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TitreGravity gradiometer data analysis in mineral exploration
TéléchargerTéléchargement (publication entière)
AuteurPilkington, M; Keating, P
SourceTargeted Geoscience Initiative 4: Canadian nickel-copper-platinum group elements-chromium ore systems -- fertility, pathfinders, new and revised models; par Ames, D E (éd.); Houlé, M G (éd.); Commission géologique du Canada, Dossier public 7856, 2015 p. 167-173, https://doi.org/10.4095/296687
Année2015
ÉditeurRessources naturelles Canada
Documentdossier public
Lang.anglais
DOIhttps://doi.org/10.4095/296687
Mediaen ligne; numérique
Référence reliéeCette publication est contenue dans Ames, D E; Houlé, M G; (2015). Targeted Geoscience Initiative 4: Canadian nickel-copper-platinum group elements-chromium ore systems -- fertility, pathfinders, new and revised models, Commission géologique du Canada, Dossier public 7856
Formatspdf
Sujetsgravité; interprétations de la pesanteur; méthodes analytiques; levés au gradiomètre; géophysique
ProgrammeÉtude des gîtes magmatiques de Ni-Cu-EPG, Initiative géoscientifique ciblée (IGC-4)
Diffusé2015 06 22
Résumé(disponible en anglais seulement)
Gravity gradiometer surveys are becoming increasingly important in the search for and characterization of mineral deposits. Measurement of the full gravity gradient tensor provides the opportunity for processing and interpretation of single tensor components or combinations of components. To effectively use these components and combinations thereof, it is necessary to characterize the information content in order to interpret the gradiometer data correctly. We use linear inverse theory to evaluate different components and their combinations and find that which concatenated components produce the smallest modelling errors. Of the single tensor components, the Tzz component was found to provide the best performance overall. Since airborne gradiometer data are collected in a highly dynamic environment, noise is ever-present and must be compensated for to produce a clean signal for interpretation. Two approaches were investigated for removing noise: kriging and directional filtering. The kriging and directional filtering results show a similar level of smoothness, the main difference being the increased smoothing along strike of the directionally filtered data. Since kriging is a data-driven procedure, it provides an objective estimate of the data noise level and degree of smoothness. Based on the kriging results, processing parameters can be chosen to give a similar level of smoothness and noise suppression for directional filtering, that more effectively delineates geological trends in the data.
GEOSCAN ID296687