Use of Gestalt Theory and Random Sets for Automatic Detection of Linear Geological FeaturesMathematical Geosciences - Tập 47 - Trang 249-276 - 2015
Dafni Sidiropoulou Velidou, Valentyn A. Tolpekin, Alfred Stein, Tsehaie Woldai
This paper presents the calibration and application of a Gestalt-based line segment method for automatic geological lineament detection from remote sensing images. This method involves estimation of the scale factor, the angle tolerance and a threshold on the false alarm rate. It identifies major lineaments as objects characterized by two edges on the image, which appear as transitions from dark t...... hiện toàn bộ
Contaminant Source Identification in Aquifers: A Critical ViewMathematical Geosciences - Tập 54 - Trang 437-458 - 2021
J. Jaime Gómez-Hernández, Teng Xu
Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the cor...... hiện toàn bộ
Multivariate Analysis of an LA-ICP-MS Trace Element Dataset for PyriteMathematical Geosciences - Tập 44 - Trang 823-842 - 2012
Lyron Winderbaum, Cristiana L. Ciobanu, Nigel J. Cook, Matthew Paul, Andrew Metcalfe, Sarah Gilbert
Application of multivariate statistics to trace element datasets is reviewed using 164 multi-element LA-ICP-MS spot analyses of pyrite from the Moonlight epithermal gold prospect, Queensland, Australia. Multivariate analysis of variance (MANOVA) is used to demonstrate that classification of pyrite on morphological and other non-numeric factors is geochemically valid. Parallel coordinate plots and ...... hiện toàn bộ
Improving Automated Geological Logging of Drill Holes by Incorporating Multiscale Spatial MethodsMathematical Geosciences - Tập 53 - Trang 21-53 - 2020
E. June Hill, Mark A. Pearce, Jessica M. Stromberg
Manually interpreting multivariate drill hole data is very time-consuming, and different geologists will produce different results due to the subjective nature of geological interpretation. Automated or semi-automated interpretation of numerical drill hole data is required to reduce time and subjectivity of this process. However, results from machine learning algorithms applied to drill holes, wit...... hiện toàn bộ