  Determining earthquake hypocenters and focal mechanisms requires precisely
measured P-wave arrival times and first-motion polarities. Automated algorithms
for estimating these quantities have been less accurate than estimates by human
experts, which is problematic for processing large data volumes. Here, we train
convolutional neural networks to measure both quantities, which learn directly
from seismograms without the need for feature extraction. The networks are
trained on 18.2 million manually picked seismograms for the southern California
region. Through cross-validation on 1.2 million independent seismograms, the
differences between the automated and manual picks have a standard deviation of
0.023 seconds. The polarities determined by the classifier have a precision of
95% when compared with analyst-determined polarities. We show that the
classifier picks more polarities overall than the analysts, without sacrificing
quality, resulting in almost double the number of focal mechanisms. The
remarkable precision of the trained networks indicates that they can perform as
well, or better, than expert seismologists.
