Probabilistic Seismic Hazard Analysis

Probabilistic Seismic Hazard Analysis
Overview History Allin Cornell BSSA paper Rapid development since that time

Ground motion parameters Ground motion parameters
Probabilistic Seismic Hazard Analysis Overview Deterministic (DSHA) Assumes a single “scenario” Select a single magnitude, M Select a single distance, R Assume effects due to M, R Probabilistic (PSHA) Assumes many scenarios Consider all magnitudes Consider all distances Consider all effects Ground motion parameters Ground motion parameters

Ground motion parameters
Probabilistic Seismic Hazard Analysis Overview Probabilistic (PSHA) Assumes many scenarios Consider all magnitudes Consider all distances Consider all effects Why? Because we don’t know when earthquakes will occur, we don’t know where they will occur, and we don’t know how big they will be Ground motion parameters

Consists of four primary steps: 1. Identification and characterization of all sources 2. Characterization of seismicity of each source 3. Determination of motions from each source 4. Probabilistic calculations PSHA characterizes uncertainty in location, size, frequency, and effects of earthquakes, and combines all of them to compute probabilities of different levels of ground shaking

Uncertainty in source-site distance Need to specify distance measure Based on distance measure in attenuation relationship rhypo rseis rrup rjb Seismogenic depth Vertical Faults

Uncertainty in source-site distance Need to specify distance measure Based on distance measure in attenuation relationship Dipping Faults rhypo rseis rrup rjb=0 rseis & rrup rjb

Uncertainty in source-site distance Where on fault is rupture most likely to occur? Source-site distance depends on where rupture occurs

Uncertainty in source-site distance Where is rupture most likely to occur? We don’t know Source-site distance depends on where rupture occurs

Uncertainty in source-site distance Approach: Assume equal likelihood at any point Characterize uncertainty probabilistically rmin fR(r) rmax r rmin rmax pdf for source-site distance

Uncertainty in source-site distance Two practical ways to determine fR(r) Draw series of concentric circles with equal radius increment Measure length of fault, Li, between each pair of adjacent circles Assign weight equal to Li/L to each corresponding distance rmin rmax

Uncertainty in source-site distance Two practical ways to determine fR(r) Divide entire fault into equal length segments Compute distance from site to center of each segment Create histogram of source-site distance. Accuracy increases with increasing number of segments rmin rmax Linear source

Uncertainty in source-site distance Divide source into equal area elements Compute distance from center of each element Create histogram of source-site distance Areal Source

Uncertainty in source-site distance Divide source into equal volume elements Compute distance from center of each element Create histogram of source-site distance

Uncertainty in source-site distance Unequal element areas? Create histogram using weighting factors - weight according to fraction of total source area

Uncertainty in source-site distance Quick visualization of pdf? Use concentric circle approach - lets you “see” basic shape of pdf quickly

Characterization of maximum magnitude Determination of Mmax - same as for DSHA Empirical correlations Rupture length correlations Rupture area correlations Maximum surface displacement correlations “Theoretical” determination Slip rate correlations Also need to know distribution of magnitudes

Distribution of earthquake magnitudes Given source can produce different earthquakes Low magnitude - often Large magnitude - rare Gutenberg-Richter Southern California earthquake data - many faults Counted number of earthquakes exceeding different magnitude levels over period of many years

Distribution of earthquake magnitudes NM log NM M M

Distribution of earthquake magnitudes log lM Mean annual rate of exceedance lM = NM / T M

(recurrence interval)
Probabilistic Seismic Hazard Analysis Distribution of earthquake magnitudes log lM 0.01 100 yrs Return period (recurrence interval) TR = 1 / lM 0.001 1000 yrs log TR M

Distribution of earthquake magnitudes log lM 10a b Gutenberg-Richter Recurrence Law log lM = a - bM log TR M

