Bad assumptions or bad luck:

1
Bad assumptions or bad luck Tohokus
embarrassing lessons for earthquake hazard
mapping Whats going wrong and what to do?
Seth Stein, Northwestern University Robert
Geller, University of Tokyo Mian Liu,
University of Missouri 
CNN
NY Times
Tohoku, Japan March 11, 2011 M 9.1
2
Japan spent lots of effort on national hazard
map, but 2011 M 9.1 Tohoku, 1995 Kobe M 7.3
others in areas mapped as low hazard In
contrast map assumed high hazard in Tokai gap
Geller 2011

3

• Hazard maps fail because of
• - bad physics (incorrect description of
  earthquake processes)
• bad assumptions (mapmakers choice of poorly
  known parameters)
• bad data (lacking, incomplete, or
  underappreciated)
• bad luck (low probability events)
• and combinations of these (Tohoku!)

4

Detailed model of segments with 30 year
probabilities
Off Sanriku-oki North M8 0.2 to 10
Off Sanriku-oki Central M7.7 80 to 90
Assumption No M gt 8.2
Off Miyagi M7.5 gt 90
Off Fukushima M7.4 7
Off Ibaraki M6.7 M7.2 90
Sanriku to Boso M8.2 (plate boundary) 20 Sanriku
to Boso M8.2 (Intraplate) 4-7
Expected Earthquake Sources 50 to 150 km
segments M7.5 to 8.2 (Headquarters for
Earthquake Research Promotion)
J. Mori
5

Giant earthquake broke all of the segments
2011 Tohoku Earthquake 450 km long fault, M
9.1
Expected Earthquake Sources 50 to 150 km
segments M7.5 to 8.2 (Headquarters for
Earthquake Research Promotion)
(Aftershock map from USGS)
J. Mori
6
Planning assumed maximum magnitude 8 Seawalls
5-10 m high
NYT
Stein Okal, 2011
Tsunami runup approximately twice fault slip
(Plafker, Okal Synolakis 2004) M9
generates much larger tsunami
CNN
7
Didnt consider historical record of large
tsunamis
NYT 4/20/11
8
Lack of M9s in record seemed consistent with
model that M9s only occur where lithosphere
younger than 80 Myr subducts faster than 50 mm/yr
(Ruff and Kanamori, 1980)
Disproved by Sumatra 2004 M9.3 and dataset
reanalysis (Stein Okal, 2007) Short record at
most SZs didnt include larger multisegment
ruptures
Stein Okal, 2011
9
NY Times 3/21/11
Why?
10
Hazard maps are hard to get right success
depends on accuracy of four assumptions
Where will large earthquakes occur? When will
they occur? How large will they be? How strong
will their shaking be?
Uncertainty map failure often result because
these are often poorly known
11
2010 M7 earthquake shaking much greater than
maximum predicted for next 500 years
2001 hazard map
http//www.oas.org/cdmp/document/seismap/haiti_dr.
htm
12
2008 Wenchuan earthquake (Mw 7.9) was not
expected map showed low hazard based on lack of
recent earthquakes
Didnt use GPS data showing 1-2 mm/yr ( Wasatch)
13
Maps are like Whack-a-mole - you wait for the
mole to come up where it went down, but its
likely to pop up somewhere else.
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What to do
Continue research on fundamental scientific
questions (partly UNAVCO communitys
job!) Realistically assess map uncertainties and
present them to help users decide how much
credence to place in maps Develop methods to
objectively test hazard maps and guide future
improvements
15
Comparing map predictions shows the large
uncertainties (3X) resulting from different
assumptions Shows uncertainties without
subjective weighting
Stein et al, 2012
16
Testing analogy evidence-based medicine
objectively evaluates widely used treatments,
often with embarrassing results Although more
than 650,000 arthroscopic knee surgeries at a
cost of roughly 5,000 each were being performed
each year, a controlled experiment showed that
"the outcomes were no better than a placebo
procedure."
17
Test maps by comparison to what happened after
they were published.
Bad luck or bad map? Compare maximum
acceleration observed to that predicted by both
map and null hypotheses. A simple null
hypothesis is regionally uniformly distributed
hazard. Japanese map seems to be doing worse
than this null hypothesis.
Geller 2011
18
Avoid biases from new maps made after a large
earthquake that earlier maps missed.
Before 2010 Haiti M7
After 2010 Haiti M7
4X
Frankel et al, 2010
19
A posteriori changes to a model are "Texas
sharpshooting shoot at the barn and then draw
circles around the bullet holes.
20
Challenge Users want predictions even if
theyre poor Future Nobel Prize winner Kenneth
Arrow served as a military weather forecaster. As
he described, my colleagues had the
responsibility of preparing long-range weather
forecasts, i.e., for the following month. The
statisticians among us subjected these forecasts
to verification and found they differed in no way
from chance. The forecasters themselves were
convinced and requested that the forecasts be
discontinued. The reply read approximately
"The commanding general is well aware that the
forecasts are no good. However, he needs them for
planning purposes." Gardner, D., Future Babble
Why Expert Predictions Fail - and Why We Believe
Them Anyway, 2010