Application of Neural-network Technology in Analyzing Deep-water Depositional Elements
DOI:
https://doi.org/10.51835/bsed.2003.18.1.233Abstract
The introduction of neural-network technology in 3D seismic interpretation proves to be a powerful tool in constructing depositional elements in basin plain environment. The most known software that uses neural network technology to classify the seismic facies by imitating the human's brain work is Stratimagic. Its 'magic' requires a reference surface, interval thickness (window) and number of iteration as data inputs to create a facies map.
The reference surface(s) is used as a reference to define the interval thickness. Interval thickness acts as a processing window that depends mostly on lithology complexity and the quality of the seismic data while the number of iteration defines how much trial-and-error processes are needed in search of a better correlation to the real traces. The result is a series of signal traces that represents the diversity of the signal shape over the seismic volume. In other word, Neural Network Technology trains itself actual trace shapes within a 3D seismic interval by constructing synthetic seismic traces.
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