Operational Earthquake Forecasting

Southern California Earthquake Center

Operational Earthquake Forecasting: Proposed Guidelines for Implementation

Thomas H. Jordan

Director, Southern California Earthquake Center

S33D-01, AGU Meeting

14 December 2010


Operational Earthquake Forecasting

• Seismic hazard changes with time

– Earthquakes release energy and suddenly alter the tectonic forces that

will eventually cause future earthquakes

• Statistical models of earthquake interactions can capture many of the short-term temporal and spatial features of natural seismicity

– Excitation of aftershocks and other seismic sequences

• Such models can use regional seismicity to estimate short-term

changes in the probabilities of future earthquakes

Authoritative information about the time dependence of

seismic hazards to help communities prepare for

potentially destructive earthquakes.


Operational Earthquake Forecasting

• What are the performance characteristics of current short-term

forecasting methodologies?

– Probability (information) gain problem

• How should forecasting methods be qualified for operational use?

– Validation problem

• How should short-term forecasts be integrated with long-term

forecasts?

– Consistency problem

• How should low-probability, short-term forecasts be used in

decision-making related to civil protection?

– Valuation problem

Authoritative information about the time dependence of

seismic hazards to help communities prepare for

potentially destructive earthquakes.


Supporting Documents

• Operational Earthquake Forecasting: State of Knowledge

and Guidelines for Implementation

– Final Report of the International Commission on Earthquake Forecasting for Civil Protection (T. H. Jordan, chair), Dipartimento

della Protezione Civile, Rome, Italy, 79 pp., December, 2010.

• Operational Earthquake Forecasting: Some Thoughts on

Why and How

– T. H. Jordan & L. M. Jones (2010), Seismol. Res. Lett., 81, 571-574,

2010.


Prediction vs. Forecasting

• An earthquake forecast gives a probability that a target

event will occur within a space-time domain

• An earthquake prediction is a deterministic statement

that a target event will occur within a space-time domain

RTP Alarm for California M 6.4,

15 Nov 2004-14 Aug 2005 Rupture Probability on San

Andreas System (WGCEP, 2007)

(Keilis-Borok et al., 2004)


Prediction vs. Forecasting

For operational purposes,

• deterministic prediction is only useful in a high-probability environment

• probabilistic forecasting can be useful in a low-probability environment

1.0

0.5

0 0 0.5 1.0

Earthquake Probability

Shannon E

ntr

opy

P > 0.8 P < 0.2


Operational Forecasting in California

• Organizations

– USGS - National Earthquake Prediction Evaluation Council (NEPEC)

– CalEMA - California Earthquake Prediction Evaluation Council (CEPEC)

• Operational forecasting tools

– Long-term models (WGCEP models; e.g., UCERF2)

– Short-term models (Reasenberg-Jones, STEP, ETAS, Agnew-Jones)

• Notification protocols

– Southern San Andreas Working Group (1991)

– California Integrated Seismic Network notifications

• For M 5 events, probability of M 5 aftershocks and expected

number of M 3 aftershocks


UCERF2 ratio of time-dependent to time-independent participation probabilities for M 6.7 (WGCEP, 2007)

Uniform California Earthquake Rupture Forecast (UCERF2)

0 1 2 3

1906

1857

1680

30-yr Gain

5-yr Gain

Probability Gain

UCERF2

UCERF2 1-day probability

for M > 7 Coachella rupture:

P = 3 x 10-5

Long-Term Earthquake Probability Models


- 1 hour 0 hour + 1 hour + 13 hours + 1 month + 2 months

Short-Term Earthquake Probability (STEP) Model

Probability of Exceeding MMI VI

http://earthquake.usgs.gov/earthquakes/step/

Gerstenberger et al. (2005)

2004 Parkfield Earthquake


+ 1 hour

Short-Term Earthquake Probability (STEP) Model


http://earthquake.usgs.gov/earthquakes/step/

Gerstenberger et al. (2005)

2004 Parkfield Earthquake

STEP 1-day probability gain

for MMI > VI shaking:

G > 100


Summary of Probability Gains

Method Gain

Factor

Pmax(3 day)

SAF-Coachella

Long-term renewal 1-2 1 x 10–4

Medium-term seismicity

patterns 2-4 2 x 10–4

Short-term STEP/ETAS 10-100 3 x 10–3

Short-term empirical

foreshock probability 100-1000 3 x 10–2

Prospectively

validated?

