Chapter 2: Theory

This thesis applies Machine Learning methods to 4D seismic data. In this chapter I introduce 4D seismic concepts and the motivation to acquire and analyze 4D seismic data. I go on to introduce machine learning and review the development of machine learning in itself and in the field of geoscience. The focus on this thesis is on Neural Networks, particularly Deep Learnings to geophysical problems. Considering recent developments in computer vision, a focus on Convolutional Neural Networks, the developments and break-throughs of this type of Neural Network (NN) and the innovations that lead to the recent adoption of Machine Learning in geoscience are explored.

4D seismic

4D seismic is the analysis of seismic data that was acquired over the same location after some calendar time has passed. The repeated imaging of the same subsurface location, highlights changes in the subsurface that can lead to improved understanding of subsurface processes and fluid movement. E&P companies in particular have an interest in imaging hydrocarbon reservoirs (Johnston, 2013b), however 4D seismic imaging wide ap- plications for subsurface characterization, such as observing volcanic activitiy (Londoño et al., 2018) or CO~2~ sequestration monitoring (Arts et al., 2004).

The main applications of 4D seismic analysis according to Yilmaz (2003) and Johnston (2013a) include:

4D seismic data analysis suffers from the superposition of multiple effects on the seismic imaging. These effects include changes in the acquisition equipment due to technological advances, changes in acquisition geometry (source-receiver mismatch), as well as physical changes in the subsurface (Yilmaz, 2003; Johnston, 2013b). These physical changes are in part due to fluid movement in the subsurface (Lumley, 1995), as well as, changes in the geology due to compaction and expansion (Hatchell et al., 2005a). These geomechanical effects change the position of the reflectors, the thickness of stratigraphy and the physical properties such as density and wave velocity (Herwanger, 2015).

Succesfull 4D applications rely on careful acquisition planning, closely matching the mismatch of source (∆S) and receiver (∆R). This awareness has generally improved the repeatability of seismic acquisition, however, the Normalized Root Mean Squared Error (NRMS) remains to be an important measure of noise sources that deteriorate the 4D seismic analysis. Moreover, 4D seismic analysis has brought to light that some 3D seismic processing workflows are not as repeatable and amplitude-preserving as they were thought to be (Lumley, 2001). Modern processing flows include co-processing of the base and monitor seismic volumes with specialized tools to reduce differences from processing (Johnston, 2013a).

The standard analysis tool in 4D seismic interpretation are amplitude differences (Johnston, 2013b). Differences can stem from fluid movement or replacement and changes in the rock matrix due to compaction, temperature changes, and movement of injected CO~2~ plumes. Additionally, by-passed oil zones in heterogeneous reservoirs can be identified by “low difference zones” in generally mobile reflector packets (Yilmaz, 2003). Usually, a simple difference of the 3D seismic volumes will not yield satisfactory results due to small-scale fluctuations in both arrival times and amplitudes, making time-shift analysis an important process to match the reflection events. These time-shift values have been shown to be a valuable source of information themselves (Hall et al., 2002a; Hatchell et al., 2005b), considering their sole dependence on wavefield kinematics, time shifts tend to be a more robust measurement than amplitude differences (Johnston, 2013b).

Considering normal incidence on a horizontal layer of thickness z and a P-wave velocity v with a traveltime t, we can express the changes in traveltime as:

time shifts

for homogeneous isotropic v and small changes in z and v. Originally developed in Hatchell et al. (2005b), with a rigorous integral derivation presented in MacBeth et al. (2019).

The vertical strain [ ∆z]{.underline} directly relates to the geomechanical strain ξ~zz~, describing the vertical strain on the vertical surface of a infinitesemal element (Herwanger, 2015). Independently Hatchell et al. (2005b) and Røste et al. (2006) developed a single-parameter solution to relate velocity changes and vertical strain


with R being the single parameter Hatchell-Bourne-Røste (HBR)-factor (Hatchell et al., 2005a; MacBeth et al., 2019). The HBR being a lithological constant, we can relate (2.2) and (2.1) and obtain a direct relationship between the vertical strain ξ~zz~ and the time shift ∆t for a given lithology with property R


Contingent on the assumption of zero-offset incidence, homogeneous velocity and isotropy, time shift extraction is mostly performed in z-direction by comparing traces directly. Prominently, the 1D windowed cross-correlation is used due to its computational speed and general lack of limiting underlying assumptions (Rickett et al., 2001). The main drawback of this method is, however, that the result is highly dependent on the window-size and susceptible to noise. Other methods for post-stack seismic time shift extraction include Dynamic Time Warping (DTW) (Hale, 2013a) and inversion-based approaches (Rickett et al., 2007).

More recently research into pre-stack time shift extraction and 3D-based methods is conducted. These methods relax the constraints of some assumptions of 1D applications (Ghaderi et al., 2005; Hall et al., 2002b). 3D time shifts have the ability to capture subsurface movement of reflectors and account for 3D effects of the ∆R/S acquisition mismatch, which effect seismic illumination.

Qantitative Interpretation (QI) extends the interpretation of 4D changes to estimate fluid saturation and pressure changes within the reservoir. The subsurface changes recorded by the seismic data can be related numerically to subsurface changes. The process of extracting causal information from imaging data is called inversion. The underlying phenomena interact with several possible and physical explanations for the same seismic response, which makes the inversion process non-unique and often reliant on prior information. The decoupling of pressure and saturation changes is non-trivial and relies on pre-stack or angle-stack information (Landrø, 2001). This process is, however, highly desirable with the benefit of quantifying the subsurface changes from seismic data directly.

