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The robust and automated determination of earthquake source parameters on a global and regional scale is important for many applications in seismology. We present a novel probabilistic method to invert a wide variety of (waveform) data for point‐source parameters in real time using pattern recognition. Inferences are made in the form of marginal probability density functions for point‐source parameters and incorporate realistic posterior uncertainty estimates. The neural‐network‐based method is calibrated using samples from the prior distribution, which are synthetic data vectors, and corresponding sources located in a predefined monitoring volume. Once a set of trained neural networks is available, inversions are fast with very moderate demands on computational resources: an inversion takes less than a second on a standard desktop computer. Uncertainties in the layered Earth model are taken into account in the Bayesian framework and increase the robustness of the results with respect to neglected 3D heterogeneities. Moreover, we find that the method is very robust with respect to perturbations such as observational noise and missing data and therefore is potentially well suited for automated and real‐time tasks, such as earthquake monitoring and early warning. We demonstrate the method by means of synthetic tests and by inverting an observed high‐rate Global Positioning System displacement dataset for the 2010 M w 7.2 El Mayor–Cucapah event. Our results are compatible with published point‐source estimates for this event within the respective uncertainty bounds.Online Material: Additional information on the neural network methodology and implementation details, tables on neural network parameters, crustal model and reference double‐couple solution, and figures showing prediction error, crustal models, normalized displacements, and histograms of weighted parameters.

Robust and Fast Probabilistic Source Parameter Estimation from Near‐Field Displacement Waveforms Using Pattern Recognition