Titlebook: Geophysical Applications of Artificial Neural Networks and Fuzzy Logic; William A. Sandham,Miles Leggett Book 2003 Springer Science+Busine

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Automated Picking of Seismic First-Arrivals with Neural Networksr perceptron neural network. The success of this technique depends on the statistical properties of the features input to the neural network, and the ability of the neural network to approximate a wide class of functions. Using methods from statistical pattern recognition, it is possible to determin

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Automated 3-D Horizon Tracking and Seismic Classification Using Artificial Neural Networks in the workload of an interpreter. An automatic tracker is described in this chapter, based on artificial neural networks (ANNs), which enables horizons to be tracked in three dimensions with less input from an interpreter compared to most commercial automatic trackers. More time can therefore be s

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Refinement of Deconvolution by Neural Networksptive linear combiner (ALC), in order to refine the results of deconvolution. For example, the conventional method of designing a shaping filter for a minimum-delay wavelet, requires the computation of a fixed filter by the method of least-squares. An alternative approach, considered in this chapter

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Seismic Principal Components Analysis Using Neural Networksix for different types of seismogram. Principal components analysis (PCA) using the GHA network enables the extraction of information regarding seismic reflections and uniform neighboring traces. The seismic data analyzed are seismic traces with 20, 25, and 30 Hz Ricker wavelets. The GHA network is

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