Locations and source mechanisms of microseismic events are very crucial for understanding the fracturing behavior and evolution of stress fields within the reservoir and hence facilitates the detection of hydraulic fracture growth and estimation of the stimulated reservoir volume (SRV). In the classic workflow, there are two main methods for locating microseismic events with a calibrated fixed velocity model: grid search and linear inversion. The grid search is very stable; can find a global minimum and does not need initial event locations. However, it is computationally intensive and its resolution depends on the grid size, hence, it is not suitable for real-time monitoring. On the other hand, although the linear inversion method is quite fast, the inversion may be pushed into a local minimum by thin shale layers and large velocity contrasts leading to false locations. The source mechanisms of the located events, which provide information about the magnitudes, modes and orientations of the fractures, are obtained through moment tensor inversion of the recorded waveforms. In this paper, we propose a deep neural network approach to solve the above challenges, in real-time, and increase the efficiency and accuracy of location and moment tensor inversion of microseismic events, induced during hydraulic fracturing. Location of microseismic events was considered as a multi-dimensional and non-linear regression problem and a multi-layer two-dimensional (2D) convolutional neural network (CNN) was designed to perform the inversion. The source mechanisms of the microseismic events were inverted using a multi-head one-dimensional (1D) CNN. The neural networks were trained using synthetic microseismic events with low signal to noise ratio (SNR) to imitate field data. The overall results indicate that both the 2D CNN and 1D CNN models are capable of learning the relationship between the events locations and source mechanisms and the waveform data to a high degree of precision compared to classical methods. Both the event location and source mechanism errors are less than few percent. Deep learning offers a number of benefits for automated and real-time microseismic event location and moment tensor inversion, including least preprocessing, continuous improvement in performance as more training data is obtained, as well as low computational cost.

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