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Stacked autoencoders based machine learning for noise reduction and signal reconstruction in geophysical data
Autoencoders are neural network formulations where the input and output ...
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Application of Artificial Neural Networks in Estimating Participation in Elections
It is approved that artificial neural networks can be considerable effec...
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DeepView: Visualizing the behavior of deep neural networks in a part of the data space
Machine learning models using deep architectures have been able to imple...
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Estimation for Compositional Data using Measurements from Nonlinear Systems using Artificial Neural Networks
Our objective is to estimate the unknown compositional input from its ou...
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Review: Noise and artifact reduction for MRI using deep learning
For several years, numerous attempts have been made to reduce noise and ...
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A Study on the Behavior of a Neural Network for Grouping the Data
One of the frequently stated advantages of neural networks is that they ...
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Robust Wireless Fingerprinting via Complex-Valued Neural Networks
A "wireless fingerprint" which exploits hardware imperfections unique to...
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Deep Autoassociative Neural Networks for Noise Reduction in Seismic data
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.
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