Active areas of research in 4D seismic are the use of 4D seismic data to estimate saturation and pressure changes quantitatively particularly in volumetric applications as opposed to map-based approaches. However, these approaches often depend on reliable rock-physics models, an area of research in model-based approaches. Moreover, there’s active research in moving to volumetric approaches in time-shift estimation and quantitative pre-stack analysis. Additional research in extractive data-based methods and model-based approaches investigate how much information is available directly from the data and what information is available from the modelling feedback-loop.

Machine Learning

Machine Learning (ML) is the discipline of defining a statistical or mathematical models based on data. These ML models are either trained in a supervised or unsupervised fashion, which usually results in them learning a decision boundary, or a representation or structure of the data respectively. Historically, ML has been an interest in geoscience but has not gained momentum due to sparse data, computational capability, and availability of algorithms. Geoscience data was often not available and still is often not available with a reliable ground truth. However, particularly NNs have found broad interest in geophysical applications, Bayesian methods are often used in inversion schemes and recent software developments have changed the research entirely.

Recently, the subfield Deep Learning (DL) has reignited interest in the wider field of ML by outperforming rule-based algorithms on computer vision tasks, such as image classification and segmentation (Bishop, 2016). These developments have propelled de- velopments in other non-related fields such as biology (Ching et al., 2018), chemistry (Schütt et al., 2017), medicine (Shen et al., 2017) and pharmacology (Kadurin et al., 2017). DL utilizes many-layered artificial NN to approximate an objective function. In recent years the open source movement, democratization of access to computing power and developments in the field of DL have rekindled interest in applications of ML to geoscience. The availability of free open source libraries such as skikit-learn (Pedregosa et al., 2011) has made ML methods and several tools for the application of rigorous statistical evaluation of experiments without explicit expert knowledge widely available. Furthermore, Tensorflow (Martı́n Abadi et al., 2015), PyTorch (Paszke et al., 2017), and Keras (Chollet et al., 2015) have made NNs easily accessible and provide experimentation capabilities to transfer recent developments in ML research to other scientific fields.

Algorithms and methods in ML can be organized in different ways. Two ways to categorize algorithms are based on the training or based on the learned distribution. In training, these algorithms can be categorized into supervised and unsupervised methods, where supervised methods learn the functional mapping from x, being the data, to y, being the ground truth or label for the data. When the ground truth is not known, unsupervised methods can be applied to determine structures and relationships within the data. Semi-supervised, and weakly supervised try to propagate partial labels to similarly distributed data and then learn the supervised mapping f(x) = y. Alternatively, ML algorithms can be categorized into generative methods that learn the joint probability distribution or discriminative methods that learn a decision boundary to optimally separate data. Additionally, methods can be distinguished by application. Assigning labels to data is called classification. The general, continuous application to map data from the input to the output domain is called regression. Finding relationships and agglomerations of data is called clustering. Most algorithms can be applied to several of these categories, such as support vector machines that can function as classifier and regressor.

Applications in ML are quickly evolving and many are improved by mathematical insights, engineering features and increased availability of data. This thesis focuses on the application of NNs, which come in different implementation details and particularly NN architectures are often re-implemented with slight differences that deviate from the original published architecture. Particularly in NN we have to focus on the most practical building blocks, to be able to give a comprehensive overview.

Figure 2.1: Selection of notable milestones in machine learning.

History of Machine Learning

Creativity, learning, and intelligence with regard to computers have been discussed as early as of the first programmer Ada Lovelace (Taylor, 1843).

“The Anlytical Engine has no pretensions whatever to originate any thing. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths. Its province is to assist us in making available what we are already acquainted with. This it is calculated too effect primarily and chiefly of course, through its executive faculties; but it is likely to exert an indirect and reciprocal influence on science itself in another manner.” -Note G, Page 689, Ada A. Lovelace. (Taylor, 1843); Emphasis taken from source text.

This notion was challenged by Alan Turing (Turing, 1950) who proposed the “Learning Machine”, which specifically predict genetic algorithms, a metaheuristic that finds application in optimization and search problems. Evolutionary computing and genetic algorithms specifically can perform some machine learning tasks (Goldberg et al., 1988). This is generally considered the commencement of Artificial Intelligence (AI) and ML, however, they rely heavily on earlier developments in statistics such as the Bayesian theorem (Bayes, 1763) and Markov processes (Markov, 1906; Markov, 1971). The first method, we include on the timeline in Figure 2.1 is “kriging” (Krige, 1951), which is based on Gaussian Processes, these form an important category of non-parametric machine learning these days. Gaussian processes are often also attributed to work of Kolmogorov (1939) on time series. Another method was developed to mimic the human brain, namely Neural Networks (NNs). The construction of the first NN machine by Minsky (Russell et al., 2010) was soon followed by the “Perceptron”, a binary decision boundary learner (Rosenblatt, 1958). The decision is made according to

which describes a linear system of the input data x, the weights w and bias b and a binary activation funtion σ. The linear system is still used in modern neurons, however, the activation σ is usually a Rectifier function. Shortly after, Belson (1959) describe the first Decision Tree (DT), which learns hierarchical decision systems. The next method, k-Nearest Neighbour (KNN) search, was introduced by Cover et al. (1967) to solve the traveling salesman problem. Two decades later Q-learning (Watkins, 1989) introduces a method to reinforcement learning that is still used to this day. The final two methods in the timeline were introduced in 1995. Random Forests (RFs) (Ho, 1995) introduce ensemble learning of weak learning Decision Trees (DTs). Support Vector Machine (SVM) (Cortes et al., 1995) introduce a strong learner that aims to maximize the margin between classes.

