Long range gas-geochemical anomalies of a remote earthquake

Geochemical Journal, Vol. 45, pp. 137 to 156, 2011
Long range gas-geochemical anomalies of a remote earthquake recorded
simultaneously at distant monitoring stations in India
HIROK CHAUDHURI ,1* WASEEM B ARI,2 NASEER IQBAL,2 R AKESH K. BHANDARI,1 DEBASIS GHOSE ,1
PRASANTA S EN3 and BIKASH S INHA1
1
2
Variable Energy Cyclotron Centre, 1/AF Bidhannagar, Kolkata - 700 064, India
Physics Department, University of Kashmir, Hazratbal, Srinagar -190 006, India
3
Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata - 700 064, India
(Received June 5, 2010; Accepted November 7, 2010)
Continuous He, 222Rn concentrations and gamma dose rate were simultaneously monitored for pre-seismic signatures
at two thermal springs in India that are separated at a distance of approximately 1612 km. The recordings of six month
monitoring period (September 15, 2007–March 15, 2008) are presented here. In this paper, we make a cross correlation
study of the simultaneously recorded multi-component gas-geochemical data from the two thermal springs. In the monitoring period a number of prominent fluctuations beyond the regular diurnal variations in the gas composition were recorded. We attempt to find out the linkage between the recorded distinct variations with local and remote seismic activities. Anomalous fluctuations in the spring gases were observed during the period December 24–27, 2007 at both the
thermal springs and these anomalies may be correlated to the distant (~1000 km) China earthquake of magnitude M = 6.3
that occurred on January 09, 2008. Based on the obtained sequence of data points a time series analysis to relate earthquake magnitude, epicentral distance and precursor time through statistical methods and empirical equations related to the
zone of influence is made. Concurrent monitoring of multi-component gas-geochemical anomalies recorded at a number
of distant sites appears to be a potential tool to deal with the commonly debated question of earthquake precursors.
Keywords: thermal spring, gas-geochemical monitoring, He,
quake precursor
222
Rn, gamma dose, pre-seismic anomalies, time series analysis, earth-
mechanisms are well understood (Weinlich et al., 2006).
Seismicity as well as associated geochemical precursory
changes in ground waters, mineral spring gases (ambient
temperature), thermal spring gases and soil gases are both
intrinsically linked to the time dependant interaction between tectonic stress, fluid flow, pore-pressure changes
in fluid-rock interactions and rock deformation (Parotidis
et al., 2003). Consequently the subject matter of the study
of seismo-geochemical precursors has drawn worldwide
attention of a large base of scientific community over the
last several decades to carry out and experimentally improve gas-geochemical monitoring methods as a means
of deriving pre-seismic information (Scholz et al., 1973;
Sugisaki, 1978; King, 1980, 1986; Hauksson and
Goddard, 1981; Kawabe, 1984, 1987; Barsukov et al.,
1984; Satake et al., 1985; Sato et al., 1986; Sugisaki and
Sugiura, 1986; Wakita et al., 1988; Thomas, 1988; Reimer,
1990; Nagamine and Sugisaki, 1991; Virk and Baljinder,
1994; Virk, 1995; Sugisaki et al., 1996; Toutain and
Baubron, 1999; Heinicke and Koch, 2000; Ito, 2000; Biagi
et al., 2000; Virk et al., 2001; Gülec et al., 2002; Planinic
et al., 2004; Zmazek et al., 2005; Hartmann, 2005a, b;
Yang et al., 2006; King et al., 2006, 2008; Das et al.,
2009; Cicerone et al., 2009; Chaudhuri et al., 2010b). Pre-
INTRODUCTION
“Potential earthquake precursory signals”, often called
“anomalies” have been well documented over the last few
decades. Anomalies in the gas-geochemical and watergeochemical signals preceding major seismic events have
been recognized as short-term earthquake precursors
(Talwani et al., 1980; Mogro-Campero et al., 1980;
Fleischer, 1981; Hauksson, 1981; Sugisaki, 1981; King,
1986; Heinicke et al., 1995; Igarashi et al., 1995; Igarashi
and Wakita, 1995; Tsunogai and Wakita, 1995, 1996; Virk,
1995; Wakita, 1996; Virk et al., 1997; Toutain and
Baubron, 1999; Ito et al., 1999; Heinicke and Koch, 2000;
Favara et al., 2001; Chyi et al., 2005; Yang et al., 2005;
Hartmann, 2005a; Hartmann and Levy, 2005, 2006;
Weinlich et al., 2006; Cicerone et al., 2009). Changes in
the chemical composition of ground waters, deep earth
gases and ascending fluids are linked with seismic activities and could be used as earthquake precursors if the
*Corresponding author (e-mail: [email protected])
Copyright © 2011 by The Geochemical Society of Japan.
137
seismic geochemical changes are observed in the ground
water (Tsunogai and Wakita, 1995; Favara et al., 2001)
and to a greater degree in the mineral spring gases
(Weinlich et al., 2006), thermal spring gases (Das et al.,
2006a; Chaudhuri et al., 2007; Horiguchi and Matsuda,
2008) and soil gases (Virk and Baljinder, 1994; Hartmann,
2005a, b) monitored in a tectonically sensitive region.
Seismic induced gas-geochemical fluctuations are often
superimposed on diurnal fluctuations that are caused by
the effects of earth’s tides. The criteria adopted to attribute
an observed gas-geochemical signal as genetically due to
a seismic occurrence, is by observing anomalous characteristics in gas concentration amplitude, form and duration of stay that occur preceding earthquakes (Toutain and
Baubron, 1999). Explicit rules to unequivocally distinguish earthquake related signals in a monitoring time series have not been developed as yet. However, a variety
of rules to identify and organize earthquake related signals have been proposed by several workers (Dobrovolsky
et al., 1979; Kawabe, 1987; Igarashi and Wakita, 1990;
Hartmann and Levy, 2005). Concurrently quantitative
changes in gas-geochemical time series data exceeding
2σ (σ is the standard deviation of the time series data)
spread over durations of several hours to two, three or
more days are often considered as precursory indicative
parameters and are treated to be of seismic origin
(Hauksson and Goddard, 1981; King, 1986). The maximum distance at which geochemical precursors are observed are governed by power law dependant algorithms
(Giuseppe et al., 1997). Therefore, it is likely that for
earthquakes of magnitude M > 6 the precursory signals
may be observed at a monitoring site which is few thousand km away from the epicenter (Dobrovolsky et al.,
1979; Fleischer and Mogro-Campero, 1985; Rikitake,
1988; Morgounov and Malzev, 2007; Walia et al., 2005;
Cicerone et al., 2009). However, local and regional settings play an important role in influencing and determining the occurrence and pattern of geochemical anomalies. Although geochemical techniques appear to be a prospective tool for earthquake precursor study, however,
analysis of geochemical signals related to earthquake still
poses a challenging task, since geochemical markers
pointing unambiguously to a definite earthquake occurrence, out of long duration data received from a single
site, are hard to come by. This is an inherent natural restriction that illustrates the limitation of single site
geochemical observation. To get out of this difficulty, in
the present paper major focus has been made towards
multi-component and multi-station approach to record and
analyze the pre-seismic gas-geochemical anomalies. Conventionally, the “one earthquake-one signal” hypothesis
is often used to relate the pre-seismic signals with the
impending earthquake. However, this approach neglects
the continuum of stress-strain changes (Hartmann, 2005a).
