Martijn van den Ende

Martijn van den Ende

rock squeezer by choice, seismologist by accident...

Research interests

Physics-Based Machine Learning

Earthquake detection

Over a relatively short span of time, machine learning has become an indispensible tool in seismology and geophysics. Benefitting from the vast data sets available to the community, machine learning as a data-driven approach excels in performing non-linear tasks such as automated earthquake detection, phase picking, and signal processing. On the other hand, it is easy to forget that the seismological community is already equiped with traditional techniques that are based on the physics of wave propagation.

Instead of relying on purely data-driven methods, can we unite data-driven and model-driven methods by incorporating physical models into machine learning, or machine learning into physical models?

Recently, I started to explore a number of avenues that address this question. To help me navigate through the rapidly evolving landscape of machine learning, I take inspiration from recent advances in, for instance, medical imaging, Hamiltonian mechanics, and fluid dynamics. With those computational concepts in the back of my mind, I revisit seismological problems such as seismic array beamforming, finite fault inversion, and seismic source characterisation.

Key publication van den Ende & Ampuero (2020), Automated Seismic Source Characterisation Using Deep Graph Neural Networks, Geophysical Research Letters, doi:10.1029/2020GL088690

Distributed Acoustic Sensing

3IA/UCA logo

Being the backbone of our digital infrastructure, fibre-optic cables can be found everywhere on Earth, both on land and in the oceans. When you send a laser pulse through an optical fibre, tiny imperfections in the fibre cause scattering of the light that can be measured as "optical echoes". By analysing these echoes, one can infer stretching and shrinking of the fibre at different locations along the cable, a technique called Distributed Acoustic Sensing (DAS).

We can use DAS to record various types of vibrations along the cable, including earthquakes, cars (when the cable is on land), and ocean waves and boats when the cable is underwater. This opens up a plethora of possibilities to use existing fibre-optic cables (which we use for our everyday internet) as sensitive seismometers.

Key publication van den Ende & Ampuero (2020), Evaluating Seismic Beamforming Capabilities of Distributed Acoustic Sensing Arrays, Solid Earth Discuss. [preprint], doi:10.5194/se-2020-157

Earthquake Source Mechanics

Earthquakes (which can potentially grow to hundreds of kilometres in size) are the catastrophic result of processes that operate at the scale of micrometres (and even smaller). Even though we cannot directly resolve these processes with seismological methods, efforts have been made to explore the connection between the micro-scale and kilometre-scale through numerical modelling.

Together with other collaborators, I have developed a quasi-dynamic earthquake cycle code: QDYN. With this seismic cycle simulator, we can simulate numerous earthquake cycles and study the relation between local fault properties, such as fault zone width and rock solubility, and large scale emergent behaviour, such as slow earthquakes and run-away ruptures. Similarly, this code can be used to tackle inverse problems, extracting fault zone properties that explain natural observations of earthquake ruptures. The QDYN code is open-source available from GitHub.

Key publication van den Ende et al. (2018), A comparison between rate-and-state friction and microphysical models, based on numerical simulations of fault slip, Tectonophysics 733(9), doi:10.1016/j.tecto.2017.11.040

Selected publications