Microearthquakes -- low-intensity earthquakes that register 2.0 or less magnitude on the moment magnitude scale -- rarely cause property damage. And as a result of background noise, small events, and false positives, they're not always picked up by seismic monitoring systems. A possible solution is described in a new paper from the Department of Geophysics at Stanford University, where scientists have developed an AI system -- dubbed Cnn-Rnn Earthquake Detector, or CRED -- that can isolate and identify a range of seismic signals from historical and continuous data. It builds on the work of Harvard and Google, which in August created an AI model capable of predicting the location of aftershocks up to one year after a major earthquake. The researchers' system consists of neural network layers -- interconnected processing nodes that loosely mimic the function of neurons in the brain -- of two types: convolutional neural networks and recurrent neural networks.