Forecasting and Nowcasting Major Earthquakes
An Automated Cloud-Based ApproachJohn Rundle
Distinguished Professor, University of California
Chairman, Open Hazards GroupTopics:
ScienceApplications

Tohoku, Japan
Earthquake and Tsunami
March 11, 2011

How do we estimate risk
from these events?
What does this mean for theinsurance industry?

Nowcasting
Forecasting is a probability of future activity in the hazard (earthquake) cycle
Nowcasting describes the current state of the hazard cycle
The term “Nowcasting” was first used to describe the current state of theeconomic/business cycleEarthquake Cycle Example: Nankai Trench, Japan
M Ando, Tectonophysics, v27, p112 (1975)• Data from historic writings in Japan• The basic idea of the earthquake “cycle”
started in Japan using historical data

The Earthquake Cycle Arises fromElastic ReboundFault Stress

Report of the 1906 Earthquake Investigation (1910)(Idealized)

Time, tFault Slip

Tt1 t2

Time, tHarry Fielding Reid (1859-1944)

But: How do we measure the time-dependent state of stress?

Example: Magnitude-Frequency Data
is Modeled by the Gutenberg-Richter Law
1000 km Around Tokyo (Accessed April 24, 2018)

The model
The data

Before the 3/11 M9.1 Mainshock

After the M7.7 Aftershock

Basic ideas of Nowcasting:
Deficiency of large earthquakes must be filled in eventually
Example: For every M>6 earthquake, there are on average 1000 M>3 earthquakes

Basic Nowcasting Includes Elastic Reboundvia Small Earthquake Proxy Data
Natural Time = Count of Small Earthquakes

Natural Time Since
the Last Large
Earthquake

Example:“Large” EQ: ML > 6“Small” EQ: 6 > MS > 4(Idealized)

Total Natural Time Elapsed

Accumulating small earthquakes arereadily observable proxy data, unlike stressSeismicity around Los Angeles

Circle of radius 200 km,
events at depths < 100 km

Los Angeles

M7.1 Searles Valley, California
Earthquake 2019/7/5
is 195 km from Los Angeles

M7.1

China Lake

Earthquake Potential Score

Cumulative
Distribution
Function of small
earthquake
intervals with
68% confidence
error estimate

Histogram of small
earthquake numbers
between large
earthquake intervals

2019/7/5 M7.1 Searles
Valley earthquake is 195
km from Los Angeles,
within the 200 km circle
Los Angeles

Example: Nowcasting the Sanriku-Japan Trench

Complex Polygon

Earthquake Potential Score, Sanriku Trench
Computed 3/31/2017. Depths < 50 kmSanriku

Nowcasting Great Earthquake and Tsunamis
Source Regions
Aleutians W

Aleutians EAlaska PWS

Kamchatka

Cascadia N

SagamiCascadia S
Sanriku

California-NVNankai
Sumatra N

Sumatra S

Concepcion
Valdivia

Global Seismicity, 1900 - 2018

Data probably
complete after 1950
= “cutoff date”

Summary Data

Sanriku Coast Polygon Source Region

Enhanced Nowcast for Sanriku Source Region

From Nowcasts to Forecasts
Forecasting with the Natural Time Weibull MethodJR Holliday et al. (2014)• We begin by counting small earthquakes since the last large earthquake (Nowcasting)• We build on the Nowcast by projecting the count forward in time using the current rateof small earthquake activity• We combine these ideas with Weibull (1952) statistics, which are commonly used forengineering failure analysis• The result is a fully automated computation of probability of future large earthquake
occurrence• Automation allows backtesting and optimization• We have built this technology into a series of automated cloud-based web sites:www.openhazards.com

Open Hazards PSHA Cloud Computational Framework
User Interface (UI)
Javascript, Python, HTML
XML/JSON, Web Services

GlobalData
Cloud-Based PSHA Compute Engine
Database
Middleware(PHP, MySQL)

Earthquake
forecast
model

GMPE
model

Damagemodel

Automated
forecasting
and risk
analysisallowsrigorousbacktesting.
Expertelicitation
generally does
not allow
rigorous
backtestingAutomated Forecastingin the Cloud
www.openhazards.com

Automated California Forecast – Updated Nightly

July 5, 2019, M7.1
Searles Valley
Earthquake Epicenter

Forecasting in a Circular Region

RegionSelectionTool

200 km circle
aroundLos Angeles

66% g acceleration
computed for Ridgecrest
City during the M7.1
earthquake.
Mercalli Intensity IX(violent)

ShakingIntensityTool

Ground Shaking Tool

From Earthquakes to Tsunami Early WarningGlobal Navigation Satellite System (GTEWS)
Tsunami Early Warning (TEW)• The GNSS Tsunami Early Warning Systems workshop (GTEWS 2017)
was held in Sendai, Japan on July 25-27, 2017
• It was supported by NASA in collaboration with:• The Global Geodetic Observing System (GGOS) of the International
Association of Geodesy (IAG)• The Association of Pacific Rim Universities (APRU) Multi-Hazards Hub atTohoku University in Sendai, and• The International Research Institute of Disaster Science (IRIDeS) at TohokuUniversity.

