Presentation on theme: "USING EARTHQUAKE SCIENCE TO PREDICT EARTHQUAKE HAZARDS AND REDUCE EARTHQUAKE RISKS: USING WHAT WE KNOW AND RECOGNIZING WHAT WE DON’T Seth Stein Department."— Presentation transcript:
USING EARTHQUAKE SCIENCE TO PREDICT EARTHQUAKE HAZARDS AND REDUCE EARTHQUAKE RISKS: USING WHAT WE KNOW AND RECOGNIZING WHAT WE DON’T Seth Stein Department of Earth & Planetary Sciences Northwestern University email@example.com http://www.earth.northwestern.edu/people/seth/Export/CEA
WE CAN HAVE AS MUCH SEISMIC SAFETY AS WE WANT TO PAY FOR But it takes resources away from other needs Need to understand earthquake hazards and risks to decide what to do
Hazard is natural occurrence of earthquakes and the resulting ground motion and other effects. Risk is the danger the hazard poses to life and property. High hazard areas can have low risk because few people live there, and modest hazard areas can have high risk due to large populations and poor construction. Hazards can’t be reduced by human actions - but risks can. HAZARDS VERSUS RISKS
Newman et al., 2001 Seismic hazard - predicted shaking - is not something we measure or know We define it on policy grounds We predict it based on what we think happened in the past and what will happen in the future Different assumptions predict very different hazards
$100M seismic retrofit of Memphis hospital, removing nine floors, bringing it to California standard Does this make sense? How can we help society decide? Mitigating hazard (reducing risk) from earthquakes or other natural disasters involves economic and policy issues as well as scientific and engineering ones.
Systems Analysis for Hazard Mitigation What’s the hazard? What do we know & not know? What are we trying to accomplish? What strategies are available? What are the costs & benefits of each? What is an optimum strategy given uncertainty? Our goal is to decide how much is enough.
Stochastic model Optimal level of mitigation minimizes total cost = sum of mitigation cost + expected loss Expected loss = ∑ (loss in i th 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 Stein & Stein, 2012 For earthquake, mitigation level is construction code Loss depends on earthquake & mitigation level
Including risk aversion & uncertainty Consider marginal costs C’(n) & benefits Q’(n) (derivatives) More mitigation costs more But reduces loss Optimum is where marginal curves are equal, n* Uncertainty in hazard model & mitigation efficiency 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 consider hazard model uncertainty cost Benefit (loss reduction ) Stein & Stein, 2012
QUESTIONS: 1) Why is predicting earthquake (or other natural) hazards so hard? 2) How does the challenge differ between plate boundary, plate boundary zone, and intraplate earthquakes? 3) What are the difficulties in hazard mapping? 4) What are the issues in cost-effective hazard mitigation policy? Some US experience may be useful in China
TOPIC 1: Why is predicting earthquake (or other natural) hazards so hard?
We have learned a lot about earthquakes, but In general, we have not done well at short-term predictions (narrow window in space and time) We do better at long-term forecasting, because of the wider window in space and time, but often fail
WANT TO AVOID False negative - unpredicted hazard Fail to identify & prepare for real hazard False positive - overpredicted hazard Waste resources, public loses confidence
PREDICTING HAZARDS IS HARD BECAUSE Scientific issues - The earth is complicated -There’s a lot we don’t know Human issues - Often we know less than we think we do - We interpret data to fit wrong models
PREDICTING HAZARDS IS HARD BECAUSE Scientific issues - The earth is complicated -There’s a lot we don’t know - No adequate theory - Rare events - Short time history
Bulge was an artifact of errors in referring the vertical motions to sea level via a traverse across the San Gabriel mountains. Davidson et al 2002 USGS director McKelvey expressed his view that a great earthquake would occur in the area possibly within the next decade that might cause up to 12,000 deaths, 48,000 serious injuries, 40,000 damaged buildings, and up to $25 billion in damage. PALMDALE BULGE UPLIFT 1975
PARKFIELD, CALIFORNIA SEGMENT OF SAN ANDREAS In 1985, expected next in 1988; predicted at 95% confidence by 1993 M 5-6 earthquakes about every 22 years: 1857, 1881, 1901, 1922, 1934, and 1966 2004 $20 million project set up seismometers, strainmeters, creepmeters, GPS receivers, tiltmeters, water level gauges, electromagnetic sensors, and video cameras were set up to monitor what would happen before and during the earthquake.
