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Discrimination of mirco-earthquake on source depth plays an important role in the field of micro-seismic monitoring. Conventional machine learning methods for data classification rely on carefully hand-engineered features that are vulnerable to low signal-to-noise ratio. Convolutional neural networks (CNNs) demonstrate some merits in dealing with structured data modelling where a set of meaningful features can be automatically extracted from sample learning. This paper explores the use of machine learning techniques for discrimination between deep and shallow mirco-seismic events. A benchmarked dataset including 444 micro-earthquakes from an underground cavern collapse in South Louisiana is employed for performance evaluation in this study, where several feature-based classifiers are compared against the CNN classifier. Empirical results show that the deep learning method outperforms the conventional classification techniques in discriminating the source depth. [ABSTRACT FROM AUTHOR]