Facies maps are constructed from paleontologic and lithologic data to depict major and subtle depositional environment differences across a region during a specified time span. Ratio maps and three-component maps exhibit a lack of discrimination because they cannot incorporate all available data. Factor analysis and cluster analysis techniques can be used to construct truly multivariate facies maps. Earlier attempts at factor or cluster analysis multivariate facies maps had one or more deficiencies: (1) inability to handle a sufficient number of variables and locations; (2) inability to handle mixed-mode data (presence-absence, coded states, integer counts, and continuously variable measurements); (3) inability to take into account redundant or highly correlated variables and (4) inability to accommodate to missing data.

A new cluster analysis classification computer program has been written to overcome these deficiencies. The FORTRAN IV program can utilize up to 200 variables on as many as 1,000 stations. It performs a distance function principal components analysis to compute orthogonal (uncorrelated) factor measurements for a distance function cluster analysis of locations. This combination will handle mixed-mode data and will adjust to missing data.

From the resulting multivariate classification of paleontologic and lithological data, a facies map showing the distribution of the various classes was constructed and compared with previously published facies maps. An example using multivariate lithologic data from coded AmStrat sample-description logs from central Montana demonstrates the potentialities of this method.

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