Science & Technology: Machine Learning Can Help Map Geothermal Resources

Machine learning picks out hidden vibrations from earthquake data (Phys.org)

Credit: Christine Daniloff, MIT

The new method could allow researchers to artificially synthesize the low-frequency waves that are hidden in seismic data, which can then be used to more accurately map the Earth's internal structures.

"The ultimate dream is to be able to map the whole subsurface, and be able to say, for instance, 'this is exactly what it looks like underneath Iceland, so now you know where to explore for geothermal sources,'" says co-author Laurent Demanet, professor of applied mathematics at Massachusetts Institute of Technology (MIT). "Now we've shown that deep learning offers a solution to be able to fill in these missing frequencies."

As they improve the neural network's predictions, the team hopes to be able to use the method to extrapolate low-frequency signals from actual seismic data, which can then be plugged into seismic models to more accurately map the geological structures below the Earth's surface. The low frequencies, in particular, are a key ingredient for solving the big puzzle of finding the correct physical model.

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