Transcript Document

What to do given that earthquake
hazard maps often fail
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
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NY Times 3/31/2011
Japan spent lots of
effort on national
hazard map, but
Geller
2011
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”
Hazard map crucial for mitigation strategy
Optimal level of mitigation minimizes
total cost = sum of mitigation cost + expected loss
Expected loss = ∑ (loss in ith expected event
x assumed probability of that event)
Less mitigation decreases
construction costs but increases
expected loss and thus total cost
More mitigation gives less
expected loss but higher total cost
Because assumed probability taken from hazard
map, inaccurate map biases mitigation - too low
or too high
Stein & Stein, 2012
Including risk aversion & uncertainty
Consider marginal costs C’(n) & benefits Q’(n) (derivatives)
Benefit
(loss reduction)
cost
More mitigation
costs more
But reduces loss
Optimum is where
marginal curves
are equal, n*
Uncertainty in hazard model causes uncertainty in expected
loss. We are risk averse, so add risk term R(n) proportional
to uncertainty in loss, yielding higher mitigation level n**
Crucial to understand hazard model
uncertainty
Stein &
Stein, 2012
Too expensive
to rebuild for
2011 sized
tsunami
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Choosing
policy involves
politics,
economics,
geoscience
“In 30 years
there might be
nothing left
there but fancy
breakwaters
and empty
houses.”
NY Times 11/2/2011
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!)
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%
Off Miyagi
~M7.5 > 90%
Off Fukushima
~M7.4 7%
Assumption:
No M > 8.2
Off Ibaraki
~M6.7 – M7.2 90%
Expected Earthquake Sources
50 to 150 km segments
M7.5 to 8.2
(Headquarters for Earthquake Research Promotion)
Sanriku to Boso M8.2 (plate boundary)
20%
Sanriku to Boso M8.2 (Intraplate)
4-7%
J. Mori
Giant earthquake broke five segments
Expected Earthquake Sources
50 to 150 km segments
M7.5 to 8.2
(Headquarters for Earthquake Research Promotion)
2011 Tohoku Earthquake
450 km long fault, M 9.1
(Aftershock map from USGS)
J. Mori
Planning assumed maximum magnitude 8
Seawalls 5-10 m high
Stein & Okal, 2011
NYT
Tsunami runup
approximately twice fault
slip (Plafker, Okal &
Synolakis 2004)
M9 generates much
larger tsunami
CNN
Didn’t consider
historical record of
large tsunamis
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NYT 4/20/11
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
didn’t include
rarer, larger
multisegment
ruptures
Stein & Okal, 2011
Why?
NY Times 3/21/11
Hazard maps are hard to get right: success
depends on accuracy of four assumptions over
500-2500 years
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 hard to assess
2001 hazard map
2010 M7 earthquake shaking
much greater than maximum
predicted for next 500 years
http://www.oas.org/cdmp/document/seismap/haiti_dr.htm
2008 Wenchuan earthquake (Mw 7.9) was not expected:
map showed low hazard based on lack of recent
earthquakes
Didn’t use GPS data showing
1-2 mm/yr (~ Wasatch)
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Earthquakes prior to the 2008 Wenchuan event
Aftershocks of the Wenchuan event delineating the rupture zone
Maps are like ‘Whack-a-mole’ - you wait for
the mole to come up where it went down,
but it’s likely to pop up somewhere else.
What to do
Continue research on fundamental scientific
questions (geoscience community’s 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 - which hasn’t been done despite their
wide use - and thus guide future improvements
Comparing map predictions shows the large
uncertainties (~3X) resulting from different
assumptions
Stein et al, 2012
Newman et
al, 2001
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."
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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
Avoid biases from new maps made after a large
earthquake that earlier maps missed.
Before 2010 Haiti M7
After 2010 Haiti M7
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4X
Frankel et al, 2010
A posteriori changes to a model are "Texas
sharpshooting:” shoot at the barn and then draw
circles around the bullet holes.
Challenge: Users want predictions even if they’re 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