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Just as the residents of L’Aquila, Italy, were preparing for bed
on a chilly Sunday evening in April 2009, they felt a pair of
tremors, each barely more perceptible than the rumbling of a
distant freight train. The first earthquake, which occurred just
before 11 P.M. local time, measured 3.9 on the magnitude scale,*
a frequency strong enough to rattle nerves and loosen objects but
little else. The second was even weaker, a magnitude 3.5; it
would not have been powerful enough to awaken a sound

But L’Aquila was on edge about earthquakes. The town, which
sits in the foothills of the Apennine Mountains and is known for
its ski resorts and medieval walls, had been experiencing an
unusually large number of them—the two that Sunday were the
seventh and eighth of at least magnitude 3 in the span of about a
week. Small earthquakes are not uncommon in this part of the

world, but the rate is normally much less—about one such
earthquake every two or three months. These were coming
almost one hundred times as often.

Meanwhile, the citizens of a town a mountain pass away,
Sulmona, had just survived an earthquake scare of their own. A
technician named Giampaolo Giuliani, who worked at Italy’s
National Institute of Nuclear Physics, claimed to have detected
unusually high levels of radon in the area. He theorized this
might be a precursor to an earthquake and went so far as to tell
Sulmona’s mayor that an earthquake would strike the town on the
afternoon of March 29. The mayor, impressed by the prediction,
ordered vans carrying loudspeakers to drive about town,
warning residents of the threat.1

No earthquake hit Sulmona that day. After the prediction
failed, Giuliani was reported to local authorities for procurato
allarme (bringing about alarm)—in essence, having yelled fire
in a crowded theater. He was forced to remove his predictions
from the Internet for fear of triggering further panic.

Authorities in L’Aquila told the residents the earthquake
swarm* was nothing to worry about; the fault was helpfully
discharging energy, explained Bernardo De Bernardinis, the
deputy chief of Italy’s Civil Protection Department,2 reducing the
threat of a major earthquake. He agreed with a reporter that they
should sit back and enjoy a glass of wine;3 De Bernardinis

recommended a local specialty, a Montepulciano.
A major earthquake did hit L’Aquila, however. Measuring at

magnitude 6.3, it came at 3:32 A.M. local time on Monday
morning. Shaking houses from their foundations, caving in roofs,
and turning furniture into projectiles, the quake killed more than
300 residents, left another 65,000 homeless, and caused more
than $16 billion in damage.4

What We Do When Our Foundations Are Shaken

L’Aquila ought to have been better prepared. The city sits near a
particularly violent type of fault known as a subduction zone,
where the African Plate, one of the eight major tectonic plates
that cover the earth’s surface, slips slowly and inexorably
beneath the Eurasian one. Its first significant earthquake was
recorded in 1315, and earthquakes struck again in 1349, 1452,
1461, 1501, 1646, 1703, and 1706;5 the most serious one, in
1786, had killed more than 5,000 people. Each time, often on
direct order of the pope,6 the town was rebuilt and repopulated.

Since then, L’Aquila had tempted fate for more than two
centuries. An earthquake hit in 1958, but it was fairly minor—

magnitude 5.07—and only the town’s oldest residents would have
remembered it. The 2009 earthquake was much more powerful.
The magnitude scale is logarithmic; a one-point increase in the
scale indicates that the energy release has multiplied by thirty-
two. Thus, the 2009 earthquake, magnitude 6.3, was about
seventy-five times more powerful than the one that had hit
L’Aquila in 1958. And it was about 3,000 times more powerful
than the tremors—foreshocks to the major earthquake—that
L’Aquila had experienced earlier that evening.

Still, while the 2009 earthquake was large by Italian
standards, it was barely a hiccup on the global scale. The
earthquake that devastated Japan in 2011 measured at magnitude
9.0 or 9.1—almost 11,000 times more powerful. And the largest
earthquake recorded since reliable estimates were possible,
which hit Chile in 1960 and measured magnitude 9.5, was about
60,000 times stronger than the L’Aquila quake.

Why, then, did L’Aquila—a fairly well-to-do town in a
wealthy, industrialized nation—sustain such significant damage?
One reason was the city’s geology—L’Aquila sits on an ancient
lake bed, which tends to amplify the earth’s shaking. Mexico City
was also built on an ancient lake bed,8 and 10,000 were killed
there in 1985 from an earthquake whose epicenter was more than
two hundred miles away.

But the major reason was simply that the town had become

complacent about the seismic danger that lay just fifteen
kilometers underground. There was nothing resembling the
proper level of earthquake readiness:9 building codes,
emergency supplies, community drills. Not only were centuries-
old buildings leveled by the tremor, but so too were many
modern ones, including a wing of a hospital that had been
erected as recently as 2000. A little bit of warning would have
saved untold lives there.

Had Giampaolo Giuliani provided that warning? In the Italian
tabloids, he had become something of a savant and a martyr.
Soft-spoken and disheveled, and often wearing the colors of the
local soccer team, he played the role of the humble civil servant
or absentminded professor whose insights had been ignored by
the scientific establishment. He claimed that he had warned
friends and family about the L’Aquila quake and was prevented
from telling others only because of the police order against him.
He demanded an apology from the authorities—not to him, he
said, but to the people of L’Aquila.

Never mind that Giuliani had not actually predicted the
earthquake. His prediction had been very specific: Sulmona, not
L’Aquila, was at greater risk, and the earthquake would come in
March rather than April. In fact, he had suggested to a local
newspaper that the danger had passed. “To simplify the
concepts,” he said before launching into a rambling explanation

about the lunar cycle, “the Earth-Moon system has come to visit
at perihelion . . . the minimum distance from Earth, and aligned
with the planet Venus. . . . I feel I can reassure my fellow
citizens because the swarm will be diminishing with the end of

Perihelion with the planet Venus? Radon gas? What did any of
this have to do with earthquakes? And what about Giuliani’s
failed prediction in Sulmona? It didn’t matter. When catastrophe
strikes, we look for a signal in the noise—anything that might
explain the chaos that we see all around us and bring order to the
world again. Giuliani’s rambling explanations were the closest
thing available.

