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Abstract

The quality of earthquake prediction is usually characterized by a two-dimensional diagram n versus τ, where n is the rate of failures-to-predict and τ is a characteristic of space-time alarm. Unlike the time prediction case, the quantity τ is not defined uniquely. We start from the case in which τ is a vector with components related to the local alarm times and find a simple structure of the space-time diagram in terms of local time diagrams. This key result is used to analyze the usual 2-d error sets n, τ w in which τ w is a weighted mean of the τ components and w is the weight vector. We suggest a simple algorithm to find the (n, τ w ) representation of all random guess strategies, the set D, and prove that there exists the unique case of w when D degenerates to the diagonal n + τ w = 1. We find also a confidence zone of D on the (n, τ w ) plane when the local target rates are known roughly. These facts are important for correct interpretation of (n, τ w ) diagrams when we discuss the prediction capability of the data or prediction methods

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References

  • Bolshev, L. N. and Smirnov, V. N. Tables of Mathematical Statistics (Nauka, Moscow 1983).

    Google Scholar 

  • Harte, D., and Vere-Jones, D. (2005), The entropy score and its uses in earthquake forecasting, Pure Appl. Geophys. 162, 1229–1253.

    Article  Google Scholar 

  • Hanssen, A. W., and Kuiper, W. J. A. (1965), On the relationship between the frequency of rain and various meteorological parameters, Modedeelingen en Verhandelingen, Royal Notherlands Meteorological Institute, 81.

    Google Scholar 

  • Jolliffe, I. T. and Stephenson, D. B. (eds.), Forecast Verification: A Practitioner’s Guide in Atmospheric Science (John Wiley & Sons, Hoboken 2003).

    Google Scholar 

  • kagan, Y. Y. (2007), On earthquake predictability measurement: information score and error diagram, Pure Appl. Geophys. 164, 1947–1962.

    Article  Google Scholar 

  • Keilis-Borok, V. I. and Soloviev, A. A. (eds.), Nonlinear Dynamics of the Lithosphere and Earthquake Prediction (Springer-Verlag, Berlin-Heidelberg 2003).

    Google Scholar 

  • Kossobokov, V. G. (2005), Earthquake prediction: principles, implementation, Perspect. Comput. Seismol. 36-1, 3–175, (GEOS, Moscow).

    Google Scholar 

  • Lehmann, E. L., Testing Statistical Hypotheses (J. Wiley & Sons, New York 1959).

    Google Scholar 

  • Marzocchi, W., Sandri, L., and Boschi, E. (2003), On the validation of earthquake-forecasting models: The case of pattern recognition algorithms, Bull. Seismol. Soc. Am. 93,5, 1994–2004.

    Article  Google Scholar 

  • Molchan, G. M. (1990), Strategies in strong earthquake prediction, Phys. Earth Planet. Inter. 61(1-2), 84–98.

    Article  Google Scholar 

  • Molchan, G. M. (1991), Structure of optimal strategies of earthquake prediction, Tectonophysics 193, 267–276.

    Article  Google Scholar 

  • Molchan, G. M. (1997), Earthquake prediction as a decision making problem, Pure Appl. Geophys. 149, 233–247.

    Article  Google Scholar 

  • Molchan, G. M., Earthquake prediction strategies: A theoretical analysis. In Nonlinear Dynamics of the Lithosphere and Earthquake Prediction (eds. Keilis-Borok, V.I. and Soloviev, A.A.) (Springer-Verlag, Berlin-Heidelberg 2003), pp. 209–237.

    Google Scholar 

  • Molchan, G. M., and Kagan, Y. Y. (1992), Earthquake prediction and its optimization, J. Geophys. Res. 97, 4823–4838.

    Article  Google Scholar 

  • Molchan, G. M. and Keilis-Borok, V. I. (2008), Earthquake prediction: Probabilistic aspect, Geophys. J. Int. 173, 1012–1017.

    Article  Google Scholar 

  • Shcherbakov, R., Turcotte, D. L., Holliday, J. R., Tiampo, K. F., and Rundle, J. B. (2007), A Method for forecasting the locations of future large earthquakes: An analysis and verification, AGU, Fall meeting 2007, abstract #S31D-03.

    Google Scholar 

  • Shen, Z.-K., Jackson, D. D., and Kagan, Y. Y. (2007), Implications of geodetic strain rate for future earthquakes, with a five-year forecast of M 5 Earthquakes in Southern California, Seismol. Res. Lett. 78(1), 116–120.

    Article  Google Scholar 

  • Swets, J. A. (1973), The relative operating characteristic in psychology, Science 182,4116, 990–1000.

    Article  Google Scholar 

  • Tiampo, K. F., Rundle, J. B., McGinnis, S., Gross, S., and Klein, W. (2002), Mean field threshold systems and phase dynamics: An application to earthquake fault systems, Europhys. Lett. 60(3), 481–487.

    Article  Google Scholar 

  • Zechar, J.D. and Jordan, Th., H. (2008), Testing alarm-based earthquake predictions, Geophys. J. Int. 172, 715–724.

    Article  Google Scholar 

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Molchan, G. (2010). Space—Time Earthquake Prediction: The Error Diagrams. In: Savage, M.K., Rhoades, D.A., Smith, E.G.C., Gerstenberger, M.C., Vere-Jones, D. (eds) Seismogenesis and Earthquake Forecasting: The Frank Evison Volume II. Pageoph Topical Volumes. Springer, Basel. https://doi.org/10.1007/978-3-0346-0500-7_5

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