Distribution of earthquake magnitudes Gutenberg-Richter Recurrence Law log lM = a - bM Implies that earthquake magnitudes are exponentially distributed (exponential pdf) Can also be written as ln lM = a - bM

Distribution of earthquake magnitudes Then lM = 10a - bM = exp[a - bM] where a = 2.303a and b = 2.303b. For an exponential distribution, fM(m) = b e-b m

Distribution of earthquake magnitudes Neglecting events below minimum magnitude, mo lm = n exp[a - b(m - mo)] m > mo where n = exp[a - b mo]. Then, fM(m) = b e-b (m-mo)

Distribution of earthquake magnitudes For worldwide data (Circumpacific belt), log lm = M M = 6 lm = 148 /yr TR = yr M = 7 lm = TR = 0.062 M = 8 lm = TR = 0.562 M = 12 lm = TR = 2.29 M > 12 every two years?

Distribution of earthquake magnitudes Every source has some maximum magnitude Distribution must be modified to account for Mmax Bounded G-R recurrence law

Distribution of earthquake magnitudes Every source has some maximum magnitude Distribution must be modified to account for Mmax Bounded G-R recurrence law log lm Bounded G-R Recurrence Law Mmax M

Distribution of earthquake magnitudes Characteristic Earthquake Recurrence Law Paleoseismic investigations Show similar displacements in each earthquake Inividual faults produce characteristic earthquakes Characteristic earthquake occur at or near Mmax Could be caused by geologic constraints More research, field observations needed

Distribution of earthquake magnitudes log lm Characteristic Earthquake Recurrence Law Seismicity data Geologic data Mmax M

Predictive relationships Standard error - use to evaluate conditional probability log lm ln Y P[Y > Y*| M=M*, R=R*] Y = Y* ln Y M = M* R = R* log R Mmax M

Predictive relationships Standard error - use to evaluate conditional probability ln Y P[Y > Y*| M=M*, R=R*] ln Y Y = Y* M = M* R = R* log R M

Temporal uncertainty Poisson process - describes number of occurrences of an event during a given time interval or spatial region. 1. The number of occurrences in one time interval are independent of the number that occur in any other time interval. 2. Probability of occurrence in a very short time interval is proportional to length of interval. 3. Probability of more than one occurrence in a very short time interval is negligible.

Temporal uncertainty Poisson process where n is the number of occurrences and m is the average number of occurrences in the time interval of interest.

Temporal uncertainty Poisson process Letting m = lt Then

Temporal uncertainty Poisson process Consider an event that occurs, on average, every 1,000 yrs. What is the probability it will occur at least once in a 100 yr period? l = 1/1000 = 0.001 P = 1 - exp[-(0.001)(100)] =

Temporal uncertainty What is the probability it will occur at least once in a 1,000 yr period? P = 1 - exp[-(0.001)(1000)] = 0.632 Solving for l,

Temporal uncertainty Then, the annual rate of exceedance for an event with a 10% probability of exceedance in 50 yrs is The corresponding return period is TR = 1/l = 475 yrs. For 2% in 50 yrs, l = /yr TR = 2475 yrs

Summary of uncertainties Location Size Effects Timing fR(r) fM(m) P[Y > Y*| M=M*, R=R*] P = 1 - e-lt Source-site distance pdf Magnitude pdf Attenuation relationship including standard error Poisson model

Combining uncertainties - probability computations P[A] = P[A] = P[A|B1]P[B1] + P[A|B2]P[B2] + … + P[A|BN]P[BN] U P[A B1] + P[A B2] + … + P[A BN] B1 B2 B3 B4 B5 A Total Probability Theorem

Combining uncertainties - probability computations Applying total probability theorem, where X is a vector of parameters. We assume that M and R are the most important parameters and that they are independent. Then,

Combining uncertainties - probability computations Above equation gives the probability that y* will be exceeded if an earthquake occurs. Can convert probability to annual rate of exceedance by multiplying probability by annual rate of occurrence of earthquakes. where n = exp[a - bmo]

Combining uncertainties - probability computations If the site of interest is subjected to shaking from more than one site (say Ns sites), then For realistic cases, pdfs for M and R are too complicated to integrate analytically. Therefore, we do it numerically.

