The 2012 Pollino (Calabria, Italy) seismic sequence, culminating in the Mw 5.2 earthquake of 25 October 2012, is investigated, exploiting data collected during a long-term continuous radon monitoring experiment performed in the epicentral area from late 2011 to the end of 2014. We analyse data collected both using a phenomenological approach based on quantitative evidence and a purely numerical analysis including the following: (i) correlation and cross-correlation investigations; (ii) an original approach aimed at limiting the impact of meteorological parameters variations on the interpretation of measured radon levels; (iii) a change point analysis; (iv) the implementation of an original detection algorithm aimed at highlighting the connections between radon emission variations and major seismic events occurrence. Results from both approaches suggest that radon monitoring stations can be subject to massive site effects, especially regarding rainfall, making data interpretation harder. The availability of long-term continuous measurements is crucial to precisely assess those effects. Nevertheless, statistical analysis shows a viable approach for quantitatively relating radon emanation variations to seismic energy release. Although much work is still needed to make radon time series analysis a robust complement to traditional seismological tools, this work has identified a characteristic variation in radon exhalation during the preparation process of large earthquakes.

One of the most challenging problems in seismology is presently the study of
preparatory processes for strong earthquakes. Seismometric data still
represent the most informative observations available to researchers who
investigate their association with signals emitted by faults before
catastrophic ruptures. In this respect, new features in seismometric records
have been discovered and studied in the recent past

Geographical setting of the study area located in the Calabria
peninsula, southern Italy (see inset). Green triangles show the location of a
radon monitoring stations MMN and MMNG. Yellow circles represent the
earthquakes recorded by

Evidence gathered in recent years indicates that, in specific seismotectonic
settings, fluid transport and dynamics could play an important role in
seismogenic processes

From the beginning of 2010 the Pollino Range area, in the southern Apennines
on the border between Calabria and Basilicata, has experienced a seismic
sequence. The seismic activity is characterized by frequent periods of
intense output with others of relative quiescence and culminated on
25 October 2012 with a Mw 5.2 mainshock

In late 2011, we started a long-term experiment in the Pollino area of
Southern Italy, installing a high sensitivity, high efficiency active radon
monitoring station based on a Lucas cell

Several world-wide compilations of radon emission anomalies that could be
associated with variations in the seismic activity and/or occurrence of a
single earthquake are available in the literature

It is widely accepted that meteorological parameters play an important role
in modulating soil radon emanations

MMN radon concentration in (Bq m

In the following, we propose an articulate approach, taking advantage of
different investigative tools, to better assess the questions described
above. In particular, we will consider the problem both from a quantitative
phenomenological point of view and by means of suitable numerical analyses.
The presentation of our results is organized as follows: in
Sect.

MMNG radon concentration in (Bq m

We installed two radon monitoring stations in the Pollino area, equipped with
prototype detectors based on a Lucas cell that continuously acquired radon
concentration data, with a sampling interval of 2 hours. Station MMN was
co-located with the homonymous seismic station belonging to the INSN, Italian
National Seismic Network, at Mormanno (

Station MMN shows a high variability in radon concentration, with sharp peaks
and rapidly changing values ranging from a few tens up to 2500 Bq m

The evidence of the impact of meteorological parameters on radon observations
and at the same time the strong site-dependent nature of the characteristics
of radon emissions introduce uncertainties into the comprehension of the
problem. These complexities suggest the problem should be approached from a
phenomenological point of view in order to supplement the indications
retrieved by means of a purely quantitative analysis. First of all, we focus
on the “sealing” effect induced by precipitation on soil radon emanation.
Such effect has already been suggested and established by several studies

In the following we try both to assess the impact of meteorological parameters on radon signals on a quantitative basis and to outline an original approach aimed at removing (or at least mitigate) the effects of meteorological events on the detected time series. Our goal is to maximize the informative power of radon emanation variations potentially related to a variation in seismic energy release.

Even though the effects of meteorological conditions on temporal radon
time series have been investigated for the last 50 years by means of
different approaches and methodologies

For the following analyses, we decided to use only radon time series from
station MMN, since it was the only one installed before the main events of
the sequence (Mw 4.3 on May 2012 and Mw 5.2 on October 2012), corresponding
to the major changes in cumulative seismic moment release rate
(Fig.

Cross-correlation function (CC) evaluated for 2012–2013–2014
separately, between radon concentration Rn and temperature

In order to quantitatively assess the phenomenological evidences described
above by means of numerically objective procedures, we perform a series of
statistical evaluations on our dataset. Figure

Since the Pearson coefficient reflects mainly a linear relationship between
variables, we estimated the correlation between variables using both the
Pearson coefficient

We decided to exclude rainfall from this analysis since, differently from
other meteorological variables, it is intrinsically characterized by a
strongly discontinuous, spike-like behaviour being the majority of the
sampling times characterized by a null value. In fact, during the time window
of our most relevant analyses, we have null rain values ranging from 65 to

The results regarding the correlation analysis in terms of Pearson
coefficient are summarized in Table

Pearson correlation coefficient (

Within the cross-correlation analysis, whose results are shown in
Fig.

