| 23 Sep 2026
Nonlinear Event-Space Analysis of Radon and Meteorological Parameters for Seismic Anomaly Identification
Abstract. Radon (²²²Rn) dissolved in soil gas serves as a well-established geochemical tracer for crustal deformation and seismic precursory research. However, radon concentration time series recorded at near-surface monitoring stations are systematically contaminated by meteorological parameters, principally atmospheric temperature, humidity, barometric pressure, and precipitation and rigorous characterization of these influences is a prerequisite for reliable earthquake precursor signals. Conventional linear correlation analyses are fundamentally limited in their capacity to capture transient, event-driven, and nonstationary radon responses that are most likely to encode seismically significant precursory signals. This study presents a comprehensive nonlinear event-space analytical framework applied to 11,572 hourly soil-gas radon data recorded at the Jiaosi monitoring station in northeastern Taiwan spanning January 2021 to April 2022. The methodology integrates time-series first-order differencing to isolate event-based change signatures, classification of hike, drop, and normal meteorological change states, and Locally Weighted Scatterplot Smoothing (LOWESS) for nonparametric characterization of radon response curves in change space. In the linear amplitude domain, temperature (r = 0.500), atmospheric pressure (r = −0.384), and humidity (r = 0.158) emerge as the dominant meteorological correlates of radon variability. Critically, these correlations weaken markedly when evaluated in the nonlinear event domain, revealing that temperature, pressure, and humidity primarily modulate quasi-stationary and periodic background radon signals rather than transient excursions. In striking contrast, rainfall which exhibits negligible linear correlation with radon reveals a pronounced U-shaped nonlinear response in event space. The nonlinear rainfall response remained stable across all threshold configurations and accumulation windows, confirming the robustness of the event-space framework. in the event domain that is consistent across all precipitation accumulation windows (24-hour, 72-hour, and 240-hour), and is mechanistically attributable to the rainfall capping effect, piston effect, and soil drainage dynamics. This pattern constitutes the primary source of false positives in radon-based precursor identification. The event-space LOWESS framework substantially enhances the discriminability of meteorologically driven noise from potentially geophysically significant anomalies, providing a computationally accessible complement to Empirical Mode Decomposition–Hilbert-Huang Transform (EMD-HHT) approaches. The methodology has direct implications for operational earthquake precursor detection frameworks and multi-station radon monitoring networks.
The manuscript egusphere-2026-5420 regards a comprehensive, nonlinear event-space analytical framework to evaluate hourly soil-gas radon time series recorded in Northeast Taiwan from January 2021 to April 2022. To isolate event-based change signatures, the applied methodology combines first-order differencing of the time series with the classification of meteorological states into hikes, drops, and normal conditions. Nonparametric characterization of radon response curves within the change space is then achieved using Locally Weighted Scatterplot Smoothing (LOWESS). The key findings demonstrate that: (i) linear domain correlations provide a robust baseline; (ii) radon variability is primarily correlated with temperature (r = +0.500), atmospheric pressure (r = −0.384), and soil humidity (r = +0.158); (iii) meteorological parameters drive the quasi-periodic background variability; and (iv) rainfall acts as the primary nonlinear driver of transient radon fluctuations.
I think the results reported in the manuscript could be noteworthy and very interesting for the scientific community studying soil-gas radon, as potential seismic precursor and, more precisely, the influence of meteorological parameters on soil-gas radon measurements. After having carefully read it, I suggest accepting with “minor revisions”.
Main comment
Line 605. The conclusions paragraph I think it is quite too long and maybe redundant, therefore, for improving its readability, I suggest rewriting it to make shorter.
Minor comments
I suggest enlarging the font of text to improve the readability of figures 1, 2, 3, 4, 5, 6, 7, 11, 12.
Figure 1. Improve the captions of map and b and scheme c. For map a add in the caption the legend of acronyms HC SJ, JS and HH; for map b add the graphic scale, locate the radon monitoring station, and enlarge the font of text; for scheme c enlarge the font of text and add in the caption the definition of the radon network units.
Figure 2. Add the date (months) in the X-axis.
Figure 4. Add the unit of measure for radon.
Figure 8. Typo “Temprature”, add the unit of measure for radon change.
Figure 10. Define in the caption text the terms normal, hike, drop.
Figure 11. Add the unit of measure for radon.