Computer Science ETDs

Publication Date

Summer 7-15-2022

Abstract

The current seismic data processing pipeline is surprisingly human-dependent. With the rapid increase of seismic-sensor data availability, all manual data processing approaches fail to detect, classify, and analyze seismic activity within a reasonable amount of time. An automated, fast, and reliable seismic data processing pipeline is desired for the meaningful analysis of massive seismic datasets. In this thesis, we show how advanced time-series data-mining and machine learning techniques can be leveraged to resolve this issue. We precisely focus on seismic activity detection, classification, and inspection using our techniques that would help us better understand the surrounding earth structure, earthquake evaluation, and seismic monitoring

In this dissertation, (a) we demonstrate a semi-supervised motif discovery algorithm that forms a nearest neighbor graph to discover novel seismic events from static continuous waveforms. (b) We exhibit a seismic data repository system that can extract thousands of seismic waveforms including annotations using complex queries within seconds. (c) We design and implement a hierarchical neural network that can predict seismic depth from seismograms and classify deep and shallow earthquakes with 86.5% F1 score.

Keywords

Machine learning, Data mining, Seismology, Explosion monitoring, Time series

Document Type

Dissertation

Degree Name

Computer Science

Level of Degree

Doctoral

Department Name

Department of Computer Science

First Committee Member (Chair)

Abdullah Mueen

Second Committee Member

Shuang Luan

Third Committee Member

Huiping Cao

Fourth Committee Member

Trilce Estrada

Fifth Committee Member

Jonathan MacCarthy

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