These methods have been improved upon over the decades. Specific milestones that accelerated further developments in NN are automatic differentiation (Linnainmaa, 1970) and consequently applying this to backpropagate errors in Deep Neural Networks (DNNs) (Rumelhart et al., 1988). Backpropagation itself as a concept existed earlier (Kelley, 1960; Bryson, 1961), followed by a simplification by using the chain rule (Dreyfus, 1962). These enable effective implementation of NNs today. Moreover, open sourcing the Torch library (Collobert et al., 2002) made and assembling the Im- ageNet database (Deng et al., 2009) has accelerated developments in computer vision and enabled modern developments in deep learning. In the same year of 2009 the library Scikit-Learn (Pedregosa et al., 2011) was established, which introduced a common open source Application Programming Interface (API) (Buitinck et al., 2013) for a diverse and growing set of shallow machine learning models (e.g. SVMs, RFs, KNNs, shallow NNs). Scikit-learn has had a profound impact on machine learning applications across the sciences and the API is modelled in other open source libraries. Chang et al. (2011) introduced a widely used implementation for Support Vector Machine (SVM), which is also used in Scikit-Learn. Recently, the Tensorflow library (Martı́n Abadi et al., 2015) was introduced for open source deep learning models, with some different design choices than Pytorch. In this open environment fueled by competitions (e.g. ImageNet (Russakovsky et al., 2013), Netflix Prize (Bennett et al., 2007), Kaggle (Goodfellow et al., 2013)) XGBoost (Chen et al., 2016), a library for extreme gradient tree boosting was developed.

Recent developments in deep learning are based in Neural Networks (NNs), hence, we highlight some key developments in Figure 2.1. Convolutional Neural Networks (CNNs) are essential in the modern computational vision systems, they were inspired by the concept of Neocognitron (Fukushima, 1980; LeCun et al., 2015). In the same decade Recurrent Neural Networks (RNNs) were introduced implemented as Hopfield Networks (Hopfield, 1982). While Hopfield networks are not a general RNN, they provide content- adressable memory with the internal state memory. Hochreiter et al. (1997) implement the Long Short-Term Memory (LSTM), which contain internal states (i.e. memory) that can process temporal sequences, still used and performing to the state-of-the-art in sequence analysis and Natural Language Processing (NLP) to this day. Recently, Generative Adversarial Network (GAN) (Goodfellow et al., 2014c) introduced a system of NNs that can create new samples from a distribution. The GAN consists of two NNs, a generator and a discriminator, which generate samples from a noise distribution and judge the validity of the sample respectively. We discuss NNs in more detail in Section 2.2.2

Neural Networks (NNs)

Neural Network (NN) as a class of ML algorithms are very diverse and versatile. NNs have persisted for decades and their nomenclature has changed in this time. NNs were long called Artificial Neural Network (ANN), which has changed to simply NN, usually prepended with the class of Neural Network, namely Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Deep Neural Network (DNN), which I will discuss in more detail.

Figure 2.2: Basic NN with three inputs that are densely connected to three output neurons by weights.

Neural Networks (NNs) can be approached from several theoretical bases. Mathematically, NNs are directed acyclical graphs with edges and nodes. In neural computation, these are generally referred to as weights and nodes or neurons. In Figure 2.2, we present a simple densely connected Multi-Layer Perceptron (MLP) with three inputs and three outputs. This configuration is equivalent to a linear regression model. The inputs are distributed across the nodes, and each weight is multiplied with a weight inherent to that graph edge. During the training of this machine learning model, these weights get adjusted to obtain a generalizable result. Each node sums the contributions of these weights and possibly a bias, which is trainable but does not take input data. This amounts to each node performing


with a signifying the activation at a node, i, j being the index of the source and target node respectively, w being the trainable weight, and b being the trainable bias, and σ representing an activation function. Activation functions are an active topic of research, but the generally perform a non-linear transformation of the activation at the node.

(a) Linear activation (b) Sigmoid activation (c) Tanh activation

(d) ReLU activation (e) PReLU activation (α = .5) (f) ELU activation (α = 1)

Figure 2.3: Common Activation functions (red) and derivatives (blue). The linear activation does not modify the data. The sigmoid and tanh functions are mainly used to limit output activations to a range of values. The ReLU, PReLU and ELU activations are different iterations of rectifiers that are used in Deep Neural Networks.

In Figure 2.3 I present common activation functions used in NNs. The activation functions introduce non-linearities into the network to transform the linearly scaled input to arbitrary non-linear outputs. The mathematical function in Figure 2.3(b) and Figure 2.3(c) are used less, because of the vanishing gradient problem (Hochreiter, 1991). These occur in the extrema of both functions, where the function saturates and the gradient is close to zero for large values of x. Rectifiers presented in Figures 2.3(d) to 2.3(f) circumvent this problem by one-sided saturation.

