Science of OEF Meeting

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Meeting Goals:

Building on our Potential Uses of OEF meeting and associated report, the goal of this meeting is to discuss and identify best available science (in the context of likely uses), and to construct a prioritized implementation plan for a California OEF.

Background and Meeting Strategy:

Having surveyed the range of possible OEF uses at a previous Powell Center meeting, we now move on to identifying the best available science, both for near-term deployments and for more advanced capabilities anticipated in the future. Because all models are only an approximation of the system, best available science must be ascertained in the context of specific uses. Also, and as emphasized at our previous meeting, it is important to identify and work directly with a set of early adopters, which for our planning purposes can be considered as follows:

  1. CEPEC and NEPEC

  2. CalEMA

  3. PG&E

  4. California Earthquake Authority (perhaps through the commercial loss modeling companies they rely upon)

Table 1, on the following page, lists various potential OEF products, ranging from a magnitude-probability distribution, to a full earthquake-rupture forecast, to various risk estimates.

Because OEF aims to “to help communities prepare for potentially destructive earthquakes” (Jordan et al., 2014), usefulness will be dictated by reliability and skill at larger magnitudes (e.g., M≥6.5 in California). Table 2 lists various informational constraints that could be utilized in forecasting such events. As indicated therein, an explicit goal of UCERF3-ETAS has been to incorporate finite faults, elastic rebound, and characteristic magnitude frequency distributions. Interestingly, we find that you cannot combine ETAS with finite faults without also including elastic rebound, and adding the latter in turn implies a need for characteristic magnitude-frequency distributions on faults. Considerable time will be spent discussing UCERF3-ETAS at this meeting, especially given the complexity of the model. However, we will also review and discuss both simpler and more advanced approaches (e.g., physics-based simulators).

We currently plan to have two follow up Powell Center meetings, one to address operationalization challenges and one on verification and validation.

Table 1. Potential products that could be produced either directly by an operational earthquake forecast system, or by further processing in downstream risk calculations. Each of these will constitute an estimate in that uncertainties will need to be quantified (taken from the Potential Uses of OEF report).



Magnitude Probability Distribution

The likelihood of having earthquakes of different magnitudes in a given area and for a specified timespan.

Spatial Distribution of Triggering

Specifying where triggered events are most likely to occur.

Fault Triggering Probabilities

A prioritized list of faults that could nucleate or participate in large triggered events, accounting for any elastic-rebound effects.

Full Earthquake Rupture Forecast (ERF)

Specifying the likelihood of all possible events (at some discretization level) for a given timespan.

Stochastic Event Sets

Synthetic catalogs of triggered events as implied by an ERF.

Hazard Estimates

The probability of exceeding hazard-related intensity measures (e.g., PGA, PGV, PGD, SA, MMI) as a function of time and space.

Sequence Duration

Time needed for some measure (e.g., earthquake rate) to drop back to some level

Scenario Earthquakes

Representative examples of earthquakes that could be triggered

Ground Deformation Probabilities

Forecast of future fault offsets (e.g., due to creep) and/or other types of ground deformation

Landslide Probabilities

The likelihood of triggering landslides

Liquefaction Probabilities

The likelihood of triggering liquefaction

-------------------------------------------- Hazard-Risk Separation Interface --------------------------------------------

Population and/or Infrastructure Exposed

The number of people, houses, commercial properties, schools, hospitals, etc. with a certain likelihood of experiencing certain shaking or other hazard thresholds


Risk estimates with respect to human physical health and survival.

Damage Level and Collapse Probability

Loss and collapse estimates with respect to built infrastructure.


Likelihood and length of disruption to business, power, water, waste disposal, telecom, and communication systems.

Inspection Priority or Concern Level

A customized and prioritized list of assets that may require attention (e.g., as provided by the ShakeCast system).



Table 2. Some of informational constraints (observed or model inferred) that one might use to forecast the probability of triggered earthquakes (taken from the UCERF3-ETAS documentation).

Observation or Model Inference



Global or regional triggering statistics (generic aftershock parameters)

Sequence-specific deviations from generic aftershock parameters

Spatial and temporal variation in aftershock parameters (within a sequence)


Spatial variation in long-term event rates (background rates)


Location of recent events (i.e., areas lighting up with microseismicity)

Proximity to active faults, especially with respect to triggering larger-magnitude events


The long-term magnitude-frequency distribution inferred for faults


Elastic-rebound implied stress on faults (e.g., time since last event relative to recurrence interval)


Dynamic or static stress changes imposed by any previous events



Transient deformation




Meeting Participants




Blanpied, Michael

USGS, Reston; NEPEC>

Dieterich, James

UC, Riverside>

Fialko, Yuri

UC, San Diego>

Field, Ned

USGS, Golden>

Gerstenberger, Matt

GNS New Zealand>

Gilchrist, Jacquelyn

UC, Riverside>

Gomberg, Joan

USGS, Seattle>

Hardebeck, Jeanne

USGS, Menlo>

Jordan, Thomas


Michael, Andrew


Milner, Kevin


Page, Morgan

USGS, Pasadena>

Powers, Peter

USGS, Golden>

Rhoades, David

GNS New Zealand>

Richards-Dinger, Keith

UC, Riverside>

Shaw, Bruce


Trugman, Daniel

UCSD grad student>

van der Elst, Nicholas

USGS, Pasadena>

Werner, Maximilian

U. Bristol>