Microseismic event detection and localization

Microseismic event detection and localization: A migration-based and machine-learning approach using full waveforms

Vinard, N. (TU Delft Applied Geophysics and Petrophysics)

Drijkoningen, G.G. (promotor)
Verschuur, D.J. (promotor)

Delft University of Technology

2022-09-21

When humans started started exploiting the abundant underground natural resources the Earth has to offer such as hydrocarbons, minerals and heat, we started to experience earthquakes that are related to this exploitation, so called induced earthquakes. Under certain conditions those can damage local infrastructure. However, most events are weak and only sensed by seismic sensors. Microseismic monitoring plays a vital role to optimize and insure the safety of these underground activities and new technologies such as carbon capture and storage. One key task besides the detection of microseimsic events is to determine the source location of these events using data recorded at the surface. In this thesis we investigate a method to localize weak microseismic events, using a deterministic approach, assuming a dense network of sensors. In simple words this method takes the seismic signals recorded at the Earth’s surface and sends them back into the Earth, where the signals start to focus at the point they originated from. This focusing method uses one-way wavefield extrapolation with an estimate of the background velocity model. The advantage of this method is that the weak signals recorded by the different sensors at the surface are amplified as they approach the location of the event that emitted the signal due to constructive interference. However, this is not enough to reliably recover the source location because typically earthquakes do not radiate seismic waves evenly; complex radiation patterns are typically observed depending on the mechanical properties of the rupture. To obtain a strong focused signal at the optimal source location we therefore perform a grid search over possible source mechanisms and increase the strength of the signal by deconvolution. Without taking the source mechanism into account we are not able to obtain accurate source locations, especially at low signal-to-noise ratios. However, by taking the source mechanism into account we are able to retrieve accurate source locations while also retrieving information about the source mechanism. Good results were obtained for 2D synthetic data for both a simple subsurface model as well as the realistic Annerveen salt model even when realistic noise was added...

Machine learning
induced seismicity
microseismic monitoring

https://doi.org/10.4233/uuid:3a065972-1fbc-483a-b9d6-7bc4570d4bef

Institutional Repository

doctoral thesis

© 2022 N. Vinard