Training the Model Before training, each weight and bias is assigned an initial number that is drawn from a distribution appropriate to the network architecture and data (LeCun et al., 2012; Glorot et al., 2010; He et al., 2015). These strategies collectively initialize weights in a pseudo-random way within limits. The data is then passed through the network, which calculates a result. This result is then compared to the ground truth, using a loss function (e.g. Mean Absolute Error (MAE), Mean Squared Error (MSE)). The resulting error ∆t is then used to correct the weights and biases in the network, calculating the correction per weight ∆w~ij~ recursively (for many-layered networks).


with η being the learning rate and δ being

Therefore, hidden nodes are reliant on the result δ~j~ ~1~ of the node at index j 1 (Goodfellow et al., 2016). The training of the model can be done on a per-sample basis, which is Stochastic Gradient Descent (SGD) or in the case of noisy inputs, the mean error of several samples can be calculated to perform mini-batch gradient descent. Iteration over forward and backward passes adjusts the weights to predict the correct result.

Modern deep NNs are trained on Graphical Processing Units (GPUs) that are optimized for matrix multiplications instead of Central Processing Units (CPUs) that are magnitudes slower. However, more recently task-specific hardware such as Field Programmable Gate Arrays (FGPAs) and Tensor Processing Units (TPUs), which work closely with the Tensorflow (TF) library are being developed and made available in cloud infrastructures.

The optimization of the backpropagation is performed using SGD or other gradientbased optimizers such as the Adam optimizer (Kingma et al., 2014). However, during training of the NN, it is important to ensure that the network learns a general relationship instead of memorizing the input data. This memorization is called overtraining, or overfitting. Overfitting can be avoided by regularizations like weight decay (Krogh et al., 1992) and Nesterov momentum (Sutskever et al., 2013), which modify the optimization loop. Alternatively, methods like Dropout (Hinton et al., 2012) and Batch Normalization (BN) (Ioffe et al., 2015) modify the network at training time. Moreover, a diverse training set and train-val-test split help avoid overfitting and ensure generalization of the trained model.

The train-val-test split separates the data into three parts. The training and validation set are available during training and hyperparameter tuning, the test set, however, should only be used once to ensure generalization of the model. The train test is used during the optimization loop, the actual training of the model, with the validation set ensuring generalization of the model to unseen data within the loop. In and of itself, the train and validation data would be sufficient, if no other changes to the model were made based on the results of the validation data. Since hyperparameter tuning and model selection are a common procedure today, these present an implicit source of information leakage from the validation set into the data. The hyperparameter tuning will often pose an optimization loop in itself that optimizes based on the results on the “unseen” validation set, essentially implicitly fitting the model to the validation data, therefore, a separate test set is necessary to ensure true generalization.

Feed Forward Networks

Feed forward Neural Networks (NNs) or MLPs are the simplest for of NN. In its simplest form it uses a set of linear equations to approximate a function. The network can be described as a graph with edges and nodes. In the neural information community the nodes are often named neurons. These neurons are arranged into layers in Figure 2.4. The first layer in a NN is the input layer with a number of nodes corresponding to the number of input data points. The input nodes are connected to the next layer by the graph’s edge. The next node can be the output layer. The weights between subsequent are floating point numbers that scale each input point and determine the value at the output nodes.

Figure 2.4: Feed forward NN with three input neurons that are connected to a single hidden layer with three neurons. The hidden layer is densely connected to two output neurons.

NNs gain their powerful learning capabilities from adding layers (see Figure 2.4) in between the input and output node and applying a non-linear activation function. Non- linear activations scale the input from the edge at each neuron. Historically, these have been straight-forward mathematical functions such as tanh() and sig() (cf. Figure 2.3). These suffer from some short-comings that were overcome to leverage multi-layered Deep Neural Networks (DNNs).

Deep Neural Networks (DNNs)

Improvements in computational power made it possible to train many-layered NNs (see Figure 2.5). These Deep Neural Networks (DNNs) are at the core of recent developments in Deep Learning (DL), leading to the re-implementation of many algorithms into openly available libraries, which has led to further innovative uses of these building blocks. These networks leverage the combinatorial power of NN layers. In deep NNs gradient propagation led to exploding or vanishing gradients before. New non-saturating activation functions lead to stabilization of training DNN (cf. Figure 2.3).

Figure 2.5: Deep Feed forward NN with two hidden layers with three neurons each, densely connected to three inputs and two output neurons. Deep networks are NNs that contain more than one hidden layer.

Self-Organizing Maps (SOMs)

Self-Organizing Map (SOM), also named Kohonen-networks (Kohonen, 1982) are a special case of networks that do not modify the flow of data from the input to the output nodes. They treat each data point as a node and adjust the weights between each node in on a similarity metric. These tend to perform well on spatially correlated data and find good adoption in geoscience.

Recurrent Networks

A special configuration of NN is the Recurrent Neural Network (RNN). These networks use edges that feed back into the network. RNNs are used in two applications in ML. They can preserve hidden states, which gives them temporal context sensitivity. Application two is time series analysis similar to feed-forward NNs, where the input is a time step that can be analyzed within the context of surrounding time steps. These RNN represent cyclic directed graphs of computation, as opposed to the other types of NN we discuss, which are acyclic directed graphs. In Figure 2.6 we show the changes of a simple RNN graph compared to a feed forward NN in Figure 2.4. The RNN loops back into itself, which is often regarded as the internal state or feedback. This internal state enables content addressable memory and good performance on sequential data such as time series and language.

Hopfield Networks are one type of recurrent networks that model the human memory. Hopfield networks and their subclasses can be used for pattern recognition. They are guaranteed to find a pattern, however, they are known to converge to local minima. Boltzman machines are configured like Hopfield networks, in contrast to deterministic Hopfield networks, their response to an input is stochastic. Boltzman machines draw from a joint distribution, making them a generative model.

