Testing Earthquake Forecasts David D. Jackson Yan Y. Kagan Yufang Rong.

Slides:

Advertisements

Similar presentations

Modeling of Data. Basic Bayes theorem Bayes theorem relates the conditional probabilities of two events A, and B: A might be a hypothesis and B might.

Advertisements

The goal… We provide a spatial-temporal distribution of large earthquakes (M 5.5+) occurred in Italy in the last 4 centuries We provide a NONPARAMETRIC.

The trouble with segmentation David D. Jackson, UCLA Yan Y. Kagan, UCLA Natanya Black, UCLA.

Christine Smyth and Jim Mori Disaster Prevention Research Institute, Kyoto University.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA ,

Extreme Earthquakes: Thoughts on Statistics and Physics Max Werner 29 April 2008 Extremes Meeting Lausanne.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , EARTHQUAKE.

7 June 06 1 UW Point Source Detection and Localization: Compare with DC2 truth Toby Burnett University of Washington.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , EARTHQUAKE PREDICTABILITY.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , Testing.

Yan Y. Kagan, David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles, CA ,

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , Statistical.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , Full.

Epidemic Type Earthquake Sequence (ETES) model  Seismicity rate = "background" + "aftershocks":  Magnitude distribution: uniform G.R. law with b=1 (Fig.

Earthquake predictability measurement: information score and error diagram Yan Y. Kagan Department of Earth and Space Sciences, University of California.

Yan Y. Kagan, David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles, CA ,

Yan Y. Kagan, David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles, CA ,

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , Forecast.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , Global.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , Rules of the Western.

New Earthquake Catalogs For Southern California And Their Use In Earthquake Forecasting Yan Y. Kagan, David D. Jackson and Yufang Rong, University of California,

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , Earthquake.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , STATISTICAL.

The Empirical Model Karen Felzer USGS Pasadena. A low modern/historical seismicity rate has long been recognized in the San Francisco Bay Area Stein 1999.

If we build an ETAS model based primarily on information from smaller earthquakes, will it work for forecasting the larger (M≥6.5) potentially damaging.

Statistics of Seismicity and Uncertainties in Earthquake Catalogs Forecasting Based on Data Assimilation Maximilian J. Werner Swiss Seismological Service.

FULL EARTH HIGH-RESOLUTION EARTHQUAKE FORECASTS Yan Y. Kagan and David D. Jackson Department of Earth and Space Sciences, University of California Los.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA ,

An Introduction to Seismic Eruption software and an associated classroom activity Michael Hubenthal, IRIS Educational Specialist.

Holt’s exponential smoothing

Toward urgent forecasting of aftershock hazard: Simultaneous estimation of b-value of the Gutenberg-Richter ’ s law of the magnitude frequency and changing.

Application of the RI model to forecasting future large earthquakes in Japan Kazu Z. Nanjo (ERI, Univ. of Tokyo) International symposium “Toward constructing.

Southern California Earthquake Center Triggering Models vs. Smoothed Seismicity PG = 1.35/eqk PG = 10/eqk Information gain per earthquake Reference forecast.

Random stress and Omori's law Yan Y. Kagan Department of Earth and Space Sciences, University of California Los Angeles Abstract We consider two statistical.

Source: Time Series Data Library MONTHLY MINNEAPOLIS PUBLIC DRUNKENNESS INTAKES JAN.’66-JUL’78 Meghan Burke.

Yan Y. Kagan & David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles, CA ,

1. Difficulty of point process model evaluation. 2. RELM and CSEP. 3. Numerical summaries (L-test, N-test, etc.). 4. Functional summaries (error diagrams,

5 a day - Earthquakes Day How many seismic stations does it take to locate an earthquakes epicenter? 2. What is the name of the technique that we.

Some Properties of “aftershocks” Some properties of Aftershocks Dave Jackson UCLA Oct 25, 2011 UC BERKELEY.

TOHOKU EARTHQUAKE: A SURPRISE? Yan Y. Kagan and David D. Jackson Department of Earth and Space Sciences, University of California Los Angeles Abstract.

Business Processes Sales Order Management Aggregate Planning Master Scheduling Production Activity Control Quality Control Distribution Mngt. © 2001 Victor.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , Evaluation.

GLOBAL EARTHQUAKE FORECASTS Yan Y. Kagan and David D. Jackson Department of Earth and Space Sciences, University of California Los Angeles Abstract We.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA ,

Earthquake size distribution: power-law with exponent ? Yan Y. Kagan Department of Earth and Space Sciences, University of California Los Angeles Abstract.

Graphing. Graphs Data must be shown in a way that allows others to understand your results easily and rapidly. There are many types of graphs. The type.

Finding the Epicenter …I hope you really like graphs!

California Earthquake Rupture Model Satisfying Accepted Scaling Laws (SCEC 2010, 1-129) David Jackson, Yan Kagan and Qi Wang Department of Earth and Space.

Plate-tectonic analysis of shallow seismicity: Apparent boundary width, beta, corner magnitude, coupled lithosphere thickness, and coupling in 7 tectonic.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , STATISTICAL SEISMOLOGY–

Forecasting is the art and science of predicting future events.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA ,

GNS Science Testing by hybridization – a practical approach to testing earthquake forecasting models David Rhoades, Annemarie Christophersen & Matt Gerstenberger.

Statistics 350 Review. Today Today: Review Simple Linear Regression Simple linear regression model: Y i =  for i=1,2,…,n Distribution of errors.

Random stress and Omori's law Yan Y. Kagan Department of Earth and Space Sciences, University of California Los Angeles Abstract We consider two statistical.

Metrics, Bayes, and BOGSAT: Recognizing and Assessing Uncertainties in Earthquake Hazard Maps Seth Stein 1, Edward M. Brooks 1, Bruce D. Spencer 2 1 Department.

Jiancang Zhuang Inst. Statist. Math. Detecting spatial variations of earthquake clustering parameters via maximum weighted likelihood.

Yan Y. Kagan Dept. Earth and Space Sciences, UCLA, Los Angeles, CA ,

Statistical earthquake forecasts

Comments on physical simulator models

Global smoothed seismicity models and test results

Yan Y. Kagan Dept. Earth, Planetary, and Space Sciences, UCLA, Los Angeles, CA , NEGATIVE.

Tips Need to Consider When Organizing a College Event

Maximum Earthquake Size for Subduction Zones

هل يمكن أن نكون جيل الأرض الأخير؟

Leave-one-out cross-validation

Tohoku earthquake: A surprise?

Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.

ماجستير إدارة المعارض من بريطانيا

K. Z. Nanjo (ERI, Univ. Tokyo)

Presentation transcript:

Testing Earthquake Forecasts David D. Jackson Yan Y. Kagan Yufang Rong

Types of test Prospective: all parameters and rules set Retrospective; adustments made to fit data better Pseudo-prospective Learning and test periods Qualitative adjustments may be made

What to test Number of events Magnitude distribution of those events Locations of those events

Smoothed seismicity forecast test

Concentration plot of the earthquake potential model and actual earthquakes

Number test and Likelihood test The forecast passed the tests at 95% confidence level.