PROFESSIONAL ETHICS AND PRACTICES - COLUMN 160
are cropping up so that they can be corrected. Only be rigorous application of QA/QC will demonstrate that the analytical results are reliable. While tak- ing the additional samples and having them analyzed adds additional costs to a sampling program, the demonstration of reliability is more than worth the extra cost.
Demonstrating reliability is true not only for chemically analyzed samples but also applies to geophysical and other types of data collection. Are your instru- ments properly calibrated, maintained, and cleaned? For example, one uranium exploration program that was finding no anomalous radiation was calibrating its detectors at the program’s field camp that was assumed to be away from any deposit. In fact, the camp sat on top of a uranium deposit and so the calibration checks set the radiation background way too high. I once discovered that a magnetic base station I was using for a particular survey, which was chosen for repeated magnetic readings to measure diurnal variations, was for some reason a point at which magnetic readings fluctuated dramatically over short time intervals and was not in fact a reliable base station. Taking repeated readings over a relatively brief time was required to determine if a location would make a good base station. These examples demonstrate that QA/QC procedures should be included in any data collection program.
The Problem With Geologic Models
Computer modeling and 3D graphics programs provide colorful images that can be rotated around multiple axes and allow the examination of geologic data in ways that simply were not possible in the days when graphics were hand drawn with India ink. The problem with such illustrations is that they are not “Truth” but are interpretations, either based entirely on an algorithm or a combination of human and mathemati- cal inputs. Martin Geach pointed this problem out in his “3D model: stepping back” in the June 2016 Geoscientist.2 Geach “believes we must understand what happens in the ‘black box’ if we are to ensure the quality of our outputs.” “This is most important when applying computer-based algorithms to produce geological surfaces that are spatially
incomplete and difficult to refine.” Geach illustrates his point by using three differ- ent interpolation techniques on the same data set to determine the difference in volumes between a geologic surface (for example, the upper contact of a particu- lar formation) below a fixed plane. Table 1 presents Geach’s results.
Table 1
Geach’s Interpolation Modeling Results Using a search radius of 30 data points
Interpolation method Ordinary kriging
Calculated volume 15,564 km3
Inverse distance weighting 15,239 km3 Radial basis function
37,794 km3
While the ordinary kriging and inverse distance weighting algorithms generated similar results, use of the radial basis function calculated a volume over twice as large as the other methods. Which method generated the best esti- mate of volume between a surface and the overlying plane? That is something the geoscientist working with the data must decide. But calculating the result using the different interpolation meth- ods is important in assessing the answer and the uncertainties involved.
Roadblocks to Communicating with Others
The starting place for this discussion
is J.B. Maynard, M.T. Bowers, and P.E. Potter’s article, What do you mean moisture is 110%! Twenty roadblocks to communicating engineering geology, in following is Edward Gates’ Letter to the Editor in the same issue. Finally, President Helen Hickman and Past President Foster Sawyer reviewed the draft of the comments on the SEC’s proposed new mining disclosure rules I wrote on behalf of AIPG.3 Effective com- munication including clear explanation of terms and units of measure are at the heart of each of these documents.
Maynard, Bowers, and Potter review the differences in terms used by geolo- gists and engineers for various rock and soil properties. They identify technical terms that have different definitions in geology and engineering. They conclude that, “It is tempting for the geologist to regard failures in construction or excava- tion as the failure of the civil engineer to consider geologic factors. We opine
2. www.
http://geolsoc.org.uk/Geoscientist/June-2016/3D-models-stepping-back. 3.
https://www.sec.gov/comments/s7-10-16/s71016.htm, accessed 9/1/16.
www.aipg.org
instead that it is the geologist who has failed to communicate the existence and importance of these factors. Because the flow of communication is almost always downstream from the geologist to the engineer, the bulk of the responsibility falls on geologists to clearly convey the significance of their results to engi- neers.” While I would argue that engi- neers working in geologic environments ought to know some geology in order to be professionally competent, the point that providing clear explicit explanations is important.
Gates makes this point in his com- ment on Robert Font’s Test your knowl- edge question 4 from the Apr/May/Jun TPG. Gates notes that Font converted pounds to ounces using the conversion 1 lb = 16 oz. While this is true for avoirdu- pois weights—the weights we commonly use—gold and other precious metals are weighed in troy ounces. One troy ounce = 1.09714 avoirdupois ounces. Mining geologists know that precious metals are weighed in troy ounces but frequently forget that the rest of the population has generally never heard of troy weights. Life might be simpler if we all used metric units—1 troy ounce = 31.10348 grams and 1 troy ounce per short ton = 34.28568 grams per tonne—but most of AIPG’s membership, let alone the general public, doesn’t have these unit conversions memorized.
Finally, Hickman’s and Sawyer’s com- ments on my draft comments reminded me to spell out acronyms and explain technical terms. Doesn’t everyone know that CRIRSCO stands for the Combined Reserves International Reporting Standards Committee, now known as the Committee for Mineral Reserves International Reporting Standard? Likewise, doesn’t everyone know that the unrefined gold sold by mining compa- nies is known as doré or that the purity of gold is expressed in fineness, which is given in parts per thousand (per mil (or per mille), ‰; like batting averages). A particular sample of mineral gold may have a fineness of 864 (most of the remaining 136 per mil is silver) and that pure gold is 999 fine.
The point being that we must be clear and explicit about our reports. Don’t assume the reader knows the techni- cal terms and short hand or clearly understands all the units of measure. Remember the space probe that was
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