Earthquake Detection in Zeerijp
A Study on the Usage of Template Matching and Neural Networks for Detection of Small Earthquakes in Zeerijp
J.J.M. van Deventer (TU Delft - Civil Engineering & Geosciences)
Femke C. Vossepoel – Mentor (TU Delft - Reservoir Engineering)
Elmer Ruigrok – Graduation committee member (Royal Netherlands Meteorological Institute (KNMI))
Guy Drijkoningen – Graduation committee member (TU Delft - Applied Geophysics and Petrophysics)
C Weemstra – Graduation committee member (TU Delft - Applied Geophysics and Petrophysics)
H.A. Diab Montero – Graduation committee member (TU Delft - Reservoir Engineering)
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Abstract
In this thesis, we discuss two pattern recognition techniques, template matching, and neural networks. We discuss how these techniques have been used for the development of two earthquake detection algorithms. The first algorithm is based on template matching and the second is based on deep learning. The algorithms are designed for the detection of small <0.5M events in the subsurface of Zeerijp, Groningen. These two algorithms have been compared to assess their earthquake detectability and practicality. The systems have been compared using field data from Zeerijp. The algorithm based on deep learning (a neural network) produced too many false positives considering the amount of seismic data we would like to use it for. The algorithm based on template matching did not produce any false positives during testing. The template matching system has been fed six months of continuous seismic data fromZeerijp. This resulted in the detection of at least 22 new events.