Publications

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Backprojection techniques are a class of methods for detecting and locating events that have been successfully implemented at local scales for dense networks. This article develops the framework for applying a backprojection method to detect and locate a range of event sizes across a heteorogeneous regional network. This article extends previous work on the development of a backprojection method for local and regional seismic event detection, the Waveform Correlation Event Detection System (WCEDS). The improvements outlined here make the technique much more flexible for regional earthquake or explosion monitoring. We first explore how the backprojection operator can be formulated using either a travel-time model or a stack of full waveforms, showing that the former approach is much more flexible and can lead to the detection of smaller events, and to significant improvements in the resolution of event parameters. Second, we discuss the factors that influence the grid of event hypotheses used for backprojection, and develop an algorithm for generating suitable grids for networks with variable density. Third, we explore the effect of including different phases in the backprojection operator, showing that the best results for the study region can be obtained using only the Pg phase, and by including terms for penalizing early arrivals when evaluating the fit for a given event hypothesis. Fourth, we incorporate two parallel backprojection computations with different distance thresholds to enable the robust detection of both network-wide and small (sub-network-only) events. The set of improvements are outlined by applying WCEDS to four example events on the University of Utah Seismograph Stations (UUSS) network.

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Most of the commonly used seismic discrimination approaches are designed for teleseismic and regional data. To monitor for the smallest events, some of these discriminants have been adapted for local distances (< 200 km), with mixed level of success. For this, we take advantage of the variety of seismic sources, including nontraditionally studied anthropogenic sources and the existence of a dense regional seismic network in the Utah region to evaluate amplitude ratio seismic discrimination at local distances. First, we explored phase-amplitude Pg-to-Sg ratios for multiple frequency bands to classify events in a dataset that comprises populations of single-shot surface explosions, shallow and deep ripple-fired mining blasts, mining-induced events (MIEs), and tectonic earthquakes. We achieved a success rate of about 59%–83%. Then, for the same dataset, we combined the Pg-to-Sg phase-amplitude ratios with Sg-to-Rg spectral amplitude ratios in a multivariate quadratic discriminant function (QDF) approach. For two-category pairwise classification, seven of ten population pairs show misclassification rates of about 20% or less, with five pairs showing rates of about 10% or less. The approach performs best for the pair involving the populations of single-shot explosions and MIEs. By combining both Pg-to-Sg and Rg-to-Sg ratios in the multivariate QDFs, we are able to achieve an average improvement of about 4%–14% in misclassification rates compared with Pg-to-Sg ratios alone. When all five event populations are considered simultaneously, as expected, the potential for misclassification increases, and our QDF approach using both Pg-to-Sg and Rg-to-Sg ratios achieves an average success rate of about 74% compared with the rate of about 86% for two-category pairwise classification.

The quality of automatic signal detections from sensor networks depends on individual detector trigger levels (TLs) from each sensor. The largely manual process of identifying effective TLs is painstaking and does not guarantee optimal configuration settings, yet achieving superior automatic detection of signals and ultimately, events, is closely related to these parameters. We present a Dynamic Detector Tuning (DDT) system that automatically adjusts effective TL settings for signal detectors to the current state of the environment by leveraging cooperation within a local neighborhood of network sensors. After a stabilization period, the DDT algorithm can adapt in near-real time to changing conditions and automatically tune a signal detector to identify (detect) signals from only events of interest. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. This system provides an important new method to automatically tune detector TLs for a network of sensors and is applicable to both existing sensor performance boosting and new sensor deployment. With ground truth on detections from a local neighborhood of seismic sensors within a network monitoring the Mount Erebus volcano in Antarctica, we show that DDT reduces the number of false detections by 18% and the number of missed detections by 11% when compared with optimal fixed TLs for all sensors.