A combined seismic phase classification and back-azimuth regression neural network for array processing pipelines

Array processing is routinely used to measure apparent velocity and back-azimuth of seismic arrivals. Being an integral part of automatic processing pipelines for seismic event monitoring at the IDC and NDCs, this processing step usually follows seismic phase detection in continuous data and precedes event association and location. The apparent velocity is used to classify the type of the detected phase, while the measured back-azimuth is assumed to point towards the event epicentre. Phase type and back-azimuth are usually determined under the plane wave assumption using Frequency-Wavenumber (FK) analysis or other wave front fitting algorithms such as Progressive Multi-Channel Correlation (PMCC). However, local inhomogeneities below the seismic array as well as regional sub-surface structures can lead to deviations from the plane wave character and to differences between the measured back-azimuth and the actual source direction. This can also affect the slowness estimates and, thus, the accuracy of phase type classification. Previous attempts to take these issues into account were based for example on empirical array-dependent slowness vector corrections.

Here, we suggest a neural network architecture to learn from past observations and to determine the seismic phase type and back-azimuth directly from the arrival time differences between all combinations of stations of a given array (the co-array), without assuming a certain wavefield geometry. In particular, input data are phase differences measured for multiple frequencies from the cross-spectrum of each co-array element. The neural network is a combined classification (phase type) and regression (back-azimuth) network and is trained using P and S arrivals of over 30,000 seismic events from the reviewed regional bulletins in Scandinavia of the past three decades and seismic noise examples. Hence, phase types are classified without first measuring the apparent velocity and without using pre-set velocity thresholds, and an unbiased back-azimuth is determined pointing directly towards the source. Training data are selected based on coherency thresholds to avoid training with too noisy arrivals included in the bulletins where for example the analysist placed a pick based on additional information. Furthermore, we test augmenting training data with time differences corresponding to plane waves to add source directions which are underrepresented in the bulletins. Models are trained and evaluated for regional seismic phase observations at the ARCES, NORES and SPITS arrays. Very good performance for seismic phase type classification (97% accuracy) and low source back-azimuth misfits were obtained. A systematic and careful test of the performance compared to FK analysis in NORSAR’s automatic processing (FKX) was conducted to evaluate potential improvements for event association and location. Taking the reviewed bulletins as reference, our first results suggest that the machine learning phase classifier performs equally well as FKX processing when it comes to phase classification and better for source back-azimuth estimation.