No type of catastrophe is more jarring to our sense of order
than an earthquake. They quite literally shake our foundations.
Whereas hurricanes descend upon us from the heavens and have
sometimes been associated with metaphors for God’s
providence,* earthquakes come from deep underneath the
surface and are more often taken to be signs of His wrath,11

indifference,12 or nonexistence. (The Lisbon earthquake of 1755
was a major spark for the development of secular philosophy.13)
And whereas hurricanes—along with floods, tornadoes, and
volcanoes—can often be forecasted in advance, earthquakes
have defied centuries of efforts to predict them.

Magic Toads and the Search for the Holy Grail

Pasadena, California, has long been the world’s epicenter for
earthquake research. It is home to the California Institute of
Technology, where Charles Richter developed his famous
logarithmic scale in 1935. The United States Geological Survey
(USGS) also has a field office there, where most of its
earthquake specialists reside. I traveled there in September 2009
to meet with Dr. Susan Hough, who is one of the USGS’s top
seismologists and who has written several books about
earthquake prediction. She had watched Giuliani’s television
interviews with suspicion and had written a blistering editorial
in the New York Times 14 that criticized both Giuliani and the
attention paid to him.

Hough’s editorial argued that Giuliani’s success was merely
coincidental. “The public heard about Mr. Giuliani’s prediction
because it appears to have been borne out,” she wrote. “But
there are scores of other [incorrect] predictions that the public
never hears about.”

If you have hundreds of people trying to make forecasts, and
there are hundreds of earthquakes per year, inevitably someone
is going to get one right. Giuliani’s theories about radon gas and
lunar cycles had been investigated many times over15 by

credentialed seismologists and had shown little or no ability to
predict earthquakes. Giuliani had been lucky: the monkey who
typed Shakespeare; the octopus who predicted the World Cup.

Hough’s office at the USGS sits near a quiet corner of the
Caltech campus where there are more eucalyptus trees than
students. She seemed a little road weary when I met her, having
just returned from a trip to Turkey where she’d been to study a
system of earthquake faults. She has soft features and frizzy hair
and her eyes are dark, tired—skeptical. “What’s your day job?”
she quizzed me a few moments after I greeted her.

At one point, she pulled a pocket-size globe off her desk, the
sort that looks like it was bought at an airport gift shop. She took
her index finger and drew a line across the surface of the globe,
starting in the Sea of Japan and moving east–southeast.

“They are really concentrated in this belt—stretching from
southern China through Greece,” Hough explained, referring to
the world’s most destructive earthquakes. “It’s a complicated
earthquake zone, a lot of buildings with vulnerable construction.
If you put a big earthquake under Tehran, you could kill a million

Indeed, almost all the deadliest earthquakes in modern history
(figure 5-1) have occurred along the path that Hough outlined,
one which passes through the Cradle of Civilization in the
Middle East and through some of the most densely populated

regions of the planet, including China and India. Often poor and
crowded, these areas lack the luxury to prepare for a onceper-
three-hundred-year catastrophe. But the death tolls can be
catastrophic when earthquakes hit, stretching into the hundreds of


Earthquakes kill more people than hurricanes, in fact,16 despite
seeming like the rarer phenomenon.17 Perhaps that is because they
are so seldom predicted successfully. Whereas the landfall
position of hurricanes can be forecasted at least three times more
accurately now than they were even twenty-five years ago, the
science of earthquake forecasting seems barely to have evolved

since the ninth century A.D., when the Japanese first claimed to be
able to anticipate earthquakes by looking at the behavior of
catfish.18 (Cows, pigs, eels, rats, parakeets, seagulls, turtles,
goldfish, and snakes have also been reported at various times to
behave unusually in advance of an earthquake.)

Kooks like Giuliani are still taken seriously, and not just in
the Italian tabloids.19 The California Earthquake Prediction
Council receives hundreds of unsolicited earthquake forecasts
per year, most of which, the agency says, “discuss the strange
behavior of household pets, intuition, Aunt Agatha’s aching
bunions, or other mysterious signs and portents that scientists
simply don’t understand.”20 Meanwhile, some of the stuff in
academic journals is hard to distinguish from ancient Japanese
folklore. A 2010 paper 21 in a relatively prestigious journal, The
Journal of Zoology, observed that toads in a pond fifty miles
from L’Aquila had stopped spawning five days before the major
earthquake there.22 Remarkably, it asserted that this was evidence
that they had predicted the earthquake.

It’s research like this that exhausts Hough. “If you look back in
time, certainly going back to the 1970s, people would come up
with some idea—they’d be optimistic—and then you wait ten
years and that method would be debunked,” she told me. “Ten
years later, you have a new method and ten years later it’s
debunked. You just sort of sense a theme. Most top scientists at

this point know better than to chase after a Holy Grail that
probably doesn’t exist.”

But while Giuliani’s close encounters with Venus or the toads
are easy to dismiss, is there really no way at all to predict an
earthquake? What about the swarm of smaller quakes around
L’Aquila just before the Big One hit? Was that just a
coincidence? The seismological community has a reputation for
being very conservative. It was very slow to accept the theory of
plate tectonics, for instance23—the now broadly accepted notion
that the shifting of the earth’s continental plates is the primary
cause for earthquakes—not adopting it into their canon until the
1960s even though it was proposed in 1912. Had Hough’s
skepticism crossed the line into cynicism?

The official position of the USGS is even more emphatic:
earthquakes cannot be predicted. “Neither the USGS nor Caltech
nor any other scientists have ever predicted a major earthquake,”
the organization’s Web site asserts. 24 “They do not know how,
and they do not expect to know how any time in the foreseeable

Earthquakes cannot be predicted? This is a book about
prediction, not a book that makes predictions, but I’m willing to
stick my neck out: I predict that there will be more earthquakes
in Japan next year than in New Jersey. And I predict that at some
point in the next one hundred years, a major earthquake will hit

somewhere in California.25

Both the USGS and I are playing some semantic games. The
terms “prediction” and “forecast” are employed differently in
different fields; in some cases, they are interchangeable, but
other disciplines differentiate them. No field is more sensitive to
the distinction than seismology. If you’re speaking with a

1. A prediction is a definitive and specific statement about
when and where an earthquake will strike: a major
earthquake will hit Kyoto, Japan, on June 28.