Combining uncertainties - probability computations Dividing the range of possible magnitudes and distances into NM and NR increments, respectively This expression can be written, equivalently, as

Combining uncertainties - probability computations What does it mean? All possible distances are considered - contribution of each is weighted by its probability of occurrence All possible magnitudes are considered - contribution of each is weighted by its probability of occurrence All sites are considered All possible effects are considered - each weighted by its conditional probability of occurrence

Combining uncertainties - probability computations NM x NR possible combinations Each produces some probability of exceeding y* Must compute P[Y > y*|M=mj,R=rk] for all mj, rk m1 m2 r1 m3 mNM rNR

Combining uncertainties - probability computations Compute conditional probability for each element on grid Enter in matrix (spreadsheet cell) log R ln Y M=m2 r1 Y = y* P[Y > y*| M=m2, R=r2] r2 r3 rN P[Y > y*| M=m2, R=r1] P[Y > y*| M=m2, R=r3]

Combining uncertainties - probability computations “Build” hazard by: computing conditional probability for each element multiplying conditional probability by P[mj], P[rk], ni Repeat for each source - place values in same cells m1 m2 r1 m3 mNM rNR P[Y > y*| M=m2, R=r1] P[Y > y*| M=m2, R=r3] P[Y > y*| M=m2, R=r2]

Combining uncertainties - probability computations When complete (all cells filled for all sources), Sum all l-values for that value of y* ly* m1 m2 r1 m3 mNM rNR P[Y > y*| M=m2, R=r1] P[Y > y*| M=m2, R=r3] P[Y > y*| M=m2, R=r2]

Combining uncertainties - probability computations Choose new value of y* Repeat entire process Develop pairs of (y*, ly*) points Plot m1 m2 r1 m3 mNM rNR P[Y > y*| M=m2, R=r1] P[Y > y*| M=m2, R=r3] P[Y > y*| M=m2, R=r2] Seismic Hazard Curve log ly* log TR y*

Combining uncertainties - probability computations Seismic hazard curve shows the mean annual rate of exceedance of a particular ground motion parameter. A seismic hazard curve is the ultimate result of a PSHA. log lamax log ly* log TR log TR amax y*

Using seismic hazard curves amax=0.30g log TR log lamax 0.001 Probability of exceeding amax = 0.30g in a 50 yr period? P = 1 - e-lt = 1 - exp[-(0.001)(50)] = = 4.9% In a 500 yr period? P = = 39.3%

Using seismic hazard curves What peak acceleration has a 10% probability of being exceeded in a 50 yr period? 10% in 50 yrs: l = or TR = 475 yrs Use seismic hazard curve to find amax value corresponding to l = log lamax log TR 0.0021 475 yrs amax=0.21g

Using seismic hazard curves Contribution of sources Can break l-values down into contributions from each source Plot seismic hazard curves for each source and total seismic hazard curve (equal to sum of source curves) Curves may not be parallel, may cross Shows which source(s) most important Total log lamax 2 log TR 1 3 amax

Using seismic hazard curves amax, Sa log TR log lamax Can develop seismic hazard curves for different ground motion parameters Peak acceleration Spectral accelerations Other Choose desired l-value Read corresponding parameter values from seismic hazard curves

0.1 2% in 50 yrs Peak acceleration Crustal 0.01 lamax Intraplate 0.001 Interplate 0.0001

0.1 2% in 50 yrs Sa(T = 3 sec) 0.01 Crustal lamax Intraplate 0.001 Interplate 0.0001

Uniform hazard spectrum (UHS) Find spectral acceleration values for different periods at constant l All Sa values have same l-value same probability of exceedance Sa T Uniform Hazard Spectrum

Disaggregation (De-aggregation) Common question: What magnitude & distance does that amax value correspond to? 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 25 km 75 km 100 km 125 km 150 km 175 km 200 km 50 km 0.01 0.02 0.03 0.04 0.05 0.00 0.06 0.09 0.08 Total hazard includes contributions from all combinations of M & R.