Figure 2.6: Recurrent NN that connects two input neurons to two recurrent neurons. These recurrent neurons feed back into themselves, which signifies the state of the neuron. RNN neurons are more complicated internally than the neurons in CNNs accomodating the state memory.

Long Short-Term Memory (LSTM) is a type of RNN that models memory. Details differ in implementations of Long Short-Term Memory (LSTM), however the main criteria are three gates and an inner cell.

The input gate regulates the contribution of input values to the internal cell. The forget gate regulates the persistence of values in the cell. Finally, the output gate regulates the contribution of the input value to the output value convolved with the cell state.

Convolutional Networks

Convolutional Neural Network (CNN) were developed in computer vision to automatically learn a filter that spatially correlates data. The convolutional kernels are computationally efficient due to weight sharing, making them feasible for very deep networks (cf. Section CNNs have had the biggest influence on the renaissance of modern ML. These building blocks for NNs are very good for image data and data where spa- tially correlated information provides valuable context. It has therefore quickly gained attention in seismic interpretation and seismic data analysis. CNNs like other NNs are optimized by SGD, optimizing a defined loss over the chosen task.

For a two-dimensional CNN, the convolution of the m n-dimensional image G with a filter matrix f can be expressed as:


Figure 2.7: Schematic of a CNN filter (purple) in the image data (orange) in 2D. The filter passes over the image, extracting a filtered representation of the input image. The image is downsampled spatially by striding or pooling. Convolutional filters are efficient due to weight sharing.

resulting in the central result G^∗^ around the coordinate c. Realistically, the calculation is done in the Fourier domain, due to the Convolution theorem reducing the computational complexity from O(n^2^) to O(n log n) with


with f denoting the Fourier transform of f and k being a normalization constant. This reduces the convolution to a simple multiplication in the Fourier domain, sped up by Fast Fourier transform (FFT).

Figure 2.7 shows the schematic of connected convolutional layers in a CNN. The network learns a specified number of 3 3 filters from the initial image. Strided con- volutions with a step-size larger than 1 or Pooling layers are used to reduce the spatial extent of the image. The repeated downsampling of the image and extraction of convolutional filters has been shown to work for computer vision tasks. Historically, the CNN architecture AlexNet (Krizhevsky et al., 2012) was the first CNN to enter the ImageNet challenge and improved the classification error rate from 25.8 % to 16.4 % (top-5 accuracy). This has propelled research in CNNs, resulting in error rates on ImageNet of

2.25 % on top-5 accuracy in 2017 (Russakovsky et al., 2015).

Generative Adversarial Networks

Goodfellow et al. (2014b) introduced Generative Adversarial Network (GAN) as a combination of two CNNs. These Deep Convolutional Generative Adversarial Networks (DCGANs) exist in different modifications that draw from the original GAN, these modifications add more regularization and other feedback loops, as GANs are notoriously difficult to train without careful fine-tuning. These modifications include Wasserstein losses (Arjovsky et al., 2017), and gradient penalization (Gulrajani et al., 2017) for regularization, or cycle-consistent loss for unsupervised training (Zhu et al., 2017).

Figure 2.8: Schematic of a Generative Adversarial Network. The generator samples a latent space to generate fake data. The discriminator randomly obtains real or fake data and decides whether it was created by the generator or a real sample. The networks learn by gradient descent gaining information regardless of the discriminator being right.

Figure 2.8 shows the basic working of GANs. The arrows are colored in blue and grey, where the blue paths show network feedback and grey shows the progression of data. These networks learn from each other, where the generator draws from latent space (a noise vector) to create a fake version of a target. The discriminator tries to discern whether the presented data is real or generated from the adversarial generator. These networks leverage game theory to outperform each other and comparative networks. They reach a Nash equilibrium during training, which describes the concept on a noncooperative game reaching steady state (Nash, 1951).

Neural Architectures

Neural Networks can generally be assembled in different architectures. In Figure 2.10 we present reported performances of neural architectures on the classification task of the ImageNet challenge. The colors in this figure express different classes of architectures. Early networks that broke ground as the new state-of-the-arts in image classification are the AlexNet, VGG-16, and VGG-19. These networks clearly do not leverage some tricks that modern CNNs implement, the VGG-16 with a relatively high amount of parameters is known to generalize well on transfer learning tasks however (Dramsch et al., 2018c).

Figure 2.9: Resnet Block with two 1 1 convolutional layers that frame a 3 3 convolutional layer with ReLU activation each. The result being added with the original data, also known as identity..

Research into deep convolutional networks showed that the data in the network would lose signal with increasing depth. Hence, the limitation of VGG at 19 layers. Residual blocks introduced a solution to this problem by implementing a shortcut between the original data and the output from the block. Figure 2.9 presents the original ResNet block architecture, which was used in ResNet-50 and ResNet-101 in Figure 2.10 (He et al., 2016). Details on ResNet blocks differ, the main take-away being the sum or concatenation of the original data with the block output. DenseNets (Huang et al., 2017a) and Inception-style networks (Szegedy et al., 2015) are other approaches to build deeper NNs.

