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Abstract
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 encoderdecoder 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.
Original language | English |
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Publisher | Department of Physics, Technical University of Denmark |
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Number of pages | 324 |
Publication status | Published - 2020 |
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Dive into the research topics of 'Machine Learning in Geoscience Applications of Deep Neural Networks in 4D Seismic Data Analysis'. Together they form a unique fingerprint.Projects
- 1 Finished
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4D Seismics for Fracture Characterization
Dramsch, J. S. (PhD Student), Lüthje, M. (Main Supervisor), Christensen, A. N. (Supervisor), Macbeth, C. (Supervisor), Jørgensen, T. M. (Examiner), Mosegaard, K. (Examiner) & Naeini, E. Z. (Examiner)
Technical University of Denmark
15/10/2016 → 10/02/2021
Project: PhD