2. Whereas a forecast is a probabilistic statement, usually
over a longer time scale: there is a 60 percent chance of
an earthquake in Southern California over the next thirty

The USGS’s official position is that earthquakes cannot be
predicted. They can, however, be forecasted.

What We Know About How Earthquakes Behave

If you explore the USGS Web site, in fact, you’ll find that it
makes lots of tools available to help you forecast


One particularly neat one is an application that lets you type in
the longitude and latitude at any point in the United States; it will
estimate the long-term probability of an earthquake there.26 In
figure 5-2, I’ve listed the probabilities for earthquakes in a
variety of major U.S. cities as provided by the USGS Web site.

We all know that California is very seismically active; the
USGS estimates that an earthquake of magnitude 6.8 or higher
will hit San Francisco about once every thirty-five years. Many
of you will also know that Alaska has many earthquakes—the
second largest one in recorded history, magnitude 9.4, hit
Anchorage in 1964.


1 per 30 years
San Francisco
1 per 35 years
Los Angeles
1 per 40 years
1 per 150 years
1 per 180 years
San Diego
1 per 190 years
Salt Lake City
1 per 200 years

Portland, OR
1 per 500 years
Charleston, SC
1 per 600 years
Las Vegas
1 per 1,200 years
1 per 2,500 years
1 per 7,500 years
New York
1 per 12,000 years
1 per 15,000 years
1 per 17,000 years
St. Louis
1 per 23,000 years
1 per 30,000 years
1 per 40,000 years
Washington, DC
1 per 55,000 years
1 per 75,000 years
1 per 100,000 years
1 per 130,000 years
1 per 140,000 years

But did you know about Charleston, South Carolina? It is
seismically active too; indeed, it experienced a magnitude 7.3
earthquake in 1886. The USGS estimates that there will be
another big earthquake there about once per six hundred years. If

you live in Seattle, you should probably have an earthquake plan
ready; it is more earthquake-prone than many parts of California,
the USGS says. But you don’t need one if you live in Denver,
which is a safe distance away from any continental boundaries.

This seems like an awful lot of very specific and user-friendly
information for an organization whose party line is that it is
impossible to predict earthquakes. But the USGS’s forecasts
employ a widely accepted seismological tool called the
Gutenberg–Richter law. The theory, developed by Charles
Richter and his Caltech colleague Beno Gutenberg in 1944, is
derived from empirical statistics about earthquakes. It posits that
there is a relatively simple relationship between the magnitude
of an earthquake and how often one occurs.

If you compare the frequencies of earthquakes with their
magnitudes, you’ll find that the number drops off exponentially as
the magnitude increases. While there are very few catastrophic
earthquakes, there are literally millions of smaller ones—about
1.3 million earthquakes measuring between magnitude 2.0 and
magnitude 2.9 around the world every year.27 Most of these
earthquakes go undetected—certainly by human beings and often
by seismometers.28 However, almost all earthquakes of
magnitude 4.5 or greater are recorded today, however remote
their location. Figure 5-3a shows the exponential decline in their
frequencies, based on actual records of earthquakes from

January 196429 through March 2012.30

It turns out that these earthquakes display a stunning regularity
when you graph them in a slightly different way. In figure 5-3b,
I’ve changed the vertical axis—which shows the frequency of
earthquakes of different magnitudes—into a logarithmic scale.*
Now the earthquakes form what is almost exactly a straight line

on the graph. This pattern is characteristic of what is known as a
power-law distribution, and it is the relationship that Richter
and Gutenberg uncovered.

Something that obeys this distribution has a highly useful
property: you can forecast the number of large-scale events from
the number of small-scale ones, or vice versa. In the case of

earthquakes, it turns out that for every increase of one point in
magnitude, an earthquake becomes about ten times less frequent.
So, for example, magnitude 6 earthquakes occur ten times more
frequently than magnitude 7’s, and one hundred times more often
than magnitude 8’s.

What’s more, the Gutenberg–Richter law generally holds
across regions of the globe as well as over the whole planet.
Suppose, for instance, that we wanted to make an earthquake
forecast for Tehran, Iran. Fortunately, there hasn’t been a
catastrophic earthquake there since its seismicity began to be
measured. But there have been a number of medium-size ones;
between 1960 and 2009, there were about fifteen earthquakes
that measured between 5.0 and 5.9 on the magnitude scale in the
area surrounding the city.31 That works out to about one for every
three years. According to the power law that Gutenberg and
Richter uncovered, that means that an earthquake measuring
between 6.0 and 6.9 should occur about once every thirty years
in Tehran.

Furthermore, it follows that an earthquake that measured 7.0
or greater would occur about once every three hundred years
near Tehran. This is the earthquake that Susan Hough fears. The
Haiti earthquake of 2010, which measured magnitude 7.0 and
killed 316,000,32 showed the apocalyptic consequences that
earthquakes can produce in the developing world. Iran shares

many of Haiti’s problems—poverty, lax building codes, political
corruption33—but it is much more densely populated. The USGS
estimates, on the basis of high death tolls from smaller
earthquakes in Iran, that between 15 and 30 percent of Tehran’s
population could die in the event of a catastrophic tremor there.34

Since there are about thirteen million people in Tehran’s metro
area, that would mean between two and four million fatalities.

What the Gutenberg–Richter law does not tell us anything
about is when the earthquake would strike. (Nor does it suggest
that Tehran is “due” for an earthquake if it hasn’t experienced
one recently.) Countries like Iran and Haiti do not have the
luxury of making contingency plans for a once-every-three-
hundred-year event. The earthquake forecasts produced using the
Gutenberg–Richter law provide for a good general guide to the
hazard in an area. But like weather forecasts determined from
statistical records alone (it rains 35 percent of the time in
London in March), they don’t always translate into actionable
intelligence (should I carry an umbrella?). Geological time
scales occupy centuries or entire millennia; human life spans are
measured in years.

