Graphic Biostratigraphic Correlation Using Genetic Algorithms |
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| Authors: | Tao Zhang and Roy E Plotnick |
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| Institution: | (1) Wireless Broadband Systems, Motorola, Inc., 1475 W Shure Drive, Arlingtong Heights, IL 60004, USA;(2) Department of Earth and Environmental Sciences, University of Illinois at Chicago, 845 W. Taylor St., Chicago, IL 60607, USA |
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| Abstract: | The most generally used method for estimating the basin-wide sequence and scaling of first and last occurrences, based on
their occurrence in local sections, is Shaw’s graphic correlation method. The key step in this method is the determination
of the line of correlation (LOC), which represents the best estimate of the correlation between two local sections, or between
a local section and a composite standard. In general, available techniques for fitting the LOC for multiple sections are tedious,
subjective, or computationally expensive. A new method employing genetic algorithms can dramatically reduce the effort involved
in determining the LOC and produces stable biostratigraphic correlations and composite range charts objectively and efficiently.
Genetic algorithms are an artificial intelligence technique that excels in locating the optimum solution from a large number
of alternative choices. In the case of the LOC, the alternative choices are the number of line segments comprising the complete
line and the positions of each segment’s beginning and end points. For a given number of segments, a wide range of alternative
LOCs can be rapidly evaluated and a potential optimum fit determined. It is also possible to estimate the point when no further
refinement of the fit by adding line segments is necessary. Genetic algorithms can also be applied to other methods for quantitative
biostratigraphy. |
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| Keywords: | biostratigraphy correlation artificial intelligence genetic algorithms |
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