28 June 2021 to 2 July 2021
Europe/Vienna timezone

A neural network architecture for detecting repeating events using seismic arrays

1 Jul 2021, 09:00


e-Poster T3.6 - Artificial Intelligence and Machine Learning T3.6 e-poster session


Mr Steffen Maeland (Norwegian Seismic Array (NORSAR), Kjeller, Norway)


Recent advances in convolutional neural networks (CNNs) have brought impressive detection capabilities to one- and three-component seismic stations. Still, the highest sensitivity to repeating events is obtained by beamforming signals over a seismic array. We propose a new neural network architecture that combines the two, by introducing a two-dimensional convolutional layer that encodes the propagation time delays between array stations. This results in a purely empirical model, which does not rely on the plane-wave approximation of traditional beamforming. We demonstrate the model by detecting and classifying repeating blasts from multiple mining sites in northern Fennoscandia. Results are compared to those obtained by empirical matched field processing, a highly sensitive method which, similarly, does not require signals to be coherent under the plane-wave model.

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We present a machine learning method for seismic arrays, which aims to improve verification capabilities through higher detection sensitivity and better identification of repeating sources.

Primary authors

Mr Steffen Maeland (Norwegian Seismic Array (NORSAR), Kjeller, Norway) Mr Andreas Kohler (Norwegian Seismic Array (NORSAR), Kjeller, Norway) Mr Ben Dando (Norwegian Seismic Array (NORSAR), Kjeller, Norway)

Presentation materials