shakealert testing procedure discussion philip maechling 26 march 2010 1

ShakeAlert Testing Procedure Discussion

Philip Maechling

26 March 2010

SCEC has the opportunity to define a testing approach for the CISN ShakeAlert System.– Testing approach should be consistent with USGS

interests in the ShakeAlert System.– CTC effort should provide a longitudinal study of

ShakeAlert Capabilities– Science-oriented testing focus (rather than engineering

focus) is more consistent CSEP model– CTC effort provides SCEC with an opportunity to

demonstrate the general capabilities of CSEP infrastructure other problems.

ShakeAlert Testing

CTC plan must be implemented within funded level of effort approximately 12 hours per month.– SCEC should establish scientific framework for

ShakeAlert Testing– Initial testing approach should be simple– Initial testing should provide value to USGS and

ShakeAlert developers– Initial Testing should communicate value of EEW testing

to SCEC community and CISN

Scale of SCEC CTC Activity

Bridging the gap between science and engineering: avenues for collaborative research

Christine Goulet, PhDSr Geotechnical Engineer, URS

Lecturer, UCLA

christine_goulet@urscorp.com

2009 Annual Meeting: Palm Springs, CA

Conclusion

• Collaboration is an outcome-driven process (mission, vision, etc.)

• We can benefit from collaboration if we commit toSpend time and effort in the processKeep an open mindKeep a eye on the goal

• Benefit for engineersA better understanding and integration of seismological

phenomena = better design

• Benefit for scientistsThe application and dissemination of their results into the

built world = greater impact

On collaboration

Collaboration is a process through which people work together, pooling their ressources to achieve a shared desired result or outcome.

The collaboration process:• Involves a catalyst (common interest, reaction to an event)• Provides a broader insight into a problem and its potential

solutions• Allows a knowledge transfer by which each participant’s

specialty benefits the group (knowledge optimization) • Gives access to new problems and ideas

Successful collaboration requires: • Effective communication• A clearly defined goal or vision

Collaboration is an outcome-driven process

On communication

To communicate is human…

…it does not mean we’re naturally good at it.

Key elements for a better communication:• Sharing a common language• Saying what you mean• Developing improved active listening skills • Using feedback techniques (“What I understood is… Is this

correct?”)• Keeping an open mind

A shared vision?

Scientists Engineers

Interest

Goal/desired outcome

Earthquakes

Understanding Design a product

Interface(s)

• Source effects Fault mechanism,

magnitude and location Recurrence models

• Travel paths

• Site effects Wave propagation to the

surface Basin effects Topographic effects Directivity

• Structural response Including foundation

• Loss analysis

Geologists &SeismologistsSeismologists &

Engineers

Geotechnical Engineers &Seismologists

Geotechnical &Structural Engineers

Engineers, loss modelers

Establish Testing Emphasis with USGS and CISN Development Groups

ShakeAlert Forecast Evaluation Problems:– Scientific publications provide insufficient information for

independent evaluation– Data to evaluate forecast experiments are often

improperly specified– Active researchers are constantly tweaking their codes

and procedures, which become moving targets– Difficult to find resources to conduct and evaluate long

term forecasts– Standards are lacking for testing forecasts against

reference observations

Problems in Assessing Forecasts

SCEC Annual Meeting, Palm Springs, Sept. 14-16, 2009

Warner Marzocchi

INGV, Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy

In collaboration with: Anna Maria Lombardi (INGV), Gordon Woo (RMS), Thomas van Stiphout (ETH), Stefan Wiemer (ETH)

Long- and short-term operational earthquake forecasting in Italy: the case of the April 6, 2009, L'Aquila

earthquake

Long- and short-term operational earthquake forecasting in Italy: the case of the April 6, 2009, L'Aquila

earthquake

Design of Testing Experiment

The EEW tests we implement should be valid for CISN and any other EEW implementation including commercial systems and community contribution-based systems.