Sendai Framework• The GTEWS 2017 workshop sought to implement the vision
articulated by IUGG 2015 Resolution #4 to encourage broader
cooperation within the Indo-Pacific community of APEC economies
for the adoption of GTEWS.

• The GTEWS 2017 workshop was aligned with the goals and priorities
the UNDRR Sendai Framework for Disaster Risk Reduction 2015-2030(https://www. unisdr.org/we/coordinate/sendai-framework).
• The recommendations of GTEWS 2017 workshop support the Sendai
Framework goal to substantially reduce disaster mortality through theapplication of multi-national investments

Action Priorities of the Sendai Framework
Understand disaster risk:• Short term disaster risk will be improved by rapid and accurate
tsunami disaster warnings for a clearer understanding of impending
disaster risk.
• More rapid accurate information will also improve the community
response to warnings and will save lives in the medium term.
• The GTEWS network improvements will provide better long term
estimates of disaster risk through better scientific understanding ofthe evolving geologic forces.

Summary• Forecasting: Computing the probability of future activity• Nowcasting: Determining the current state of progress
through the hazard cycle• Uses: Ranking the current seismic risk of cities and tsunami
source regions world wide
• Tsunami Early Warning using Global Navigation SatelliteSystems• A cooperative project of NASA, NOAA, APRU-MH, APEC, GGOS, andothers• Consistent with the Sendai Framework for DRR

Thank you for your attention
Much more information at:www.openhazards.com

Sensitivity Analysis
Varying region sizes and completeness magnitudes
Fixed Conditions:
1) Computed for M>6.5 2) 200 km radius around cities

3) Depth < 100 km

1) Change the large region diameter: 1500 km, 2000 km, 3000 km, 4000 km
Larger1500 x 1500 km

2000 x 2000 km

3000 x 3000 km

4000 x 4000 km

2) Change the completeness threshold for San Francisco and Los Angeles
calculations from M3.0 to M4.0
Result: Under reasonable conditions, EPS changes by only about +/- 10%

Enhanced Nowcasting adds
Two Simple Model Ideas• Definition: Cutoff Date. Date after which catalog is assumed to be complete at
the defined completeness level• For M>6, cutoff date is apparently 1950 (next slide)• Sensitivity tests show cutoff date of 1960 gives similar results• Idea 1. Small earthquakes that occur after the cutoff date are counted. Small
earthquake numbers prior to the cutoff date are estimated using the average ratefrom after the cutoff date
• Idea 2. Current estimated magnitude m is computed as:

m = mC + (1/b) Log10(N)Where:
N = Natural time count of small earthquakes since last large earthquake
mC = Catalog completeness magnitude

Example: Vancouver Island Earthquakes, NTW Forecast
Latest Significant Event was M6.6 on 4/24 /2014
JR Holliday et al. (2014)
Chance of M>6 earthquake in circular regionof radius 200 km for next 1 year.
Data accessed 4/26/2014

m6.6
11/17/2009

Observed
Behaviorm6.0,6.1
9/3,4/2013

Idealized
Expected
Behavior
Time, tOptimized 12 month California-Nevada forecast:
Probabilities (%) vs. Time for Magnitude ≥ 6Forecast as of 2011/11/30

AutomatingForecasts
AllowsBacktestingandOptimizing!

Scatter Plot
1997-present
Observed Frequency vs.
Computed Probability

Optimal California-NV Forecast
Optimal forecasts via backtesting, using common
validation and verification testing procedures.

Bootstrap ErrorAnalysis

TemporalReceiver Operating
Characteristic
1997-present

Earthquake Forecasting
Current Practice• Expert elicitation is frequently used in forecasting, meaning thatbacktesting is not possible• Most/many current forecasts use time-independent Poisson statistics
in forecasting• Poisson forecasts have the property that they have no memory ofpast events• An example is the current UCERF3 forecast for California, which has
not been backtested

Aleutians E Polygon Source Region

Enhanced Nowcast for Aleutians E

The Science
Nowcasting and ForecastingPricing Earthquake Risk: Current Practice
Economics

Products

Groundshakingmodel

Economicloss model

Pricing of
insurance &financial
securities

Attenuation
PGA/PGV
Lquifaction
Surface geology
Basin structure
Etc.

EP curves
Building damage
Socioeconomic data
Supply chain disruption
Lost income
Lifelines
Etc.

ScienceEngineering

Earthquake
forecast
model

PoissonLog Normal
BPT
Fault models
Pattern Analysis
AMRETAS
NTW
NowcastEtc.

CAT Bonds
SPVs
WarrantsTraders
Hedge Funds
Options
Muni Bonds
Equities
Etc.

Nowcasting Earthquakes
JBR et al. (2016)

Landers(M7.3),
1992/06/28

Joshua Tree(M6.1),
1992/04/22

Northridge(M6.7),
1994/02/17