No

Yes

Yes

No

• The probability gains of short-term, seismicity-based forecasts can

be high, but the absolute probabilities of large earthquakes typically remain low, even in seismically active areas, such as California.



• W. H. Bakun et al., Parkfield, California, Earthquake Prediction Scenarios and

Response Plans. USGS OFR 87-192, 1987.

• Southern San Andreas Working Group, Short-Term Earthquake Hazard Assessment

for the San Andreas Fault in Southern California, USGS OFR 91-32, 1991.

* # instances

many

~10

2



• Earthquake forecasting in a “low-probability environment” is

already operational in California, and the dissemination of

forecasting products is becoming more automated

– Level-A probability threshold of 25% has never been reached

– Level-B threshold of 5-25% has been exceeded only twice (Joshua Tree and

Parkfield)

• However, procedures are deficient in several respects:

– CEPEC has generally relied on generic short-term earthquake probabilities or

ad hoc estimates calculated informally, rather than probabilities based on

operationally qualified, regularly updated seismicity forecasting systems

– Procedures are unwieldy, requiring the scheduling of meetings or telecons,

which lead to delayed and inconsistent alert actions

– How the alerts are used is quite variable, depending on decisions at different

levels of government and among the public


The Validation Problem

• To be fit for operational purposes, short-term

forecasting methods should demonstrate reliability and

skill with respect to long-term (e.g., time-independent) models

– Forecasting methods intended for operational use should be

scientifically tested against the appropriate data for reliability and skill,

both retrospectively and prospectively

• All operational models should be under continuous

prospective testing competing time-dependent models

– Collaboratory for the Study of Earthquake Predictability (CSEP)

provides the infrastructure for this testing


Los Angeles

Zurich

Tokyo

Wellington

GNS Science

Testing Center

Japan

New Zealand

ERI

Testing Center

Italy

EU

Testing Center

California

SCEC

Testing Center

Western Pacific

Testing Center

Upcoming

Testing Region

Upcoming

Global

Beijing

China

Testing Center

North-South

Seismic Belt

CSEP Testing Regions & Testing Centers


CSEP Evaluation of Short-Term Models in

the California Testing Region (Rhoades T-test, M 3)

IG = 0.3, PG = 1.35/eqk

IG = 2.6, PG = 13.5/eqk

Information gain per earthquake

Refe

ren

ce f

ore

cast

STEP Model


The Consistency Problem

• Spatiotemporal consistency is an important issue for dynamic risk

management, which often involves trade-offs among multiple

targets and time frames

• In lieu of physics-based forecasting, consistency must be

statistically enforced — a challenge because:

– Long-term renewal models are less clustered than Poisson

– Short-term triggering models are more clustered than Poisson

• Consistency can be lacking if long-term forecasts specify

background seismicity rates for the short-term models (e.g., STEP)

– Seismicity fluctuations introduced by earthquake triggering can occur on time

scales comparable to the recurrence intervals of the largest events

• Model development needs to be integrated across all time scales of

forecast applicability

– Approach adopted by WGCEP for development of UCERF3


The Valuation Problem

• Earthquake forecasts acquire value through their ability to influence

decisions made by users seeking to mitigate seismic risk and

improve community resilience to earthquake disasters

– Societal value of seismic safety measures based on long-term forecasts has

been repeatedly demonstrated

– Potential value of protective actions that might be prompted by short-term

forecasts is far less clear

• Benefits and costs of preparedness actions in high-gain, low-

probability situations have not been systematically investigated

– Previous work on the public utility of short-term forecasts has anticipated that

they would deliver high probabilities of large earthquakes (deterministic

prediction)