Cross-correlation function (CC) between radon concentration Rn and
seismic moment release

Of course the relationship between variations of radon emanation and
seismotectonic processes would be better assessed if we would be able to
remove, or at least reduce, the bias of meteorological parameters on the
radon measured concentration. To this aim, we implement an empirical
correction procedure for temperature, pressure and precipitation variations.
Basically, given an observed radon concentration value

In Fig.

Correction coefficients for temperature (

The problem of detecting changes in time series is well known in climate
literature: the definition and identification of discontinuous steps, or
change points, may be subjective and it also depends on the form of the trend
one expects between changes. Several methods have been implemented to solve
the change point problem both for short and long climatic time series. We
refer the readers to

We applied to the measured radon intensity time series an algorithm developed
in the realm of Earth's climate system studies in order to calculate, by
means of a Bayesian approach, the posterior probability of multiple change
points in a generic climatic time series (Bayesian Change Point algorithm,

Applying the BCP algorithm to the whole MMN time series, we obtain an
indication of most likely two change points that are potentially associable
with the two largest events of the sequence. Figure

Change point analysis applied to time series of radon concentration at MMN. The black solid line represents the radon concentration at MMN filtered with a 14-day moving average, while the green dashed line represents the model predicted by the Bayesian Change Point (BCP) algorithm. The red line represents the probability of a change point at each time. Yellow stars represent the occurrence of the earthquakes Mw 4.3 on 28 May 2012 and Mw 5.2 on 25 October 2012.

The different time advances of the change points found by the BCP algorithm
with respect to the two associated earthquakes occur (20 days and 3 days
before, respectively) is not determinant for our investigations, since the
dynamics of radon emission is intrinsically complex, as shown also by

Change point analysis applied to cumulative seismic moment release.
The black solid line represents the cumulative seismic moment release
filtered with a 14-day moving average, while the green dashed line
represents the model predicted by the BCP algorithm. The red curve indicates
the probability of a change point at each time. The two blue dashed vertical
lines mark the occurrence of the second and of the third change point
represented in Fig.

We point out the fact that a standard change point analysis uses always the
whole time series, since to identify a change point at a time

Flow chart representing the detection algorithm.
(

Figure

Output of the detection algorithm applied to time series of radon concentration at MMN. The red triangles represent all the issued alarms, yellow stars represent the greatest seismic events occurred in the 40 days following each alarm. For each year the two greatest seismic events have been also displayed (white stars), with corresponding occurrence date and magnitude.

Therefore, both the cross-correlation analysis and the change point analysis,
as well as the application of our detection algorithm, indicate that a
physical relation between the variation of soil radon emanation and seismic
moment release exists. While change point and detection algorithm both succeed
in finding some useful radon signal before the variation in seismic moment
release, the cross-correlation investigations seem to behold the radon
signature after the seismic moment release variation. Relying on the change
point analysis and detection algorithm, we have verified if also the
cross-correlation analysis is compatible with a radon signal preceding the
seismic moment release signal. To investigate this possibility, we have
repeated the procedure described in Sect.

The same as Fig.

The same as Table

We have performed a detailed analysis of the temporal variations of radon emanations from late 2011 to 2014 in a seismically active area during a seismic sequence that culminated at the end of 2012 with a Mw 5.2 event. We exploited several different approaches to carry out our investigations. Namely: (i) phenomenological analysis; (ii) correlation and cross-correlation investigations; (iii) empirical correction of the meteorological parameters effect on radon time series and its impact on cross-correlation; (iv) change point analysis; (v) detection algorithm.

We can split the main results of our work in two classes: (a) those concerning the impact of meteorological parameters variation on the observed radon time series and (b) those concerning the existence of a physical connection between the observed radon time series and the seismic moment release temporal variations. Converging indications coming from both classes represent an important outcome of our work. Regarding class (a), we have indications that, in the investigated setting, soil radon emanation is strongly anti-correlated with precipitation and weakly anti-correlated with temperature, while we do not get significant and univocal evidence of correlation (positive or negative) with pressure variations. In this context, approaches (i), (ii) and (iii) give remarkably consistent indications and we see as particularly significant the agreement between the strength of the correlation evidenced by (i) and (ii) and the magnitude of the corresponding correction factor found with (iii). These results, when compared with previous findings, confirm that the environmental impact on radon observations is strongly site dependent. The correlation between radon variations and temperature is, in this sense, a clear example: many works found it positive, as several others (including ours), negative. This observation suggests that a specific characterization is needed for each station, when implementing an observational network (see, for example, the dependence on the varying soil characteristics as porosity, permeability, and pre-rain moisture state). Regarding class (b), all of our analyses univocally indicate the existence of a non-accidental correlation between the temporal evolution of soil radon emanation and seismic moment release. The primary output of approach (ii) suggests that the radon signal follows the seismic moment variation, while approaches (i), (iv) and (v) indicate that it is possible to retrieve the radon signal also before the seismic moment variation. Remarkably, we have found that even if approach (ii) gives as primary result a shifted forward temporal correlation also the solution with the radon signal preceding the seismic moment variation is acceptable at a barely lower significance level.

We thank P. Einarsson and R. C. Tiwari for their constructive comments and careful reviews. We are grateful to F. Pio Lucente for discussion about the Pollino 2010 seismic sequence. Edited by: I. Koulakov Reviewed by: P. Einarsson and R. C. Tiwari