NHESSNatural Hazards and Earth System ScienceNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus GmbHGöttingen, Germany10.5194/nhess-14-3169-2014Study of the seismicity temporal variation for the current seismic hazard
evaluation in Val d'Agri, ItalyBaskoutasI.D'AlessandroA.antonino.dalessandro@ingv.itInstitute of Geodynamics, National Observatory of Athens, Athens, GreeceIstituto Nazionale di Geofisica e Vulcanologia, Centro Nazionale Terremoti, Rome, ItalyA. D'Alessandro (antonino.dalessandro@ingv.it)2December201414123169317422April201419June20141October20142October2014This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.nat-hazards-earth-syst-sci.net/14/3169/2014/nhess-14-3169-2014.htmlThe full text article is available as a PDF file from https://www.nat-hazards-earth-syst-sci.net/14/3169/2014/nhess-14-3169-2014.pdf
This study examines the temporal variation of the seismicity in the Val d'Agri (southern Italy) and adjacent areas, for the current seismic hazard
evaluation. The temporal variation of the seismicity is expressed as time
series of the number of earthquakes, b value of Gutenberg–Richter
relationship or b value of the frequency–magnitude distribution and the
seismic energy released in the form of logE2/3. The analysis was
performed by means of a new research tool that includes visualizing
techniques, which helps the interactive exploration and the interpretation
of temporal variation changes. The obtained time series show a precursory
seismicity pattern, characterized by low and high probability periods,
which preceded earthquakes of magnitude M≥4.0. The 75 % of the
examined cases were successfully correlated with a change in seismicity
pattern. The average duration of the low and the high probability periods is
10.6 and 13.8 months respectively. These results indicate that the
seismicity temporal variation monitoring in a given area and the recognition
of the low and high probability periods can contribute to the evaluation,
in regular monthly intervals, of current seismic hazard status.
Introduction
Val d'Agri is the most seismically active sector of the central-western
Mediterranean region, having repeatedly been struck by destructive earthquakes in
1561 (M=6.5), 1857 (M=7) and 1980 (M=6.9). Earthquakes are
characterized by predominant normal-faulting focal mechanisms, with
NW-oriented nodal planes, occur within a narrow seismic belt, about 20 to 40 km wide, centred on the axis of the Apennine chain (Amato et al., 1997; Valensise and Pantosti, 2001). Seismicity occurs mainly
along the major seismogenic structures, such as the Irpinia Fault, which
slipped during the 1980 M=6.9 normal-faulting event, but also at the
boundary between adjacent fault segments of the active belt.
The Val d'Agri instrumental seismicity recorded in the last 30 years is low
and sparse showing only two small seismic swarms recorded between 1996
April–June and 2002 February–December. The first swarm was characterized
by low-magnitude events (Md= 1.8–3.4), to the south of the basin at 2–7 km depth (Cucci et al., 2004). The second swarm consists of very few
earthquakes with magnitudes ranging between 2.2 ML and 3.2ML, also clustering
to the south of the Agri basin (Frepoli et al., 2005).
In this area there are many environmentally protected zones and natural parks,
a sequence of shallow and deep aquifers and hydraulic network systems for
storage and supply water for agricultural and civil purposes; therefore, the
seismic hazard evaluation is a very important task. Usually to assess the
seismic hazard, the most widespread and internationally accepted method is
to estimate the peak ground motion expected in a given place by applying
probabilistic or deterministic methodologies.
Nevertheless, the study of the properties of the time distribution of the
seismicity can contribute also to the evaluation of the seismic danger in a
given area. There are several methods that can statistically describe the
properties of the time distribution of the seismicity. Among them the
non-extensive statistical mechanics seem to be an interesting approach, for
the description of the frequency–moment and the time and distance
distributions between strong events. (Leonard et al, 2001; Parsons and
Geist, 2012; Michas et al., 2013; Papadakis t al., 2013; Vallianatos and
Sammonds, 2013; Antonopoulos et al., 2014).