The Temptation of the Signal

What seismologists are really interested in—what Susan Hough
calls the “Holy Grail” of seismology—are time-dependent
forecasts, those in which the probability of an earthquake is not
assumed to be constant across time.

Even seismologists who are skeptical of the possibility of
making time-dependent earthquake forecasts acknowledge that
there are some patterns in the earthquake distribution. The most
obvious is the presence of aftershocks. Large earthquakes are
almost always followed by dozens or even thousands of
aftershocks (the 2011 earthquake in Japan produced at least
1,200 of them). These aftershocks follow a somewhat
predictable pattern.35 Aftershocks are more likely to occur
immediately after an earthquake than days later, and more likely
to occur days later than weeks after the fact.

This, however, is not terribly helpful when it comes to saving
lives. This is because aftershocks, by definition, are always less
powerful than the initial earthquake. Usually, if a particular fault
produces a sufficiently powerful earthquake, there will be a few
aftershocks and then that’ll be the end of the fireworks for a
while. This isn’t always the case, however. For example, the
incredibly powerful earthquake that hit the New Madrid Fault on

the Missouri-Tennessee border on December 16, 1811,
evaluated by seismologists as magnitude 8.2, was followed just
six hours later by another shock of about the same magnitude.
And the fault was not yet quiesced: the December 16 quakes
were succeeded by another magnitude 8.1 earthquake on January
23, and then yet another, even more powerful 8.3 earthquake on
February 7. Which ones were the foreshocks? Which ones were
the aftershocks? Any interpretation is about as useless as any

The question, of course, is whether we can predict
earthquakes before the fact: can we tell the foreshocks and
aftershocks apart in advance? When we look at data that shows
the distribution of earthquakes across time and space, it tempts
us with the possibility that there might be some signal in the

Figure 5-4a, for instance, shows the distribution of
earthquakes near L’Aquila36 from 2006 until the magnitude 6.3
earthquake hit in 2009.37 All the data in this chart, except the
large black circle that indicates the main earthquake, shows
earthquakes that occurred before the main shock. In the case of
L’Aquila, there does seem to be a discernible pattern. A big
cluster of earthquakes, measuring up to about magnitude 4,
occurred just before the main shock in early 2009—much higher
than the background rate of seismic activity in the area.

A more debatable case is the Japan earthquake of 2011. When

we make one of these plots for the region (figure 5-
4b), we see, first of all, that it is much more seismically active
than Italy. But are there patterns in the timing of the earthquakes
there? There seem to be some; for instance, there is a cluster of
earthquakes measuring between magnitude 5.5 and magnitude 7.0
in mid-2008. These, however, did not precipitate a larger
earthquake. But we do see an especially large foreshock,
magnitude 7.5, on March 9, 2011, preceding the magnitude 9.1

earthquake38 by about fifty hours.
Only about half of major earthquakes are preceded by

discernible foreshocks,39 however. Haiti’s was not (figure 5-4c).
Instrumentation is not very good in most parts of the Caribbean,
so we don’t have records of magnitude 2 and 3 earthquakes, but
seismometers in the United States and other areas should be able
to pick up anything that registers at 4 or higher. The last time
there had been even a magnitude 4 earthquake in the area was in
2005, five years before the magnitude 7.0 earthquake hit in 2010.
There was just no warning at all.

Complicating matters further are false alarms—periods of
increased seismic activity that never result in a major tremor.
One case well known to seismologists is a series of smaller
earthquakes near Reno, Nevada, in early 2008. The Reno
earthquake swarm looks a lot like the one we saw before
L’Aquila in 2009. But it never amounted to anything much; the
largest earthquake in the series was just magnitude 5.0 and no
major earthquake followed.


This is just a taste of the maddening array of data that
seismologists observe. It seems to exist in a purgatory state—not
quite random and not quite predictable. Perhaps that would
imply that we could at least get halfway there and make some
progress in forecasting earthquakes—even if we can never get to
hard-and-fast predictions. But the historical record of attempts to
predict earthquakes is one of almost complete failure.

A Parade of Failed Forecasts

Hough’s 2009 book, Predicting the Unpredictable: The
Tumultuous Science of Earthquake Prediction, is a history of
efforts to predict earthquakes, and is as damning to that
enterprise as Phil Tetlock’s study was to political pundits. There
just seems to have been no progress at all, and there have been
many false alarms.

Lima, Peru

One of the more infamous cases involved a geophysicist named
Brian Brady, who had a Ph.D. from MIT and worked at
Colorado School of Mines. Brady asserted that a magnitude 9.2
earthquake—one of the largest in recorded history—would hit
Lima, Peru, in 1981.40 His prediction initially had a fair amount
of support in the seismological community—an early version of
it had been coauthored with a USGS scientist. But as the theory
became more elaborate—Brady would eventually invoke
everything from the rock bursts he had observed in his studies of
mines to Einstein’s theory of relativity in support of it—
colleagues had started telling him that theory was beyond their
understanding:41 a polite way of saying that he was nuts.
Eventually, he predicted that the magnitude 9.2 earthquake
would be just one in a spectacular series in Peru, culminating in

a magnitude 9.9 earthquake, the largest in recorded history, in
August 1981.42

The prediction was leaked to the Peruvian media and terrified
the population; this serious-seeming American scientist was sure
their capital city would be in ruins. Their fear only intensified
when it was reported that the Peruvian Red Cross had requested
100,000 body bags to prepare for the disaster. Tourism and
property values declined,43 and the U.S. government eventually
dispatched a team of scientists and diplomats to Peru in an effort
to calm nerves. It made front-page news when there was no
Great Peruvian Earthquake in 1981 (or even a minor one).

Parkfield, California

If Lima had provided a warning that false alarms can extract a
substantial psychological and economic cost on the population, it
did not stop seismologists from seeking out the Holy Grail.
While Brady had been something of a lone wolf, there were
cases when earthquake prediction had much more explicit
backing from the USGS and the rest of the seismological
community. These efforts did not go so well either.

