|Titre||A log-barrier approach for airborne gamma-ray spectrometry inversion|
|Auteur||Weihermann, J D; Oliveira, S P; Li, Y; Fonseca Ferreira, F J; Silva, A M; Fortin, R|
|Source||Computers and Geosciences vol. 147, 104682, 2020 p. 1-15, https://doi.org/10.1016/j.cageo.2020.104682|
|Séries alt.||Ressources naturelles Canada, Contribution externe 20200659|
|Document||publication en série|
|Media||papier; en ligne; numérique|
|Sujets||spectomètres à rayons gamma; Traitement des données; géophysique; Sciences et technologie; géomathématique|
|Illustrations||photographies; diagrammes; tableaux; diagrammes|
|Diffusé||2020 12 29|
|Résumé||(disponible en anglais seulement)|
The standard processing of airborne gamma-ray spectrometry (AGRS) data provides useful preliminary information to interpretation in several contexts, such as
environmental studies, geological mapping, and analysis of mineral deposits. For optimal results, the acquisition conditions should be nearly constant, and the flight height should be uniform. However, abrupt changes in flight height (often
originated from mountainous terrain) are common and lead to spurious variations and incorrect anomaly interpretation. Moreover, the commonly used corrections applied to radiometric data do not consider the effective sampling area of a survey,
especially the overlap between successive fields of view. As a consequence, the concentrations estimates of potassium (K), uranium (eU), and thorium (eTh) are not sharp. A solution to deal with this problem is to compute the concentration from the
AGRS data through an inversion algorithm. Inversion of AGRS data has shown to be an effective approach to suppress the significant overlap between successive fields of view. We introduce a logarithmic barrier approach for the radiometric inversion to
avoid spurious negative values in the radioelement concentration models. We choose calibration ranges from Brazil and Canada to test the proposed methodology. We produced concentration models for K, eU and eTh and compared the standard approach model
with the recovered model from both ranges. The predicted data from inversion of both calibration ranges are mostly consistent with the survey data, not over smoothing the data or fitting noise. The predicted data from inversion showed more
consistency with the observed data than the one predicted from the standard procedure.
|Sommaire||(Résumé en langage clair et simple, non publié et disponible en anglais seulement)|
This article deals with a method for improving the accuracy of data collected by airborne gamma-ray
spectrometry (AGRS). AGRS data is used in various fields, such as environmental studies and geological mapping, but abrupt changes in flight height, often caused by flying over mountainous terrain, can lead to errors in the collected data.
standard corrections applied to this data often don't account for the overlap between different readings, leading to imprecise estimates of the concentrations of elements like potassium, uranium, and thorium.
To address this issue, the researchers
introduced an inversion algorithm, a mathematical technique to enhance the quality of AGRS data. They used a logarithmic barrier approach to prevent the introduction of incorrect negative values in their models.
They tested their method using data
from calibration ranges in Brazil and Canada and compared the results with the standard approach. The inversion method provided more consistent and accurate data, reducing errors and improving the quality of information used in environmental and
This publication is essential for geologists, environmental scientists, and anyone using AGRS data because it offers a more accurate way to interpret the information and make informed decisions in various applications, from
mineral exploration to environmental impact assessments.