Poster Presentation

Recently, machine learning pickers have surpassed traditional pickers in terms of both accuracy and the quantity of earthquake detections; however the spatial detection variability of machine learning pickers has not yet been fully explored. This study investigates the spatial probability of earthquake detection to quantify the improvements in a machine learning catalog compared to a traditional catalog. We derived the spatially varying probabilistic magnitude of completeness from both catalogs and analyzed their differences. We also compare the spatial detection probabilities of P- and S-waves between the two catalogs to investigate the strengths and weaknesses of the machine learning picker.