A Machine Learning Approach for Material Type Logging and Chemical Assaying from Autonomous Measure-While-Drilling (MWD) Data
Understanding the structure and mineralogical composition of a region is an essential step in mining, both during exploration (before mining) and in the mining process. During exploration, sparse but high-quality data are gathered to assess the overall orebody. During the mining process, boundary positions and material properties are refined as the mine progresses. This refinement is facilitated through drilling, material logging, and chemical assaying. Material type logging suffers from a high degree of variability due to factors such as the diversity in mineralization and geology, the subjective nature of human measurement even by experts, and human error in manually recording results. While laboratory-based chemical assaying is much more precise, it is time-consuming and costly and does not always capture or correlate boundary positions between all material types. This leads to significant challenges and financial implications for the industry, as the accuracy of production blasthole logging and assaying processes is essential for resource evaluation, planning, and execution of mine plans. To overcome these challenges, this work reports on a pilot study to automate the process of material logging and chemical assaying. A machine learning approach has been trained on features extracted from measurement-while-drilling (MWD) data, logged from autonomous drilling systems (ADS). MWD data facilitate the construction of profiles of physical drilling parameters as a function of hole depth. A hypothesis is formed to link these drilling parameters to the underlying mineral composition. The results of the pilot study discussed in this paper demonstrate the feasibility of this process, with correlation coefficients of up to 0.92 for chemical assays and 93% accuracy for material detection, depending on the material or assay type and their generalization across the different spatial regions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Large-scale machine-learning-assisted exploration of the whole materials space
Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two millio…
Graph AttentionGenerative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials
A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit b…
Generative Adversarial NetworkvalidTopogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials ha…
Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data
Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data fo…
Transfer LearningRomanian micro-blogging named entity recognition including health-related entities
This paper introduces a manually annotated dataset for named entity recognition (NER) in micro-blogging text for Romanian language. It contains gold annotations for 9 entity classes and expressions: persons, locations, o…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+1