Additional Goal for Testing

Many CSEP testing principles are applicable to CISN EEW Testing. The following definitions need to be made to evaluate forecasts:– Exact definition of testing area– Exact definition of a forecast– Exact definition of input data used in forecasts– Exact definition of reference observation data– Measures of success for forecasts

Design of an Experiment

Design of EEW Science Testing introduces elements that CSEP has not had to consider– Must decide whether to test both forecast and “alerts”– Different algorithms produce different forecasts

• Some (e.g. On-site) produce site-specific information (PGA), event magnitude, but no origin time or distance to event

• Some (e.g. Vs) produces full event parametric information.• Some (e.g. Elarms) produce site specific ground motion

estimates on a regular grid.• Some produce single values (On-site)• Some produce time-series with updates (Vs,Elarms)

Design of EEW Science Testing introduces elements that CSEP has not had to consider– More difficult to determine information used in forecast

especially with Bayesian approach is fully implemented– More difficult to determine what data is used in forecast

at any time.– Time-basis of forecast (forecast term e.g. 60 second …1

second) varies by event– Greater interest in summary of performance on an event

by event basis. Should support push-based distribution of results after significant events.

Example of stations that could contribute to forecasts.

The 1-day forecasts (the palette represents the rate of M 4+)Daily forecasts released at 8:00 AM (no overlaps)

Testing the forecasts (using M 2.5+ events)

N-test Spatial test

2. GMPE prediction, distance-scaling term

Image: J. Stewart, L. Star

1 10 100

Rrup (km)

CB (2008)PGA Original

SA,T=1s Original

SA,T=10s Original

Strike-slip EQVS30=540m/s

Propose Time Dependent tests as forecasts before origin (or peak ground motion at site)– Could produce a peak ground motion map at origin time

and later. Forecasts produce ground motion maps and any regions that have not received peak ground motion contribute to the forecast. Series of forecast maps for each algorithm as they produce them. Any regions in any maps that have not experienced their time of PGV is credited. Map regions will fall over time eventually reaching zero forecasts to be evaluated for the event.

– For next test maybe we can ignore whether sites receive a warning.

– Plot the forecast by time like slide 15 with improvement in forecast with shorter forecast times.

• First test is to reproduce the ShakeMap

• Map of reporting stations used in Shakemap

Propose Time Dependent tests as forecasts before origin (or peak ground motion at site)– Introduce the use of first provided estimate as important

measure. – Introduce use of announcers as a new system that

provides forecasts. Announcers would be easy to add and easy to remove.

– Which side of the interface is the probability set? They provide forecasts and probabilities, or do we set tests at probability level and let them figure out whether it meets the specified level.

Point to bring home on short-term forecasts

We perform daily aftershock forecasts in real-time. From the test on the first months, the forecast seems well calibrated, describing correctly the space-time evolution of the aftershock sequence.

The same model (retrospectively) detected an increase in probability before the main event; the (daily) probability did not reach a value of 1%.

The Challenge is for scientists to articulate uncertainty without losing credibility and to give public officials the information they need for decision-making

Scientists

Public officials

this requires to bridge the gap between scientific output (probability) and the boolean logic (YES-NO) of decision-makers

Introducing the problem

Design of EEW Science Testing introduces elements that CSEP has not had to consider– CISN seems to be distinguishing event module

(produces event parameters) and user module which produces site-specific ground motion estimates

– User modules are likely to vary by tolerance for false alarms and by conversion from location/magnitude to site-specific ground motion estimates.

– I recommend we make it easy to add new forecast sources, and remove old ones so that we can support experimentation on forecasters by CISN.

New Waveform Processing LibraryAlgorithm Code Memory

buffersImport from Delays

On-site algorithm compact internal Multicast Network or

Earthworm

< 0.01 seconds

Virtual Seismologist

compact internal Waveform Data Area (WDA)

3-5 seconds

ElarmS 4 modules + ElarmS program

shared Waveform Data Area (WDA)

3-5 seconds + delays caused by

writing/ reading to shared memory

buffers

Development of a new Waveform Processing Library (based on the same idea already used by the On-site algorithm): The old framework used GCDA (Generic Continuous Data Area) to store waveforms which slowed down the read/write access to the waveforms and overall processing thread. To avoid that problem the new version will use internal memory buffers and work in a single process multi-threaded environment.

Decision Module (DM)

• The Decision Module is expected to - receive short, independent messages from the three Event Detectors - be running on different machines than the Event Detectors.

