A Machine-Learning Approach for Earthquake Magnitude Estimation

In this study, we present a fast and reliable method for end-to-end estimation of earthquake magnitude from raw waveforms recorded at single stations. We design a regressor (MagNet) composed of convolutional and recurrent neural networks that is not sensitive to the data normalization, hence waveform amplitude information can be utilized during the training. The network can learn distance-dependent and site-dependent functions directly from the training data. Our model can predict local magnitudes with an average error close to zero and standard deviation of similar to 0.2 based on single-station waveforms without instrument response correction. We test the network for both local and duration magnitude scales and show a station-based learning can be an effective approach for improving the performance. The proposed approach has a variety of potential applications from routine earthquake monitoring to early warning systems.