Evidences of complexity of magnitude distribution, obtained from a non-parametric testing procedure

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1 Evidences of complexity of magnitude distribution, obtained from a non-parametric testing procedure Stanislaw Lasocki

2 Complex magnitude distributions Singh, et. al., 1983, BSSA 73, Southern California Knopoff, 2000, PNAS 97, Main, 1995, BSSA 85,

3 How to reach statistical (measurable) significance of observational evidences?

4 How to reach statistical (measurable) significance of observational evidences? Two problems with goodness of fit testing procedures:

5 How to reach statistical (measurable) significance of observational evidences? Two problems with goodness of fit testing procedures: 1.

6 How to reach statistical (measurable) significance of observational evidences? Two problems with goodness of fit testing procedures: 1.

7 How to reach statistical (measurable) significance of observational evidences? Two problems with goodness of fit testing procedures: 1.

8 How to reach statistical (measurable) significance of observational evidences? Two problems with goodness of fit testing procedures: 1. : exponential (G-R) : tapered G-R (Jackson, Kagan, 1999) gamma for M 0 (Kagan, 1999) generalized Pareto for M 0 (Pisarenko, Sornette, 2003) Weibull (Lasocki, 1993) double exponential (Lomnitz-Adler, Lomnitz, 1978) normal (Niazi,1964) Utsu (1971), Makjanić (1972), Saito et al. (1973), Purcaru (1975), Seino et al. (1989)...

9 2. Exponential-like shape of f act (M)

10 2. Exponential-like shape of f act (M)???

11 2. Exponential-like shape of f act (M)??? An artifact due to depopulation?

12 2. Exponential-like shape of f act (M)??? An artifact due to depopulation? A statistical scatter?

13 2. Exponential-like shape of f act (M)??? An artifact due to depopulation? A statistical scatter? A real break in scaling law?

14 Model-free testing: The smoothed bootstrap test for multimodality (Silverman,1986; Efron, Tibshirani, 1998):

15 Model-free testing: The smoothed bootstrap test for multimodality (Silverman,1986; Efron, Tibshirani, 1998): Mode = a local maximum of PDF Bump = [a,b]: PDF concave over [a,b] and not over any larger interval

16 Model-free testing: The smoothed bootstrap test for multimodality (Silverman,1986; Efron, Tibshirani, 1998): Mode = a local maximum of PDF Bump = [a,b]: PDF concave over [a,b] and not over any larger interval Both, more than one mode and more than one bump in PDF are descriptive features indicating mixing of components. (Cox, 1966) Low significance of either of H 0 evidences complexity of f act (M)

17 TESTING PROCEDURE SCHEME EQ DATA RANDOMIZATION TESTING FOR MULTIMODALITY EVALUATING CRITICAL SMOOTHING FACTOR SMOOTH BOOTSTRAPPING ESTIMATING H 0 SIGNIFICANCE TEST CALIBRATION FITTING G-R DISTRIBUTION MODEL MC SIMULATIONS TESTS FOR MULTIMODALITY OF GENERATED DATA SETS CALIBRATED SIGNIFICANCE OF H 0

18 TESTING PROCEDURE SCHEME EQ DATA RANDOMIZATION TESTING FOR MULTIMODALITY EVALUATING CRITICAL SMOOTHING FACTOR SMOOTH BOOTSTRAPPING ESTIMATING H 0 SIGNIFICANCE TEST CALIBRATION FITTING G-R DISTRIBUTION MODEL MC SIMULATIONS TESTS FOR MULTIMODALITY OF GENERATED DATA SETS CALIBRATED SIGNIFICANCE OF H 0

19 DATA RANDOMIZATION To avoid a spurious complexity of f act (M) due to repetitions the observed magnitudes M obs are exponentially randomized within their round-off intervals δm (=0.1)

