Fine tuning a deep neural network to localize low magnitude earthquakes

Abstract

A main challenge in microseismic monitoring is that the seismic signals recorded at the Earth's surface are weak and thus localization of those microseismic earthquakes becomes challenging. Diffraction stacking is a traditional method used to localize weak earthquakes, which involves stacking the waveforms along precomputed travel-time curves from different locations, where the maximum is used to determine the source location. In this work we aim to recover the source location of weak microseismic earthquakes using a deep neural network (DNN) that resembles the U-Net but uses fewer skip connections. However, the size of the field data is too small to train the DNN from scratch. Thus, we propose to pretrain a DNN using synthetic data that resembles the field data and that learns to map the source location in terms of a 3D Gaussian distribution directly from the seismic signals. This pretrained DNN is capable of localizing the higher magnitude earthquakes in the field data, but fails for the weaker earthquakes. To be able to localize the weaker magnitude earthquakes we therefore, fine tune the pretrained DNN using the higher magnitude field-data earthquakes. We observe that the updated model is able to extrapolate the information learned during the fine tuning step from higher magnitude earthquake data to lower magnitude earthquake data.