The present work examines the temporal variation of the seismicity in the
Agri valley (southern Italy) and adjoining region in the period 1983–2014 in
order to evaluate the current status of the seismic danger and the time
period between strong events, by means of FastBEE tool (Papadopoulos and
Baskoutas, 2009, 2011).
This tool is suited to visualize simultaneously the temporal variation
curves of common seismicity parameters like the number of earthquakes
logN that occur in a certain magnitude range, the b value of the frequency–magnitude distribution relation and the seismic energy released, supposing
that they depict the influence of the tectonic stress, and to explore their
temporal behaviour in terms of probability periods for an earthquake
occurrence. In fact among these parameters, it is well established that
b value is related to the seismogenic procedure containing information on
differences in the physics of the process that generates earthquakes (Aki,
1965; Smith, 1986, Imoto 1991; Monterroso 2003) and that depends on the
stress condition and on the homogeneity of the material in the focal region
being useful for seismicity interpretation (Mogi, 1967; Scholz, 1968; Wyss,
1973; Wiemer, 2002). On the other hand, the seismic energy released reveals the
build-up and release of stress, depending on geotectonic characteristics.
Keilis-Borok (1959) and Sadovski and Pisarenko (1983) have proposed that
seismic energy in the form logE2/3, being from the physical
point of view proportional to the rate of accumulation of the dynamic
ruptures in the strong earthquake preparation process area, may reflect the
variations of the tectonic stress in the region of the observation.
Method
FastBEE algorithm, based on the character of the seismicity parameters
described previously, assumes that the temporal variation curves of these
parameters represent distinct phases of a phenomenological model for the
strong earthquake preparation (Popandopoulos and Baskoutas, 2011) and can
be explained according to the classical models for the preparation of
earthquakes and especially the phases of the consolidation model
(Dobrovolski, 1991).
The characteristic onset of the temporal change, where the amount of the
released seismic energy logE2/3 decreases and the simultaneous b value
increases as well the consequent changes of both parameters (i.e. increases
of the seismic energy released and decreases of b value), is characterized by two
distinct and consecutive low and high probability stages before a strong
earthquake occurrence (Fig. 1). This behaviour was formulated as a precursory
seismicity pattern (Baskoutas et al. 2011; Baskoutas and Popandopoulos,
2014).
Characteristic FastBEE output schematic general
trend of the temporal prognostic anomaly (solid blue lines) before a strong
earthquake occurrence. The open rectangular parallelogram denotes the first
low probability stage, since the prognostic anomaly beginning, followed by a
second higher probability stage, which concludes with the strong earthquake
occurrence. Vertical red arrow shows the earthquake origin time from
Baskoutas and Popandopoulos (2014).
Based on the above consideration, the methodology for the current seismic
hazard evaluation consists of the following: first in the construction, by means of FastBEE tool, of the temporal
variation series of a set of seismicity parameters, like the number of
earthquakes N, b value and the seismic energy released in the form
logE2/3; and second in the correlation of the observed temporal variation series changes
with the significance of the area earthquakes.
The magnitude of these events represents the lower magnitude that correlates
better with the observed temporal changes and was determined by retrospective
analysis of all available seismic data in the examined area. According to
the FastBEE algorithm, these events depend on the seismotectonic
characteristics of the area and represent, from the physical point of view,
a representative response of the medium to the local tectonic stress acting
in the area.
The number of earthquakes per unit time, logN, is obtained by means of the
following formula:
logN(t)=log∑i=n(t-w)n(t)i,
where i is the serial number of an earthquake with magnitude ML≥Mmin. Mmin is the
completeness magnitude for sampling data, t is the time (in months), w is a
temporary window smoothing (in months), and n(t-w) is the initial number of
earthquakes in the window smoothing, and n(t) is the finite number of earthquakes
in the window smoothing.
Seismicity map of Val d'Agri area and surrounding in the period
1983–2013 (Rectangle denotes the limits of the study area. Solid stars show the strong earthquakes, ML≥4.0).
The standard error of the calculation is given by the relation σlogN=0,4343/N.