The categories of AmoebaNet, NASNet, and EfficientNet are a more recent development in neural architecture research, based on Neural Architecture Search (NAS). The AmoebaNet is based on Evolutionary Computing and hand-tuning the solution to search for an ideal neural architecture to solve the task (Real et al., 2019). The NASNet fixes the overall architecture, but uses a controler RNN to modify the blocks within the architecture (Zoph et al., 2018). The EfficientNet architecture was also acquired by NAS, by optimizing for both accuracy and FLOPS to reduce the computational cost (Tan et al., 2019b). Moreover, Tan et al. (2019b) derives a method of compound scaling for deep neural networks. While ResNet-50 and ResNet-101 differ only in depth, the authors derive a relationship between depth, width and resolution-scaling of deep neural networks.

Apart from building deeper networks for image classification, the neural architectures can serve as a forcing function to the task the network is built for. Encoder-Decoder networks will compress the data with a combination of downsampling layers, which in the case of a computer vision could either be strided convolutions or pooling layers after convolutional layers. During these operations, the number of filters increases, while the

Figure 2.10: Top-5 Accuracies of Neural Architectures on ImageNet plotted against Million Parameters, color-coded to similar network type. Data and references shown in Table A.1.

spatial extent is diminished significantly. This encoding operation is equivalent to a lossy compression, with the low-dimensional layer called “code” or “bottleneck”. The bottleneck is then upsampled by either strided transpose Convolutions or upsampling layers that perform a specified interpolation. This is the Decoder of the Encoder-Decoder pair. These networks can be used for data compresssion in AutoEncoders (AEs), where the decoder restores the original data as good as possible (Hinton et al., 2006). Alternatively, the Decoder can learn a dense classification task like semantic segmentation or seismic interpretation.

U-Nets present a special type of encoder-decoder networks, that learn semantic segmentation on from small datasets (Ronneberger et al., 2015). They form a special kind of Fully Convolutional Network (FCN) shown in Figure 2.11. Originally developed on biomedical images, the network found wide acceptance in label sparse disciplines. The Unet implements shortcut connections between convolutional layers of equal extent in the Encoder and Decoder networks. This alleviates the pressure of the network learning and reconstructing the output data from the bottleneck in isolation.

Figure 2.11: Unet after Ronneberger et al. (2015) using 2D convolutional layers (yellow) with ReLU activation (orange) and skip connection between equaldimensional layers. The Encoder uses pooling (red), while the Decoder uses Upsampling layers (blue), witha final SoftMax layer (purple) for classification / semantic segmentation.

2.3 Machine Learning in Geoscience

The development of the subfield of deep learning has lead to advances in many scientific fields that are not directly related to the larger field of artificial intelligence. This section focuses on historic use-cases of machine learning models in geoscience and eval- uate these in the context of recent advances in deep learning. I provide an overview of supervised and unsupervised methods that have persevered. Furthermore, I distinguish implementations of deep neural network topologies and advanced machine learning methods in geoscientific applications. I go on to investigate where these methods differ from previously unsuccessful attempts at application.

Early on Machine Learning (ML) has been reviewed in a geophysical context. Early publications of ML in geoscience apply NNs to geophysical problems. Particularly seismic processing lends itself to explore NNs as general functional approximator (Hornik et al., 1989). McCormack (1991) review of the emerging tool of neural networks in 1991. He highlights the application of pattern recognition and is very succinct in describing basic math associated with neural computing. The wording of most parts has changed, as compared to today. Generally this gives a good baseline and McCormack gives a good illustration and overview with examples in well log classification and trace editing. The author summarizes NN applications over the 30 year prior to the review and hightlights automated well-log analysis and seismic trace editing. The review comes to a conclusion that these methods show promise as general approximators.

Baan et al. (2000) review the most recent advancements in Neural Networks (NNs) in geophysical applications. It goes into much detail on the neural networks employed in 2000 and the difficulties in building these models and training them. They identify the following subsurface geoscience applications through history: First-break picking, electromagnetics, magnetotellurics, seismic inversion, shear-wave splitting, well log analysis, trace editing, seismic deconvolution, and event classification. The authors evaluate the application of NNs as subpar to physics-based approaches. The paper concludes that neural networks are too expensive and complex to be of real value in geoscience. Generally, this review focuses very much on exploration geoscienc.

Mjolsness et al. (2001) review ML in a broader context outside of exploration geoscience. They illustrate recent successes of ML in analyzing sattelite data and computer robotic geology. The authors include graphical models, Random Markov Models (RMMs), Hidden Markov Models (HMMs), and SVMs. They further highlight limitations to vector data, therefore failing richer data such as graphs and text data. Moreover, the authors from NASA JPL go into detail on pattern recognition in automated rovers to identify geological prospects on Mars. They state:

“The scientific need for geological feature catalogs has led to multiyear human surveys of Mars orbital imagery yielding tens of thousands of cataloged, characterized features including impact craters, faults, and ridges.” (Mjolsness et al., 2001)

The authors evaluate how especially the introduction of SVM have allowed the identification of geomorphological features without modeling the processes behind. Further they mention recurrent neural networks in gene expression data, a method that has experienced a renaissance in deep learning.

History of Machine Learning in Geoscience

Machine learning, statistical, and mathematical models have a long history in geoscience. Markov models have been used to describe sedimentology as early as the 1970s (Schwarzacher, 1972) and the use of k-means in geoscience as early as 1964 (Preston et al., 1964). In geophysics applications of NNs to perform seismic devonvolution were published in the 1980s Zhao et al. (1988). Early tree-based methods were chiefly used in economic geology and exploration geophysics for prospectivity mapping with Decision Trees (DTs) (Newendorp, 1976; Reddy et al., 1991). SVM has early on been applied to AVO classification Li et al., 2004 and geological facies delineation for hydrological analysis (Tartakovsky, 2004). This thesis mostly focuses on the application of NNs, however, we give an additional overview of geoscientific applications of shallow ML.

