Detection of hidden earthquakes after the 2011 Tohoku earthquake by automatic hypocenter determination combined with machine learning

Abstract

After the 2011 Mw 9.0 Tohoku earthquake, seismicity became extremely active throughout Japan. Despite enormous efforts to detect the large number of earthquakes, microearthquakes (M < 2 inland, M < 3 offshore) were not always cataloged and many have remained undetected, making it difficult to understand the detailed seismicity after the 2011 Tohoku earthquake. We developed an automatic hypocenter determination method combined with machine learning to detect microearthquakes. Machine learning was used for phase classification with convolutional neural networks and ensemble learning to remove false detections. We detected > 920,000 earthquakes from March 2011 to February 2012, triple the number of the conventional earthquake catalog (~ 320,000). This represents a great improvement in earthquake detection, especially in and around the Tohoku region. Detailed analysis of our merged catalog more clearly revealed features such as (1) swarm migrations, (2) small foreshock activity, and (3) increased microseismicity preceding repeating earthquakes. This microseismic catalog provides a magnifying glass for understanding detailed seismicity.Graphical