The basic results and prospects of MEE algorithm for the medium-term forecast of earthquakes Alexey Zavyalov Schmidt Institute of Physics of the Earth Russian Academy of Science Str. B.Gruzinskaya, 10, Moscow , Russia,

INTRODUCTION The International Geophysical Year 1957 has given a powerful pulse to development of geophysical researches in the USSR. Under its aegis detailed seismological supervision in sesmoactive areas of Soviet Union: on the Far East (east coast of Kamchatka, Kuriles), Caucasus, the Baikal region have been started. One of the important results of Geophysical Year for seismology was creation of a seismic stations network which became a basis of the future Uniform Network of Seismic Supervisions of USSR (UNSS), nowadays functioning within the framework of Geophysical Service of Russian Academy of Science. Earthquake catalogues of UNSS/GS RAS of different level of detail became the basic information base for revealing regularities of seismic process and development of various algorithms for strong earthquakes forecast.

The Map of Expected Earthquakes (MEE) algorithm was established in 1984 by G. Sobolev, T. Chelidze, A. Zavyalov and L. Slavina on the basis of ideas about failure process of rocks and geological medium as a self-similar and self-organizing system of blocks of different scales. Based on the kinetic conception of strength of solid materials authors have made the image of anomaly behavior of different seismological parameters before strong (M  5.5) earthquakes.

MEE algorithm uses the principle of space- time scanning of the earthquake catalog within the limits of the studied seismoactive region. Using the Bayesian approach maps of conditional probability distribution of strong earthquake occurrence P(D 1 |K) where calculated. These maps were named as Maps of Expected Earthquakes (MEE). During last twenty years MEE algorithm have been tested on regional earthquake catalogs of Caucasus, Kamchatka, Kuril Arch, Kopet-Dag, Kirgizstan, Southern California, North-East and South-West China, Greece and Western Turkey.

Choice of precursors clear physical meaning of precursors; physically substantiated relation of each precursor to the earthquake preparation process; availability of observation data for each precursor; these data should be representative in time (long-terms series of prognostic parameter values) and in space (the possibility of their mapping); availability of a formal procedure for the identification of anomalies in prognostic parameters based on a model of their behavior during the earthquake preparation; possibility of obtaining estimates for retrospective statistical characteristics of each precursor: probability of successful prediction (probability of detection), probability of a false alarm, prognostic efficiency (informativeness), and so on.

What parameters have been used: b-value (maximum likelihood estimate) Model of b-value behavior under preparation of earthquake

fractures concentration parameter K f where μ - volumetric density (concentration) of ruptures, identified on happened earthquakes, - average size of faults in a cell, n - the number of events in a cell. The quantity has the meaning of the average inter-rupture distance between the centers of the ruptures. Model of K f behavior under preparation of earthquake

number of earthquakes N seismic activation; seismic quiescence Model of parameter N behavior under preparation of earthquake

released seismic energy of weak events seismic activation; seismic quiescence Model of parameter behavior under preparation of earthquake

Efficiency of the used precursors (in time)

Efficiency of the used precursors (on square)

Bayesian approach for MEE calculations where P(K i |D 1 ) – conditional probability of strong EQ occurrence use a prognostic indicator K i ; P(K i |D 2 ) – conditional probability of false alarm; P(D 1 ) is unconditional probability of strong EQ occurrence in the spatial cell under consideration; P(D 2 )=1-P(D 1 ) is unconditional probability of absence of a strong EQ in the spatial cell under consideration; K i is the presence of anomaly of the i-th prognostic indicator in the spatial cell. Conditional probability of strong EQs occurrence in elementary spatial cell is calculating as

Map of Expected Earthquakes for Caucasus for the period (compiled in May, 1988 by G.Sobolev, L.Slavina, A.Zavyalov, T.Chelidze)  , K=12.7; 2  , K=13.8 (Paravan EQs); 3  , K=12.6; 4  , M=6.8 (Spitak EQs); 5  , M=5.1; 6  , K=13.0.

Map of Expected Earthquakes for Kuril for the period

Map of Expected Earthquakes for Kuril for the period (compiled in August, 2007)

Efficiency of MEE algorithm on regions Number of strong EQs Average area of expectation

Map of Expected Earthquakes for Kronotskoe EQ (Kamchatka, Dec. 5, 1997) preparation zone (Prognostic period begins from Jan. 1, 1997) H = km H = km

MEE algorithm in real-time prediction The Greece MEE for the period 1996–2002 (compiled in May, 1997)

Lacks of MEE algorithm One of essential lacks of MEE algorithm will be, that it does not give the answer to a question, in which area of the increased probability there will be a next strong earthquake.

Prospects of the further development of MEE algorithm localization of the seismic process traveltime ratio of P and S waves (parameter  ) parameter Ksf including the fractal correction earthquake clustering parameter RTL parameter MEE algorithm is open for inclusion in it new physically and statistically proved predictors satisfying requirements described above. In such approach the author sees one of ways of development and perfection of a technique.

C o n c l u s i o n The analysis of all set of received Maps of Expected EQs for the studied seismoactive regions has shown, that the efficiency of MEE algorithm at the retrospective forecast of strong earthquakes J=3-4. Up to 70% of strong earthquakes occur in zones of the increased conditional probability. In addition the area of these zones does not exceed 30% from the total area of supervision. Results of long-term testing allow to recommend developed MEE algorithm for strengthening of supervisions in the allocated zones with high (more than 70%) level of conditional probability over precursors of another geophysical nature having more short-term character in comparison with used, and for acceptance necessary preventive measures on reduction of probable economic and social damage from the future strong EQ. It is possible to improve prognostic abilities of MEE algorithm by insertion of additional precursors.