FORECASTING STRONG AFTERSHOCKS IN GREEK EARTHQUAKE CLUSTERS - Using the NESTORE machine learning algorithm - Master’s thesis

Aug 5, 2026

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Any predictive algorithm that learns from data to generate forecasts is part of machine learning (ML). ML methods are increasingly used in seismology and geophysics, supporting tasks such as pattern recognition and feature extraction to improve understanding of the physical world. This work applies Greek seismicity data together with the NESTORE machine learning algorithm to forecast the occurrence of strong aftershocks after a mainshock. The study focuses on anticipating when damaging events may follow and how models can refine risk expectations in earthquake clusters.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages177

Reading Time7 h 26 min

AccessFree to download

Acknowledgements

Abstract

Declaration of Non-Infringement of Intellectual Property

Hellenic Republic National and Kapodistrian University of Athens Department of Geology and Geoenvironment Sector of Geophysics and Geothermy ELENI APOSTOLIA ANYFANTI REGISTRATION NUMBER: 7114112100012 FORECASTING STRONG AFTERSHOCKS IN GREEK EARTHQUAKE CLUSTERS USING THE NESTORE MACHINE LEARNING ALGORITHM Master’s degree: Earth Science and Environment Specialisation: Applied Geology-Geophysics ATHENS 2023 Three‐member Committee of Examination Filippos Vallianatos (Supervisor) Professor, Department of Geophysics and Geothermy, National and Kapodistrian University of Athens Stefania Gentili (Co-Supervisor) Researcher, Center for Seismological Research, National Institute of Oceanography and Applied Geophysics George Kaviris Asc. Professor, Department of Geophysics and Geothermy, National and Kapodistrian University of Athens DECLARATION OF NON-INFRINGEMENT OF INTELLECTUAL PROPERTY The total or partial reproduction of another person's work or the presentation of another person's work as the author's personal work is considered an infringement of intellectual property. The Department of Geology and Geoenvironment takes very seriously and condemns the resort to such practices by Graduate Students. In cases of obvious or intentional infringement of intellectual property, the competent bodies of the Department may impose as a sanction up to and including permanent withdrawal from the MSc. During the preparation, submission, examination and publication of the Specialization Thesis, the Postgraduate Students must adhere to the following guidelines: 1. The Dissertation must be the work of the student submitting it. 2. Copying or paraphrasing the work of a third party is an infringement of intellectual property and constitutes a serious offence. This offence includes both the infringement of another student's intellectual property and copying from published sources such as books, papers or scientific articles. Plagiarised material may come from any source. Copying or using material from the Internet or an online encyclopedia is just as serious as using material from a printed source or database. 3. The use of extracts from the work of others is acceptable provided that the source of the extract is acknowledged. In the case of an excerpt from the work of another, the use of quotation marks or a footnote is necessary so that the source of the excerpt can be identified. 4. Paraphrasing text is an infringement of intellectual property. 5. The sources of quotations used should be fully listed in a bibliography table at the end of the paper. 6. Infringement of intellectual property is subject to penalties. In deciding on the appropriate sanctions, the relevant departmental bodies will take into account factors such as the scope and size of the part of the work that is the result of copyright infringement. Sanctions will be imposed in accordance with Article 7(7) of the Academic Regulations. I certify that the Thesis, which I am submitting, does not contain any elements of intellectual property infringement as specified in the above statement, the terms of which I have read and accept. I give my consent for an electronic copy of my thesis to be subjected to an electronic audit to identify any evidence of copyright infringement. Date Candidate's Signature 20/12/2023 Acknowledgements First and foremost, I would like to thank my supervisor Dr. Filippos Vallianatos, because without his assistance and expertise I would not have been able to complete this thesis. He was always by my side, with his technical skills and experience, to assist me in answering the issues that came from the study and drawing the appropriate conclusions. I am looking forward to future collaborations with him since his consistency, ingenuity, critical and innovative thinking, discretion and understanding contributed to a pleasant working atmosphere which made the whole procedure fruitful. I would also like to thank Dr.

FAQs

What is the main research objective of the thesis?

To forecast the timing of strong aftershocks following a mainshock using Greek seismicity data and the NESTORE machine learning algorithm.

Why is machine learning relevant to seismology in this study?

Machine learning supports prediction from data and enables tasks such as pattern recognition and feature extraction to improve understanding of seismic phenomena.

What intellectual property rules does the thesis declaration require?

The submitted dissertation must be the student’s own work; copying or paraphrasing others without proper acknowledgement is prohibited, and quotation sources must be listed in a bibliography.

Aug 5, 2026

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