In 1985, expected next in 1988; predicted at 95% confidence by 1993 Didn’t occur till 2004 (16 years late) Poor statistics: shifted 1934 event to improve fit & hence reduce uncertainty 2004
So far, no clear evidence for observable behavior before earthquakes. Maybe lots of tiny earthquakes happen frequently, but only a few grow by random process to large earthquakes In chaos theory, small perturbations can have unpredictable large effects - flap of a butterfly's wings in Brazil might set off a tornado in Texas WHY SHORT-TERM PREDICTIONS DO POORLY
A AA simple example of chaos Consider a system whose evolution in time is described by the equation x(t+1) = 2x(t) 2 -1 Runs starting off at time t=0 with slightly different values, x(0) = 0.750 and x(0) = 0.749, yield curves that differ significantly within a short time.
The fact that small differences grow is part of the reason why weather forecasts get less accurate as they project further into the future - tomorrow's forecast is much better than one for the next five days. An interesting thought experiment, suggested by Lorenz (1995), is to ask what the weather would be like if it weren't chaotic. In this case, weather would be controlled only by the seasons, so year after year storms would follow the same tracks, making planning to avoid storm damage easy. In reality, storms are very different from year to year Tracks of North Atlantic hurricanes, tropical storms, and depressions for two very most active hurricane seasons
If there’s nothing special about the tiny earthquakes that happen to grow into large ones, the time between large earthquakes and their locations are highly variable and nothing observable happens before them. If so, earthquake prediction is either impossible or nearly so. “It’s hard to predict earthquakes, especially before they happen” WHY SHORT-TERM PREDICTIONS DO POORLY
LONG-TERM FORECASTS SOMETIMES DON’T DO WELL Hazard map didn’t predict locations of future earthquakes GSHAP NUVEL-1 Argus et al., 1989
GSHAP 1998 NUVEL-1 Argus et al., 1989 2004 2003 LONG-TERM FORECASTS SOMETIMES DON’T DO WELL Hazard map didn’t predict locations of future earthquakes
Years # of recurrence events time 100 1 100 500 11 45 1000 20 50 2000 35 57 3000 56 54 4000 73 55 5 o W 10 o E Latitude M > 7 PROBLEM: HAZARD MAP BASED ON LAST EARTHQUAKES When recurrence time is long, short record shows apparent seismic gaps & high hazard zones even if hazard is uniform Swafford & Stein, 2007
2001 hazard map http://www.oas.org/cdmp/document/seismap/haiti_dr.htm 2010 M7 earthquake shaking much greater than predicted for next 500 years 6 mm/yr fault motion
USGS 2008 Wenchuan earthquake (Mw 7.9) was not expected: map showed low hazard
Hazard map - assumed steady state - relied on lack of recent seismicity Didn’t use GPS data showing 1-2 mm/yr Earthquakes prior to the 2008 Wenchuan event Aftershocks of the Wenchuan event delineating the rupture zone M. Liu
Japan seemed ideal for hazard mapping Fast moving (80 mm/yr ) & seismically very active plate boundary with good instrumentation & long seismic history 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
Tsunami runup approximately twice fault slip (Plafker, Okal & Synolakis 2004) M9 generates much larger tsunami Planning assumed maximum magnitude 8 Seawalls 5-10 m high CNN NYTStein & Okal, 2011
Due to short history, didn’t recognize danger of damaging earthquakes on closer but buried thrust faults 1994 Northridge M 6.7 58 deaths, $20B damage UNTIL RECENTLY, EARTHQUAKE HAZARD STUDIES IN THE LOS ANGELES AREA FOCUSED ON THE SAN ANDREAS FAULT SAF broke in 1857: M 7.9
BECAUSE STRONG GROUND MOTION DECAYS RAPIDLY WITH DISTANCE A SMALLER EARTHQUAKE NEARBY CAN DO MORE DAMAGE THAN A LARGER ONE FURTHER AWAY M 7 M 6
PREDICTING HAZARDS IS HARD BECAUSE Human issues - We often think we know more than we really do - Rely on inadequate model - Uncertainties are hard to assess and usually underestimated - Data selected or interpreted to fit existing idea - Groups convince themselves - Researchers go along with others even when their data say otherwise (“Bandwagon”)
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
SUGGESTIONS Do our best to assess hazards, but Be realistic about what we know & what we don’t Think carefully about what the evidence for conventional ideas is Try to realistically assess uncertainties & bear them in mind Don’t discard new data because they don’t fit model Accept that the earth is more complicated than we know, and may surprise us