Abstract
Computational neural network applications are promising for automation of some labor-intensive tasks in seismic data processing, such as trace editing, travel-time picking, and velocity analysis as well as applications in seismic interpretation. Among the most promising applications are those that involve seismic attribute processing. A seismic attribute can be broadly defined as any property that is obtained or derived from seismic data. As large three dimensional (3-D) seismic surveys have become more common and computing techniques more powerful and rapid, it has become possible to extract and use more information from seismic data. Hundreds of seismic attributes have been studied, although only 30-50 are commonly used in seismic interpretation. The greatest amount of research has been conducted on the application of neural networks to analyze and interpolate physical seismic attributes, those that describe lithology, wave propagation, and other physical parameters. This chapter reviews neural network applications to waveform recognition, first break identification, velocity estimation, trace editing, deconvolution, multiple removal, and inversion.
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