138 H. Chaudhuri et al.
Based on the “multiple signal-one earthquake” and/or
“one signal-multiple earthquake” hypotheses, comprehensive monitoring of multi-component and multi-station
data-sets has begun to result in the detection of more reliable precursory signals. Often these proposed precursors simultaneously appear at several monitoring locations and contain two or more observations (Igarashi and
Wakita, 1995; Hartmann, 2005a; Hartmann and Levy,
2005). In this paper attempt has been made to verify the
feasibility of “multiple signal-one earthquake” and/or
“one signal-multiple earthquake” hypotheses. Consequently the aim is to carry out direct inter-comparison
and cross-correlation study of the pre-seismic gasgeochemical anomalies recorded simultaneously at two
identically equipped monitoring stations placed in totally
dissimilar geological environments and in different seismic zones.
The choice of monitoring station is a crucial factor
for the pre-seismic gas-geochemical monitoring. The essential condition for successful monitoring of preseismic gas-geochemical signal is the appropriate choice
of a location for the emplacement of a monitoring station
(Weinlich et al., 2006). According to King et al. (2006)
the sensitivity of a site to pre-seismic (and post-seismic)
change depends upon its proximity to active faults. The
monitoring station may be overlying in (i) a thermal spring
located in a highly fractured, extensively faulted and
tectonically sensitive region (say, geothermal region with
high heat flow) or (ii) in the vicinity of an active fault or
(iii) directly in a seismically active region. These regions
serve as easy escape channels for the deep earth gas emission due to high permeability and porosity of the underlying bed rock and the presence of a large number of gas
emanation vents that are deeply rooted, possibly into the
in-situ bedrock. Seismic induced variations in thermal
spring gases and soil gases are best examined if the time
series data are taken in a tectonically sensitive, highly
fractured and extensively faulted region, against benefit
of background statistics recorded over a long duration
(Das et al., 2006a; Chaudhuri et al., 2007). With this in
view, we set-up a laboratory at Bakreswar thermal spring
in the eastern part of India during December 2004 (Das
et al., 2005) to measure continuously hourly concentration changes in He, CH4, 222Rn and gamma dose rate in
thermal spring gases. Time series data obtained thereafter revealed several large anomalies that were subsequently correlated to possible major seismic disturbances
(local and/or remote) that occurred in and around India
(Das et al., 2005, 2006a, 2006b, 2007, 2009; Chaudhuri
et al., 2007, 2010b). With an aim to obtain better seismic
correlation and to enhance the reliability, we established
a second monitoring station at Tatta Pani thermal spring
of the north-western part of the country during August
2007. The instrumentation and on-line, continuous [24×7]
Fig. 1. Geological settings of Bakreswar (modified after Majumdar et al., 2005) and Tatta Pani (modified after Hassan and Baig,
2006) geothermal areas along with the location of the China earthquake.
monitoring as well as data recording facility in both the
laboratories are identical.
GEOLOGICAL DESCRIPTION OF
THE M ONITORING STATIONS
The monitoring stations are placed in dissimilar geological environments and in different seismic zones of
the country and are separated by a distance of 1612 km.
One of the monitoring stations is located at thermal spring
site at Bakreswar (23°52′30″ N, 87°22′30″ E) which lies
in the eastern part of the country in a moderate risk zone—
“seismic zone III”. The other monitoring station is situated at Tatta Pani thermal spring site (33°14′16″ N,
74°24′43″ E) which falls in the north-western part of the
country in a high risk zone—“seismic zone IV”. The
monitoring spring at Bakreswar lies close to the extinct
(115 Ma) Rajmahal volcanism of the Chotanagpur
gneissic complex in West Bengal while the spring at Tatta
Pani is located in a non-volcanic geothermal area that lies
within the mountain folds of Jammu and Kashmir, situated in proximity to the Main Boundary Thrust (MBT) of
the Lesser Himalayas. The locations and geological settings of the two monitoring sites are shown in Fig. 1.
Bakreswar
Bakreswar geothermal area is situated in a geologically mixed faulted area about 250 km north-west of
Kolkata and consists of a cluster of seven thermal springs
with differing water temperatures (Majumdar et al., 2009).
Long range pre-seismic gas-geochemical anomalies 139
The thermal springs lie in an area with Precambrian rocks
and is associated with a 1.2 km long shear zone linked to
the extinct (115 Ma) Rajmahal volcanic activity (Nagar
et al., 1996). The geological conditions of the region
maintain a constant flow of hot waters that issue out of
fractures in a reactivated composite mass comprising predominantly Precambrian granitic rocks (Majumdar et al.,
2000). This geothermal zone has been subjected to different cycles of plate movements with intervening periods of iso-static readjustments during the Precambrian to
Cenozoic era (Sarkar, 1982; Ravi Shankar, 1991a). The
spring gas along with the soil air in the region is characterized by the presence of high helium as well as high
radon flux (Ghose et al., 1998, 2002; Chaudhuri et al.,
2010a, b). The Bakreswar geothermal province is characterized by high average heat flow of ~200 mW/m 2
(Chandrasekharam, 2000). This is about three times the
recent estimates of the mean heat flow 65 ± 1.6 mW/m2
for all continents and twice for all oceans 101 ± 2.2 mW/
m 2 (Gilat and Vol, 2005). The monitoring spring
“Agnikunda” has a water temperature of ~69°C at surface, with water discharge rate of ~790 slpm. Bakreswar
thermal spring water is alkaline with a pH value of 9.2.