Among the most studied seismic zones in the world is
Parkfield, California, which sits along the San Andreas Fault

somewhere between Fresno, Bakersfield, and the next exit with
an In-N-Out Burger. There had been earthquakes in Parkfield at
what seemed to be regular intervals about twenty-two years
apart: in 1857, 1881, 1901, 1922, 1934, and 1966. A USGS-
sponsored paper44 projected the trend forward and predicted
with 95 percent confidence that there would be another such
earthquake at some point between 1983 and 1993, most likely in
1988. The next significant earthquake to hit Parkfield did not
occur until 2004, however, well outside of the prediction

Apart from being wrong, the Parkfield prediction also seemed
to reinforce a popular misconception about earthquakes: that
they come at regular intervals and that a region can be “due” for
one if it hasn’t experienced an earthquake in some time.
Earthquakes result from a buildup of stress along fault lines. It
might follow that the stress builds up until it is discharged, like a
geyser erupting with boiling water, relieving the stress and
resetting the process.

But the fault system is complex: regions like California are
associated with multiple faults, and each fault has its own
branches and tributaries. When an earthquake does strike, it may
relieve the stress on one portion of a fault, but it can transfer it
along to neighboring faults, or even to some faraway portion of
the same fault.45 Moreover, the stress on a fault is hard to

observe directly—until an earthquake hits.
What this means is that if San Francisco is forecasted to have

a major earthquake every thirty-five years, it does not imply that
these will be spaced out evenly (as in 1900, 1935, 1970). It’s
safer to assume there is a 1 in 35 chance of an earthquake
occurring every year, and that this rate does not change much
over time regardless of how long it has been since the last one.

Mojave Desert, California

The Brady and Parkfield fiascoes seemed to suppress efforts at
earthquake prediction for some time. But they came back with a
vengeance in the 2000s, when newer and seemingly more
statistically driven methods of earthquake prediction became the

One such method was put forward by Vladimir Keilis-Borok,
a Russian-born mathematical geophysicist who is now in his late
eighties and teaches at UCLA. Keilis-Borok had done much to
advance the theory of how earthquakes formed and first achieved
notoriety in 1986 when, at a summit meeting in Reykjavík with
Mikhail Gorbachev, President Reagan was handed a slip of
paper predicting a major earthquake in the United States within
the next five years, an event later interpreted to be the Loma

Prieta quake that struck San Francisco in 1989.46

In 2004, Keilis-Borok and his team claimed to have made a
“major breakthrough” in earthquake prediction.47 By identifying
patterns from smaller earthquakes in a given region, they said,
they were able to predict large ones. The methods that Keilis-
Borok applied to identify these patterns were elaborate and
opaque,48 representing past earthquakes with a series of eight
equations, each of which was applied in combination with the
others at all conceivable intervals of time and space. But, the
team said, their method had correctly predicted 2003
earthquakes in San Simeon, California, and Hokkaido, Japan.

Whether the San Simeon and Hokkaido predictions were
publicly communicated ahead of time remains unclear;49 a search
of the Lexis-Nexis database of newspapers reveals no mention
of them in 2003.50 When we are evaluating the success of a
forecasting method, it is crucial to keep “retrodictions” and
predictions separate; predicting the past is an oxymoron and
obviously should not be counted among successes.51

By January 2004, however, Keilis-Borok had gone very
public with another prediction:52 an earthquake measuring at least
magnitude 6.4 would hit an area of the Mojave Desert in
Southern California at some point within the subsequent nine
months. The prediction began to attract widespread attention:
Keilis-Borok was featured in the pages of Discover magazine,

the Los Angeles Times, and a dozen or so other mainstream
publications. Someone from Governor Schwarzenegger’s office
called; an emergency panel was convened. Even the famously
skeptical USGS was willing to give some credit; their Web site
conceded that “the work of the Keilis-Borok team is a legitimate
approach to earthquake prediction research.”53

But no major earthquake hit the Mojave Desert that year, and
indeed, almost a decade later, none has. The Keilis-Borok team
has continued to make predictions about earthquakes in
California, Italy, and Japan but with little success: a 2010
analysis found three hits but twenty-three misses among
predictions that they had clearly enunciated ahead of time.54

Sumatra, Indonesia

There is another type of error, in which an earthquake of a given
magnitude is deemed unlikely or impossible in a region—and
then it happens. David Bowman, a former student of Keilis-
Borok who is now the chair of the Department of Geological
Sciences at Cal State Fullerton, had redoubled his efforts at
earthquake prediction after the Great Sumatra Earthquake of
2004, the devastating magnitude 9.2 disaster that produced a
tsunami and killed 230,000 people. Bowman’s technique, like

Keilis-Borok’s, was highly mathematically driven and used
medium-size earthquakes to predict major ones.55 However, it
was more elegant and ambitious, proposing a theory called
accelerated moment release that attempted to quantify the amount
of stress at different points in a fault system. In contrast to
Keilis-Borok’s approach, Bowman’s system allowed him to
forecast the likelihood of an earthquake along any portion of a
fault; thus, he was not just predicting where earthquakes would
hit, but also where they were unlikely to occur.

Bowman and his team did achieve some initial success; the
massive aftershock in Sumatra in March 2005, measuring
magnitude 8.6, had its epicenter in an area his method identified
as high-risk. However, a paper that he published in 200656 also
suggested that there was a particularly low risk of an earthquake
on another portion of the fault, in the Indian Ocean adjacent to
the Indonesian province of Bengkulu. Just a year later, in
September 2007, a series of earthquakes hit exactly that area,
culminating in a magnitude 8.5. Fortunately, the earthquakes
occurred far enough offshore that fatalities were light, but it was
devastating to Bowman’s theory.

Between a Rock and a Hard Place

After the model’s failure in 2007, Bowman did something that
forecasters very rarely do. Rather than blame the failure on bad
luck (his model had allowed for some possibility of an
earthquake near Bengkulu, just not a high one), he reexamined
his model and decided his approach to predicting earthquakes
was fundamentally flawed—and gave up on it.

