Publications

Abstract not provided.

Waveform cross-correlation is a sensitive phase-matched filtering technique that can detect seismic events for nuclear explosion monitoring. However, there are outstanding challenges with correlation detectors, most notably a direct dependence on the completeness of the waveform template library. To ameliorate these challenges, we investigate how dynamic time warping (DTW) may make waveform correlation more robust. DTW analyzes the differences between two time series and attempts to “warp” one time series relative to another in a recursive manner. We apply DTW to synthetic earthquake and recorded explosion templates to expand the capability of correlation detectors. We explore what conditions (e.g., source, station distance, frequency bands) and/or DTW algorithms generate stronger correlation scores. We show that DTW performs well on noisy signals and can dramatically improve the cross-correlation coefficient between a template and data-stream waveform. We conclude with recommendations on how to utilize DTW in nuclear monitoring detection.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Aftershock sequences are a burden to real-time seismic monitoring. Cross-correlation can be used because aftershocks exhibit similar waveforms, but the method is computationally expensive. Deep learning may be an alternative, as it is computationally efficient, but great attention to training and testing is required in order to trust that the model can generalize to new aftershock sequences. This is problematic for aftershock sequences, because large-magnitude earthquakes are unpredictable and are globally widespread. Here, we test several paired neural network (PNN) models trained on a augmented (noise-added) earthquake dataset, to determine whether they can be generalized to process real aftershock sequences. Two aftershock datasets that were originally detected by cross-correlation and subsequently validated by an expert analyst were used. We found that current PNN models struggle to generalize to aftershock sequences. However, we identify approaches to improve training future PNN models and believe that improvements may be achieved by transfer learning.