Transformation of Geochemical Log Data to Mineralogy Using Genetic Algorithms
J. H. Fang, C. L. Karr, D. A. Stanley
There is a pressing need for developing a technique for transforming
geochemical data obtained with the newly developed geochemical log tool (GLT)
into constituent minerals of formations. A novel technique of genetic algorithm
(GA) is found to be both appropriate and efficaceous. GA is a global
optimization method that searches for solutions based on an analogy between
optimization and natural selection. In this approach, the problem is represented
as binary
strings of 0's and 1's. Initially, the population of solutions is
generated randomly and at each subsequent iteration three probabilistic
processes are applied. The first, reproduction, imposes a survival of
the fittest criterion to select a new population of strings or solutions; the
second, crossover,</ M> produces an efficient exchange of information
between surviving solutions; the third, mutation, introduces a purely
random element that maintains diversity in the new population. Compared to the
traditional approaches of least squares and linear programming, the genetic
algorithm does not require the calculation of partial derivatives and produces
the same or better results. The method is illustrated with two examples--a
sandstone and a volcanic rock.
AAPG Search and Discovery Article #91020©1995 AAPG Annual Convention, Houston, Texas, May 5-8, 1995