The alkaline water contains few water dissolved minerals like F– (0.17 ppm), Cl– (21.80 ppm), SO42– (26.0 ppm),
B (0.21 ppm) and water dissolved 222Rn (38.90 kBq/m3)
at ambient temperature and pressure. Spring gases ascend
to the earth’s surface through the intricate network of fissures and faults with a gas flux of ~3.5 slpm and at 1.6
bar pressure. Bakreswar spring gas is primarily composed
of He (~1.45 vol%), O 2 + Ar (~3.00 vol%), N2 (~92.00
vol%), CO 2 (~0.10 vol%) and CH4 (~3.40 vol%). The
spring gas is highly radioactive with an average 222Rn
concentration of 828.85 kBq/m 3. The geothermal area
connects the eastern edges of two major fault systems that
cut across the northern and central parts of India—the
“SONATA” (by name of the Indian rivers Sone, Narmada
and Tapti) and the “ONGC” (by name of the Oil and Natural Gas Corporation, India) faults (Gupta et al., 1975; Ravi
Shankar, 1991a; Majumdar et al., 2005) incising through
the central India state of Madhya Pradesh. The crustal
thickness at this area is only 24 km as compared to an
average of 38 km of the Precambrian craton
(Mukhopadhyay et al., 1986; Majumdar et al., 2000;
Ghose et al., 2002).
Tatta Pani
Tatta Pani thermal springs are about 35 km south-east
of Rajouri town in the state of Jammu and Kashmir. Tatta
Pani is located on the Himalayan Pir-Panjal mountain
range and lies in a region of considerable structural complexity that is typical of the highly heterogeneous
geotectonics of the Lesser-Himalayas (Wadia, 1928;
Gokam et al., 2002). Tatta Pani lies close to the Main
140 H. Chaudhuri et al.
Boundary Thrust (MBT) at an altitude of about 800 m
above mean sea level. The rocks of this region have ages
ranging from Precambrian to recent. There are two thermal springs at Tatta Pani with temperatures of 55°C and
46°C. The higher temperature spring is used by pilgrims
for bathing. The monitoring spring temperature is ~46°C
with water discharge rate of 360 slpm. The spring water
at Tatta Pani is acidic having a pH value of 6.41. The
spring water contains F – (2.11 ppm), Cl – (9.90 ppm),
SO42– (55.00 ppm), B (0.46 ppm) and water dissolved
222
Rn (46.90 kBq/m 3) at ambient temperature and pressure. Spring gases flow out of the spring vents with a gas
flux of ~1.5 slpm and at 1.3 bar pressure. The spring gas
comprises of He (~1.15 vol%), O2 + Ar (~3.30 vol%), N2
(~89.50 vol%), CO2 (~4.90 vol%) and CH4 (~0.40 vol%).
The 222Rn activity of the spring gas is reasonably high
(899.21 kBq/m3). The thermal springs fall in the vicinity
of geologically conjectured fault and contact zones of the
mountain formation. These thermal springs issue out of
the deep rooted faults of Tertiary age in the MBT which
lies across the north of Jammu and regionally separates
the sandstone conglomerate rock formations known as the
“Shiwaliks” from the other sedimentary sequences of the
Lesser-Himalayas and the Indo-Gangetic alluvials (Ravi
Shankar, 1991b; Hassan and Baig, 2006). The heat flow
in this geothermal area is around 180 mW/m 2 (Ravi
Shankar, 1988).
THEORY ON EMPIRICAL ALGORITHMS
A number of empirical algorithms, given in the literature, facilitate to identify the patterns of precursory signals that characteristically occur prior to earthquakes
(Scholz et al., 1973; Yoshino et al., 1992; Giuseppe et
al., 1997; Toutain and Baubron, 1999; Walia et al., 2005;
Cicerone et al., 2009). The empirical algorithms have been
proposed on the basis of statistical comparison of precursory data with seismic ones, from an existing data base
recorded by means of geochemical, hydrological, seismological and electromagnetic techniques. A few of the
existing empirical algorithms also illustrate the relationships between the (i) earthquake magnitude-epicentral
distance, (ii) magnitude-precursor time, (iii) magnitudesignal duration, (iv) precursor time-signal duration, (v)
precursor time-epicentral distance and (vi) signal
duration-epicentral distance (Hartmann and Levy, 2005).
The relationship between magnitudes and epicentral distances
The preparation area (or the zone of influence) of an
earthquake scales directly with the magnitude M. The
maximum limit (Rmax) of this zone of influence (or the
maximum epicentral distance) is essentially a geodesic
distance (in km) that includes the epicenter of the earth-
quake and the monitoring stations where significant
anomalies can be expected due to seismic activity.
The maximum distances at which precursory signals
may be observed was estimated by Dobrovolsky et al.
(1979) through an empirical algorithm and expressed as
ship between precursor time (T) and earthquake magnitude (M) was first suggested by Tsubokawa (1973) entirely on an empirical basis (Rikitake, 1975).
Scholz et al. (1973) proposed a similar relationship
and expressed as
RDob = 100.43M for M ≥ 3
T Sch = 10(0.685M–1.57).
(1)
(7)
where, M (in Richter scale) is the magnitude of the earthquake and R (in km) is the radius of the earthquake preparation area, which essentially is the zone of influence.
Hauksson (1981) proposed a relationship as follows
The precursor time (T) was estimated by Talwani (1979)
through an empirical algorithm and expressed as
RHau = 10(M+0.43)/2.4.
Subsequently, Rikitake (1987) proposed a relationship
between precursor time and earthquake magnitude as
(2)
The formulation put forward by Rikitake (1988) is given
by
RRkt = 10 (0.38M+0.33).
(3)
Virk (1996) proposed the following four relations
RVrk = 100.32M when 10 < RVrk < 50
RVrk = 10
0.43M
RVrk = 10
0.56M
(4b)
when 100 < RVrk < 500
(4c)
(4d)
Relatively recently a multiple-fracture model of preseismic phenomena developed by Morgounov and Malzev
(2007) gives
RMog = 10 (0.5M–0.27).
TRkt = 10[(–0.47±0.73)+(0.28±0.12)M].
(8)
(9)
Out of the various empirical relations to evaluate precursor time (T), the formulation put forward by
Sultankhodzhayev et al. (1980) offers the maximum value
Tmax and given by
(4a)
when 50 < RVrk < 100
RVrk = 100.63M when 500 < RVrk < 1250.
TTal = 10[(M+0.07)/2].
TSul,M≥3 = [10(0.63M+0.15)]/R for M ≥ 3
(10a)
T Sul,M<3 = [10(0.63M–0.15)]/R for M < 3.
(10b)
T, M and R are expressed in days, Richter scale and km
respectively.
