Deep Learning for Efficient Microseismic Location Using Source Migration-Based Imaging

Migration-based location methods (e.g., time-reverse imaging based on wave equation, Kirchhoff summation, and diffraction stacking) can effectively locate events of low signal-to-noise ratios by stacking waveforms from many receivers. The methods have been widely applied for surface microseismic monitoring. However, these methods may not produce accurate results if there are polarity reversals in the surface records for a double-couple or even a general moment tensor event. Various imaging conditions have been developed to solve the non-focus image problem for a non-explosive source. Here, we propose a deep convolutional neural network to predict a better-focused image from a regular migration image that contains a quasi-symmetric pattern in both space and time. To train the network, we first simulate a large number of surface records from sources with various locations and mechanisms. We then compute diffraction stacking images from the records and take the images as the input to the network. We define the corresponding training labels as images (with the same size as the input) with Gaussian distributions centered at the true sources. This network, trained by only synthetic datasets, works well in field data to detect source locations from images for unknown events. Both synthetic tests and field data applications demonstrate that the proposed method can effectively improve diffraction stacking images for efficient microseismic location.