling. Currently, I focus on using ambient seismic noise to detect changes in the subsurface, particularly related to groundwater dynamics, as a tool to improve drought monitoring.
DSTrain project
Machine learning enhanced seismic noise monitoring of groundwater dynamics: application to central Scandinavia (SeisMonAI)
Due to climate change, droughts are becoming more frequent and severe. This is also the case in Scandinavia, where severe events such as the 2018 drought in eastern Norway and southern Sweden have highlighted the need for more robust groundwater monitoring. Traditional groundwater observations, such as groundwater level wells and GRACE satellite data, are either spatially sparse or limited in resolution and depth sensitivity. Ambient seismic noise interferometry offers a promising alternative for groundwater monitoring by exploiting ambient seismic noise to detect seismic velocity changes (Δv/v) linked to groundwater fluctuations. Using sensitivity kernels, Δv/v can be derived not only in time, but also in space and at depth, providing a spatiotemporal resolution of groundwater changes that complement existing groundwater datasets.
A key challenge with ambient seismic noise interferometry is the assumption of uniform noise sources and the need for dense seismic networks, which is difficult to obtain in Norway and Sweden. The SeisMonAI project aims to develop an optimized interferometry workflow specifically for regions with sparse seismic networks and heterogeneous noise conditions. Combining methods from seismology, hydrology, and data science, the project will use ambient seismic noise to improve understanding of aquifer dynamics and enhance groundwater monitoring in the region of interest.
Figure 1: Schematic figure showing the overall workflow of the project, from recording of seismic noise (bottom left), to cross-correlation of seismic noise over time and measurement of time shift related to changes in seismic velocity (upper left), to comparison of seismic velocity changes (Δv/v) with groundwater level data (top right) to the final spatial inversion and spatiotemporal resolution of Δv/v.
Publications
DSTrain publications
ORCID ID: https://orcid.org/0000-0002-9570-8145
Previous publications
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