The passing of messages between the three Event Detectors to the DM as well as the broadcast of the outputs of the DM to users will likely be based on

Apache ActiveMQ (public-subscribe messaging system; asynchronous message passing and persistent message storage).

• Preliminary API is almost finished• challenging: association & up-dates of messages

• up-date DM event, if possible; if misfit is too large, disassociate all messages of the event and create a new DM event (similar to Binder)

• requires that the On-site algorithm provides eventIDs (done)

- most probable… Mw

… location… origin time… ground motion and uncertainties

- probability of false trigger, i.e. no earthquake

- CANCEL message if needed

Bayesian approachup-dated with time

Decision Module(Bayesian)

τc-Pd

On-site Algorithm

(VS)ElarmS

Single sensor Sensor network Sensor network

Task 1: • increase reliability

CISN ShakeAlert

USER Module- Single site warning- Map view

CISN EEW Testing Center Test users

Task 1: • increase reliabilityTask 2: • demonstrate &

enhance

• predicted and observed ground motions

• available warning time• probability of false alarm•…

Decision Module(Bayesian)

CISN ShakeAlertτc-Pd

On-site Algorithm

(VS)ElarmS

Single sensor Sensor network Sensor network

Methodology development

slide courtesy of Holly Brown

Presented 23 June 2009 at

Joint Meeting of MeteoAlarmand the

WIS CAP Implementation Workshop on Identifiers

by Eliot Christian <echristian@wmo.int>

Identifiers and the Common Alerting

Protocol (CAP)

World Meteorological Organization (WMO)Observing and Information Systems Department

WMO Information System (WIS)

June 23, 2009Common Alerting Protocol (CAP) 35

Outline

What is CAP? Why and How would

MeteoAlarm use CAP? What are the issues with

Identifiers?

What is CAP?

The Common Alerting Protocol (CAP) is a standard message format designed for All-Media, All-Hazard, communications: over any and all media (television, radio,

telephone, fax, highway signs, e-mail, Web sites, RSS "Blogs", ...)

about any and all kinds of hazard (Weather, Fires, Earthquakes, Volcanoes, Landslides, Child Abductions, Disease Outbreaks, Air Quality Warnings, Beach Closings, Transportation Problems, Power Outages, ...)

to anyone: the public at large; designated groups (civic authority, responders, etc.); specific people

Structure of a CAP AlertCAP Alert messages

contain: Text values for human

readers, e.g., "headline", "description", "instruction", "area description", etc.

Coded values useful for filtering, routing, and automated translation to human languages

Filtering and Routing Criteria

Date/Time Geographic Area

(polygon, circle, geographic codes)

Status (Actual, Exercise, System, Test)

Scope (Public, Restricted, Private)

Type (Alert, Update, Cancel, Ack, Error)

Filtering and Routing Criteria

Event Categories (Geo, Met, Safety, Security, Rescue, Fire, Health, Env, Transport, Infra, Other)

Urgency: Timeframe for responsive action (Immediate, Expected, Future, Past, Unknown)

Severity: Level of threat to life or property (Extreme, Severe, Moderate, Minor, Unknown)

Certainty: Probability of occurrence (Very Likely, Likely, Possible, Unlikely, Unknown)

Typical CAP-based Alerting System

http://www.weather.gov/alerts

Existing proposals for EEW Testing Agreements

We propose that initial CTC testing supports science groups first, engineering second.

– Accuracy and timeliness of event-oriented parameters (location, magnitude)

– Accuracy and timeliness of ground motion forecasts (pgv, psa, intensity) for both site-specific and grid-based site specific forecasts

Are the 3 CSEP regions valid for EEW ?

