Forecasting and Nowcasting Earthquakes and Tsunamis:
Anticipating Great Natural Disasters Using Machine Learning

John Rundle
Distinguished Professor, University of California
Chairman, Open Hazards Group

Topics:ScienceApplications
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Motivation:
Tohoku, Japan
Earthquake and Tsunami
March 11, 2011

How do we estimate risk
from these events?
What does this mean for the
insurance industry?
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Forecasting and Nowcasting
Forecasting is a probability of future activity in the hazard (earthquake) cycle• We wish to calculate the probability of a future large earthquake• We need to estimate the time, location, and magnitude of the event

Nowcasting describes the current state of the hazard cycle• First used to describe the current state of the economic/business cycle• Also used in weather and climate applications3

Machine Learning• Machine learning is an application of artificial intelligence (AI)• Provides systems the ability to automatically learn and improve from experience• Machine Learning (“ML”) focuses on the development of computer programs that
can access data and build models of the process• Aims to learn patterns in past data and predict future data
• Now commonly used in data science applications including advertising in Google,Amazon, Facebook, etc.
• Several sophisticated ML libraries are now readily available on the web as opensource4Machine Learning•••••ML methods are based on defining labeled “feature vectors”.
Labels are target variables for each feature vector.
Typical feature vectors are statistical quantities characterizing the system
Labels could be “1” for presence of a desired characteristic, “0” for its absence
As an example, label “1” could be a major earthquake in the next year, “0” otherwise5

Machine Learning Open Source Libraries• Scikit-Learn (Classification andRegression)• Tensorflow (Neural Networksincluding Deep Learning)• PyTorch (Deep Learning)• Keras (Deep Learning)

Example of a decision tree in Scikit-Learn application(Rundle et al., 2020)
Example of an Artificial
Neural Network6

Workflow for Forecasting/Nowcasting with Machine Learning
User Interface (UI)
Javascript, Python, HTML
XML/JSON, Web Services

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

Machine Learning
Libraries include:••••

Scikit-Learn
Tensorflow
PyTorchKeras

LabeledFeature
VectorsMachine
Learning
Libraries

Automatedmachine learningalgorithms involve
training and testinglabeled featurevectors
A new label implies
a forecast for
future data

Predicted
Labels

The result ispredicted labels for
future unlabeled
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feature vectors

Example: The Earthquake Cycle in the 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

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The Earthquake Cycle
Fault Stress

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

Time, tFault Slip

T

t1 t2

Time, tHarry Fielding Reid (1859-1944)
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Applications of ML to Earthquake Forecasting(Rouet-Luduc et al., 2017,2019)• Earthquake forecasting in the laboratory• Laboratory experiments on model
earthquakes faults showed sudden slip
accompanied by bursts of acousticemissions (AE: small laboratory”earthquakes”)• R-L used a machine learning techniquecalled ”Random Forest” to predict theonset of sliding from the AE• The Random Forest method is an
example of “supervised learning”
10Applications to Cascadia(Rouet-Luduc et al., 2017,2019)• Episodic Tremor and Slip (ETS) is aprocess of sudden small slip on faults
accompanied by a burst of small
earthquakes• Similar to the laboratory friction
experiments• Rouet-Leduc applied the method to
the Pacific Northwest subduction zone
and found similar quality results topredict the ETS events

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Applications:
Earthquakes along the west
coast of the US , Mexico andCanada since 1980
These earthquakes occur irregularly
in time and do not show the rather
periodic patterns shown in thelaboratory or in Cascadia

12Earthquake “Bursts”• Major earthquakes are accompanied by manysmall small magnitude earthquake burst events• One type of burst is represented by earthquakeaftershocks• Other types of burst events are called “swarms”,
where there is no large main event• We have used ML classification techniques toshow that the size of these bursts varies
systematically in time prior to large earthquakes
in California (Rundle and Donnellan, 2020)13

Earthquake “Bursts”
Rundle and Donnellan (2020)

Vertical red dashed lines:
Vertical black dashed lines:M ≥ 7 earthquakes
6 ≤ M ≤ 7 earthquakes

Decreasing Radius• Horizontal radius of small
earthquake bursts as a functionof time
• Region is a 600 km radius circle
around Los Angeles, CA• Data show that burst radiusdecreases with time prior to M>7earthquakes• Most recent earthquake is a
M7.3 earthquake in east-central
California on July 5, 2019, theSearles Lake Ridgecrest,
California earthquake

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Application: Nowcasting Tsunamis and Runup
for Tsunami Early Warning• We would like to provide anestimate of risk from a major
earthquake and tsunami• Both long term (months toyears) risk as well as early
warning (minutes)• For this we need not only anearthquake model, but atsunami inundation model

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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

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Sanriku Coast Source Region17Earthquake Potential Score

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

Histogram of small
earthquake numbers
between large
earthquake intervals

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Simulating Great Tsunamis: Tohoku Earthquake and Tsunami
March 11, 2011
Simulations by David Grzan and John
Wilson with help from Steve Ward

Coastal Inundation

Red: TSGreen: ROMS*
Blue: Data

Verification*ROMS: Regional Ocean Modeling System(Finite Difference)

Tsunami Squares is a simulation method that cancompute coastal runup and inundation, as well asbasin-wide tsunamis. We verify results of thesimulations with buoy data and runup data. As
part of the simulation, we conserve mass,momentum and energy.

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Pacific Basin – Wide Simulation by Tsunami Squares Method21Summary• Great earthquakes and tsunamis present major risks to populated
regions• Anticipating these events by means of forecasting and nowcastingremains an important problem• Modern methods of Machine Learning are promising tools foradvancing the science• Early warning depends on advances in models and technology

Thank you for your attention!
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