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Autoencoders are neural network formulations where the input and output of the network are identical and the goal is to identify the hidden representation in the provided datasets. Generally, autoencoders project the data nonlinearly onto a lower dimensional hidden space, where the important features get highlighted and interpretation of the data becomes easier. Recent studies have shown that even in the presence of noise in the input data, autoencoders can be trained to reconstruct the noisefree component of the data from the reduced-dimensional hidden space. In this paper, we explore the application of autoencoders within the scope of denoising geophysical datasets using a data-driven methodology. The autoencoder formulation is discussed, and a stacked variant of deep autoencoders is proposed. The proposed method involves locally training the weights first using basic autoencoders, each comprising a single hidden layer. Using these initialized weights as starting points in the optimization model, the full autoencoder network is then trained in the second step. The applicability of denoising autoencoders has been demonstrated on a basic mathematical example and several geophysical examples. For all the cases, autoencoders are found to significantly reduce the noise in the input data., Comment: 21 pages

Machine learning is currently a trending topic in various science and engineering disciplines, and the field of geophysics is no exception. With the advent of powerful computers, it is now possible to train the machine to learn complex patterns in the data, which may not be easily realized using the traditional methods. Among the various machine learning methods, the artificial neural networks (ANNs) have received enormous attention. A variant of ANNs, autoassociative neural network (autoNN) tries to learn the reconstruction of input itself using backpropagation. In an autoNN, the input and output are the same, and an approximation to the identity mapping is obtained in a nonlinear setting. AutoNNs have primarily been used to extract sparse internal representations of any input and reduce its dimensionality. In this paper, we explore the potential of autoNNs in reducing random noise in geophysical data. In this paper, the first results of this study are presented. The synthetic mathematical example demonstrates the concept of autoNN. For the test seismic data, it is observed that autoNN can significantly remove the vertical time- and frequency-local noise, however, the resolution of the output signal is compromised to a certain extent. Future work includes testing larger examples with several different types of noise, and using deep-stacked-autoNNs to further reduce the noise, ensuring minimal compromise with the resolution of the signal., Comment: 6 pages; Accepted at 80th EAGE Annual Conference & Exhibition 2018

Rock physics models (RPMs) are used to estimate the elastic properties (e.g. velocity, moduli) from the rock properties (e.g. porosity, lithology, fluid saturation). However, the rock properties drastically vary for different geological conditions, and it is not easy to find a model that is applicable under all scenarios. There exist several empirical velocity-porosity transforms as well as first-principle-based models, however, each of these has its own limitations. It is not very straight-forward to choose the correct RPM, and templates exist, which are overlapped with the log data to decide on the correct model. In this work, we use deep machine learning and explore the concept of designing a supermodel that can be used for several different lithological conditions without any parameter tuning. In this paper, this test is restricted to only empirical velocity-porosity transforms, however, the future goal is to design a rock physics supermodel that can be used on a variety of rock properties. The goal of this paper to is to combine the advantages of several existing empirical velocity-porosity transforms under a single framework, and design a velocity-porosity supermodel (VPS) using artificial neural networks (ANN) based deep learning. Two test cases are used and based on the results presented in this paper, it is clear that deep neural networks can be a potential tool to develop a supermodel for lithological modeling and characterization., Comment: 6 pages