Equations (10a) and (10b) give the maximum value
Tmax as
Tmax,Sul,M≥3 = [10 (0.63M+0.15)]/Rmax,Fls,M≥3 for M ≥ 3 (11a)
(5)
Tmax,Sul,M<3 = [10 (0.63M–0.15)]/Rmax,Fls,M<3 for M < 3 (11b)
Out of the various empirical relations that connect M to
R, the equation
where R max,Fls confers the largest value to the radius of
influence and can be estimated using equations (6a) for
M ≥ 3 and (6b) for M < 3.
Relatively recently, Hartmann and Levy (2005) proposed an empirical formula to evaluate the maximum precursor time, expressed as
Rmax,Fls,M≥3 = 10 0.480M for M ≥ 3
(6a)
Rmax,Fls,M<3 = 100.813M/16.6 for M < 3
(6b)
and
put forward by Fleischer (Fleischer and Mogro-Campero,
1985; Fleischer, 1981) confer the largest value (Rmax) to
the radius of influence (R).
The relationship between magnitudes, precursor time and
epicentral distances
The precursory time of a pre-seismic signal is the time
interval between the recorded anomaly at a station and
the actual occurrence of an earthquake. A linear relation-
T max,Hrt = 43.6975M – 0.3046R – 32.7731
(12)
where T max is the maximum precursor time in days, M is
the magnitude of the earthquake in Richter scale and R is
the epicentral distance in km.
EXPERIMENTAL TECHNIQUE
Thermal springs are a steady source of hot water and
gases that bubble intermittently out of various places at
the base of the spring. The spring pool is 2.5 m deep. In
Long range pre-seismic gas-geochemical anomalies 141
order to minimize the influence of meteorological parameters and associated noise in the data-sets thermal spring
gases are trapped under a column of hot water. The method
is a potential way to avoid masking of weak gasgeochemical variations by various kinds of signals come
from the earth’s tidal effect and associated noise. Consequently a stainless steel (SS) funnel about 1m diameter
and 0.5 m height is placed inverted under water at the
location where maximum gas bubbles are expelled. Gas
trapped under the funnel is led through a stainless steel
pipe into the monitoring laboratory located at a distance
within 5 m from the spring edge. The moisture laden gas
stream is led into a series of glass traps filled with chemical desiccants (KOH and anhydrous CaCl2). The dry gas
leaving the traps is directly led to a set of instruments
that have comprehensive measurement functions. The
experimental hardware section consists of two low power
automated microprocessor controlled field-deployable
equipment—(i) a gas chromatograph and (ii) a radon
monitor. Both instruments operate on-line in continuous
mode. The schematic diagram of the installed experimental arrangement is shown in Fig. 2.
A programmable gas chromatograph type Varian CP
4900 (manufactured by Varian Inc., The Netherlands),
provided with narrow bore 20 m long 15N silica capillary column coated with 5A molecular sieve and micro
thermal conductivity detector, analyses the spring gas for
its stable constituents such as He, O2 + Ar, N2 and CH4.
Ultra pure (>99.998 vol%) hydrogen gas is used as the
carrier gas in the gas chromatograph. The setting of the
sampling interval (1 hour) and data acquisition of the
chromatograph is controlled by the on-line chromatographic data managing software “Star Workstation”. The
gas chromatograph column is periodically regenerated to
maintain the separation efficiency in the chromatograph
column.
A radon monitor type Alpha-GUARD PQ2000 PRO
(manufactured by Saphymo, GmbH; formerly Genitron
GmbH, Germany) registers the spring gas radon ( 222Rn)
concentration. This instrument consists of an open type
pulse ionization chamber and uses alpha spectroscopy to
detect 222Rn. The two common isotopes of radon (222Rn
and 220Rn) are discriminated through energy-specific windows. The signal generated from 222Rn is converted to a
digital output and the data are recorded on an hourly basis. The radon monitor can determine concentrations of
222
Rn from 2 Bq/m3 to 2 × 106 Bq/m3 with a resolution of
1 Bq/m 3. On account of high radon activity level in the
spring gas, this instrument is yearly sent to the manufacturer to remove contamination and for recalibration. Besides measuring 222Rn, this instrument is equipped with
an unshielded Geiger-Muller (GM) counter which records
the total gamma ray activity on account of the combined
effects of (a) short-lived gamma emitting 222Rn decay
142 H. Chaudhuri et al.
Fig. 2. Integrated experimental set-up of the automated, continuous and online monitoring system to detect gas-geochemical
precursory signals.
products 214Pb and 214 Bi, present in the spring gas, (b)
terrestrial gamma background radiation generated by the
decay of β emitting decay products 214Bi, 208Tl and 40K
of the three primordial radio nuclides viz. U, Th and K,
respectively, present in the rocks and soils of the spring
area and (c) gamma environment in proximity of the
spring primarily on account of the radioactivity of the
mineral waters due to the presence of dissolved radon and
radium salts. The Alpha-GUARD registers the gamma
dose rate in units of nSv/hr. The 222Rn concentration and
gamma dose rate data recorded in the Alpha-GUARD is
automatically logged on the control computer using the
on-line and automatic data management professional software “Data-Expert”. To produce analytically consistent
data-sets, both monitoring equipment are configured to
perform with low influence of external noise. Numeric
data received from the installed instruments are automatically stored in a local server computer (IBM make). A
back-up power pack supply ensures continuous and stable power supply in case of a power failure. To carry out
proximate analysis of the time series, all data stored in
the server computer installed at field laboratory are transmitted automatically by means of secured broad band
wireless internet connectivity (VSAT, ERNET India) to
our centre at Variable Energy Cyclotron Centre (VECC)
and Saha Institute of Nuclear Physics (SINP) situated in
the same campus at Kolkata to prepare update
Fig. 3. Helium time series plot of Bakreswar thermal spring recorded during the six month period of September 15, 2007 to
March 15, 2008.
geochemical precursory signals databank.
A seismological data sheet is also prepared routinely
by collecting diurnal seismic updates from the India Meteorological Department (IMD) and the US Geological
Survey (USGS) website reports (IMD & USGS websites)
for seismic events that occurred in India and its
neighborhood.
RESULTS
The hourly data of He, 222Rn and gamma dose rate
time series span six month from September 15, 2007 to
March 15, 2008. Figure 3 shows the helium concentration time series recorded at Bakreswar during this period.
The average helium abundance was 1.38 vol%. The helium time series show a fluctuating pattern having standard deviation of 0.11. In Fig. 4 we show concurrent spring
gas 222 Rn and gamma activity patterns recorded at
Bakreswar and Tatta Pani during the same time period.
