am-thomas/EQ-Chicago: EQ-Chicago

EQ-Chicago is a Random Forest model that is trained on global earthquake seismograms from STEAD (Mousavi et al., 2019) and Chicago seismic noise. The model detects earthquake and blast signals in noisy, urban seismic data. This release provides the code and data files needed to download, process, and apply seismic data for event detection using EQ-Chicago. It also provides the code we used to apply EQTransformer (Mousavi et al., 2020), the popular deep-learning model that we compared with EQ-Chicago. The README.md file provides more details and sample code.