“I’m a failed predictor,” Bowman told me in 2010. “I did a
bold and stupid thing—I made a testable prediction. That’s what
we’re supposed to do, but it can bite you when you’re wrong.”

Bowman’s idea had been to identify the root causes of
earthquakes—stress accumulating along a fault line—and
formulate predictions from there. In fact, he wanted to
understand how stress was changing and evolving throughout the
entire system; his approach was motivated by chaos theory.

Chaos theory is a demon that can be tamed—weather
forecasters did so, at least in part. But weather forecasters have
a much better theoretical understanding of the earth’s atmosphere
than seismologists do of the earth’s crust. They know, more or
less, how weather works, right down to the molecular level.
Seismologists don’t have that advantage.

“It’s easy for climate systems,” Bowman reflected. “If they

want to see what’s happening in the atmosphere, they just have to
look up. We’re looking at rock. Most events occur at a depth of
fifteen kilometers underground. We don’t have a hope of drilling
down there, realistically—sci-fi movies aside. That’s the
fundamental problem. There’s no way to directly measure the

Without that theoretical understanding, seismologists have to
resort to purely statistical methods to predict earthquakes. You
can create a statistical variable called “stress” in your model, as
Bowman tried to do. But since there’s no way to measure it
directly, that variable is still just expressed as a mathematical
function of past earthquakes. Bowman thinks that purely
statistical approaches like these are unlikely to work. “The data
set is incredibly noisy,” he says. “There’s not enough to do
anything statistically significant in testing hypotheses.”

What happens in systems with noisy data and underdeveloped
theory—like earthquake prediction and parts of economics and
political science—is a two-step process. First, people start to
mistake the noise for a signal. Second, this noise pollutes
journals, blogs, and news accounts with false alarms,
undermining good science and setting back our ability to
understand how the system really works.

Overfitting: The Most Important Scientific Problem
You’ve Never Heard Of

In statistics, the name given to the act of mistaking noise for a
signal is overfitting.

Suppose that you’re some sort of petty criminal and I’m your
boss. I deputize you to figure out a good method for picking
combination locks of the sort you might find in a middle school–
-maybe we want to steal everybody’s lunch money. I want an
approach that will give us a high probability of picking a lock
anywhere and anytime. I give you three locks to practice on—a
red one, a black one, and a blue one.

After experimenting with the locks for a few days, you come
back and tell me that you’ve discovered a foolproof solution. If
the lock is red, you say, the combination is 27-12-31. If it’s
black, use the numbers 44-14-19. And if it’s blue, it’s 10-3-32.

I’d tell you that you’ve completely failed in your mission.
You’ve clearly figured out how to open these three particular
locks. But you haven’t done anything to advance our theory of
lock-picking—to give us some hope of picking them when we
don’t know the combination in advance. I’d have been interested
in knowing, say, whether there was a good type of paper clip for
picking these locks, or some sort of mechanical flaw we can

exploit. Or failing that, if there’s some trick to detect the
combination: maybe certain types of numbers are used more
often than others? You’ve given me an overly specific solution to
a general problem. This is overfitting, and it leads to worse

The name overfitting comes from the way that statistical
models are “fit” to match past observations. The fit can be too
loose—this is called underfitting—in which case you will not
be capturing as much of the signal as you could. Or it can be too
tight—an overfit model—which means that you’re fitting the
noise in the data rather than discovering its underlying structure.
The latter error is much more common in practice.

To see how this works, let’s give ourselves an advantage that
we’ll almost never have in real life: we’ll know exactly what the
real data is supposed to look like. In figure 5-5, I’ve drawn a
smooth parabolic curve, which peaks in the middle and trails off
near the ends. This could represent any sort of real-world data
that you might like: as we saw in chapter 3, for instance, it
represents a pretty good description of how baseball players
perform as they age, since they are better in the middle of their
careers than at the end or the beginning.

However, we do not get to observe this underlying
relationship directly. Instead, it manifests itself through a series
of individual data points and we have to infer the pattern from
those. Moreover, these data points are affected by idiosyncratic
circumstances—so there is some signal, but there is also some
noise. In figure 5-5, I’ve plotted one hundred data points,
represented by circles and triangles. This looks to be enough to
detect the signal through the noise. Although there is some
randomness in the data, it’s pretty clear that they follow our

What happens, however, when we have a more limited

amount of data, as will usually be the case in real life? Then we
have more potential to get ourselves in trouble. In figure 5-6a,
I’ve limited us to about twenty-five of our one hundred
observations. How would you connect these dots?

Knowing what the real pattern is supposed to be, of course,
you’ll still be inclined to fit the points with some kind of curve
shape. Indeed, modeling this data with a simple mathematical
expression called a quadratic equation does a very good job of
recreating the true relationship (figure 5-6b).

When we don’t know the Platonic ideal for our data, however,
sometimes we get greedy. Figure 5-6c represents an example of
this: an overfit model. In figure 5-6c, we’ve devised a complex
function57 that chases down every outlying data point, weaving up
and down implausibly as it tries to connect the dots. This moves
us further away from the true relationship and will lead to worse

This seems like an easy mistake to avoid, and it would be if
only we were omniscient and always knew about the underlying
structure of the data. In almost all real-world applications,
however, we have to work by induction, inferring the structure
from the available evidence. You are most likely to overfit a
model when the data is limited and noisy and when your
understanding of the fundamental relationships is poor; both
circumstances apply in earthquake forecasting.

If we either don’t know or don’t care about the truth of the

relationship, there are lots of reasons why we may be prone to
overfitting the model. One is that the overfit model will score
better according to most of the statistical tests that forecasters
use. A commonly used test is to measure how much of the
variability in the data is accounted for by our model. According
to this test, the overfit model (figure 5-6c) explains 85 percent of
the variance, making it “better” than the properly fit one (figure
5-6b), which explains 56 percent. But the overfit model scores
those extra points in essence by cheating—by fitting noise rather
than signal. It actually does a much worse job of explaining the
real world.58

As obvious as this might seem when explained in this way,
many forecasters completely ignore this problem. The wide
array of statistical methods available to researchers enables
them to be no less fanciful—and no more scientific—than a
child finding animal patterns in clouds.* “With four parameters I
can fit an elephant,” the mathematician John von Neumann once
said of this problem.59 “And with five I can make him wiggle his

Overfitting represents a double whammy: it makes our model
look better on paper but perform worse in the real world.
Because of the latter trait, an overfit model eventually will get
its comeuppance if and when it is used to make real predictions.
Because of the former, it may look superficially more

impressive until then, claiming to make very accurate and
newsworthy predictions and to represent an advance over
previously applied techniques. This may make it easier to get the
model published in an academic journal or to sell to a client,
crowding out more honest models from the marketplace. But if
the model is fitting noise, it has the potential to hurt the science.