Estimates of b value are obtained using the maximum likelihood estimation method by
means of the relationship proposed by Gusev (1974) as follows:
b(t)=log1=N∑(t-w)∑i=oni⋅NMmin+iΔM(t-w)/ΔM,
where t is the time (in months), w is a temporary window smoothing (in months),
NΣ is the total number of earthquakes in time interval t-w,
Mmin is the minimum completeness magnitude for earthquakes
sampling data, n=1+(Mmax-Mmin)/ΔM is the number of the
increment (bins), NMmin+iΔM is the
earthquake number in the ith bin, and ΔM is a value of binned data (here ΔM=0.20).
The standard error of the b value estimates is obtained by means of the
relation σb(t)=b(t)/√NΣ.
Finally logE2/3, which expresses the mean seismic energy released per unit
time, is obtained using the following relation:
logE2/3(t)=log1N(t-w)∑i=n(t-w)n(t)Ei2/3,
where E is the seismic energy of the earthquake, i is the serial number of an
earthquake with magnitude ML≥Mmin. Mmin is the minimum completeness magnitude for
sampling data, t is the time (in months), w is a temporary window smoothing (in
months), n(t-w) is the initial number of earthquakes in the window smoothing,
n(t) is the finite number of earthquakes in the window smoothing, and N(t-w) is
the total number of earthquakes in the temporary window smoothing. The
confidence limits, for temporal variation of logE2/3, were defined as an
rms (root mean square), which is calculated with the range of the examined time
period.
Data and analysis
This analysis uses seismic data, in the period 1983–2013, from an area
bounded by the coordinates 39.7∘–41.1∘ N and
15.1∘–16.5∘ E, which includes the two main sources in the neighbourhood of
seismotectonic Val d'Agri region. The data, which were taken from the
earthquake catalogue of the National Institute of Geophysics and
Volcanology (INGV) of Rome (Fig. 2), are complete for events with magnitudes
M≥2.5 for the entire examined period. Their completeness was examined
by means of the discrete frequency–magnitude distribution (Fig. 3).
Non-cumulative frequency–magnitude distribution,
in the period 1983–2013, denoting the changes of catalogue completeness.
Temporal variation of the seismic parameters
logN(t), b value and logE2/3, with their
respective standard errors. Origin time and magnitude of all strong
earthquakes with magnitude ML≥4.0 are shown as
numbered arrows perpendicular to the time axis in the period 1983–2013.
Temporal variation of logE2/3. Numbered arrows perpendicular to
the time axis denote the origin time of all strong earthquakes, with
magnitude ML≥ 4.0. Solid rectangle shows the low
probability periods for an earthquake occurrence which is followed by the
consecutive high probability periods.
Figure 4 shows, from the top to the bottom, the temporal variation of the
seismic parameters logN, b value and logE2/3. Their rms scatter corresponding to a
70 % confidence interval can be seen as horizontal lines on either side of
the average value of the parameters logN and logE2/3. The standard error of the
b value, which is shown by vertical lines, refers to the monthly estimates.
The temporal variation curves of the examined parameters were obtained using
a moving window of w=13 months and with step of 1 month
The numbered arrows, perpendicular to the time axis, show the origin times of
all events with M≥4.0 (Table 1); their epicentre can be seen as solid
stars in Fig. 2. This magnitude, as it was pointed out earlier, represents
the lower magnitude that correlates better with the majority of the observed
temporal changes, in the examined area. Due to low seismicity the magnitude
range and the number of events does not permit the reliable calculation of
b value (Fig. 3), and hence the temporal variation of b value curve (Fig. 3) will not be
taken into account in the analysis.
Strong earthquakes, Mw≥5.6, in the time period 1970–2009.
s/nyyyy mm ddOrigin timeLatLonghM11986 Jul 2308:1940.6415.7764.221990 May 0507:2140.6815.85105.031990 May 0507:3840.6815.7954.441990 Aug 2819:0240.7015.87104.151991 May 2612:2640.6115.7054.561995 May 2920:4440.2316.1054.071996 Apr 0313:0440.6815.5354.581998 Sep 0911:2740.0115.9595.092004 Sep 0300:0440.7015.6854.0102012 May 2801:0639.8516.1234.3112012 Oct 2523:0539.8816.0165.0
Dates of the appearance of the relative maximum and
minimum of the logE2/3 time series and the low and high probability periods
duration.
