In this paper we present the results of characterising time series of the argon content of groundwater recorded in the Kamchatka area of Russia. The problems of correlating anomalies in the argon data with seismic activity are explored. A new statistical technique for relating anomalies to geophysical observations based on Markov Chain Monte Carlo modelling methods is outlined

Anderson, C.W.

Biagi, P.F.

Derlien, P.J.

Ermini, A.

Gordeev, E.I.

Khatkevich, Y.M.,

Kingsley, S.P.

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IL NUOVO CIMENTO Maggio-Agosto 1999VOL. 22 C, N. 3-4Analysis of argon concentration anomalies in underground waterin Kamchatka (Russia) (*)S. P. KINGSLEY (1), C. W. ANDERSON (1), P. F. BIAGI (2), P. J. DERLIEN (1)A. ERMINI (3), E. I. GORDEEV (4) and Y. M. KHATKEVICH (4)(1) Sheffield Centre for Earth Observation Science, University of Sheffield, UK(2) Dipartimento di Fisica, Università di Bari, Italy(3) Dipartimento di Scienze e Tecnologie Fisiche ed EnergeticheUniversità di Roma “Tor Vergata”, Italy(4) Experimental and Methodical Seismological DepartmentGeophysical Service, Russian Academy of Sciences - Russia(ricevuto il 9 Giugno 1998; approvato il 18 Dicembre 1998)Summary. — In this paper we present the results of characterising time series of theargon content of groundwater recorded in the Kamchatka area of Russia. Theproblems of correlating anomalies in the argon data with seismic activity areexplored. A new statistical technique for relating anomalies to geophysicalobservations based on Markov Chain Monte Carlo modelling methods is outlined.PACS 91.35.Gf – Structure of the crust and upper mantle.PACS 91.65.Dt – Isotopic compositionOchemistry.PACS 01.30.Cc – Conference proceedings.1. – IntroductionIn over 100 years of research, deterministic earthquake (EQ) prediction has still notbeen satisfactorily demonstrated, despite the wide variety of techniques that have beeninvestigated. On the one hand, it is widely thought that the chaotic, highly non-linearnature of earthquakes may mean that short term prediction is unrealisable althoughless specific, longer term forecasting remains a possibility [1, 2]. On the other hand, the“anomalous” geophysical signals recorded by experimental scientists before someearthquakes are real measurements that are not easily explicable in terms of randomprocesses such as noise. The possibility remains that these signals might somehow beused to improve the warning of impending EQs but there are several problemsconfronting those trying to pursue this research line. There is the difficulty of(*) Paper presented at the “Fourth International Conference on Rare Gas Geochemistry”,Rome, October 8-10, 1997.G Società Italiana di Fisica 407S. P. KINGSLEY, C. W. ANDERSON, P. F. BIAGI, ETC.408collecting a data series of sufficient duration for an anomaly to be definedunambiguously with respect to the normal statistical fluctuations in the data. A moredifficult problem is to prove in some objective way that these anomalies are related tothe stresses and strains that build up in advance of EQs, and that they have realpredictive power.One of the most consistently promising techniques over the last century has beenthe analysis of hydrochemicals in groundwater from sources such as wells and springs.There are a number of obvious mechanisms that could link seismic activity (andassociated geological stress) with the increase in the chemical content and flow rate ofwater percolating underground. In some cases subjective observation of the datasuggests that there might be evidence for co-seismic and post-seismic effects, but howgood is the evidence for any statistically significant pre-seismic effects? And how canany such evidence be assessed in an objective and quantifiable way? In order toaddress these questions we have analysed some of the best data sets available—up to20 years of data recorded in the Kamchatka region of eastern Russia where the ion andgas content of three geographically separated groundwater sources have beenmeasured regularly every 3 days [3].2. – The time series dataThe Kamchatka peninsula is an active volcanic area in the far East of Russia.Frequent earthquakes occur along a nearby offshore subduction zone with hypocentraldepths ranging from a few kilometres to 500 km. At sites near the city of Petro-pavlovsk, natural and artificial springs have been sampled roughly every three daysand the dissolved gas and ion content analysed. Recordings at the first site—a naturalspring at the foot