Machine Learning Applications in Geoscience

Early applications of neural networks were prominent in seismic data processing and analysis. Zhao et al. (1988) use a NN to perform seismic deconvolution early on. An application of seismic inversion with NNs was published by Röth et al. (1994). Early ML-based electromagnetic geophysics performs subsurface localization (Poulton et al., 1992) and magnetotelluric inversion via Hopfield NNs (Zhang et al., 1997). Feng et al. (1998) applied NN to model geomechanical microfractures in triaxial compression tests. Interestingly, Legget et al. (1996) used a combination of Self-Organizing Map (SOM) and back-propagation NNs that function similar to modern day Convolutional Neural Networks (CNNs) to perform 3D horizon tracking (Leggett et al., 2003). With the recent DL explosion, papers on Automatic Seismic Interpretation (ASI) have gotten very popular, given the similarity to 2D segmentation tasks (cf. Table B.1).

Modern CNNs have been applied to a wide variety of geoscience problems including seismic inversion (Araya-Polo et al., 2018), and applications in seismology such as first break picking (Ross et al., 2018a) or event classification (Zhu et al., 2018; Ross et al., 2018b). In 2017 the application of Generative Adversarial Networks in geoscience in digital rock modelling (Mosser et al., 2017), geostatistical modelling (Laloy et al., 2017) and seismic inversion (Mosser et al., 2018d; Mosser et al., 2018c). Further applications extend to geochemical anomaly detection (Zuo et al., 2018) using Variational AutoEncoders (VAEs) and hydrogeological modelling (Sahoo et al., 2017). Common applications include Ground Penetrating Radar (GPR), various applications in seismic processing, analysis and interpretation, as well as seismology, listed in detail in Table B.1. Recently, some applications of Deep Neural Networks to predict earthquake aftershocks (DeVries et al., 2018) has been called into question by the publication “One neuron versus deep learning in aftershock prediction” (Mignan et al., 2019b). Criticizing the original publication for over-engineering a problem that is well-defined on less input data. A common critique of ML and big data analytics by classical statistics and rigorous data science (Mignan et al., 2019a).

Support Vector Machines have early-on been used for seismic data analysis (Li et al., 2004) and the popular approach of Automatic Seismic Interpretation (Liu et al., 2015; Di et al., 2017b; Mardan et al., 2017). Additionally, early applications include seismological volcanic tremor classification (Masotti et al., 2006; Masotti et al., 2008) and Ground Penetrating Radar analysis (Pasolli et al., 2009; Xie et al., 2013). The 2016 SEG ML challenge was introduced using a SVM baseline (Hall, 2016), with several other investigations into SVMs for well log analysis (Anifowose et al., 2017a; Caté et al., 2018; Gupta et al., 2018; Saporetti et al., 2018). Moreover, this method has been applied to seismology for event classification (Malfante et al., 2018) and magnitude determination (Ochoa et al., 2018). Considering the strong mathematical foundation of SVMs, they have been applied to applied to a variety of geoscience problems such as microseismic event classification (Zhao et al., 2017b), seismic well ties (Chaki et al., 2018), landslide susceptibility (Marjanović et al., 2011; Ballabio et al., 2012), and digital rocks (Ma et al., 2012).

Random Forests and other tree-based methods, including gradient boosting, have gained increased attention with the implementation into scikit-learn (Buitinck et al., 2013). Similar to NN applications, RFs are applied to Automatic Seismic Interpretation (Guillen et al., 2015) with limited success. Seismological applications including localization (Dodge et al., 2016), event classification in volcanic tremors (Maggi et al., 2017) and slow slip analysis (Hulbert et al., 2018). Further geomechanical applications include fracture modelling (Valera et al., 2017) and fault failure prediction (Rouet‐Leduc et al., 2017; Rouet‐Leduc et al., 2018). Gradint Boosted Trees were the most performant models in the 2016 SEG ML challenge (Hall et al., 2017) for well-log analysis, propelling a variety of publications in facies prediction (Bestagini et al., 2017; Blouin et al., 2017; Caté et al., 2018; Saporetti et al., 2018). Moreover, random forests were applied to detect reservoir property changes from 4D seismic data (Cao et al., 2017).

Furthermore, various methods have been applied to various doomains. Hidden Markov Models were used on seismological event classification (Ohrnberger, 2001; Beyreuther et al., 2008; Bicego et al., 2013), well-log classification (Jeong et al., 2014; Wang et al., 2017a), and landslide detection from seismic monitoring (Dammeier et al., 2016). KNN

has been used for well-log analysis (Caté et al., 2017; Saporetti et al., 2018), seismic well ties (Wang et al., 2017b) combined with DTW and fault extraction in seismic interpretation (Hale, 2013b). The unsupervised k-means equivalent has been applied to seismic interpretation (Di et al., 2017a), ground motion model validation (Khoshnevis et al., 2018), and seismic velocity picking (Wei et al., 2018). The biologically inspired anttracking algorithm is commonly used for seismic interpretation (Pedersen et al., 2002) and in conjunction with NNs (Zheng et al., 2014). Graph modelling has been applied to seismology in modelling the earthquake parameters (Kuehn et al., 2011), basin modelling (Martinelli et al., 2013), seismic interpretation (Ferreira et al., 2018) and flow modelling in Discrete Fracture Networks (DFNs) (Karra et al., 2018).