LowHighprobabilityprobabilitys/nDate of MaxDate of Minperiodperiod(group of eqs.)appearanceappearancedurationdurationScore1Dec 1984May 1985514Success2May 1987Sep 19881520Success3Dec 1990Nov 19933010False (till Sep 1994 )4Oct 1994Feb 199543Success5Sep 1996Jan 1998158Success6Feb 1999Jul 20001614False (till Oct 2001)7Oct 2001Nov 20021222Success8Jan 06–Dec 07 46Stable seismicity 9Nov 2010Jan 20111316Success
Therefore the analysis will be based on the identification of the clear low
and high probability periods of the precursory seismicity pattern (Fig. 1)
on parameter logE2/3 time series only (Fig. 5). The inspection of
Fig. 5 shows the existence of consecutive relative minima and maxima, over
and above the mean value of the examined parameter. In the same figure, it can
be seen also that all earthquakes with magnitude M≥4.0 (Table 1) are
correlated with the ascending periods of the parameter logE2/3, while the
descending periods (solid rectangles in Fig. 5) show a
complete absence of significant earthquakes. This behaviour indicates two
clear and distinct periods of low and high stage in terms of probability.
Practically the appearance of the relative minimum, in the majority of the
examined cases, signalizes the beginning of the alarm period lasting until
the earthquake occurrence, unless this behaviour changes.
Results
The identification of the relative maxima and minima that represent the
beginning of the two probability periods can be measured directly on these
graphs or by analysing the appropriate temporal variation series.
Table 2 reports the dates of the appearance of the relative maximum that
practically signalizes the beginning of the low probability period and
relative minimum which signalizes the high and their respective duration, in
months, which were measured using the temporal variation estimates of the
parameter logE2/3.
Both qualitative and quantitative analyses show that six of the eight
cases (i.e. 75 % of the relative minima that appears on logE2/3
curve) were followed by one or by a group of earthquakes with magnitude
M≥4.0. Instead there are two relative minima (i.e. 25 % of the cases
that were not followed by an earthquake); therefore, these cases can be
considered as false alarms. The first of them started November 1993
and ended October 1994, and the second started July 2000 and ended
October 2001.
It is interesting to point out that the temporal variation analysis
of the seismic parameters reveals a period of stable seismic activity, which
started at the beginning of 2006 and ended in 2009.
Conclusions
The seismicity temporal variation analysis shows significant temporal
changes in all three examined seismicity parameters, using data from the
Val d'Agri region in the period 1983–2012.
The form of these changes, which fluctuates around parameter mean
values in the examined over 30-year period of observations, shows clear
regularity corresponding to phases of a phenomenological model of earthquake
preparation process. Therefore, these changes were considered to
depict the response of the medium in the examined period due to the stress
acting in the wider area.
Six of the eight cases of earthquakes with magnitude M≥4.0 that occurred in
this area were successfully correlated with the precursory seismicity
pattern, low and high probability periods. The mean duration of the low
probability period for an earthquake occurrence with magnitude M≥4.0
is about 10.6 months, while the respective high probability period is 13.8
months.
In accordance with the above findings, the evaluation of the temporal
variation of seismic parameters until 2013 shows that the region is going
through a period of low probability of earthquake occurrence with a
magnitude above M≥4.0, unless the temporal behaviour changes. These
results indicate that the continuous monitoring of the seismicity temporal
variation, by means of FastBEE tool, permits the evaluation of the current
seismic hazard in regular intervals, in a given area.
Acknowledgements
The authors are thankful to two anonymous reviewers for their comments
that helped improve the manuscript.
Edited by: F. Vallianatos
Reviewed by: two anonymous referees
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