Neural network modeling of geological field of mineral deposit

Prediction of the produced ore quality using neural networks is discussed as a casestudy of iron ore and copper–molybdenum deposits. Currently mines recover ore at different sites of an ore field, remote from each other horizontally and vertically, and while the mass of the extracted ore is usually known, the qualitative properties of ore are characterized by indicators which are the quantification of the averaged qualitative properties of ore in a certain production site. A brief analysis of characteristics of a mineral deposit geology (geochemistry) is given; these characteristics can help predict ore qualities using the detailed exploration data. The issues of poor formalization of spatial variation patterns in subsoil are addressed. A procedure is proposed for selecting a deterministic component suitable by a certain criterion of the neural network model in the conditions of a limited set of data. The statistical analysis of the results is carried out with a regression model of dispersion in a random component of a spatial variable, which enables assessment of data precision at any point of a spatially variable field. It is proved that it is possible to construct a sufficiently precise model of geological field of a mineral deposit given the neural network has revealed certain regular data patterns.

Bondarenko I.S. Neural network modeling of geological field of mineral deposit. MIAB. Mining Inf. Anal. Bull. 2023;(6):19-38. [In Russ]. DOI: 10.25018/0236_1493_2023_ 6_0_19.

I.S. Bondarenko, Cand. Sci. (Eng.), Assistant Professor, National University of Science and Technology «MISiS», 119049, Moscow, Russia, e-mail: innasbondarenko@gmail.com, ORCID ID: 0000-0002-4160-8413.

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