The mean 222 Rn concentrations at Bakreswar and Tatta
Pani thermal springs were 828.85 kBq/m3 and 899.21 kBq/
m3, respectively. Throughout the entire monitoring period temperatures of the monitoring springs at Bakreswar
and Tatta Pani appeared to be constant and were well
above the maximum value (40°C) of ambient temperatures recorded during the said six month monitoring period. Besides, the hydrostatic pressures at which the bubbling gases emerge from the spring vents are 1.6 bar and
1.3 bar for Bakreswar and Tatta Pani, respectively which
are above the atmospheric pressure. Therefore, meteorological parameters have little effect, if any, on the diurnal as well as seismic induced variations of the monitoring spring gases. The nature of the time series data-sets
recorded at both monitoring stations reveal an oscillating
mode for 222Rn and gamma activity.
Figure 5 shows the temporal variations of helium,
222
Rn concentrations and gamma dose rates monitored at
both the monitoring springs during the one-month period
of December 14, 2007 to January 18, 2008. Noteworthy
fluctuations in helium, 222Rn concentrations and gamma
dose rates were observed at Bakreswar laboratory during
December 22–27, 2007. Comparison of data from identical measurements recorded at the spring at Tatta Pani displayed similar anomalies for 222Rn and gamma at about
the same time period. The logged time series data at
Bakreswar show a large helium fluctuation during December 22–24, 2007 as seen in the lower window of Fig.
5. Starting December 22, the helium concentration scaled
Long range pre-seismic gas-geochemical anomalies 143
Fig. 4. Time series plots of 222Rn concentrations and gamma dose rates recorded at Bakreswar and Tatta Pani thermal springs
during the six month period of September 15, 2007 to March 15, 2008.
to 1.96 vol% against its six-month baseline value of 1.38
vol% and remained at the elevated level for over 44 hours.
This concentration rise is 42%, and is well above 2σ . The
helium concentration profile for the corresponding pe144 H. Chaudhuri et al.
riod at Tatta Pani could not be recorded on account of a
temporary failure of the on-line gas chromatograph installed there.
The recorded data for 222Rn concentrations and gamma
Fig. 5. Temporal variations of helium, 222Rn concentrations and gamma activities of Bakreswar and Tatta Pani thermal springs
monitored during the one month period of December 14, 2007 to January 18, 2008 with significant anomalies observed during
December 22–27, 2007 prior to the China earthquake (M = 6.3) that occurred on January 9, 2008.
Long range pre-seismic gas-geochemical anomalies 145
Fig. 6. Pre-seismic anomalies in 222Rn concentrations and gamma dose rates recorded at Bakreswar and Tatta Pani in the period
of December 24–27, 2007.
146 H. Chaudhuri et al.
dose rates during the same time period, as seen in the
upper window of Fig. 5, at both Bakreswar and Tatta Pani
show prominent fluctuations. To focus more closely on
the anomalous part of the total time series we have taken
a short-time period of seven days from December 23–29,
2007 during which significant anomalies were recorded
as shown in Fig. 6. Characteristic concentration of 222Rn
at Bakreswar scaled up to 888.00 kBq/m3 on December
24, 2007 over its resident mean of 818.38 kBq/m3. The
anomaly remained for about 16 hours. A similar pattern
of increase in 222Rn, but having a time delay by about 20
hours was observed at Tatta Pani on December 25, 2007.
The broad time scale (~18 hours) anomaly in 222Rn concentration at Tatta Pani was 945.00 kBq/m3 against its
mean 888.13 kBq/m3. Besides 222Rn, well-defined gamma
dose rate anomalies were recorded at both sites during
the same time span. Anomalous levels for 222Rn as well
as gamma dose rate at the two sites were in excess of 2σ
with occupying durations between one to two days. An
approximate 24-hour time lag in gamma dose rate
anomaly with respect to the occurrence of radon anomaly
was noted at each of the monitoring stations.
ANALYTICAL TECHNIQUE
In order to assign the probable seismic events that
sourced the observed spring gas perturbations, we take
all the 20 earthquakes (magnitude > 2 M) that occurred
in India and its neighborhood during the two month time
period December 1, 2007 to January 31, 2008 encompassing a time of about two weeks on either side of the time
window (December 14, 2007–January 15, 2008) during
which anomalous fluctuations were recorded. The regional
occurrences of the listed earthquakes were within a radius of 3500 km from the center of the line joining the
monitoring sites. Table 1 shows the seismological data
(earthquake magnitude, depth, location and date of occurrence) collected from the India Meteorological Department (IMD) and the US Geological Survey (USGS)
website reports (IMD & USGS websites) for the given
earthquake events. Table 1 also includes precursory time
in days T B and TT for Bakreswar and Tatta Pani, respectively. The geodesic distance in km, which essentially is
epicentral distance between the earthquake epicenters and
the monitoring stations at Bakreswar and Tatta Pani, are
given in Table 1 (DB and DT respectively in our case) as
well.
We consider the possibility whether the observed gasgeochemical anomalies could have arisen on account of
any one, or a combination thereof, of the earthquakes
listed in Table 1, to determine the possible existence of a
direct relationship of the recorded anomalies to the listed
earthquake events. Therefore, based on the experimental
data recorded at Bakreswar and Tatta Pani we make a time
series analysis to relate magnitude and epicentral distances
and precursor time through various empirical relationships
mentioned earlier. Among these, Eqs. (6a), (6b), (11a) and
(11b) spell the conditions whether an impending earthquake of a certain magnitude can be viewed against an
occurring geochemical anomaly at a thermal spring. These
equations are also valid for intra-plate earthquakes and
long distance seismic events (Lomnitz and Lomnitz, 1978;
Fleischer, 1981; Igarashi and Wakita, 1990; Virk, 1996;
Shalimov and Gokhberg, 1998; Das et al., 2009). To make
a comparative study of the (i) maximum epicentral distance Rmax and (ii) maximum precursor time Tmax observed
at the two monitoring sites dispersed at locations about
1612 km apart, we employ the theoretical expressions
(6a), (6b), (11a) and (11b). Accordingly, we contest the
maximum radius of influence R max (=Rmax,Fls) and the
maximum precursor times T max (=T max,Sul) of all the 20
earthquakes given in Table 1 against the possibility
whether any of the listed seismic events could have generated an anomaly at any one or both the Bakreswar and
Tatta Pani thermal springs. We put forward the argument
that an impending earthquake will generate anomalies in
spring gas concentrations provided the distance between
the monitoring spring and the earthquake epicenter (D B
and DT respectively in our case) is approximately close
to or less than Rmax i.e., (i) D B and/or DT ~ Rmax or (ii) D B
and/or DT ≤ Rmax. Table 1 also shows DB and DT for the
listed earthquakes vs. corresponding calculated maximum
radius of influence Rmax.
