Innovative Data Science Approaches to Automatic Aftershock Detection
Abstract
The aim of this contract was to explore innovative data mining techniques for automatic aftershock detection in order to intelligently monitor for nuclear explosions. In a nuclear explosion monitoring system, a burst of aftershocks (small earthquakes that occur for days to years following a large earthquake) is uninteresting and could be mislabeled as the target events. Such a burst of uninteresting events overloads the human analysts of the monitoring system. To reduce the load, at the onset of a sequence of events (e.g., aftershocks), a human analyst can label a few of these events and start an online classifier to filter out subsequent uninteresting events. In this project, we develop an online classification framework for aftershocks. Our specific technique uses a learned model to classify an event as an aftershock and exploits another classifier to decide if the model should be re-trained with the newly detected events. The framework has been tested on two large events: the 7.8Mw earthquake in Gorkha, Nepal on April 2015 and the 8.2Mw earthquake in Chiapas, Mexico on September 2017. We measure that a tolerable false-positive rate of 5 percent will allow us to automatically remove 83 percent of the Nepal aftershocks and 90 percent of the Chiapas aftershocks using the data from only one three-component station. This result shows that the proposed framework can save enough human effort to outweigh the effort needed to carefully judge the false positives. We demonstrate that our method, named FewSig, learns better than existing data science methods from only a few confirmed aftershock instances. We demonstrate that the performance of the framework improves with more confirmed instances and with better-placed stations.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 28, 2022
- Accession Number
- AD1195980
Entities
People
- Abdullah Mueen
- Shen Zhong
Organizations
- Department of Computer Science, University of Oxford