Machine learning picks out hidden vibrations from earthquake data

摘要： Technique may help scientists more accurately map vast underground geologic structures.

Over the last century, scientists have developed methods to map the structures within the Earth’s crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns. The way these waves bounce and scatter through the Earth can give scientists an idea of the type of structures that lie beneath the surface.

There is a narrow range of seismic waves — those that occur at low frequencies of around 1 hertz — that could give scientists the clearest picture of underground structures spanning wide distances. But these waves are often drowned out by Earth’s noisy seismic hum, and are therefore difficult to pick up with current detectors. Specifically generating low-frequency waves would require pumping in enormous amounts of energy. For these reasons, low-frequency seismic waves have largely gone missing in human-generated seismic data.

Now MIT researchers have come up with a machine learning workaround to fill in this gap.

In a paper appearing in the journal Geophysics, they describe a method in which they trained a neural network on hundreds of different simulated earthquakes. When the researchers presented the trained network with only the high-frequency seismic waves produced from a new simulated earthquake, the neural network was able to imitate the physics of wave propagation and accurately estimate the quake’s missing low-frequency waves.

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FULL TEXT： MIT NEWS

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