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Despite advanced seismological techniques, automatic source characterization for microseismic earthquakes remains difficult and challenging since current inversion and modelling of high-frequency signals are complex and time consuming. For real-time applications such as induced seismicity monitoring, the application of standard methods is often not fast enough for true complete real-time information on seismic sources. In this paper, we present an alternative approach based on recent advances in deep learning for rapid source-parameter estimation of microseismic earthquakes. The seismic inversion is represented in compact form by two convolutional neural networks, with individual feature extraction, and a fully connected neural network, for feature aggregation, to simultaneously obtain full moment tensor and spatial location of microseismic sources. Specifically, a multibranch neural network algorithm is trained to encapsulate the information about the relationship between seismic waveforms and underlying point-source mechanisms and locations. The learning-based model allows rapid inversion (within a fraction of second) once input data are available. A key advantage of the algorithm is that it can be trained using synthetic seismic data only, so it is directly applicable to scenarios where there are insufficient real data for training. Moreover, we find that the method is robust with respect to perturbations such as observational noise and data incompleteness (missing stations). We apply the new approach on synthesized and example recorded small magnitude (M <= 1.6) earthquakes at the Hellisheioi geothermal field in the Hengill area, Iceland. For the examined events, the model achieves excellent performance and shows very good agreement with the inverted solutions determined through standard methodology. In this study, we seek to demonstrate that this approach is viable for microseismicity real-time estimation of source parameters and can be integrated into advanced decision-support tools for controlling induced seismicity.
Global earthquake locations are often associated with very large systematic travel-time residuals even for clear arrivals, especially for regional and near-regional stations in subduction zones because of their strongly heterogeneous velocity structure. Travel-time corrections can drastically reduce travel-time residuals at regional stations and, in consequence, improve the relative location accuracy. We have extended the shrinking-box source-specific station terms technique to regional and teleseismic distances and adopted the algorithm for probabilistic, nonlinear, global-search location. We evaluated the potential of the method to compute precise relative hypocentre locations on a global scale. The method has been applied to two specific test regions using existing P- and pP-phase picks. The first data set consists of 3103 events along the Chilean margin and the second one comprises 1680 earthquakes in the Tonga-Fiji subduction zone. Pick data were obtained from the GEOFON earthquake bulletin, produced using data from all available, global station networks. A set of timing corrections varying as a function of source position was calculated for each seismic station. In this way, we could correct the systematic errors introduced into the locations by the inaccuracies in the assumed velocity structure without explicitly solving for a velocity model. Residual statistics show that the median absolute deviation of the travel-time residuals is reduced by 40-60 per cent at regional distances, where the velocity anomalies are strong. Moreover, the spread of the travel-time residuals decreased by similar to 20 per cent at teleseismic distances (>28 degrees). Furthermore, strong variations in initial residuals as a function of recording distance are smoothed out in the final residuals. The relocated catalogues exhibit less scattered locations in depth and sharper images of the seismicity associated with the subducting slabs. Comparison with a high-resolution local catalogue reveals that our relocation process significantly improves the hypocentre locations compared to standard locations.