• Region Under Test• Catalog Event Region• Buffer to avoid catalog issues

Caltech Tauc-Pd RT/AL:

For each triggered station ≤ Dist-max, send one alert of:– M-est with Talert and Talgorithm– PGV-est with Talert and Talgorithm

For each M ≥ M-min, send one alert of:– Number of reporting and non-reporting stations ≤ Dist-max as a function of Talert

and Talgorithm

UC Berkeley ElarmS RT and ETH VS:

For each triggered event, send one alert of:– M-est as a function of Talert– Loc-est as a function of Talert– PGA-est at each station ≤ Dist-max without S-wave arrival as a function of Talert– PGV-est at each station ≤ Dist-max without S-wave arrival as a function of Talert

• Number of reporting and non- reporting stations ≤ Dist-max as a function of Talert

Input to forecasts are based on CISN real-time data

– If system performance (e.g. missed events) are to be evaluated, CTC will need station-list in use at any time

– Existing CISN often has problems keeping track of which stations are being used in forecasts

Two authorized data sources have been integrated into the current CTC:

– ANSS Catalog• Earthquake Catalog

– ShakeMap Shake_RssReader • Event-based Observed Ground Motions delivered in

Stationlist.xml files

Summary Reports for each M ≥ M-min: Key documents is 3 March 2008 document which specifies six types of tests.

– Summary 1: Magnitude– Summary 2: Location– Summary 3: Ground Motion– Summary 4: System Performance– Summary 5: False Triggers– Summary 6: Missed Triggers

Proposed Performance Measures

Design of Testing Experiment

Use CSEP Forecast Groups to Test different EEW information.

– Event Parameters• Magnitude• Location

– Site-specific Parameters:• Site specific ground motion intensity

Forecast Groups for different EEW Forecasting Systems.

– Event Parameters• Magnitude• Location

– Site-specific Parameters:• Site specific ground motion intensity

Forecast Group

Forecast Producer Example Forecasters

Forecast Parameters

T1 P-wave detector Commercial Alarm Peak Site Intensity

T2 On-Site Commercial Alarm, On-Site

Magnitude,Peak Site Intensity

T3 Event Parameter System

Network System Location, Magnitude

T4 Event Parameter System with User Module

Network System feeding User Modules

Location, Magnitude, Grid-based Peak Site Intensities

Summary 1.1: Magnitude X-Y Diagram

Measure of Goodness: Data points fall on diagonal line

Relevant: T2,T3,T4

Drawbacks: Timeliness element not represented

Which in series of magnitude estimates should be used in plot.

Experiment Design

Summary 1.2: Initial magnitude error by magnitude

Measure of Goodness: Data points fall on horizontal line

Relevant: T2,T3,T4

Experiment Design

Summary 1.3: Magnitude accuracy by update

Relevant: T3,T4

Experiment Design

Summary 2.1: Cumulative Location Errors

Measure of Goodness: Data points fall on vertical zero line

Relevant: T3, T4

Drawbacks: Does not consider magnitude accuracy or timeliness

Summary 2.2: Magnitude and Location error by time after origin

Measure of Goodness: Data points fall on horizontal zero line

Relevant: T3, T4

Drawbacks: Event-specific not cumulative

Experiment Design

Summary 3.1 : Intensity Map Comparisons

Measure of Goodness: Forecast map matches observed map

Relevant: T4

Drawbacks: Not a quantitative results

Summary 3.2: Intensity X-Y Diagram

Measure of Goodness: Data points fall on diagonal line

Relevant: T1,T2,T4

Which in series of intensity estimate should be used in plots T3.

Experiment Design

Summary 3.3: Intensity Ratio by Magnitude

Relevant: T1,T2,T4

Which intensity estimate in series should be used in plot.

Experiment Design

Summary 3.3: Predicted to Observed Intensity Ratio by Distance and Magnitude

Relevant: T1,T2,T4

Which intensity estimate in series should be used in plot.

Summary 3.3: Evaluate Conversion from PGV to Intensity

Group has proposed to evaluate algorithms by comparing intensities and they provide a formula for conversion to Intensity.

Summary 3.4: Evaluate Conversion from PGV to Intensity

Group has proposed to evaluate algorithms by comparing intensities and they provide a formula for conversion to Intensity.