D ISCUSSION
In this section we have discussed (a) the causes to the
oscillating nature of the spring gas, (b) the reasons behind the significant anomalies, (c) the seismogeochemical mechanism, (d) feasibility of the (i) “one
signal-one earthquake”, (ii) “multiple signal-one earthquake” and (iii) “one signal-multiple earthquake” hypotheses and (e) the validity as well as the effective range of
interaction of the existing empirical algorithms by
analyzing the recorded gas-geochemical precursory signals using two distant reference sampling locations at
Bakreswar and Tatta Pani.
Diurnal variations
As seen in Figs. 3 and 4 diurnal variations occur in
the monitored helium, 222Rn concentration and gamma
activity time series data sets independent of the seasons
and the weather conditions. Gas-geochemical parameters
recorded at Bakreswar and Tatta Pani revealed fluctuating patterns from daily minima to daily maxima. However, these fluctuations were below 2σ. During the monitoring period the helium concentration at Bakreswar
scaled upto daily maximum value of 1.61 vol% from mini-
Long range pre-seismic gas-geochemical anomalies 147
148 H. Chaudhuri et al.
Occurrence Location
date
Latitude;
Longitude
Magnitude (M)
(in Richter Scale)
December
14, 2007
December
14, 2007
December
22, 2007
6
7
8
Sumatra
Indonesia
Koyna, Maharashtra
India
Nanded, Maharashtra
India
India (Kashmir)
−China Border
35.4°N
77.2°E
19.3°N
77.7°E
17.1°N
73.7°E
1.5°N
98.0°E
5.8
2.9
2.7
5.3
2738

10.0
January
04, 2008
January
05, 2008
January
09, 2008
January
12, 2008
January
14, 2008
12
13
14
15
16
Andaman Islands
India
India (Mizoram)
−Bangadesh Border
China
N. Sumatra
Indonesia
S. Sumatra
Indonesia
10.4°N
92.5°E
22.7°N
92.6°E
5.8
5.0
6.3
84.8°E
5.4
94.8°E
32.0°N
6.0
5.9°S
103.8°E
5.3°N
33.0
33.0
96.0
28.6
15.0
21.0
19.0
16.0
12.0
11.0
1593
549
938
2213
3760
1783
1610

5.0
8.0
1122

5.0
1747
1613

8.0
747

33.0
2233
2123

188.7
4.0
3179

33.0
35.0
2803
Distance between
Bakreswar and
epicenter (DB) (in km)

Precursor time (TB)
(in days)
10.0
Depth (d)
(in km)
Seismo-geochemical features at Bakreswar
20.0
18.0
15.0
11.0
10.0
7.0
7.0
3.0








Precursor time (TT)
(in days)
3141
2131
945
3755
5342
1420
1390
3805
4302
1796
1584
352
2255
510
4746
4367
Distance between
Tatta Pani and
epicenter (DT) (in km)
Seismo-geochemical features at Tatta Pani
Anomalous fluctuations (>2σ) observed during Dec. 1, 2007−Jan. 29, 2008
Bakreswar: Dec. 24−26, 2007 (>2σ); Tatta Pani: Dec. 25−27, 2007 (>2σ); (Fluctuations >> 2σ)
9
December
N. Sumatra
5.4°N
5.2
2.7
28, 2007
Indonesia
95.8°E
10
January
Junagarh, Gujarat
21.2°N
3.5
10.5
01, 2008
India
70.6°E
11
January
Junagarh, Gujarat
21.0°N
3.6
5.0
01, 2008
India
70.3°E
December
11, 2007
5
Bakreswar: No anomaly (~2σ); Tatta Pani: No anomaly (~2σ); (Fluctuations << 2σ)
1
December
N. Sumatra
1.9°N
5.9
01, 2007
Indonesia
98.3°E
2
December
S. Sumatra
1.9°S
5.3
02, 2007
Indonesia
100.1°E
3
December
Hindukush
37.0°N
4.8
02, 2007
Afganisthan
71.2°E
4
December
Myanmar
23.5°N
5.0
07, 2007
94.7°E
Event No.
Earthquake data obtained from IMD & USGS website
608
251
1057
391
759
54
48
313
608
14
10
350
251
201
350
680
Maximum radius of
Rmax,Fls
(in km)
10.47
7.94
12.45
9.12
11.22
4.89
4.73
8.51
10.47
3.47
3.76
8.81
7.94
7.41
8.81
10.84
Maximum
precursor time
Tmax,Sul (in days)
Theoretical estimated values of the
seismic events
Table 1. Seismo-geochemical features (precursor time T and epicentral distance D) of the recorded precursory signals and seismological data of the earthquakes that
occurred in and around India during the period December 1, 2007 to January 29, 2008 along with the theoretical estimated values of the parameters—maximum radii of
influence R max and maximum precursor time Tmax
India (Arunachal Pradesh)
−China Border
January
29, 2008
20
Gajapati, Orissa
India
January
27, 2008
19
Rudraprayag, Uttaranchal
India
January
25, 2008
18
The shaded row indicates the earthquake that has been correlated with the observed geochemical anomalies recorded at geochemical monitoring laboratories at Bakreswar as well as at Tatta
Pani.
6.03
104
4.2
10.0
14.0
690

1864
4.57
43
3.4
15.0
12.0
623

1790
4.73
48

1101
10.0
13.9
3.5

2736
7.0
6.0
1.3°N
97.5°E
30.6°N
79.2°E
19.0°N
84.4°E
27.7°N
92.8°E
N. Sumatra
Indonesia
January
22, 2008
17
Bakreswar: Jan. 15−17, 2008 (<2σ); Tatta Pani: No anomaly; (Fluctuations < 2σ or ~2σ)
10.0
Depth (d)
(in km)
Magnitude (M)
(in Richter Scale)
Latitude;
Longitude
Occurrence Location
date
Event No.
572
11.22
759
4291
Maximum
precursor time
Tmax,Sul (in days)
Maximum radius of
Rmax,Fls
(in km)
Distance between
Tatta Pani and
epicenter (DT) (in km)
Precursor time (TT)
(in days)
Distance between
Bakreswar and
epicenter (DB) (in km)
Precursor time (TB)
(in days)
Theoretical estimated values of the
seismic events
Seismo-geochemical features at Tatta Pani
Anomalous fluctuations (>2σ) observed during Dec. 1, 2007−Jan. 29, 2008
Seismo-geochemical features at Bakreswar
Earthquake data obtained from IMD & USGS website
Fig. 7. The power spectrum of the helium time series recorded
at Bakreswar during the one month period of December 14,
2007 to January 18, 2008.
mum value of 1.35 vol% with a mean value of 1.38 vol%.