Machine Learning methods have been applied to various disciplines in geoscience, with the main objective increasing predictive capability or automating expensive and labour-intensive tasks. These approaches rely on diverse labelled data sets and are prone to common problems of ML in geoscience inherent to geoscientific data and the implication of cost of data acquisition.

Challenges of machine learning in geoscience

Statistical methods and machine learning are based on several assumptions and demand some pre-requisites that can cause problems in geoscience. These include the assumption that data is independent and identically distributed (iid) and the pre-requisite of a ground truth for supervised learning. In this section I discuss these challenges and present some approaches to solve these problems.

Geoscientific data is known to be very heterogeneous across vastly different scales (mm to km), which makes the system hard to model in general. Additionaly, a core assumption of statistics iid is usually in conflict with the geological processes. Regionality of depositional patterns violates the assumption data is identically distributed and time-dependent processes, such as systems tracts in sedimentology, violate the independence assumption of individual samples. This fact has to be taken into account, when choosing models and sampling methods. Expanding on the sedimentology example, the time-varying deposition can be modelled as markovian (Schwarzacher, 1972), instead of treating samples as strictly independent. Moreover, sampling of any data needs to honour the clustering in distribution of samples. Stratified sampling (Kish, 1965) can alleviate sampling bias. Additionally, stratified sampling can address the problem that geoscientific data often contains imbalanced data. Imbalanced data implies that the number of samples per class in the label data set is not uniformly distributed. These imbalances can stem from the fact that different depositional regimes cause different thicknesses in the stratigraphic columns, for example commonly leaving thicker sand columns and fine shale layers. Alternatively, imbalances can stem from the data collection process itself, be it that seismic data does not adequately image variations below 10 m or the location where data is collected, considering that e.g. E&P companies do not choose the location for 3D seismic data acquisition randomly. This imbalance due to non-uniform sampling can not be solved by sampling itself, as the bias is implicit in the available data itself.

In the computer vision community hand-labelled data sets like ImageNet, CIFAR, and PASCAL-VOC are openly available, which catalyzed the developed new architectures and approaches in deep learning. Geoscientific data is often expensive to acquire and companies are reluctant to make data available, less even for processed or inter- preted data. Early machine learning workshops often showed results on the open Dutch F3 dataset, however, national data repositories have started to change this approach to foster innovation. With data becoming more available, the next problem is the lack of ground truth. Obtaining accurate labels for seismic data is impossible, as any inversion process is non-unique and digging is not practical. In other imaging-based fields (e.g. radiology) that rely on interpretation of imaging results, studies investigate both interinterpreter variations, by making several interpretations available and intra-interpreter variability by re-interpreting the dataset after a set time interval (McErlean et al., 2013; Alikhassi et al., 2018; Al-Khawari et al., 2010). Additionally, simulations provide a ground truth, but can implicitly include modelling assumptions in the data or commit the inverse crime (Wirgin, 2004). The inverse crime presents the problem of modelling and inverting data with the same theoretical ingredients.

In geophysics itself, seismic data presents a unique challenge to computer vision problems, in that the 3̃rd percentile of amplitudes occupy large parts of the dynamic range (Forel et al., 2005). Displays of seismic data usually clip amplitudes to make most of the seismic amplitude content visible, this has also proven to be a viable preprocessing step before feeding seismic data to computer vision systems, such as convolutional neural networks. Machine learning systems have been known to be vulnerable to noise. This noise can be physical noise (i.e. low SNR) for simpler models or adversarial attacks that reverse engineer more complex models to fool said model. Adversarial attacks include a one-pixel attack on ImageNet classifiers (Su et al., 2019), humanly imperceptible noise (Goodfellow et al., 2014a), or physical stickers (Brown et al., 2017). In addition, geological data contains regions of geological interest and regions that are inconsequential, this has not been represented in metrics adequately (Purves et al., 2019).

Realistically, the sparsity of labelled ground truth data can be addressed in different ways. In the case when labels are available but not abundant, transfer learning of highly generalizable models like VGG-16 can be fine-tuned to seismic data. The VGG16 architecture can also be included in U-Nets as a decoder to leverage the benefits of transfer learning in semantic segmentation tasks (Dramsch et al., 2018c). Moreover, weakly-supervised training can preform label propagation of labeled sections of the full data set to unlabeled sets. Unsupervised or self-supvervised training can be applicable, where no reliable ground truth is available, but a desired operation on the data is known or an internal structure of the data can be exploited (Dramsch et al., 2019b). Additionally, multi-task learning has been shown to be able to stabilize network performance in Natural Language Processing (Liu et al., 2019) and Reinforcement Learning (Yu et al., 2019).

One caveat of increasingly performant but complex machine learning models is stakeholder buy-in or trust. These issues can be adressed, by benchmarking complex models against simpler models and physics-based solutions. Additionally, model explainability has become an important topic of research (Lundberg et al., 2017). Ribeiro et al. (2016) introduce the local interpretable model-agnostic explanation (LiME) method to gain insight into black-box models for individual samples. Shrikumar et al. (2017) propose a method to propagate activations in Deep Neural Networks. The Grad-CAM algorithm (Selvaraju et al., 2017) provides attention-like explanations for CNNs in computer vision tasks, to explain the main contributors to a classification output. Additionally, strict adherence to train-val-test splits and exploration of biases within the data can be essential. Considering these caveats and best practices in Machine Learning for geosciences, the following chapter introduces the main chapters of this thesis.