Experiment Design

Summary 3.5: Statistical Error Distribution for Magnitude and Intensity

Measure of Goodness: No missed events or false alarms in testing area

Relevant: T4

Drawbacks:

Experiment DesignSummary 3.6: Mean-time to

first location or intensity estimate (small blue plot)

Measure of Goodness: Peak of measures at zero

Relevant: T1,T2,T3,T4

Drawbacks: Cumulative and does not involve accuracy of estimates

Experiment Design

No examples for System Performance Summary defined as

Summary 4.1: Ratio of reporting versus non-reporting stations:

Experiment Design

Summary 5.1: Missed event and False Alarm Map

Relevant: T3, T4

Drawbacks: Must develop definitions for missed events and false alarms, Does not reflect timeliness

Experiment Design

Summary 5.2: Missed event and False Alarm Map

Relevant: T3, T4

Drawbacks: Must develop definitions for missed events and false alarms, Does not reflect timeliness

Experiment DesignSummary 6.1: Missed

Event map

Measure of Goodness: No missed events in testing region

Relevant: T3, T4

Drawbacks: Must define missed event. Does not indicate timeliness

SCEC: An NSF + USGS Research Center

Application of the CSEP Testing Approach to Earthquake Early Warning and other Seismological Forecasts

Philip MaechlingInformation Technology ArchitectSouthern California Earthquake Center (SCEC)24 September 2009

Premise: EEW In California Is Imminent

EEW in Use in Japan - JMA Issued Ground Motion Alerts

EEW in Use in Japan – Emerging commercial market for ground motion alarms

Testing of Earthquake Forecast and Earthquake Early Warning is often Retrospective without

Comparison to other Approaches

Can we Apply the CSEP Testing Approach to other Seismological Forecasts?

CISN and SCEC recently received funding from USGS to develop and evaluate prototype network-based EEW:

CISN Earthquake Early Warning (EEW) Testing Center which evaluates the system and seismological performance of the CISN real-time earthquake monitoring system.

Discussions at SCEC Annual Meeting about Needed Test Center:

Ground Motion Modeling Testing Center which verifies and validates 3D wave propagation simulations by comparing observational data against synthetic seismograms.

Testing Center System Requirements

The goals of both an EEW and Earthquake Forecast Testing Center Goals (as outlined by Schorlemmer and Gerstenberger (2007)) describe what is needed to build trust in results:

Controlled EnvironmentTransparencyComparabilityReproducibility

Applying CSEP Style Testing To Other Seismological Forecasts

CSEP collaboration has worked to define how short term earthquake forecast models can produce comparable results.

– Define standard problems– Define standard forecast definition– Define standard regions under test– Define standard evaluation criteria– Testing performed independent of forecast developers

CSEP testing approach helps to build acceptance and trust in forecast evaluations through its independent and transparent testing approach.

We believe that other seismological forecasting groups can benefit from CSEP testing approach including:

(a) Earthquake Early Warning (EEW) forecasts of final magnitude or peak ground intensity.

(b) Computer modeling of 3D earthquake wave propagation which produces synthetic seismograms.

SCEC3 Organization SCEC Director

Board of Directors

Planning Committee

External Advisory Council

CEO Program

Earthquake Geology

Tectonic Geodesy

Seismology

Fault & RuptureMechanics

EarthquakeForecasting &Predictability

LithosphericArchitecture & Dynamics

Crustal Deformation Modeling

Unified Structural Representation

Seismic Hazard& Risk Analysis

Public Outreach

K-12 & InformalEducation

PetaShake

PetaSHA-1

PetaSHA-2

Special ProjectsDisciplinaryCommittees Focus Groups CEO Activities

USEIT/SUREIntern Programs

BroadbandPlatform

CenterAdministration

InformationArchitect

KnowledgeTransfer

Ground MotionPrediction

Earthquake EarlyWarning

CSEP ACCESS Forum

PetaShake

PetaSHA-1

PetaSHA-2

BroadbandPlatform

Earthquake Early Warning

California Integrated Seismic Network (CISN) Earthquake Early Warning Evaluation

• Funded by USGS NEHRP – $120K over 3 years (ending 2012)

• Science thrust areas:– CISN Development of a single integrated

Real-time Earthquake Alerting system– Evaluation of system performance

• Computer science objectives– Unified CISN EEW system– Independent testing and analysis

Testing of EEW and STEF use Similar Science Techniques

Comparison between algorithms encourages scientists to produce a results in a common and comparable format:

• CSEP:

– e.g. RELM testing region defined for testing

– CSEP Standard Grid and forecast statement

– Standard evaluation test (N,L,R tests)

