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Image of Foundations for an Operational Earthquake Prediction System

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Foundations for an Operational Earthquake Prediction System

Angelo De Santis - Personal Name; Gianfranco Cianchini - Personal Name; Loredana Perrone - Personal Name; Maurizio Soldani - Personal Name; Habib Rahimi - Personal Name; Homayoon Alimoradi - Personal Name;

Earthquake prediction is one of the most challenging enterprises of science. Any prediction system must be based on the search for a precursor appearing during the preparation phase of an earthquake in the ground, atmosphere, or ionosphere that can anticipate its occurrence. We present methods to detect potential pre-earthquake anomalies. In particular, we show the analysis of lithospheric, atmospheric, and ionospheric data and the detection of anomalies under specific criteria. When we apply these methods retrospectively, we find that their accuracy goes from 69% to 83%. The combination of two or more methods is expected to improve the accuracy.


Availability
387550Perpustakaan BIG (Eksternal Harddisk)Available
Detail Information
Series Title
Geosciences
Call Number
550
Publisher
Switzerland : MPDI., 2025
Collation
22 hlm PDF, 2.855 KB
Language
Inggris
ISBN/ISSN
2076-3263
Classification
550
Content Type
text
Media Type
-
Carrier Type
online resource
Edition
Vol.15, Issue 2, February 2025
Subject(s)
Prediction
Earthquake
precursors
LAIC
Specific Detail Info
Geosciences
Statement of Responsibility
-
Other version/related

No other version available

File Attachment
  • Foundations for an Operational Earthquake Prediction System
    Earthquake prediction is one of the most challenging enterprises of science. Any prediction system must be based on the search for a precursor appearing during the preparation phase of an earthquake in the ground, atmosphere, or ionosphere that can anticipate its occurrence. We present methods to detect potential pre-earthquake anomalies. In particular, we show the analysis of lithospheric, atmospheric, and ionospheric data and the detection of anomalies under specific criteria. When we apply these methods retrospectively, we find that their accuracy goes from 69% to 83%. The combination of two or more methods is expected to improve the accuracy.
    Other Resource Link
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