Localizing Microseismic Events Using Semi-Supervised Generative Adversarial Networks

The performance of the microseismic monitoring technique depends greatly on the accuracy of microseismic event localization. Recently, machine learning (ML) methods have been extensively implemented for the localization of microseismic events. These neural networks are typically trained using numerous microseismic events labeled with known source locations. Obtaining enough microseismic events with good source locations can be difficult and costly. To overcome this shortcoming, we present a microseismic events localization method using semi-supervised generative adversarial networks (GANs). We utilize limited labeled seismograms and large amounts of unlabeled seismograms to train the semi-supervised GANs, thus improving the prediction ability of the networks. Finally, we evaluate the performance of the proposed method using synthetic microseismic data and field data. Comparison with the supervised learning methods on the same microseismic data shows that the proposed method can significantly improve the accuracy of locating microseismic sources in the lack of sufficient source labels.