Sensor fusion and correlation of X-ray tomography and XRF data for drill core analysisShow others and affiliations
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]
State of the art analysis techniques on drill cores for exploration purposes, including X-ray fluorescence (XRF), laser-induced breakdown spectroscopy (LIBS) or Raman spectroscopy are used to derive mineralogical information. Since this sensor data corresponds to materials that occur on the surface of the core, inclusions (e.g. diamonds) cannot be detected. In addition, information outside of the measurement position is not taken into account and may lead to misinterpretation or the miss of certain elements. X-ray computed tomography (CT) and radioscopy provide data about the entire sample as well as inlying structures based on X-ray absorption. As a drawback, CT is time-consuming and the material information is not explicit.
For the enhancement of geological interpretation, we propose to apply sensor data fusion techniques in order to unite both depth information as well as reliable material data information from surface measurement techniques. This leads to more substantial information of the drill core.
For further insights in the feasibility we investigate the correlation of XRF data at varying abstraction levels with CT data, i.e. grey value information.
The applied XRF technique involves the fact that the data is not acquired continuously but discrete point by point. This is accompanied by the circumstance that the spatial resolution of the acquired data has a different magnitude than the CT-data. Both facts result in the challenge to register the XRF data coming from a one-dimensional scan line with a micro-CT volume. The experiments must be planned in a way that location and orientation of the scan data are well-known and reproducible.
In the experiments, the acquired and registered data of a defined drill core is analysed with respect to correlation and fusion capability. The experimental setup will be presented and results will be discussed.
Place, publisher, year, edition, pages
2019.
National Category
Geology
Research subject
Ore Geology
Identifiers
URN: urn:nbn:se:ltu:diva-75803OAI: oai:DiVA.org:ltu-75803DiVA, id: diva2:1347781
Conference
International Symposium on Digital Industrial Radiology and Computed Tomography (DIR2019), Fürth, Germany, July 2-4, 2019
Projects
EIT Raw Materials Upscaling Project "Enhanced Exploration (EnEx)"2019-09-022019-09-022021-06-08Bibliographically approved