High-pass corner frequency selection for implementation in the USGS automated ground motion processing tool

Earthquake ground motion processing for next-generation attenuation (NGA) projects required human inspection to select high-pass corner frequencies (f_cHP), which is time-intensive and subjective. With growth in the number of recordings per event and interest in enhancing repeatability, we sought to develop automated procedures for f_cHP selection. These procedures consider signal-to-noise ratio (SNR) and non-physical features in the displacement time series that indicate high- and/or low-frequency noise effects. The procedures are implemented in a US Geological Survey software package (gmprocess). We extend previous procedures for SNR-based corner frequency selection to also check for low-frequency artifacts in the displacement record using a polynomial fit to improve f_cHP selection. We evaluate the performance of the SNR and polynomial fit criteria using recordings from the 2020 M5.1 North Carolina and the 2013 M4.7 Southern Ontario earthquakes. Data processed with the SNR-only criteria can have low f_cHP and displacement drift; the displacement check increases f_cHP and reduces displacement drift.