Machine Learning provides a tool for the modelling and analysis of geoscientific data. I have placed recent developments in deep learning into the greater context of machine learning by reviewing the approaches and challenges of the use of machine learning in geoscience. The thesis consists of six peer-reviewed publications and one submitted journal paper. Furthermore, five peer-reviewed publications are placed in the appendix.

The aim of this thesis is to apply recent developments in computer vision systems, neural networks, and machine learning to geoscientific data, particularly 4D seismic analysis. Neural networks are a type of machine learning that has made significant contributions to modern artificial intelligence and automation. The applicability of neural networks for their capability of being a universal function approximator was recognized within geophysics from an early stage. Following the recent interest in deep learning, neural networks have experienced a renaissance in geoscience applications, particularly in automatic seismic interpretation, inversion processes and sequence modelling.

This is followed by an exploration of unsupervised machine learning to segment chalk sediments in back-scatter scanning electron microscopy data. The next chapter shows that using neural networks pre-trained on natural images can reduce the data necessary for transfer learning to geoscience problems. This is followed by a chapter showing that complex-valued convolutions can stabilize training and data compression on non-stationary physical data. Subsequently, pressure-saturation data is extracted from 4D seismic amplitude difference maps using a novel deep dense sample-based encoder-decoder network. The network contains a low-assumption physical basis (Amplitude Versus Offset) as explicit features and learns the residual for the regression of the "inversion" data. This work shows that transfer from simulation data to field data is possible.

Finally, an unsupervised method is devised to extract 3D time-shifts from two 4D seismic cubes. The network extracts these 3D time-shifts including uncertainty measures. Commonly, time-shifts are extracted in 1D, due to processing speed, computational cost and poor performance of 3D methods. Within the training loop, the stationary velocity field is numerically integrated to obtain 3D time shifts that are constrained by the topology in a geologically consistent manner. The unsupervised implementation of the network structure ensures that biases from other time-shift extraction methods are not implicitly included in the network. This application utilizes unsupervised learning by devising a way of behaviour for the network to follow instead of supplying ground truth labels. Moreover, this results in a way to increase trust in the system, by limiting the extraction process to the deep learning system and performing well-defined operations within the network to automate the unsupervised training.