20 TESTING PROCEDURE SCHEME EQ DATA RANDOMIZATION TESTING FOR MULTIMODALITY EVALUATING CRITICAL SMOOTHING FACTOR SMOOTH BOOTSTRAPPING ESTIMATING H 0 SIGNIFICANCE TEST CALIBRATION FITTING G-R DISTRIBUTION MODEL MC SIMULATIONS TESTS FOR MULTIMODALITY OF GENERATED DATA SETS CALIBRATED SIGNIFICANCE OF H 0

21 TESTING PROCEDURE SCHEME EQ DATA RANDOMIZATION TESTING FOR MULTIMODALITY EVALUATING CRITICAL SMOOTHING FACTOR SMOOTH BOOTSTRAPPING ESTIMATING H 0 SIGNIFICANCE TEST CALIBRATION FITTING G-R DISTRIBUTION MODEL MC SIMULATIONS TESTS FOR MULTIMODALITY OF GENERATED DATA SETS CALIBRATED SIGNIFICANCE OF H 0

22 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor

23 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor Nonparametric, kernel PDF estimate:

24 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor Nonparametric, kernel PDF estimate: Its shape depends on h:

25 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor Nonparametric, kernel PDF estimate: Its shape depends on h:

26 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor Nonparametric, kernel PDF estimate: Its shape depends on h:

27 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor Nonparametric, kernel PDF estimate: Its shape depends on h:

28 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor Nonparametric, kernel PDF estimate: Its shape depends on h:

29 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor Nonparametric, kernel PDF estimate: Its shape depends on h:

30 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor Nonparametric, kernel PDF estimate: Its shape depends on h:

31 TESTING FOR MULTIMODALITY Evaluating critical smoothing factor Nonparametric, kernel PDF estimate: Its shape depends on h: Critical smoothing factor h cr :

32 TESTING FOR MULTIMODALITY Smooth bootstrapping

33 TESTING FOR MULTIMODALITY Smooth bootstrapping Smooth bootstrapping = Sampling from

34 TESTING FOR MULTIMODALITY Smooth bootstrapping Smooth bootstrapping = Sampling from Standard bootstrap:

35 TESTING FOR MULTIMODALITY Smooth bootstrapping Smooth bootstrapping = Sampling from Standard bootstrap: Smooth bootstrap: Silverman (1986): Efron, Tibshirani (1998):

36 TESTING FOR MULTIMODALITY Estimating H 0 significance R bootstrap samples

37 TESTING PROCEDURE SCHEME EQ DATA RANDOMIZATION TESTING FOR MULTIMODALITY EVALUATING CRITICAL SMOOTHING FACTOR SMOOTH BOOTSTRAPPING ESTIMATING H 0 SIGNIFICANCE TEST CALIBRATION FITTING G-R DISTRIBUTION MODEL MC SIMULATIONS TESTS FOR MULTIMODALITY OF GENERATED DATA SETS CALIBRATED SIGNIFICANCE OF H 0

38 TESTING PROCEDURE SCHEME EQ DATA RANDOMIZATION TESTING FOR MULTIMODALITY EVALUATING CRITICAL SMOOTHING FACTOR SMOOTH BOOTSTRAPPING ESTIMATING H 0 SIGNIFICANCE TEST CALIBRATION FITTING G-R DISTRIBUTION MODEL MC SIMULATIONS TESTS FOR MULTIMODALITY OF GENERATED DATA SETS CALIBRATED SIGNIFICANCE OF H 0

39 TEST CALIBRATION

40 Test result TEST CALIBRATION

41 Test result TEST CALIBRATION If H 0 were true how many samples like {M i } would result in?

42 TEST CALIBRATION Fitting G-R distribution model

43 TEST CALIBRATION Fitting G-R distribution model

44 TEST CALIBRATION Fitting G-R distribution model - supports H 0 - could underlay {M i }

45 TEST CALIBRATION

46 TEST CALIBRATION

47 TEST CALIBRATION

48 TEST CALIBRATION

49 TEST CALIBRATION p * vs ν : calibration curve

50 SELECTED RESULTS REGIONAL CATALOGS Southern-California earthquakes (SCSN Format EQ Catalog) 983 EQ-s from M 4.0 Area definition: Nordquist (1964) Sample selection: Knopoff (2000) Prob{H 01 : unimodality} = Prob{H 02 : one bump} = 0.091