The 222Rn concentrations recorded at Bakreswar and Tatta
Pani showed the similar diurnal variations from daily
maxima of 872 kBq/m 3 and 888 kBq/m 3 from daily
minima of 782 kBq/m3 and 790 kBq/m3 with mean values of 828 kBq/m3 and 848 kBq/m3 for Bakreswar and
Tatta Pani, respectively. The fluctutaions (<2σ ) observed
are in part short term oscillations caused by changes in
lithological strain pattern of the confining rock masses
owing to aquifer dilation due to earth’s tides (Sugisaki,
1981). Figure 7 shows the power spectrum of helium time
series recorded at Bakreswar during the one month period December 14, 2007–January 15, 2008. The power
spectrum exhibits relative maxima close to periods of 24
hr (and small indications at 12 hr), which coincide with
the earth’s tide constituents (e.g., O1, S1, OO1, M2 and
S2). The O1, S1, OO1, M2 and S2 are the Darwin symbol
of the different frequencies (in cpd) of the diurnal and
semi-diurnal earth’s tides; O1 = 0.9295357, S1 =
1.0000000, OO1 = 1.0759401, M2 = 1.9322736 and S2 =
2.0000000 (Hartmann and Wenzel, 1995; Kudryavtsev,
2004). Weinlich et al. (2006) reported similar S1, P1, J1,
O1, OO1, S2 and M2 peaks in the 222Rn and CO2 spectra
during 222Rn and CO2 measurements in mineral springs.
A similar prominent S1 peak in the radon spectrum of
radon measurements inside a gypsum mine was also reported by Millich et al. (1999). Therefore, we may conclude that the 24 hr cycles observed in the helium data
are based on earth’s tides. Barnet et al. (1997) also observed a correlation between diurnal 222Rn variation and
earth’s tide in mines. Gas-geochemical time series data
are further complicated by the influence of meteorological parameters, rainfall, tectonic processes and other factors (Binchard and Libby, 1980; Sugisaki, 1981; Igarashi
and Wakita, 1991; Finkelstein et al., 1998). As mentioned
earlier meteorological parameters have little effect on the
fluctuating pattern of the Bakreswar and Tatta Pani thermal spring gas emanation and therefore the oscillating
pattern observed in the time series data is due to intrinsic
influence of the tidal effect. However, if fluctuations
Long range pre-seismic gas-geochemical anomalies 149
(<2σ) of state of stress caused by tides affect the deep
earth fluids, a complete coincidence between tide and gas
geochemical data does not necessarily have to exist. The
stress caused by tectonic movements has to be considered in addition, and that will affect the gas emanation
process. Beyond the diurnal variations (<2σ ) caused by
earth’s tides, anomalous changes (>2 σ) in the He, 222Rn
concentrations and gamma activity occur which clearly
exceed these variations and are thought to be of seismic
origin (Weinlich et al., 2006).
Seismic induced signals
Several distinct fluctuations (>2σ) beyond the diurnal variations (<2σ ) in the helium and 222Rn time series
occur at both the monitoring stations as shown in Figs. 5
and 6. These anomalies cannot be explained by the tidal
effects and/or by the changes in meteorological parameters. These alterations inevitably reveal stress variations
caused by the seismic activities. In most cases these
anomalies are noteworthy and may be accompanied by
local seismic activities and/or by the seismic signals of
remote earthquakes (Weinlich et al., 2006). Although a
remote earthquake will not result in a long-term redistribution of stress, it can cause a pressure-pulse on the deep
earth fluid systems (Weinlich et al., 2006). Pressure
changes at the Aigio fault, Gulf of Corinth, Greece induced by remote earthquakes (Rat Islands, 2003; Sumatra,
2004) were reported by Doan and Cornet (2005) which
clearly indicate an effect on the state of stress even in
remote fault systems. This coincidence indicates a relationship between the changes in the gas-geochemical data
and the distant seismic activities.
Pre-seismic excess release of helium and radon are
caused by the changes in tectonic stress-strain pattern
during the critical phase preceding an earthquake event.
This phase is accompanied by permeability changes due
to micro-crack formation in rocks prior to the seismic
event. The anomalous fluctuations observed may have
resulted from (i) relative increase in heat flow that precedes earthquakes, (ii) stress-induced pore-collapse and
(iii) stress-induced micro-fracturing. The observed effect
could be attributed to any one of the three or a possible
combination thereof (Monnin and Seidel, 1988; Ghose et
al., 1996; Chaudhuri et al., 2010b). Rock masses usually
hold systems of overlapping cracks and their interconnections in a three dimensional system. Elastic deformation due to hydrostatic compression influences the process of helium and radon release. Effectively large transformations in the gas concentrations are known to occur
prior to major seismic events in response to alterations in
rock properties, changes in aquifer chemistry and inputs
from mantle sources (Kennedy et al., 1997). The build
up of stresses preceding an earthquake or their release
thereafter can cause changes in the strain field within the
150 H. Chaudhuri et al.
Fig. 8. Earthquake magnitude (M) vs. the theoretically derived
maximum precursory time (T max) and experiential precursor
times TB and TT recorded at the two monitoring sites Bakreswar
and Tatta Pani, respectively.
earth. Because the crust largely acts as an elastic continuum the stress field domain may be large (Ouchi et
al., 1985). When the ground is squeezed, the pore-spaces
are narrowed non-linearly resulting in extrusion of helium, radon and other deep earth gases (Fleischer, 1981).
As mentioned earlier the observed gamma ray activity is due to combined effects of (a) gamma radiation
present in the spring gas, (b) terrestrial gamma background radiation and (c) ambient gamma radiation. Each
of the above contributory gamma source is responsive to
the earth stress changes (Tsvetkova et al., 2001;
Karangelos et al., 2002; Steinitz et al., 2003; Das et al.,
2006a). Accordingly the registered gamma activity would
be roughly synchronized with 222Rn and helium changes
as seen in Fig. 5.
During the anomalous fluctuations period December
22–24, 2007 an approximate 24 hour time lag was observed in both monitoring stations in gamma dose rate
anomaly with respect to the occurrence of radon anomaly.
This is purely an experimental observation and may have
a link with the geological disposition of the two monitoring sites.
During the same time period a time delay of 20 hours
was also observed in the appearance of the radon anomaly
at Tatta Pani as compared to that at Bakreswar. The time
delay may be the outcome of natural geological inhomogeneities, differences in the heat flow rate and regional settings at the two test sites that affect strain propagation and consequently spring gas emanation response.
