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dc.contributor.advisorZhang, Xiangliang
dc.contributor.authorAudretsch, James
dc.date.accessioned2020-03-22T08:37:13Z
dc.date.available2020-03-22T08:37:13Z
dc.date.issued2020-03-17
dc.identifier.citationAudretsch, J. (2020). Earthquake Detection using Deep Learning Based Approaches. KAUST Research Repository. https://doi.org/10.25781/KAUST-52098
dc.identifier.doi10.25781/KAUST-52098
dc.identifier.urihttp://hdl.handle.net/10754/662251
dc.description.abstractEarthquake detection is an important task, focusing on detecting seismic events in past data or in real time from seismic time series. In the past few decades, due to the increasing amount of available seismic data, research in seismic event detection shows remarkable success using neural networks and other machine learning techniques. However, creating high quality labeled data sets is still a manual process that demands tremendous amount of time and expert knowledge, and is stifling big data innovation. When compiling a data set, it is unclear how many earthquakes and noise are mislabeled. Another challenge is how to promote the general applicability of the machine learning based models to different geographical regions. The models trained by data sets from one location should be applicable to the detection at other locations. This thesis explores the most popular deep learning model, convolutional neural networks (CNN), to build a single location detection model. In addition, we build more robust generalized earthquake detection models using transfer learning and meta learning. We also introduce a process for generating high quality labeled datasets. Our technique achieves high detection accuracy even on low signal to noise ratio events. The AI techniques explored in this research have potential to be transferred to other domains that utilize signal processing. There are a myriad of potential applications, with audio processing probably being one of the most directly relevant. Any field that deals with waveforms (e.g. seismic, audio, light) can utilize the developed techniques.
dc.language.isoen
dc.subjectMachine Learning
dc.subjectSeismology
dc.subjectDeep Learning
dc.subjectEarthquake Detection
dc.subjectMeta Learning
dc.titleEarthquake Detection using Deep Learning Based Approaches
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberHoehndorf, Robert
dc.contributor.committeememberMai, Paul Martin
thesis.degree.disciplineComputer Science
thesis.degree.nameMaster of Science
refterms.dateFOA2020-03-22T08:37:13Z
kaust.request.doiyes


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