Machine learning and earthquake risk prediction

Homes and offices are only as solid as the ground beneath them. When that solid ground turns to liquid — as sometimes happens during earthquakes — it can topple buildings and bridges. The phenomenon is known as liquefaction, and it was a major feature of the 2011 earthquake in Christchurch, New Zealand, a magnitude 6.3 quake that killed 185 people and destroyed thousands of homes.

An upside of the Christchurch quake was that it was one of the most well-documented in history. Because New Zealand is seismically active, the city was instrumented with numerous sensors for monitoring earthquakes. Post-event reconnaissance provided a wealth of additional data on how the soil responded across the city.

“It’s an enormous amount of data for our field,” said researcher Maria Giovanna Durante. “We said, ‘If we have thousands of data points, maybe we can find a trend.'”

Durante works with Ellen Rathje, an engineer at The University of Texas at Austin and principal investigator of the U.S. National Science Foundation-funded DesignSafe effort, which supports research across the natural hazards community.

Rathje’s research on liquefaction led her to study the Christchurch event. She had been thinking about ways to incorporate machine learning into her research and this case seemed like a great place to start.

The researchers developed a machine learning model that predicted the amount of lateral movement that occurred when the Christchurch earthquake caused soil to lose its strength and shift relative to its surroundings. The results were published in Earthquake Spectra.

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