Effects on a Deep-Learning, Seismic Arrival-Time Picker of Domain-Knowledge Based Preprocessing of Input Seismograms

Abstract

Automated seismic arrival picking on large and real-time seismological waveform datasets is fundamental for monitoring and research. Recent, high-performance arrival pickers apply deep-neural-networks to nearly raw seismogram inputs. However, there is a long history of rule-based, automated arrival detection and picking methods that efficiently exploit variations in amplitude, frequency and polarization of seismograms. Here we use this seismological domain-knowledge to transform raw seismograms as input to a deep-learning picker. We preprocess 3-component seismograms into 3-component characteristic functions of a multi-band picker, plus modulus and inclination. We use these five time-series as input instead of raw seismograms to extend the deep-neural-network picker PhaseNet. We compare the original, data-driven PhaseNet and our domain-knowledge PhaseNet (DKPN) after identical training on datasets of different sizes and application to in- and cross-domain test datasets. We find DKPN and PhaseNet show near identical picking performance for in-domain picking, while DKPN outperforms PhaseNet for some cases of cross-domain picking, particularly with smaller training datasets; additionally, DKPN trains faster than PhaseNet. These results show that while the neural-network architecture underlying PhaseNet is remarkably robust with respect to transformations of the input data (e.g. DKPN preprocessing), use of domain-knowledge input can improve picker performance.