of an active volcano—began in January 1977. At a second and thirdsite, both deep artificial wells, measurements began in 1986 and 1992, respectively. Inthe period 1977 to 1986 there was no major seismic activity and magnitudes did notexceed 6.0; this can be regarded as excellent training data to study the normalfluctuations in the observations.In the second ten years, from 1986 to date, there have been five main seismic eventswith magnitudes MF6.5 and a number of smaller events. Two of these EQs occurredin 1996, the first in January 120 km to the North of Petropavlovsk followed by ashallower event in June to the South East in the subduction zone. These 1996 EQs arethe main focus of this study.3. – Data processingThe analysis presented here is based on a time series of seismic information and oneof argon concentration values. The seismic time series contained geographical informa-tion that enabled the effect of each event on the area around the spring to be calculatedthrough the seismic stress parameter. For ME5 the value of this parameter e can befound from e410(1.5M-9.18) OR 3, otherwise e410(1.3M-8.19) OR 3, where M is the magnitudeand R (km) is the epicentral distance from the EQ to the centre of the measurementsites. Figure 1a shows a plot of all the values eF0.001 and fig. 1b shows the raw argondata collected from spring 2. The time scale along the bottom of all the figuresrepresents days since 1/1/1960 and in the case of fig. 1 the axis runs from the start of1988 to the end of April 1997.ANALYSIS OF ARGON CONCENTRATION ANOMALIES IN UNDERGROUND WATER ETC. 409Fig. 1. – (a) Seismic deformation parameter e for the period 1988-1997 for values greater than0.001. (b) Measured argon concentrations for Spring 2 for the same period. (c) Time series b)interpolated to the instants of the seismic events. The units for argon concentration aremillilitre/litre 3 1022. Time is in days since 1/1/1960.A difficulty in comparing the time series 1a and 1b is that the times when argonsamples were taken do not correspond to the instants when seismic events occurred; adirect correlation between the two is therefore not possible. To circumvent thisproblem we linearly interpolated the argon data to exactly the times of the seismicevents, see fig. 1c, and then correlated the two series. Over the whole data set thecoefficient of correlation between e and the interpolated argon is 2 0.086 indicatingthat the two series are unrelated, but there are events towards the end of the figure in1996 that appear to be more interesting when a large increase in argon content appearsto occur at roughly the same time as a seismic event in June 1996.S. P. KINGSLEY, C. W. ANDERSON, P. F. BIAGI, ETC.410Fig. 2. – Rate of occurrence of seismic events and the 20 day change in the argon concentration ofgroundwater for Spring 2. Time is in days since 1/1/1960.Fig. 3. – Seismic events with eF0.001 and the argon data for Spring 3. Time is in days since1/1/1960.ANALYSIS OF ARGON CONCENTRATION ANOMALIES IN UNDERGROUND WATER ETC. 411Fig. 4. – A short section of data expanded from fig. 1. The correlation coefficient between thesetwo time series is 0.526 as compared to 2 0.086 for all the data shown in fig. 1.In figs. 2a and b we have plotted the rate of occurrence of seismic events against the20-day change in argon concentrations. The second of the 1996 EQs appears the largerin occurrence rates of fig. 2a, even though its magnitude was smaller, because it wascomposed of many small seismic events. This event was preceded by a sudden increasein the rate of change of argon. A similar effect is shown in fig. 3 where the upper traceis the same e plot as before but the bottom trace is the argon concentration measure atspring 3. Again, a large increase in the groundwater argon concentration occurs at thetime of the two large EQ events of 1996.We must be careful in our analysis though; note that around 11500 days in fig. 2band 11800 days in fig. 3b there are also sudden increases in the argon that do notcorrespond to any seismic activity. As a demonstration of how misleading datapresentations can be, we have plotted in fig. 4 a short section of about 100 days datafrom spring 2. The cross-correlation of the seismic rate in fig. 4a and the argon in 4bgives a coefficient of 0.526 which would appear to be convincing. And yet this is part ofthe same data set that gave an overall coefficient of 2 0.086. By being selective we canachieve just about any result we want.How then are we to demonstrate objectively