Abstract
A seismic arrival time picking model, SegPhase, is introduced to automatically process a large amount of seismic data recorded by large dense seismic networks with different sampling frequencies and numbers of observed components. Three models were created to address different sampling frequencies and the number of observed components in each network. The model structure uses a hierarchical Vision Transformer structure, which has not previously been used in seismic arrival time picking models and shows superior performance compared to conventional models using convolutional layers. The performance of SegPhase models was verified in terms of the relationship between arrival time residuals, output probability values, epicentral distance, signal-to-noise ratio, and magnitude, and compared to the PhaseNet models. The SegPhase models had better picking performance and number of seismic detections. Moreover, when the SegPhase models are applied to continuous waveforms, the relationship between the number of detections, O-C values and hypocenter determination error, and the threshold of output probability values used in the analysis was then investigated. It was found that when the threshold was lowered, more arrival times were used for earthquake detection not only with lower output probability values but also with higher output probability. Therefore, lowering the threshold allows the Phase association to make better use of the arrival times that the model assumes to be highly accurate. Although lowering the threshold value increases the error, its effect does not significantly impact the overall result.