• EEW:

– PGA or PGV converted to Intensity for comparison

– Defined evaluation tests (CISN EEW document March 2008)

Earthquake Catalog

Retrieve Data

FilterCatalog

Filtered Earthquake

Catalog

Earthquake Forecast

Evaluation of Earthquake Predictions

Earthquake Catalog

Forecast EQs

Evaluate Forecast

Evaluation of CSEP Forecasts

CSEP Collaboratory

Earthquake Catalog

Retrieve Data

FilterCatalog

Filtered Earthquake

Catalog

CISN EEW Performance Summary Processing

CISN EEW Testing Center and Web Site

ANSS Earthquake

Catalog

UCB/ElarmSNIEEW Data Source

CIT/OnSite EEW Data Source

Load Reports

EEW Trigger

Reports

EEW Trigger

Reports

Observed ANSS Data

CISN EEW Trigger Data

Produce Web

Summaries

CSEP Evaluation of two one day forecasts STEP and ETA using R (log likelihood ratio) Test

EEW Testing Center Provides On-going Performance Evaluation

Can CSEP Be Adapted to Support Ground Motion Synthetics

Synthetic Seismograms are in use by engineering communities:

• Development of hybrid attenuation relationships

• Seismograms for studying Tall Building Response to Strong Ground Motions

• Probabilistic Seismic Hazard Maps using 3D wave propagation as Ground Motion Prediction Equation (GMPE)

EEW Testing Center Provides On-going Performance Evaluation

Fig. 11. IM SA3.0 at POE 2% in 50 Years. Base is UCERF2 and average of 4 attenuation relationships

Fig. 11. IM SA3.0 at POE 2% in 50 Years. CyberShake 1.0 Map based on 224 Hazards curves at 10km spacing

Fig. 11. IM SA3.0 at POE 2% in50 Years. Difference between Base Map and CyberShake Map showing increase of hazard in LA Basin and in Riverside.

Fig. 6. Comparable Vs profiles across the Los Angeles Basin are shown with CVM4.0 (top) and CVM-H (bottom). The differences between the CVM 4.0 and CVM-H velocity models contribute to uncertainties in high frequency simulations. The CME collaboration is working with both velocity models in order to determine which produces best match to observation or if a new combined or merged model will be required for 2.0Hz and higher frequency deterministic wave propagation simulations for Southern California.

Dalguer et al (2008) Implications of the ShakeOut Source Description for Rupture Complexity and Near-Source Ground Motion

Ensemble Dynamic Rupture ShakeOut Simulations

Ensemble of dynamic ruptures for ShakeOut scenario produced a set of Kinematic source descriptions called the ShakeOut-D ruptures.

Fig. 7. Validating regional scale wave propagation simulation results against observed data may require thousands of comparisons between observed and simulated data. The CME has developed an initial implementation of a Goodness of Fit (GOF) measurement system and is applying these new tools to help evaluate the 2Hz Chino Hills simulations. In this GOF scale, 100 is a perfect fit. The maps (left) show how GOF values vary geographically for AWP-Olsen, Chino Hill M5.4 event, and two different SCEC Community Velocity Models, CVM4.0 (left) and CVM-H 5.7 (right).

Assertions for Discussion

1. Broad impact of seismological technologies (EEW, STEF, GMPE) are great enough to warrant significant effort for evaluation.

2. Independent evaluation for STEF, EEW, GMPE provides valuable service to agencies including CISN, USGS, CPEC, NEPC, and others.

3. Prospective must be done to before techniques will be accepted. 4. Similarities between problems lead to similar scientific techniques.5. Similarities between problems lead to similar technology approach and

potentially common infrastructure.6. “Neutral” third party testing has significant benefits to the science grous

involved in forecasting.7. CSEP infrastructure can be adapted for use in CISN EEW Testing

Centers.8. A GMPE (Ground Motion Prediction Equation) Testing Center; using

techniques similar to CSEP would have value both seismologists and building engineers.

shakealert testing procedure discussion philip maechling 26 march 2010 1

shakealert testing slide

collaboration collaboration

ca slide

testing emphasis

collaboration process

initial testing approach

outcomedriven process

simple initial testing

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