2019/2/15

Forecasting and Nowcasting Major Earthquakes
An Automated Cloud‐Based ApproachJohn Rundle
Distinguished Professor, University of California
Chairman, Open Hazards Group

Topics:ScienceApplications

Tohoku, Japan
Earthquake and Tsunami
March 11, 2011

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

1

2019/2/15

Pricing Earthquake Risk: Current PracticeScienceEngineering

Economics

Products

Earthquake
forecast
model

Groundshakingmodel

Economicloss model

Pricing of
insurance &financial
securities

PoissonLog Normal
BPT
Fault models
Pattern Analysis
AMRETASNTWNowcast
Etc.

Attenuation
PGA/PGV
Lquifaction
Surface geology
Basin structure
Etc.

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

CAT Bonds
SPVs
Warrants
Traders
Hedge Funds
Options
Muni Bonds
Equities
Etc.

The Science
Nowcasting and Forecasting22019/2/15

Nowcasting Earthquakes
JBR et al. (2016)

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

3

2019/2/15

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 from
Elastic ReboundFault Stress

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

Time, tFault Slip

T

t1 t2

Time, tHarry Fielding Reid (1859‐1944)

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

42019/2/15

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 idea of Nowcasting: Deficiency of large earthquakes must be filled in eventually
A consequence of the ergodic dynamics of seismicity (e.g., Tiampo et al., 2007)

Basic Nowcasting Includes Elastic Reboundvia Small Earthquake Proxy Data

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

Natural Time:
Cumulative Numberof Small Earthquakes
between Large
Earthquakes(Idealized)

Time, tAccumulating small earthquakes arereadily observable proxy data, unlike stress5

2019/2/15

Seismicity around Los Angeles

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

Los Angeles

Earthquake Potential Score

Cumulative
Distribution
Function ofsmall
earthquake
intervals
Histogram of small
earthquake numbers
between large
earthquake intervals

Los Angeles

6

2019/2/15

Example: Nowcasting the Sanriku Trench

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

Sanriku7

2019/2/15

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

3000 x 3000 km

2000 x 2000 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%

Nowcasting Great Earthquake and Tsunami
Source Regions
Aleutians W

Aleutians E

Alaska PWS

Kamchatka

Cascadia N

SagamiCascadia SSanriku

California‐NVNankai
Sumatra N

Sumatra S

Concepcion
Valdivia82019/2/15

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

Global Seismicity, 1900 ‐ 2018

Data probably
complete after 1950
= “cutoff date”

9

2019/2/15

Summary Data

Aleutians E Polygon Source Region10

2019/2/15

Enhanced Nowcast for Aleutians ESanriku Coast Polygon Source Region11

2019/2/15

Enhanced Nowcast for Sanriku Coast Source Region

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

122019/2/15

From Nowcasts to Forecasts
Forecasting with the Natural Time Weibull Method
JR 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

GlobalDataCloud‐Based PSHA Compute Engine
Database
Middleware(PHP, MySQL)

Earthquake
forecast
model

GMPE
model

Damagemodel

Automated
forecasting
and risk
analysisallowsrigorous
backtesting.
Expertelicitation
generally does
not allow
rigorous
backtesting13

2019/2/15

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, tOptimized 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‐present142019/2/15

Applications/Products
The Open Hazards Group
Risk Analysis and Management Reimagined

Risk: Applications and Products
The Open Hazards Group• Public web site• NTW forecasts web app• Ground shaking web app• Structural damage factor web app• Residential seismic safety reports• Commercial seismic safety reports• Cat bond analysis• Natural hazard disclosures (required in California for transfer ofproperty)• Financial trading models for hedge funds

15

2019/2/15

Automated Forecastingin the Cloud
www.openhazards.com

Automated California Forecast – Updated Nightly

16

2019/2/15

Forecasting in a Circular Region

RegionSelectionTool

Ground Shaking Tool
Shaking
IntensityTool17

2019/2/15

Residential Seismic Safety Report18

2019/2/15

Residential SeismicSafety Report

Risk Calculations: EP Curves
Residential Seismic Safety Report192019/2/15

AutomatedCommercial
PortfolioEvaluator20

2019/2/15

Pages from Sample Commercial Report

Natural Hazards Disclosures
for Transfer of Residential Real Estate in California

21

2019/2/15

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

• Sensitivity Analysis: Need to use a completeness threshold that is relatively
uniform over the entire region
 Need to ensure that statistics are relatively uniform acrossthe large geographic region

Thank you for your attention
The Open Hazards Groupwww.openhazards.com

222019/2/15

Distinguished Professor of Physics and Geology, University of California, Davis
Co‐Founder of Open Hazards Group and Chair of the Board, Davis, California
Executive Director Emeritus of the APEC Cooperation for Earthquake
Simulations (ACES)

About MeA Senior Advisor to the Association of Pacific Rim Universities (APRU)
Visiting Professor at Tohoku University at the APRU Multihazards Hub, TohokuUniversity, Sendai, JapanJohn was Chair (1994‐1996) of the scientific Advisory Council to the Southern California
Earthquake Center. He has been a Distinguished Visiting Scientist at the Jet Propulsion
Laboratory, Pasadena, CA (1995‐present), is currently an External Professor at the Santa Fe
Institute, and is a Fellow of the American Physical Society (2005), the American GeophysicalUnion (2008), and the American Association for the Advancement of Science(2017). Recently, he was a co‐winner of the NASA Software of the Year Award (2012). John
received his B.S.E from Princeton University (Magna Cum Laude, Phi Beta Kappa, Tau Beta
Pi), and M.S. (1973) and Ph.D. (1976) from the University of California at Los Angeles. Inaddition to natural hazards and earthquakes, he also has professional interests inforecasting, validation of forecasts, and quantitative finance. He currently co‐organizes(along with Michael Maouboussin, Will Tracy and Martin Lebowitz) a yearly meeting on risk
for the Santa Fe Institute, often held at Morgan Stanley, Inc., in New York. He teaches
courses in Risk and Natural Disasters; Complex Systems; and Econophysics and Quantitative
Finance at the University of California, Davis.

23