51 SELECTED RESULTS REGIONAL CATALOGS Northern-California earthquakes: Northern California Earthquake Catalog and Phase Data (Northern California Seismic Network, U.S. Geological Survey, Menlo Park; Berkeley Seismological Laboratory, University of California, Berkeley ) lat>38 o 603 EQ-s from M 4.0 Prob{H 01 : unimodality} = Prob{H 02 : one bump} = 0.074

52 SELECTED RESULTS REGIONAL CATALOGS - GREECE

53 SELECTED RESULTS REGIONAL CATALOGS - GREECE Central Ionian Islands earthquakes: 1256 EQ-s from M 4.0 Prob{H 01 : unimodality} = Prob{H 02 : one bump} = 0.080

54 SELECTED RESULTS REGIONAL CATALOGS - GREECE Central Ionian Islands earthquakes: 1256 EQ-s from M 4.0 Prob{H 01 : unimodality} = Prob{H 02 : one bump} = Northern Aegean earthquakes: 744 EQ-s from M 4.0 Prob{H 01 : unimodality} = Prob{H 02 : one bump} = 0.076

55 SELECTED RESULTS REGIONAL CATALOGS - GREECE Central Ionian Islands earthquakes: 1256 EQ-s from M 4.0 Prob{H 01 : unimodality} = Prob{H 02 : one bump} = Northern Aegean earthquakes: 744 EQ-s from M 4.0 Prob{H 01 : unimodality} = Prob{H 02 : one bump} = Thessalia earthquakes: 104 EQ-s from M 4.0 Prob{H 01 : unimodality} = 0.51 Prob{H 02 : one bump} = 0.23

56 SELECTED RESULTS REGIONAL CATALOGS GREECE Central Ionian Islands earthquakes with aftershocks removed (Reasenberg, 1985): 595 EQ-s from M 4.0 Prob{H 01 : unimodality} = Prob{H 02 : one bump} = 0.062

57 SELECTED RESULTS WORLDWIDE CATALOGS Large (M 7.0), shallow (h 70km), worldwide earthquakes: combined Pacheco-Sykes catalog (698 EQ-s, Pacheco and Sykes, 1992) and Harvard CMTS catalogs: 821 EQ-s from Prob{H 01 : unimodality} = Prob{H 02 : one bump} = 0.096

58 SELECTED RESULTS WORLDWIDE CATALOGS Worldwide earthquakes: Harvard CMTS catalog 1825 EQ-s from Mw 6.0 Prob{H 01 : unimodality} = Prob{H 02 : one bump} <

59 CONCLUSIONS

60 CONCLUSIONS The smoothed bootstrap test for multimodality makes it possible to investigate the complexity of earthquake magnitude distribution without making any specific assumptions on the distribution model.

61 CONCLUSIONS The smoothed bootstrap test for multimodality makes it possible to investigate the complexity of earthquake magnitude distribution without making any specific assumptions on the distribution model. The test evidence that the earthquake magnitude distribution frequently neither follows the Gutenberg-Richter law nor is smoothly non-linear but it is complex. Regarding magnitudes, earthquake populations are often not homogeneous. Traces of the complexity are more distinct for the regional than the worldwide data.

62 CONCLUSIONS The smoothed bootstrap test for multimodality makes it possible to investigate the complexity of earthquake magnitude distribution without making any specific assumptions on the distribution model. The test evidence that the earthquake magnitude distribution frequently neither follows the Gutenberg-Richter law nor is smoothly non-linear but it is complex. Regarding magnitudes, earthquake populations are often not homogeneous. Traces of the complexity are more distinct for the regional than the worldwide data. The complexity of magnitude distribution has also important implications for probabilistic seismic hazard assessment. When the actual magnitude distribution is complex and nonlinear features occur in a large magnitude region, the use of the presently known magnitude distribution models may yield unacceptable inaccuracy of the hazard estimates.

63

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