Cascadia's Fault - Jerry Thompson [121]
The Tangshan disaster gave prediction optimists a sharp reality check. So did the Parkfield experiment in California. On September 28, 2004, a magnitude 6 temblor finally rattled the farming town of Parkfield—at least twelve years after scientists predicted it would happen. To say that William Bakun and Allan Lindh of the USGS, who along with Tom McEvilly of the University of California at Berkeley had offered the forecast in 1985, were disappointed would probably be an understatement. Five moderate (magnitude 6) events with similar “characteristics” had occurred on the Parkfield segment of the San Andreas since 1857. By their calculations the seventh in what looked like a repeating series of nearly identical ruptures should have happened some time in 1988 but surely by the end of 1992 with a 95 percent probability.
Clearly the “time-predictable” part of their hypothesis was wrong. The idea that fault failures tend to repeat themselves like clockwork had been kicking around since 1910, when geologist Harry F. Reid of Johns Hopkins University suggested it ought to be possible to figure out when and where quakes would happen by keeping close tabs on the build-up of stress. Looking at how unevenly land had shifted along the San Andreas during the great San Francisco earthquake of 1906, Reid developed the elastic rebound hypothesis—a cornerstone of modern geology long before the advent of plate tectonics—which Bakun, Lindh, and McEvilly set out to test in Parkfield eight decades later.
Reid’s idea was that stress built up unevenly along the fault and it took a massive rupture—at the point where the strain was great enough to cause the rocks to fail—in order to relieve or recover that strain. The longer the strain built up, the bigger the shock would be. If you knew how often the fault had ruptured in the past, you could in theory estimate how long before the next one was due.
But what if the last rip did not release all of the accumulated strain? Wouldn’t that alter the timeline for the next one? When Bakun and Lindh published their forecast in the August 16, 1985, edition of Science, they noted that the 1983 magnitude 6.5 at Coalinga, eighteen miles (30 km) off the San Andreas to the northeast of Parkfield, might have done exactly that. It had just possibly relieved enough of the “tension in the spring” of Parkfield’s clock to delay the next rumble in the series. A delay of more than a dozen years, however, was way more than merely late.
Critics within the science community didn’t wait until the 2004 jolt to pounce on Parkfield. Even though the expected magnitude 6 event did happen eventually, in more or less the same location as last time and where Bakun and his colleagues said it would be, the Parkfield prediction experiment was branded a failure shortly after the original time window closed in January 1993. A long-standing and rancorous philosophical debate intensified as some seismologists turned away from divining the future and deleted “the P-word” from their vocabularies.
By the mid-1990s Robert J. Geller at Tokyo University had become the most persistent and outspoken critic of everything predictive, especially Japan’s massive and well-funded multiyear effort to anticipate the next big temblor near Tokyo. Geller had been scathing in his view of American efforts as well, his central thesis being that prediction studies have been going on for more than a century—and yet we seemed no nearer a solution to the problem than we were in the beginning.
Geller was fond of quoting Charles Richter, developer of the earliest and best-known earthquake magnitude scale and one of the most respected seismologists in the world, who in 1977 commented that he had “a horror of predictions and of predictors. Journalists and the general public rush to any suggestion of earthquake prediction like hogs toward a full trough.” Vitriol and aspersions aside, Geller’s central argument against prediction was and is based on the idea that “individual earthquakes are inherently unpredictable because of the chaotic, highly nonlinear nature