ESTIMATE ORE CONTENT BASED ON SPATIAL GEOLOGICAL DATA THROUGH 3D CONVOLUTIONAL NEURAL NETWORKS

An ore content prediction system is provided. The system receives structured geological data that is derived based on spatial geological information that is associated with an input region. The received structured geological data includes a plurality of multidimensional tensors that are derived from...

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Bibliographische Detailangaben
Hauptverfasser:	Salles Chevitarese, Daniel, Santos Rezende de Carvalho, Breno William, de Mattos Szwarcman, Daniela, Hultmann Ayala, Helon Vicente, Correia Villa Real, Lucas, Ferreira Moreno, Marcio, Zadrozny, Bianca, Cavalin, Paulo Rodrigo
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DETECTING MASSES OR OBJECTS GEOPHYSICS GRAVITATIONAL MEASUREMENTS MEASURING PHYSICS TESTING
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Beschreibung
Zusammenfassung:	An ore content prediction system is provided. The system receives structured geological data that is derived based on spatial geological information that is associated with an input region. The received structured geological data includes a plurality of multidimensional tensors that are derived from spatial geological information of a plurality of sub-regions of the input region. The spatial geological information includes one or more types of data. The system trains a prediction model to produce a prediction output based on an average grade of an ore of a target mineral type at a target region by using the received structured geological data. The system identifies a relationship of the structured geological data to the prediction output and determines a revised input region based on the identified relationship,