As you may have guessed, something like Keilis-Borok’s
earthquake model was badly overfit. It applied an incredibly
complicated array of equations to noisy data. And it paid the
price—getting just three of its twenty-three predictions correct.
David Bowman recognized that he had similar problems and
pulled the plug on his model.

To be clear, these mistakes are usually honest ones. To
borrow the title of another book, they play into our tendency to
be fooled by randomness. We may even grow quite attached to
the idiosyncrasies in our model. We may, without even realizing
it, work backward to generate persuasive-sounding theories that
rationalize them, and these will often fool our friends and
colleagues as well as ourselves. Michael Babyak, who has
written extensively on this problem,60 puts the dilemma this way:
“In science, we seek to balance curiosity with skepticism.” This
is a case of our curiosity getting the better of us.

An Overfit Model of Japan?

Our tendency to mistake noise for signal can occasionally
produce some dire real-world consequences. Japan, despite
being extremely seismically active, was largely unprepared for
its devastating 2011 earthquake. The Fukushima nuclear reactor
was built to withstand a magnitude 8.6 earthquake,61 but not a
9.1. Archaeological evidence62 is suggestive of historic tsunamis
on the scale of the 130-foot waves that the 2011 earthquake
produced, but these cases were apparently forgotten or ignored.

A magnitude 9.1 earthquake is an incredibly rare event in any
part of the world: nobody should have been predicting it to the
exact decade, let alone the exact date. In Japan, however, some
scientists and central planners dismissed the possibility out of
hand. This may reflect a case of overfitting.

In figure 5-7a, I’ve plotted the historical frequencies of
earthquakes near the 2011 epicenter in Japan.63 The data includes
everything up through but not including the magnitude 9.1
earthquake on March 11. You’ll see that the relationship almost
follows the straight-line pattern that Gutenberg and Richter’s
method predicts. However, at about magnitude 7.5, there is a
kink in the graph. There had been no earthquakes as large as a
magnitude 8.0 in the region since 1964, and so the curve seems

to bend down accordingly.
So how to connect the dots? If you go strictly by the

Gutenberg–Richter law, ignoring the kink in the graph, you
should still follow the straight line, as in figure 5-7b.
Alternatively, you could go by what seismologists call a
characteristic fit (figure 5-7c), which just means that it is
descriptive of the historical frequencies of the earthquake in that
area. In this case, that would mean that you took the kink in the
historical data to be real—meaning, you thought there was some
good reason why earthquakes larger than about magnitude 7.6
were unlikely to occur in the region.

Here is another example where an innocuous-seeming choice
of assumptions will yield radically distinct conclusions—in this
case, about the probability of a magnitude 9 earthquake in this
part of Japan. The characteristic fit suggests that such an
earthquake was nearly impossible—it implies that one might
occur about every 13,000 years. The Gutenberg–Richter

estimate, on the other hand, was that you’d get one such
earthquake every three hundred years. That’s infrequent but
hardly impossible—a tangible enough risk that a wealthy nation
like Japan might be able to prepare for it.64

The characteristic fit matched the recent historical record

from a bit more snugly. But as we’ve learned, this type
of pattern-matching is not always a good thing—it could imply
an overfit model, in which case it will do a worse job of
matching the true relationship.

In this case, an overfit model would dramatically
underestimate the likelihood of a catastrophic earthquake in the
area. The problem with the characteristic fit is that it relied on
an incredibly weak signal. As I mentioned, there had been no
earthquake of magnitude 8 or higher in this region in the forty-

five years or so prior to . However, these are rare
events to begin with: the Gutenberg–Richter law posits that they
might occur only about once per thirty years in this area. It’s not
very hard at all for a onceper-thirty-year event to fail to occur in
a forty-five-year window,65 no more so than a .300 hitter having
a bad day at the plate and going 0-for-5.66 Meanwhile, there were
quite a few earthquakes with magnitudes in the mid-to high 7’s in
this part of Japan. When such earthquakes had occurred in other
parts of the world, they had almost always suggested the

potential for larger ones. What justification was there to think

that would be a special case?
Actually, seismologists in Japan and elsewhere came up with

a few rationalizations for that. They suggested, for instance, that
the particular composition of the seafloor in the region, which is
old and relatively cool and dense, might prohibit the formation
of such large earthquakes.67 Some seismologists observed that,
before 2004, no magnitude 9 earthquake had occurred in a region
with that type of seafloor.

This was about like concluding that it was impossible for
anyone from Pennsylvania to win the Powerball jackpot because
no one had done so in the past three weeks. Magnitude 9
earthquakes, like lottery winners, are few and far between.
Before 2004, in fact, only three of them had occurred in
recorded history anywhere in the world. This wasn’t nearly
enough data to support such highly specific conclusions about the
exact circumstances under which they might occur. Nor was
Japan the first failure of such a theory; a similar one had been
advanced about Sumatra68 at a time when it had experienced lots
of magnitude 7 earthquakes69 but nothing stronger. Then the Great
Sumatra Earthquake, magnitude 9.2,70 hit in December 2004.

The Gutenberg–Richter law would not have predicted the
exact timing of the Sumatra or Japan earthquakes, but it would

have allowed for their possibility.71 So far, it has held up
remarkably well when a great many more elaborate attempts at
earthquake prediction have failed.

