Neural networks in geophysical applications
TLDR
Techniques are described for faster training, better overall performance, i.e., generalization, and the automatic estimation of network size and architecture.Abstract:
Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization, and the automatic estimation of network size and architecture.read more
Citations
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Machine learning for data-driven discovery in solid Earth geoscience
TL;DR: Solid Earth geoscience is a field that has very large set of observations, which are ideal for analysis with machine-learning methods, and how these methods can be applied to solid Earth datasets is reviewed.
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Deep-learning tomography
TL;DR: This work proposes and implements a novel concept that bypasses these demanding steps, directly producing an accurate gridding or layered velocity model from shot gathers, and relies on training deep neural networks.
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Design of neural networks using genetic algorithm for the permeability estimation of the reservoir
TL;DR: A new method for the auto-design of neural networks was used, based on genetic algorithm (GA), which was evaluated by a case study in South Pars gas field in Persian Gulf and improved the effectiveness of forecasting when ANN is applied to a permeability predicting problem from well logs.
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A comparison of classification techniques for seismic facies recognition
TL;DR: In this paper, the authors reviewed six commonly used seismic facies classification algorithms and applied them to a single 3D seismic data volume acquired over the Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural elements of a tu...
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