Scientists turn to AI while hunting quakes
Oklahoma' s chief earthquake researcher has developed cutting-edge software that uses machine learning to sniff out tiny tremors.
State seismologist Jake Walter spent time early in the coronavirus pandemic to focus on this big project.
Walter and his fellow researchers at the Oklahoma Geological Survey are using machine learning, also called artificial intelligence, to identify small earthquakes that would have otherwise been impossible to detect.
The software allows OGS to identify twice as many earthquakes as using conventional monitoring techniques. It's been in place since May, and is able to detect earthquakes that are too small to be felt by people, which OGS says is about magnitude 2.
With time on his hands during his lockdown, Walter began organizing his computer codes, each with unique approaches for detecting and locating earthquakes. When running in tandem, the codes provide a wealth of raw data to better capture the microscopic movements of the earth, OGS said.
But there was a problem. With so much data collected, his team of geoscientists couldn't efficiently analyze it themselves.
“That's when I realized we
could leverage a machinelearning picker developed at the California Institute of Technology ,” said Walter.
The CalTech machine learning pick er was designed using millions of seismogram datasets. The picker “l earned” what an earthquake, even a very small one, looks like. Able to sort through enormous amounts of data, it can immediately identify earthquakes in a waveform.
Walter and his team can feed their data into the picker and get picks f or Oklahoma's 90 individual monitoring stations. Next, they added event association and other tools onto the picker that can output a full earthquake catalog for a provided dataset.
The result is a fully operational software package that analyzes data and detects earthquakes. It is significantly more sensitive than its predecessors and operates fairly easily with a few changes to the code.
This new system has not replaced the OGS real- time earthquake detection system that monitors and reports on
all earthquake activity in the state. Rather, the two detection systems are running in parallel, and the team is now identifying smaller events thanks to the new software.
Walter' s software is believed to be the first implementation of machine-learning technology by a regional
seismic network for routine earthquake identification and alerting. The software package is open source, meaning other geoscientists can use it and even adapt it for their own needs.
“We're already hearing from fellow researchers from around the world who are using our
software on their projects,” said Walter.
The team' s findings were just published in the geo science journal Seismological Research Letters. Walter hopes that use of his software will continue t he advancement of understanding of se is mi city around the world.