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Paper No. 12
Presentation Time: 5:00 PM

MATCHING MIND AND METHOD WITH MATERIAL: JOHN IMBRIE AND QUANTITATIVE SEDIMENTARY FACIES ANALYSIS


LAPORTE, Léo F., Earth and Planetary Sciences, University of California at Santa Cruz, Santa Cruz, CA 95064, laporte@ucsc.edu

John Imbrie (b. 1925) has always had deep mathematical insight and facility. At Yale he completed his PhD (1951) under Carl Dunbar working on Middle Devonian brachiopods where he employed a statistical technique -- "reduced major axis regression" -- to differentiate several subspecies. Later, in a study with Edwin Colbert at the American Museum of Natural History, he used the same technique to determine subtle, yet significant, variations in the growth patterns of Triassic Metoposaurid amphibians (1956). At about the same time, as sedimentary facies analysis was becoming of increased interest, Imbrie sought to test what one might do with quantitative facies analysis by undertaking a decade-long study of the Lower Permian Florena Shale (Kansas) using multivariate cluster analysis to characterize different litho- and biofacies. Despite much hard work in the field and with a high decibel desk calculator, the hoped-for results were lackluster.

But neither the man nor the methods were wanting. The materials—fragmented, scattered invertebrate fossils imbedded in shales and limestones— permitted no more understanding than qualitative, eye-ball analysis. What was needed, of course, was a problem whose material components (abundant planktonic microfossils) within well-characterized stratigraphic sequences (deep-sea, Pleistocene Caribbean cores) were well matched to the man’s mind and his quantitative methodology. And, of course, the result was phenomenal: his mathematical proof of the validity of Milankovitch Cycles as the forcing factors for large scale global climate change. His scientific success was duly honored by awards, prizes, medals, and election to distinguished honorary societies.

The moral of this tale is clear. First, one can be smart and the techniques robust, but the choice of problem to be studied must also be appropriate and adequate for the desired goal. Equally important, one must be willing to recognize when intractable difficulties are so impeding success that one must abandon the effort and look elsewhere to achieve the intended goal.

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