Disaggregation (De-aggregation) Common question: What magnitude & distance does that amax value correspond to? 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 25 km 75 km 100 km 125 km 150 km 175 km 200 km 50 km 0.01 0.02 0.03 0.04 0.05 0.00 0.06 0.09 0.08 Total hazard includes contributions from all combinations of M & R. Break hazard down into contributions to “see where hazard is coming from.” M=7.0 at R=75 km

Disaggregation (De-aggregation) USGS disaggregations Seattle, WA 2% in 50 yrs (TR = 2475 yrs) Sa(T = 0.2 sec)

Disaggregation (De-aggregation) USGS disaggregations Olympia, WA 2% in 50 yrs (TR = 2475 yrs) Sa(T = 0.2 sec)

Disaggregation (De-aggregation) USGS disaggregations Olympia, WA 2% in 50 yrs (TR = 2475 yrs) Sa(T = 1.0 sec)

Disaggregation (De-aggregation) Another disaggregation parameter For low y*, most e values will be negative For high y*, most e values will be positive and large e = -1.6 ln Y M=m2 e = -0.8 e = 1.2 e = 2.2 ln Y Y = y* r1 r2 log R r3 rN

Logic tree methods Not all uncertainty can be described by probability distributions Most appropriate model may not be clear Attenuation relationship Magnitude distribution etc. Experts may disagree on model parameters Fault segmentation Maximum magnitude

Logic tree methods Attenuation Model Magnitude Distribution Mmax 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) G-R (0.7) BJF (0.5) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) Char. (0.3) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) G-R (0.7) A & S (0.5) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) Char. (0.3)

Logic tree methods Attenuation Model Magnitude Distribution Mmax 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) Sum of weighting factors coming out of each node must equal 1.0 G-R (0.7) BJF (0.5) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) Char. (0.3) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) G-R (0.7) A & S (0.5) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) Char. (0.3)

Logic tree methods Attenuation Model Magnitude Distribution Mmax 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) G-R (0.7) BJF (0.5) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) Char. (0.3) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) G-R (0.7) 0.5x0.7x0.2 = 0.07 A & S (0.5) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) Char. (0.3)

Logic tree methods Attenuation Model Magnitude Distribution w Mmax 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) 0.07 0.21 G-R (0.7) BJF (0.5) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) 0.03 0.09 Char. (0.3) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) 0.07 0.21 Final value of Y is obtained as weighted average of all values given by terminal branches of logic tree G-R (0.7) A & S (0.5) 7.0 (0.2) 7.2 (0.6) 7.5 (0.2) 0.03 0.09 Char. (0.3)

Logic tree methods Recent PSHA logic tree included: Cascadia interplate 2 attenuation relationships 2 updip boundaries 3 downdip boundaries 2 return periods 4 segmentation models 2 maximum magnitude approaches 192 terminal branches

Logic tree methods Recent PSHA logic tree included: Cascadia intraplate 2 intraslab geometries 3 maximum magnitudes 2 a-values 2 b-values 24 terminal branches

Logic tree methods Recent PSHA logic tree included: Seattle Fault and Puget Sound Fault 2 attenuation relationships 3 activity states 3 maximum magnitudes 2 recurrence models 2 slip rates 72 terminal branches for Seattle Fault 72 terminal branches for Puget Sound Fault

Logic tree methods Recent PSHA logic tree included: Crustal areal source zones 7 source zones 2 attenuation relationships 3 maximum magnitudes 2 recurrence models 3 source depths 252 terminal branches Total PSHA required analysis of 612 combinations

Probabilistic Seismic Hazard Analysis

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