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IUGG XXIV General Assembly July 2-13, 2007 Perugia, Italy
(S) - IASPEI - International Association of Seismology and Physics of the Earth's
Interior
JSS009 Oral Presentation 2028
Earthquake precursors detected through the analysis of mutual
interactions of hydrogeochemical signals
Mrs. Laura Castellana
Department of Biomedical Sciences University of Foggia IASPEI
Pier Francesco Biagi
Many studies showed that changes in hydrogeochemical time series can be related to earthquakes. In
particular, the detection of seismic precursors is traditionally carried out associating the spectral content
of the time series analyzed to the occurrence of seismic events. The limit of this approach is twofold:
first it is based on a posteriori signal analysis, second each groundwater parameter is analysed
singularly and their mutual correlations are not investigated. Here correlations are meant both as
interactions among hydrogeochemical parameters and as interactions between these parameters and
the occurrence of seismic precursors. To overcome these drawbacks, we use a classifier that,
appropriately trained on a finite and limited number of examples (training set), learns to predict the
occurrence of seismic events in new observations of hydrogeochemical data. The training set is
composed of l couple of examples {(xi,yi)}, i=1,...,l , where the vector xi is the temporal window, large
m, of the signal and yi is a label identifying the signal pattern, i.e. yi =0 or yi =1 or yi =2 if xi is a noseismic
signal or precursor signal or co-post seismic signal, respectively. Under this perspective, the
problem of detecting hydrogeochemical precursors becomes the problem to predict the correct output yj
relative to never seen before input pattern xj. The prediction accuracy and the number of false positives
provide a quantitative measure of the capability of the multivariate predictor in discriminating noseismic/
precursor/ co-post seismic signal. Furthermore, these quantities provide also an estimate of
correlations between hydrogeochemical data and earthquakes, because the higher prediction accuracy,
the better correlations among these quantities. The dataset used in this study is composed of thirteen
geochemical time series collected in Kamchatka (Russia) peninsula, since 1977. The predictor we
worked with is K Nearest Neighbour classifier. In order to compute the prediction accuracy, we use the
Leave-K-Out-Cross-Validation (LKOCV) procedure, a statistically well founded method for estimating the
accuracy of predictors by using a finite number of observations. We applied K Nearest Neighbour
classifier to study the correlations among ions (Na+, Cl-, Ca++, HCO3 and H3BO3), among parameters
(pH, Q and T) and finally among gases (N2, CO2, CH4, O2 and Ar) in order to establish if their
interactions improve the prediction accuracy of seismic precursors. The results show the model order is
proportional to the prediction accuracy. This is a proof that information collected some months before
the event under analysis are necessary to improve the classification accuracy. In particular, we obtained
a prediction accuracy of 78% in a temporal window of size m=80.
Keywords: precursors, knn classifier, prediction