https://doi.org/10.1051/epjn/2025045
Regular Article
Earthquake precursor measurements employing a network of radon sensors
1
KTH Royal Institute of Technology Stockholm, 10044, Stockholm, Sweden
2
GSI Helmholtzzentrum für Schwerionenforschung, 64291 Darmstadt, Germany
3
Istituto Nazionale di Geofisica e Vulcanologia (INGV), 00143, Roma, Italy
4
Aristotle University of Thessaloniki, 54124, Greece
5
University of L’Aquila, department of Civil, Construction-Architectural and Environmental Engineering, 67100, L’Aquila, Italy
6
National Radiation Protection Institute(SURO), Prague, 14000, Czech Republic
7
ETH Zurich, Switzerland and Swiss Seismological Service (SED), 8006, Zurich, Switzerland
8
University of L’Aquila, 67100, L’Aquila, Italy
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Received:
2
December
2024
Received in final form:
28
April
2025
Accepted:
2
July
2025
Published online: 26 August 2025
Earthquakes, as one of the most prominent natural disasters, pose a severe threat to societies in regions near active fault lines. Being able to forecast when, where and how strong an earthquake will be is beyond current scientific capabilities. At present, forecasting methods yield earthquake occurrence probabilities within a specific time frame spanning several years. Measurements of changes of radon concentrations in groundwater have shown pronounced changes preceding imminent earthquakes. The potential in using radon as a precursor for earthquakes has been explored by multiple groups over many years. Radon measurements in soil have shown large variations in activity, partly due to atmospheric influences. The ArtEmis project seeks to offer new insight into the correlation between imminent earthquakes and changes in radon emission from the upper crust by employing novel concepts for measurements and analysis. The paper presents system aspects of the ArtEmis project, a description of the ArtEmis sensor prototype and first results.
© G. Kumar et al., Published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