The Limits of Earthquakes and Our Knowledge of

The very large earthquakes of recent years are causing
seismologists to rethink what the upper bounds of earthquakes
might be. If you look at figure 5-3b, which accounts for all

earthquakes since 1964 (including Sumatra and ) it
now forms a nearly straight line though all the data points. A
decade ago, you would have detected more of a kink in the graph

(as in the chart in figure 5-7a). What this meant is that
there were slightly fewer megaquakes than the Gutenberg–
Richter law predicted. But recently we have been catching up.

Because they occur so rarely, it will take centuries to know
what the true rate of magnitude 9 earthquakes is. It will take even
longer to know whether earthquakes larger than magnitude 9.5
are possible. Hough told me that there may be some fundamental

constraints on earthquake size from the geography of fault
systems. If the largest continuous string of faults in the world
ruptured together—everything from Tierra Del Fuego at the
southern tip of South America all the way up through the
Aleutians in Alaska—a magnitude 10 is about what you’d get,
she said. But it is hard to know for sure.

Even if we had a thousand years of reliable seismological
records, however, it might be that we would not get all that far.
It may be that there are intrinsic limits on the predictability of

Earthquakes may be an inherently complex process. The
theory of complexity that the late physicist Per Bak and others
developed is different from chaos theory, although the two are
often lumped together. Instead, the theory suggests that very
simple things can behave in strange and mysterious ways when
they interact with one another.

Bak’s favorite example was that of a sandpile on a beach. If
you drop another grain of sand onto the pile (what could be
simpler than a grain of sand?), it can actually do one of three
things. Depending on the shape and size of the pile, it might stay
more or less where it lands, or it might cascade gently down the
small hill toward the bottom of the pile. Or it might do something
else: if the pile is too steep, it could destabilize the entire system
and trigger a sand avalanche. Complex systems seem to have this

property, with large periods of apparent stasis marked by sudden
and catastrophic failures. These processes may not literally be
random, but they are so irreducibly complex (right down to the
last grain of sand) that it just won’t be possible to predict them
beyond a certain level.

The Beauty of the Noise

And yet complex processes produce order and beauty when you
zoom out and look at them from enough distance. I use the terms
signal and noise very loosely in this book, but they originally
come from electrical engineering. There are different types of
noise that engineers recognize—all of them are random, but they
follow different underlying probability distributions. If you
listen to true white noise, which is produced by random bursts of
sound over a uniform distribution of frequencies, it is sibilant
and somewhat abrasive. The type of noise associated with
complex systems, called Brownian noise, is more soothing and
sounds almost like rushing water.72

Meanwhile, the same tectonic forces that carve fault lines
beneath the earth’s surface also carve breathtaking mountains,

fertile valleys, and handsome coastlines. What that means is that
people will probably never stop living in them, despite the
seismic danger.

Science on Trial

In a final irony of the L’Aquila earthquake, a group of seven
scientists and public officials were quite literally put on trial for
manslaughter in 2011.73 Prosecutors from the city of L’Aquila
alleged that they had failed to adequately notify the public about
the risk of a Big One after the earthquake swarm there.

The trial was obviously ridiculous, but is there anything the
scientists could have done better? Probably there was; there is
fairly clear evidence that the risk of a major earthquake
increases substantially—perhaps temporarily becoming one
hundred to five hundred times higher than its baseline rate74—
following an earthquake swarm. The risk was nevertheless
extremely low—most earthquake swarms do not produce major
quakes—but it was not quite right to imply that everything was
normal and that people should sit down and have a glass of

This book takes the view that the first duty of a forecaster is

always fealty to the truth of the forecast. Politics, broadly
defined, can get in the way of that. The seismological community
is still scarred by the failed predictions in Lima and Parkfield,
and by having to compete against the likes of Giuliani. This
complicates their incentives and distracts them from their
mission. Bad and irresponsible predictions can drive out good

Hough is probably right that the Holy Grail of earthquake
prediction will never be attained. Even if individual
seismologists are behaving responsibly, we nevertheless have
the collective output of the discipline to evaluate, which together
constitutes thousands of hypotheses about earthquake
predictability. The track record suggests that most of these
hypotheses have failed and that magic-bullet approaches to
earthquake prediction just aren’t likely to work.

However, the track record of science as a whole is a
remarkable one; that is also a clear signal. It is probably safe to
conclude that the same method attempted over and over with
little variation is unlikely to yield different results. But science
often produces “unpredictable” breakthroughs.

One area in which seismologists have made some progress is
in the case of very short term earthquake forecasts, as might have

been relevant in L’Aquila. Next to the Gutenberg–Richter law,
the knowledge that major earthquakes essentially always
produce aftershocks is the most widely accepted finding in the
discipline. Some seismologists I spoke with, like John Rundle of
UC Davis and Tom Jordan of the University of Southern
California, are concentrating more on these near-term forecasts
and increasingly take the view that they should be communicated
clearly and completely to the public.

Jordan’s research, for instance, suggests that aftershocks
sometimes move in a predictable geographic direction along a
fault line. If they are moving in the direction of a population
center, they can potentially be more threatening to life and
property even if they are becoming less powerful. For instance,
the magnitude 5.8 earthquake in Christchurch, New Zealand, in
2011, which killed 185, was an aftershock of a 7.0 earthquake
that occurred in September 2010 in a remote part of the country.75

When it comes to aftershocks, there is clearly a lot of signal, so
this may be the more natural place to focus.

Finally, technology is always pushing forward. Recent efforts
by NASA and by Rundle to measure fault stress through remote
sensing systems like GPS satellites have shown some promise.76

Although the efforts are crude for the time being, there is
potential to increase the amount of data at seismologists’
disposal and get them closer to understanding the root causes of


• • •

These methods may eventually produce some forward progress.
If success in earthquake prediction has been almost nonexistent
for millennia, the same was true for weather forecasting until
about forty years ago. Or it may be that as we develop our
understanding of complexity theory—itself a very new branch of
science—we may come to a more emphatic conclusion that
earthquakes are not really predictable at all.

Either way, there will probably be some failed predictions
first. As the memory of our mistakes fades, the signal will again
seem to shimmer over the horizon. Parched for prediction we
will pursue it, even if it is a mirage.

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