Improved Automated Seismic Event Extraction Using Machine Learning

Like many organizations engaged in seismic monitoring, the Preparatory Commission for the Comprehensive Test Ban Treaty Organization collects and processes seismic data from a large network of sensors. This data is continuously transmitted to a central data center, and bulletins of seismic events are automatically extracted. However, as for many such automated systems at present, the inaccuracy of this extraction necessitates substantial human analyst review effort. A significant opportunity for improvement thus lies in the fact that these systems currently fail to fully utilize the valuable repository of historical data provided by prior analyst reviews. In this work, we present the results of the application of machine learning approaches to several fundamental sub-tasks in seismic event extraction. These methods share as a common theme the use of historical analyst-reviewed bulletins as ground truth from which they extract relevant patterns to accomplish the desired goals. For instance, we demonstrate the effectiveness of classification and ranking methods for the identification of false events -- that is, those which will be invalidated and discarded by analysts -- in automated bulletins. We also show gains in the accuracy of seismic phase identification via the use of classification techniques to automatically assign seismic phase labels to station detections. Furthermore, we examine the potential of historical association data to inform the direct association of new signal detections with their corresponding seismic events. Empirical results are based upon parametric historical seismic detection and event data received from the Preparatory Commission for the Comprehensive Test Ban Treaty Organization.