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Browsing Conference papers by Subject "Machine learning"

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    IMPLICIT MODELLING OF GEOLOGICAL DOMAINS BY SUPPORT VECTOR MACHINE: TUNING THE PARAMETERS BASED ON CONSISTENCY OF THE INDICATOR VARIOGRAM
    (APCOM 2021, 2021-09) Ongarbayev, Ilyas; Madani, Nasser; Musingwini, C; Woodhall, M
    3D modelling of geological domains is an essential part of orebody modelling and resource classification. Conventionally, one applies a wireframing technique, which, in practice, is quite laborious. An automatic approach for building an implicit geological model is the application of the Support Vector Machine (SVM) algorithm. However, in this machine learning problem, the accuracy of models significantly depends on the selected parameters. Here, the authors introduce another criterion for selecting optimum parameters, in which the indicator variogram is considered as an aid. The results can be used as an instruction for implicit geomodelling, based on the SVM algorithm where one is dealing with two categories. Keywords: SVM, Support Vector Machine, indicator variogram, geological modelling, implicit geomodelling.