Seismo: Semi-Supervised Time Series Motif Discovery for Seismic Signal Detection

Session: Waveform Cross-Correlation-Based Methods in Observational Seismology
Type: Oral
Date: 4/30/2020
Time: 09:00 AM
Room: 230 + 235
Description: 

Unlike semi-supervised clustering, classification and rule discovery; semi-supervised motif discovery is a surprisingly unexplored area in data mining. Semi-supervised Motif Discovery finds hidden patterns in long time series when a few arbitrarily known patterns are given. A naive approach is to exploit the known patterns and perform similarity search within a radius of the patterns. However, this method would find only similar shapes and would be limited in discovering new shapes. In contrast, traditional unsupervised motif discovery algorithms detect new shapes, while missing some patterns because the given information is not utilized. We propose a semi-supervised motif discovery algorithm that forms a nearest neighbor graph to identify chains of nearest neighbors from the given events. We demonstrate that the chains are likely to identify hidden patterns in the data. We have applied the method to find novel events in several geoscientific datasets more accurately than existing methods.

Presenting Author: M Ashraf Siddiquee

Authors