Xrays: A fuzzy expert system for qualitative XRD analysis

Wright, David K.; Liu, C. L.; Stanley, Damian; Chen, H. C.; Fang, J. H.

doi:10.1016/0098-3004(93)90060-i

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…One common way of phase identification by XRD pattern is the comparison of the unknown pattern to selected patterns from the Powder Diffraction File, a collection of “fingerprints” of various compounds based on d‐I data derived from a combination of the intensity of signal with the d‐spacing determined from the experimentally measured angle of diffraction [30]. The PDF is complemented by the Hanawalt Search Index, a system for manual searching that characterizes groups of compounds by the three strongest d‐I lines, listing up to eight values for each compound, with provisions for expected variability in experimental data [31,32]. Tolerances for d‐spacing values in the Hanawalt Index range from ±0.2 Å at d values over 8.0 Å to ±0.01 Å for values under 3.24 Å [33].…”

Section: Resultsmentioning

confidence: 99%

Evaluation of plasma cleaning as an approach for the preparation of soil minerals for forensic comparison by photon and electron microscopy

Duggar

Kubic

2021

Journal of Forensic Sciences

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Soil, for forensic purposes, is earthen material, accidentally or deliberately collected, with some association with the matter under investigation. One way to characterize this soil is by its inorganic mineral content. The naturally occurring inorganic portion of soil is the product of the physical disintegration of bedrock by ice, water, winds, and gravity, and the chemical changes of hydrolysis, carbonation/decarbonation, hydration/dehydration, oxidation/reduction, and the formation of salts and crystals. Soil therefore varies geographically and regionally. Variation in inorganic components may also be due to anthropogenic actions (such as agriculture, building,

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Section: Resultsmentioning

confidence: 99%

Evaluation of plasma cleaning as an approach for the preparation of soil minerals for forensic comparison by photon and electron microscopy

Duggar

Kubic

2021

Journal of Forensic Sciences

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“…lattice defects), impurities and/or variable chemical compositions (Bergaya & Lagaly, 2006; Brindley & Brown, 1980; Hillier, 2000; Moore & Reynolds, 1997; Śrondoń et al, 2001; Śrondoń, 2013). Despite great technological evolution over the last decades, the current availability of standards for thousands of material systems and the abundance of computer search/match software, an accurate qualitative and quantitative analysis of minerals remains a challenging task (recent review in Zhou et al, 2018; see also Brindley & Brown, 1980; McManus, 1991; Moore & Reynolds, 1997; Snyder & Bish, 1989; Wright et al, 1993). Evidencing the previous, several authors have designed customised approaches to diffractogram interpretation (Krumm, 1999; Wright et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

“…Despite great technological evolution over the last decades, the current availability of standards for thousands of material systems and the abundance of computer search/match software, an accurate qualitative and quantitative analysis of minerals remains a challenging task (recent review in Zhou et al, 2018; see also Brindley & Brown, 1980; McManus, 1991; Moore & Reynolds, 1997; Snyder & Bish, 1989; Wright et al, 1993). Evidencing the previous, several authors have designed customised approaches to diffractogram interpretation (Krumm, 1999; Wright et al, 1993). Pre‐treatment of natural samples is also known to increase the uncertainty when dealing with XRD analysis.…”

Section: Introductionmentioning

confidence: 99%

Customised display of large mineralogical (XRD) data: Geological advantages and applications

Coimbra

Kemna

Rocha

et al. 2022

The Depositional Record

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“…Classification techniques based on fuzzy systems were introduced by Wright et al (1993) for identifying minerals from X‐ray diffractograms. In defining fuzzy patterns for each elementary phase, the system searches for the best match between the recomposed and observed patterns so that the optimal miner‐alogical composition is achieved.…”

Section: Introductionmentioning

confidence: 99%

A multi‐method geophysical approach based on fuzzy logic for an integrated interpretation of landslides: application to the French Alps

Grandjean

2012

Near Surface Geophysics

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Physics‐based, such as seismic surveying or electrical resistivity imaging, appear to be efficient in investigating landslide structures and, consequently, for understanding related mechanisms of deformation. They are non‐intrusive probing methods that allow the direct measurement of compressional (P), shear (S) wave velocities and also electrical resistivity, three geophysical parameters that are considered as determinants in defining ground properties and identifying anomalies related to structural (faults, fissures), lithological (sand to clay, or calcareous variations) and hydrological (moisture, water flow) conditions. Both the seismic and resistivity methods are commonly used for landslide investigations, having been tested over the last decade or more on various types of landslides, including mudslides, debris flows, unstable slopes, etc. The common approach in these earlier experiments has been to invert the P‐ and S‐wave velocity fields, plus the electrical resistivity field, using suitable algorithms to produce two‐dimensional P‐ and S‐wave velocities and resistivity tomograms. A coherent and integrated interpretation of the resulting information is, however, not straightforward because each geophysical method is sensitive to different soil properties. An innovative approach has thus been developed to combine the geophysical parameters imaged on tomograms and convert them into different geological or geomechanical cross‐sections. Knowing that seismic data provide information on variations in fissure density and the presence of sheared materials and that electrical resistivity data provide information on variations in water content, the final cross‐sections are computed by combining different transformation functions able to model the conversion from geophysical parameters to ground properties. The computations are realized in a framework of the fuzzy‐set mathematical theory that maintains a certain level of objectivity and is able to manage uncertainties. The basics of this approach are explained through a brief presentation of the theory of data fusion with emphasis on how uncertainties are taken into account. The results obtained from case studies in the French Alps are then discussed in terms of reliability and compared with available ground reality obtained from surface observations and borehole descriptions.

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Xrays: A fuzzy expert system for qualitative XRD analysis

Cited by 8 publications

References 8 publications

Evaluation of plasma cleaning as an approach for the preparation of soil minerals for forensic comparison by photon and electron microscopy

Evaluation of plasma cleaning as an approach for the preparation of soil minerals for forensic comparison by photon and electron microscopy

Customised display of large mineralogical (XRD) data: Geological advantages and applications

A multi‐method geophysical approach based on fuzzy logic for an integrated interpretation of landslides: application to the French Alps

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