Real-data earthquake localization using convolutional neural networks trained with synthetic data

Continuous seismic monitoring systems aid us to act on preventing strong earthquakes, such as induced by oil and gas extraction, deep geothermal systems and carbon sequestration. These systems provide the data to detect and locate such events and to determine their source magnitude and mechanism. Estimating the location of the source is one of the first parameters to be determined. Most source localization methods in the field of induced seismicity combine migration-based operators with grid-search algorithms. These are computationally intensive and therefore not instantaneous. To improve the time to locate events via grid-search based methods, we investigate the use of Convolutional Neural Networks (ConvNets) to reduce the search space. This is achieved by feeding the ConvNets with the waveforms and outputting a possible area/volume for the source location. Once the ConvNet is trained, it can produce the output almost in real-time. Therefore, it can be used by grid-search type approaches to focus the grid search over the smaller volume provided by the ConvNet. In this study we train ConvNets with synthetic data and apply them to field data. To our knowledge this is the first attempt of training ConvNets on synthetic data for the task of earthquake localization of field data.