As a result of the gas extraction in Groningen (the Netherlands) the amount of earthquakes have increased over the past decades. Understanding the induced seismicity in the Zeerijp area is important for the population living in this relatively densely populated region, but also essential to the Dutch governement and the operator of the field; the Nederlandse Aardolie Maatschappij (NAM). One way of gaining the knowledge on the dynamics of the fault systems in the area is by, for example, a full waveform inversion f recorded seismic data that gives us the moment tensor describing the earthquake mechanism. A drawback of this method is that it is computationally expensive and time intensive. In this thesis two different machine learning techniques are investigated that can lead to a faster estimation of the moment tensor. We find that, when using time traces that are modelled from the 1D velocity model of the area, there is a difference in sensitivity of the network to the individual moment tensor components. Besides that, using a feed forward neural network generally yields a better performance, but does not give us the associated uncertainties of the network. This is in contrast with the mixture density network, which performs slightly worse than the feed forward network, but it does give us the involved uncertainties when the algorithm makes the predictions. Adding different levels of Gaussian noise to the data gave us a first insight as to how the precision of the moment tensor estimations would change. It was found that a mixture density network has a more stable prediction when estimating low signal-to-noise ratio data. No test with real data from the Zeerijp area is done, however, we can conclude from our results that the estimation of the moment tensors in this region should be possible with the use of machine learning techniques.