Seismic warnings“Majority rules” when looking for earthquakes, explosions
Finding the ideal settings for each sensor in a network to detect vibrations in the ground, or seismic activity, can be a painstaking and manual process. Researchers at Sandia are working to change that by using software that automatically adjusts the seismic activity detection levels for each sensor. The new software reduces false, missed detections of seismic activity.
A dormant volcano in Antarctica helped researchers at Sandia National Laboratories improve sensor data readings to better detect earthquakes and explosions and tune out everyday sounds such as traffic and footsteps.
Finding the ideal settings for each sensor in a network to detect vibrations in the ground, or seismic activity, can be a painstaking and manual process. Researchers at Sandia are working to change that by using software that automatically adjusts the seismic activity detection levels for each sensor.
Sandia tested the new software with seismic data from the Mt. Erebus volcano in Antarctica and achieved 18 percent fewer false detections and 11 percent fewer missed detections than the original performance of the sensors on Mt. Erebus.
Sandia says that until now, the main way to ensure sensors were picking up unusual seismic activity and not reporting regular activity was to manually adjust the settings of each sensor to its specific surroundings. Unfortunately, getting those settings exactly right is difficult, especially because those ideal settings change with the seasons and weather patterns.
During a three-year project funded by Laboratory Directed Research and Development, researchers developed software that automatically adjusts the detection settings for the data coming from each sensor in a network using a ‘majority rules’ approach, which led to fewer false detections of seismic activity and fewer missed detections of actual events. The work was recently published in a Bulletin of the Seismological Society of America paper, “Dynamic Tuning of Seismic Signal Detector Trigger Levels for Local Networks” and the open source Python-based software is available for download.