Fig. 9. Overlapping circles of radii increasing by 150 km with the monitoring sites as the center showing in the earthquake risk
zone map of India.
Monitoring observations
Table 1 shows that magnitude distribution (M; in Richter scale) of the listed earthquakes that occurred at depth
2.7 km to depth about 188 km during the referred twomonth period, ranged between 2.7 M to 7.4 M covering a
spatial area within latitude 01.0°N to 26.3°S and longitude 139.4°E to 177.4°W. The values of Rmax and Tmax
vary between 10 km to 1057 km and 3.47 days to 12.45
days, respectively in our case as shown in Table 1. Out of
the 20 earthquakes there is only one instance (event No.
14 in our case) the China earthquake of magnitude 6.3 M
(January 9, 2008), where Rmax (=1057 km) exceeded DB
(=938 km) as well as D T (=945 km) simultaneously. The
remaining earthquakes have DB and/or D T >> Rmax which
violates our given condition. Precursor signal of this China
earthquake was clearly recorded as gas-geochemical
anomalies (helium, 222Rn and gamma dose rate) from both
the springs, as shown in Fig. 5. The locations of the monitoring stations Bakreswar, Tatta Pani and the epicenter
(32°00′ N; 84°48′ E) of the said 6.3 M China earthquake
is shown in Fig. 1. In the case of event No. 5, the 5.3 M
earthquake in India (Kashmir)–China border (December
11, 2007), Rmax (350 km) ~ DT (352 km) but << D B (1613
km). No noticeable concentration change (>2σ ) either for
helium or radon was observed at either of the monitoring
sites one week before or after the said event. In the case
of event No. 20 the 4.2 M earthquake in India (Arunachal
Pradesh)–China border (January 29, 2008), an evident
anomaly but somewhat lower than 2σ did take place as
seen in Figs. 3 and 4. Helium as well as 222Rn anomalies
(~2σ) indeed occurred during the period January 15–17,
2008 at Bakreswar which was absent at Tatta Pani. The
Long range pre-seismic gas-geochemical anomalies 151
calculations show that the theoretically derived precursory time Tmax falls appreciably short of the experiential
precursor times TB and T T recorded at the two monitoring
sites Bakreswar and Tatta Pani, respectively. Further, all
the experiential precursor times TB and TT do not exactly
fall on the M-T curve (for earthquakes M > 3) of the magnitude (M) vs. precursor time (T) graphical representation are plotted against the empirical relationships (6a)
and (11a) as shown in Fig. 8. It seems that the equations
applied in deriving the theoretical values for Tmax need a
revision.
CONCLUSION
Correlations between gas-geochemical anomalies and
distant earthquakes have attracted renewed interest in
India following large scale disasters caused by some major earthquakes in recent years. The helium, 222Rn and
gamma dose rate anomalies were registered during December 22–27, 2007 in India at two widely separated
(1612 km) and extensively faulted geothermal regions
prior to the 6.3 M earthquake that occurred in China on
January 9, 2008. This suggests that nucleation effects in
the form of pre-seismic changes of large earthquake events
(>6 M) may occur at tectonically sensitive regions that
may be considerably far away (~1000 km) from the eventual epicenter of that earthquake. In the present case, the
estimated maximum radius of influence is large enough
(~1057 km) and the two monitoring thermal springs at
Bakreswar and Tatta Pani, which lie in different geological areas, fall within the theoretically calculated precursory zone of the mentioned China earthquake. The observations may be interpreted as evidence that the gasgeochemical time series data monitored at a thermal spring
located in a fault network plays a significant role to monitor the pre-seismic phase even for a distant earthquake,
as it moves towards criticality. However, the results presented here are quite preliminary but they do indicate that
the area of precursory regions may be large for large earthquakes. Therefore, pre-seismic geochemical signals for
strong earthquakes (M > 6) may be identified by means
of suitable sensitive instrumentation even if they are positioned at distant (~1000 km) monitoring stations. The
given data are results of a time series measurement obtained from two stations. Addition of other such monitoring centers along with long term data recording is expected to bring about greater correlations between
geochemical data sets and seismicity. Further, the “one
earthquake-one signal” hypothesis which is frequently
used in earthquake research, neglects the continuum of
stress-strain changes (Hartmann, 2005a). It is therefore
necessary to adopt the “multiple signal-one earthquake”
and/or “one signal-multiple earthquake” hypotheses by
installing a network of multi-component monitoring sta-
152 H. Chaudhuri et al.
tions along with a simultaneous analysis of the multicomponent and multi-station data. The geochemical signals of a definite seismic event monitored in more than
one station with a possible time delay could provide an
understanding of the effects of seismic shocks on the gas
bearing fluid systems. With this view in mind, we have
more recently, set-up a third monitoring laboratory at the
Baratang mud volcano (12°07′44″ N; 92°47′29″ E) in the
Andaman and Nicobar Islands, India, having identical
instrumentation as at Bakreswar/Tatta Pani. Figure 9
shows the overlapping and concentric circles drawn upon
the earthquake risk zone map of India with radii increasing by 150 km, taking into consideration the above three
monitoring sites as individual centers. Earthquake preparation activity that occurs within the overlaps may induce
similar variations in gas concentrations in each site. A
similar concept was proposed by Hartmann and Levy
(2005) earlier. It is expected that such reasoning would
facilitate better analytical correlation and reinforce conclusions on time series data derived from all the three
laboratories relating pre-seismic gas-geochemical anomalies with regional earthquakes. Besides, a systematic approach for analysing earthquake precursory signals and a
standardised reporting scheme may be established. It is
also suggested that the empirical algorithms applied in
deriving the theoretical value for maximum epicentral
distance and maximum precursor time need revision. This
in turn, may help identify earthquake precursory signals
in a more reliable way and may assist in better understanding the earthquake preparation phenomena.
Acknowledgments—The authors would like to express their
appreciation to the Department of Atomic Energy (DAE), the
Department of Science and Technology (IS-STAC, DST), the
Ministry of Earth Sciences (Seismology Division, MoES), Government of India, for financial support. The support of the University of Kashmir, Srinagar is highly appreciated, for extending organizational help at the Tatta Pani field laboratory, Jammu
(J&K). The authors would like to thank the reviewers for their
critical and constructive comments and suggestions. Special
thanks are expressed to Dr. Takuya Matsumoto for his careful
reading and suggestions for improving the clarity and readability of the manuscript. Finally the authors would like to acknowledge the assistance of Prof. A. N. Sekar Iyengar in improving
the power spectrum plot.
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