whether or not these anomalies arerelated to EQ activity?4. – A statistical approach to the problemSuppose seismic events are observed at times and magnitudes (t i , mi ), ]i41, R , n( and the claimed precursors are observed at times and magnitudes (tj , cj ),S. P. KINGSLEY, C. W. ANDERSON, P. F. BIAGI, ETC.412] j41, R , k(. The problem then is to establish objectively the presence or absence ofa relationship between (tj , cj ) and (t i , mi ). One method of doing this is to assume thatboth the seismic events and the precursor observations are (potentially) related to anunobserved process S(t) which we might call “stress”, as shown below:MEASUREMENTS OF DISCRETE SEISMIC EVENTSHUNOBSERVED CONTINUOUS-TIME PROCESSIOBSERVATIONS OF DISCRETE ANOMALIESThe times of the occurrences and magnitudes of seismic events can be modelled by amarked Poisson process in (t , m)-space with intensity function S:Expected no. of points in (t,t1dt)3(m,m1dm)4E(N(dt, dm))4l(S(t), m) dt dmfor some function l. Suppose also that the putative precursor values c are related to Sbycj (4c(tj ) )4S(tj )1Yj ,where the Yj ] j41, R( represent random noise in the relationship.If we assume a time series co-variance structure for S and an explicit functionalform for l , then we can fit the model by Markov Chain Monte Carlo methods with theprecursor data contributing to the fit of the covariance structure. The absence of arelationship between claimed precursors and the times and/or magnitudes of seismicevents would correspond to l(S(t), m) in fact not varying with S(t). This can be testedobjectively from the fitted results.In this way the S process bridges the gap between the times t i of EQ events and thetimes tj of observations of the precursor. This formulation is general in the sense that cjcould be any function of direct measurements on some physical quantity such as argonconcentration at tj or (tj2 l), where l is some lag in time. We believe this approach hassufficient rigour to meet IASPEI (International Association for Seismology andPhysics of the Earth’s Interior) guidelines for analysing and claiming potential EQprecursor phenomena.5. – ConclusionsThe argon content of groundwater recorded in Kamchatka data suggests thepossibility of a co-seismic response to a shallow earthquake in the subduction zone offthe Eastern seaboard. When analysing the data, it was found more useful to comparethe rate of change of argon concentration with the frequency of the seismic events.These dynamic parameters appear to be more revealing than the absolute valuesrecorded.ANALYSIS OF ARGON CONCENTRATION ANOMALIES IN UNDERGROUND WATER ETC. 413An interpolation method of overcoming the differences in instantaneous timesbetween the EQ events and the argon recordings has been used, but the dangers ofbeing too selective in the data being correlated (to improve the correlation coefficient)are all too apparent.A new statistical method of analysis is put forward that attempts to compare EQand potentially anomalous geophysical data in a more rigorous and objective waythrough an unobserved stress process. This approach can quantify the relationship andhelp to meet IASPEI guidelines for those working in this research field. A newtechnique of this kind is needed if we are to move on and investigate claimedpre-seismic, rather than co-seismic, geophysical behaviour that might be used for EQprediction.It must be remembered, however, that even if some anomalous hydrogeochemicalbehaviour is shown to be precursory to an earthquake it will not necessarily have anyuseful predictive power because there are many other issues to consider, especially thefalse alarm rate [4, 5].R E F E R E N C E S[1] GELLER R. J., Astron. Geophys., 38 (1997) 16-18.[2] GELLER R. J., EOS, 78 (1997) 64-67.[3] BELLA F., BIAGI P. F., CAPUTO M., COZZI E., DELLA MONICA G., ERMINI A., GORDEEV E. I.,KHATKEVITCH Y. M., MATINELLI, G., PLASTINO, W., SCANDONE R., SGRIGNA V., ZILPIMIANI D.,Phys. Chem. Earth, 23 (1998) 921-925.[4] ANDERSON C. W., KINGSLEY S. P., NIEDER S. R. and LIPEROVSKY V. A., presented at 21stIUGG General Assembly, Boulder, USA, July, 1995.[5] ALBARELLO D., presented at Earthquake precursors at XXII General Assembly of theEuropean Geophysical Society, Vienna, 1997.

Analysis of argon concentration anomalies in underground water in Kamchatka (Russia)

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Analysis of argon concentration anomalies in underground water in Kamchatka (Russia)

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