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dc.contributor.author
Sioutas, Ioannis
en
dc.date.accessioned
2023-06-12T11:57:50Z
dc.date.available
2023-06-12T11:57:50Z
dc.date.issued
2023-06-12
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/30296
dc.rights
Default License
dc.subject
Earthquake prediction
el
dc.subject
Acoustic emissions
el
dc.title
Earthquake Prediction using Machine Learning and acoustic emissions
en
heal.type
masterThesis
en_US
heal.dateAvailable
2023-06-11
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2023-06-11
heal.abstract
Earthquake prediction, the coveted holy grail of earthquake science continues baffle Earth scientists (Johnson et al., 2021). Could we make progress by using the huge expertise and innovation of the ML’s community? We used a popular Machine Learning competition platform, Kaggle, to join the global ML community in a competition, to gain access in their dataset so as to build and enhance data analysis algorithms for a forecasting challenge including experimental laboratory earthquake data. Participants were under the challenge of predicting the remaining time until the next earthquake in consecutive laboratory earthquakes with a relatively small portion of the laboratory seismic data. We use machine learning to find hidden signals that anticipate earthquakes in datasets from shear laboratory studies. We demonstrate that machine learning can accurately forecast the time before a defect fails by listening to the acoustic signals of a laboratory fault. These forecasts do not take into account the acoustic signal's history and are entirely based on its instantaneous physical characteristics. Unexpectedly, machine learning discovers a fault zone signal that was previously assumed to be of low utility noise and authorizes failure prediction all across the laboratory earthquake cycle. (Rouet-Leduc et al., 2017). We conclude that this signal comes from continuous movements of the fault furrow as the fault blocks shift. We believe that using this method with continuous seismic data may result in important advancements in recognizing previously unrevealed signals, new understandings of the physics of faults, and the establishment of limits on fault downtime.
el
heal.advisorName
Drakaki, Maria
el
heal.committeeMemberName
Drakaki, Maria
el
heal.academicPublisher
IHU
el
heal.academicPublisherID
ihu
en_US


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