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X-ray diffraction (XRD) was an important analytical component of International Ocean Discovery Program (IODP) Expeditions 372 and 375 in the Hikurangi subduction zone. This report documents the composition of standard mineral mixtures that we used to calibrate computations of mineral abundance in bulk powder and claysized fractions of the sediment. Shipboard analyses of the bulk powders were completed on the R/V JOIDES Resolution using a Bruker D4 Endeavor diffractometer, and reduction of those data utilized two types of software (MacDiff and Bruker DIFFRAC.EVA). To evaluate precision more rigorously, we replicated the bulk powder analyses at the New Mexico Bureau of Geology and Mineral Resources (New Mexico Tech; United States) using a Panalytical X’Pert Pro diffractometer, and MacDiff software was used for data reduction. The relation between peak area (integrated intensity) and weight percent in the bulk powder mixtures is constrained in two ways: a matrix of normalization factors, derived by singular value decomposition (SVD), and polynomial regression equations. Differences in results between the two computational methods are trivial. Absolute errors (known weight percent − computed weight percent) average less than 3 wt%, which is a level of accuracy more than sufficient to satisfy the scientific goals of shipboard XRD. The clay-sized standards were analyzed only at New Mexico Tech, and MacDiff software was used for data reduction. For computations of weight percent using those raw data, we tested three computations: Biscaye weighting factors, SVD normalization factors, and regression equations. Results using the SVD normalization factors and regression equations are significantly more accurate than those using Biscaye weighting factors. Introduction X-ray diffraction (XRD) has been included as a routine part of shipboard and shore-based measurements throughout the history of scientific ocean drilling. In one category of coring objectives, the primary goal of shipboard XRD is simple: to verify occurrences of specific detrital and/or authigenic minerals through recognition of characteristic peak positions on diffractograms. In other cases, however, the goal of XRD is to make initial estimates of absolute mineral abundance. Quantitative XRD becomes more important when an expedition’s science plan includes extensive whole-round sampling of cores for shore-based measurements of frictional, geotechnical, and hydrogeological properties; this emphasis is added because many such properties are modulated by bulk sediment composition. The science plans for International Ocean Discovery Program (IODP) Expeditions 372 and 375, which drilled in the Hikurangi subduction zone offshore New Zealand, fell into the second category (Barnes et al., 2017; Saffer et al., 2017). Accordingly, considerable effort was invested to generate reasonably accurate mineral abundance values using XRD data. Practitioners of quantitative XRD consider results to be “good” if errors amount to ±10% of the amounts present for major constituents (e.g., Moore and Reynolds, 1989a). Other workers have contended that an error of 3 wt% absoM.B. Underwood et al. Data report: standard mineral mixtures, normalization factors, and determination of error lute “is about the best that can be done for typical natural rock samples containing many phases” (e.g., Calvert et al., 1989). We regard those bounds as reasonable targets for IODP shipboard analyses. The purpose of this data report is to describe the procedures for computing values of mineral weight percent from bulk powder and clay-sized XRD data during Expeditions 372 and 375. This report provides documentation of the standard mineral mixtures used for bulk powder calibration, together with a thorough analysis of the errors in accuracy for shipboard XRD measurements using two computational approaches: singular value decomposition (SVD) and polynomial regression equations. We also assess how precision was affected by the use of two X-ray diffractometers (Bruker and Panalytical) and two types of data reduction software (DIFFRAC.EVA and MacDiff ). For the clay-sized XRD data, this report documents the standards, mineral mixtures, and errors in weight percent values using three computational approaches: Biscaye (1965) weighting factors, SVD normalization factors, and regression equations. Lessons from previous studies Several strategies have been tested and refined over five decades of scientific ocean drilling to compute relative and absolute abundances of common minerals in marine sediments (e.g., Moore, 1968; Cook et al., 1975; Heath and Pisias, 1979; Johnson et al., 1985; Mascle et al., 1988; Fisher and Underwood, 1995; Underwood et al., 2001). The basic schemes multiply values of peak intensity or peak area by empirical weighting factors to account for differences in diffracting power among common minerals (e.g., Biscaye, 1965; Cook et al., 1975). Fisher and Underwood (1995) analyzed mixtures of standard minerals to derive a matrix of normalization factors via SVD. As stated above, quantitative results