The Valuation Problem

• Economic valuation is one basis for prioritizing how to allocate the

limited resources available for short-term preparedness

– In a low-probability environment, only low-cost actions are justified

• Many factors complicate this rational approach

– Monetary valuation of life, historical structures, etc. is difficult

– Valuation must account for information available in the absence of forecast

– Official actions can incur intangible costs (e.g., loss of credibility) and benefits

(e.g., gains in psychological preparedness and resilience)

Cost-Benefit Analysis for Binary Decision-Making (e.g., van Stiphout et al., 2010)

Suppose cost of protection against loss L is C < L. If the short-term earthquake

probability is P, the policy that minimizes the expected expense E:

- Protect if P > C/L

- Do not protect if P < C/L

Then, E = min {C, PL}.


Recommendations

• Utilization of earthquake forecasts for risk mitigation and earthquake

preparedness should comprise two basic components

– Scientific advisories expressed in terms of probabilities of threatening events

– Protocols that establish how probabilities can be translated into mitigation actions and

preparedness

• Public sources of information on short-term probabilities should be

authoritative, scientific, open, and timely

– Authoritative forecasts, even when the absolute probability is low, can provide a

psychological benefit to the public by filling information vacuums that can lead to informal

predictions and misinformation

– Should continuously inform the public about the seismic situation, in accordance with social-

science principles for effective public communication of warnings

– Need to convey the epistemic uncertainties in the operational forecasts

• Alert procedures should be standardized to facilitate decisions at different

levels of government and among the public, based in part on objective analysis of costs and benefits

– Should also account for less tangible aspects of value-of-information, such as gains in

psychological preparedness and resilience


Conclusions

• Current short-term forecasting methodologies can provide nominal

(unvalidated) probability gains up to 100-1000

– Issue: unification of methodologies across temporal and spatial scales

• Operational forecasting procedures should be qualified for usage according

to three standards for “operational fitness”

– Quality: correspondence between the forecasts and actual earthquake behavior

– Consistency: compatibility of methods at different spatial or temporal scales

– Value: realizable benefits relative to costs incurred

• All operational forecasting models should be under continuous prospective

testing

– Issue: evaluation of operational forecasts in terms of ground motions

• Governments should develop and maintain an open source of authoritative,

scientific information about the short-term probabilities of future earthquakes

• keep the population aware of the current state of hazard

• decrease the impact of ungrounded information

• improve preparedness

– Issue: decision-making in a low-probability environment


How Should the Time-Dependent Forecasts be

Communicated to Decision-Makers?

Earthquake Rupture

Forecast

Attenuation

Relationship

Shaking

Intensity Loss

Hazard

P(IMk) P(IMk | Sn) P(Sn)

Risk

Probabilistic Seismic Hazard and Risk Analysis

P(Lk | IMk)

Here… … here… … or here?


+ 1 hour


http://pasadena.wr.usgs.gov/step

STEP Map for 2004 Parkfield Earthquake


LA region

CyberShake 1.0 Hazard Model (225 sites in Los Angeles region, f < 0.5 Hz)

• Uses an extended earthquake rupture

forecast – Source area probabilities

– Hypocenter distributions (conditional)

– Slip variations (conditional)

• Uses reciprocity to calculate ground

motions for ~440,000 events at each site – Psuedo-dynamic fault rupture

– 3D anelastic model of wave propagation

CyberShake hazard map

PoE = 2% in 50 yrs

CyberShake seismogram

Graves et al. (2010)


CyberShake as a Platform for Short-Term

Earthquake Forecasting

• Compute probability gain from Agnew & Jones (1991)

model. Example: G = 1000 for R 10 km

• Apply probability gain to CyberShake ruptures and re-

compute ground motion probabilities for short

interval following events. Example: 1 day

Los Angeles

Bombay Beach (M4.8)

Mar 24, 2009

Parkfield (M6.0)

Sept 28, 2004

Parkfield, 2004

Bombay

Beach, 2009

(see poster by Milner et al., S51A-1926)


End