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Researchers Create Earthquake System Model with Better Detection Capabilities

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Aftermath of a 2015 earthquake in Katmandu, Nepal.

University of Wyoming researcher Pejman Tahmasebi said the new model also may be used "to monitor volcanic activities, infrastructure modeling, clean energy, geothermal systems and other problems in which processing large data is a challenge.

Credit: ReSurge International/Flickr Creative Commons

The University of Wyoming's Pejman Tahmasebi and Tao Bai have invented a machine learning (ML) model that boosts the accuracy of earthquake detection significantly over current models.

Tahmasebi said the model processes signal data recorded by seismometers, and can automatically distinguish seismic events from seismic noise.

The model combines existing long short-term memory and fully convolutional network ML models; the former captures data signal changes over time, and the latter filters out hidden features of seismic events.

Tahmasebi said the model boasts 89.1% classification accuracy, a 14.5% improvement over the state-of-the-art ConvNetQuake model.

From University of Wyoming News
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