that fall within 3 wt% of known mineral proportions are generally considered to be “highly accurate” (e.g., Calvert et al., 1989), but the challenges in achieving that level of accuracy are multifaceted (Moore and Reynolds, 1989a). To begin with, intraand interlaboratory errors can be exacerbated by differences in design among diffractometers, generator settings, slit dimensions, scanning rates, choices of fixed-step versus continuous-scan mode, use of automatic versus fixed slits, and inconsistent tuning of detectors. Furthermore, the performance of any individual instrument can change over the course of a project (e.g., fatigue of the X-ray tube or detector or upgrades of electronics). If peak intensities decrease significantly after prolonged use of an X-ray tube, then recalibration of regression equations and weighting factors might be warranted; the same is true if a tube or detector must be replaced. Different brands of software for data reduction and peak fitting, moreover, will yield results that are almost impossible to reproduce using other software; those errors can be attributed to differences among the algorithms used for setting the baseline, smoothing counts, matching peaks to ideal geometries, and computing area under a peak. All of this means that each calibration is instrument specific and tuned to an invariable set of scanning parameters and software. Tests by other practitioners of quantitative XRD have stressed the importance of sample preparation (including the disaggregation or powdering techniques), chemical pretreatments, uniformity in particle size separation, and random orientation (e.g., Raven and Self, 2017). Accuracy sometimes improves if internal standards are added (e.g., spiking specimens with known weights of such nonnative minerals as corundum). The reference intensity ratio (RIR) method is constrained by results from mixtures with a common mineral (e.g., quartz) or an internal standard (e.g., Hillier, 2000; Zhou et al., 2018). Some workers integrate the results of bulk chemical analyses or various types of spectroscopy in the calibrations (Knudsen, 1981; Środoń, 2002; Craddock et al., 2017). Another common approach is to use computer simulation programs (e.g., NEWMOD or RockJock; Yuan and Bish, 2010) and Rietveld leastsquares refinement to make iterative peak-fitting matches between natural XRD responses and either synthetic traces or diffractograms from standards. Workers can match either single-line RIRs or full patterns (Chipera and Bish, 2013; Zhou et al., 2018). In all cases, however, accuracy is reduced by the deleterious effects of preferred crystallite orientation (e.g., Ottner et al., 2000). To enhance the randomness of grain orientations, some workers have resorted to fairly elaborate and time-consuming steps in sample preparation, such as spray drying ethanol-based slurries to produce spherical aggregates of powder (e.g., Środoń et al., 2001; Hillier, 2000; Omotoso et al., 2006). Another potential pitfall with quantitative XRD calibration is the failure to match the indigenous mineral mixtures for a particular study area with standards that have similar XRD properties. As stated by Moore and Reynolds (1989a), “A most important problem, over which little control can be exercised, is choosing a standard mineral whose diffraction characteristics are identical to those of the same mineral in the unknowns. For example, all specimens of illite are not identical.” To compound that problem, many of the readily available clay standards are far from pure (e.g., illite contaminated by quartz), so it is also important to test for impurities and solve for the amounts of those impurities. Furthermore, absolute accuracy deteriorates if a natural sediment contains high concentrations of amorphous solids (e.g., biogenic silica or volcanic glass). Such assemblages can be calibrated by spiking felsic sediment samples with known weights of tephra or biogenic silica (Andrews et al., 2006, 2013). Otherwise, high contents of diatoms or volcanic glass will elevate errors in absolute weight percent for the crystalline phases. Similarly, because of differences in diffracting power, calibration factors computed for a kaolinite-rich mineral suite will generate larger errors if applied to an illite-chlorite assemblage. As another example, mixtures of crystalline illite and clay-sized white mica eroded from a greenschist-facies metamorphic source terrane will not match the more subdued XRD response of poorly crystalline detrital illite eroded from sedimentary rock sources exposed to modest levels of diagenesis. Accordingly, advanced knowledge of the natural sediment’s typical composition is a critical prerequisite to choosing and mixing site-appropriate mineral standards for XRD calibration. All of the issues summarized above need to be considered during the planning stages of any IOD

proceedings of the international ocean discovery program. expedition reports

Data report: standard mineral mixtures, normalization factors, and determination of error for quantitative X-ray diffraction analyses of bulk powders and clay-sized mineral assemblages