Data-driven analysis of muon flux variations in muography time series at the Sos Enattos Mine
Abstract
Muon flux variations in muography time series can provide information on density-related changes in geological targets. A common approach is to define regions of interest before constructing flux time series, but this choice may dilute coherent signals or mask localized variations. We present a data-driven analysis of two muon data acquisitions at the Sos Enattos Mine in Sardinia, Italy. The method aggregates lines of sight into overlapping spatial blocks, computes standardized flux deviations, and applies principal component analysis through singular value decomposition. The leading components reveal spatially coherent structures and distinct temporal evolutions. In each run, the first mode explains more than 20% of the variance, while the first eight modes explain approximately 80%. Regions selected from the spatial modes show anticorrelated and delayed flux variations. These results show that the proposed approach can identify candidate regions of coherent muon-flux variability without imposing them a priori.
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BibTeX
@misc{tramontini2026datadriven,
title = {{Data-driven analysis of muon flux variations in muography time series at the Sos Enattos Mine}},
author = {M. Tramontini and J. -C. Ianigro and J. Marteau},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.31073},
eprint = {2609.31073},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.31073},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Data-driven analysis of muon flux variations in muography time series at the Sos Enattos Mine
AU - M. Tramontini
AU - J. -C. Ianigro
AU - J. Marteau
PY - 2026
DA - 2026/09/25
PB - arXiv
DO - 10.48550/arxiv.2609.31073
UR - https://arxiv.org/abs/2609.31073
AB - Muon flux variations in muography time series can provide information on density-related changes in geological targets. A common approach is to define regions of interest before constructing flux time series, but this choice may dilute coherent signals or mask localized variations. We present a data-driven analysis of two muon data acquisitions at the Sos Enattos Mine in Sardinia, Italy. The method aggregates lines of sight into overlapping spatial blocks, computes standardized flux deviations, and applies principal component analysis through singular value decomposition. The leading components reveal spatially coherent structures and distinct temporal evolutions. In each run, the first mode explains more than 20% of the variance, while the first eight modes explain approximately 80%. Regions selected from the spatial modes show anticorrelated and delayed flux variations. These results show that the proposed approach can identify candidate regions of coherent muon-flux variability without imposing them a priori.
ER -