Oklahoma Geological Survey at OU develops program to detect previously undetectable earthquakes | News

The Oklahoma Geological Survey (OGS) at OU has developed a program that uses artificial intelligence to detect earthquakes that previously could not be detected.

According to an OU News press release, Oklahoma state seismologist Jake Walter and his team of researchers used the March lockdown caused by COVID-19 to develop “an advanced seismic detection technology.”

Using this program since early May, OGS has been able to identify twice as many earthquakes “too small to be felt by people,” according to the release.

According to the release, Walter ran into a problem in his early research when running his computer codes “each with unique techniques for detecting and identifying earthquakes” side by side. The data was “too much” for the geologists to examine.

“I realized then that we could take advantage of the machine learning picker that was developed at the California Institute of Technology,” Walter said in the release.

CalTech’s machine learning picker “learns” what an earthquake looks like by using millions of seismograph data sets to enable the picker to identify earthquakes in waveform, depending on the release. With 90 OGS stations across Oklahoma, Walter and his team can enter their data into the Capture Tool for selections from the individual stations.

Adding various tools including event coupling to the picker that can “output the complete seismic catalog of the presented dataset,” the result is a “complete software package that analyzes data and detects earthquakes” more sensitively than its predecessors, according to the release.

Although Walter’s new system did not replace the current OGS, the old and new systems now work in tandem to identify smaller events due to the new software, according to the release.

According to the release, the open-source software package is available to other Earth scientists and is believed to be the first “application of machine learning technology through a regional seismic network to identify routine earthquakes and alert”.

“We are already hearing from our fellow researchers from around the world who are using our software in their projects,” Walter said in the statement.

With a publication in the journal Seismological Research Letters, Walter hopes his software will “continue to advance” understanding earthquakes around the world, according to the statement.

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