$P$-wave first motion polarity classification of earthquake waveforms using the CFM convolutional neural network.

We present the Convolutional First Motion (CFM) network, a Deep Convolutional Neural Network (DCNN) used to classify seismic traces based on first motion polarities of $P$-waves. The network, trained on $\sim 140000$ time windows centered on $P$-wave arrival times of waveforms belonging to the INSTANCE catalogue, shows high accuracy levels ($i.e.$, $97.4%$ and $96.2%$) when tested on two independent test sets, high reliability and great generalization ability. Further testing showed that if we give the network waveforms with uncertain arrival times, it acquires robustness to this type of noise, still showing high-level of performance. We infer that the CFM network would be suitable in succession to automatic techniques that derive $P$-wave arrival times, for example techniques in which deep learning is used, in order to cover the entire data processing phase with machine learning. Given the incredible ability of DCNNs to model and process large volumes of data and their remarkable performance, it is reasonable to assume that deep learning will soon become the norm even in the context of first-motion.