Predicting geological and geomechanical rock properties using data analytics and multi-sensor core logging data

使用数据分析和多传感器岩心测井数据预测地质和地质力学岩石特性

• 批准号: RGPIN-2020-06196
• 负责人: Esmaeili, Kamran
• 金额: $ 1.89万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749336
• 关键词: Predicting; geological; geomechanical; rock; properties

A detailed modelling of geological and geotechnical rock mass properties is essential for a cost effective and safe mining operation. Despite improvements in almost every technological aspect of mine planning and design, lack of sufficient and consistently measured data for the development of a detailed geological and geotechnical model remains a challenge. A higher quality and quantity of geological and geotechnical data is required to model the spatial variation of rock mass properties and to lower uncertainties in mine planning and design. Core logging is a fundamental method in obtaining geological and geotechnical data. However, the manual core logging methods are subjective, time consuming and inconsistent, which can significantly reduce the reliability of the resulting geological and geotechnical models. To overcome these limitations, new rock measurement and analysis techniques that automate the collection and analysis of data are required. This research program aims to advance prediction of rock geological and geomechanical properties using data from a multi--sensor core logging system. This will be achieved through the collection of multi-variate rock physical, mechanical, mineralogical and structural properties from non--destructive tests, together with high resolution images of the core samples. The data will then be used to develop machine learning models to predict geological and geomechanical rock properties. Core samples from geotechnical boreholes will be logged using both the manual and the multi--sensor core logging system. The high- quality images of the core samples along with the multi--variate core logging data will be processed to develop a multi--modal dataset. The dataset will be used for predictive models of geological and geomechanical rock properties. Supervised machine learning techniques will be used to train predictive models using the multi--modal mixed data. The models will be employed to automatically: predict and classify lithological rock units and their mechanical properties; detect and characterize geometrical attributes of natural discontinuities using image analysis; predict shear behaviour of discontinuities using digital core logging data; classify geomechanical rock mass properties based on multi--parameter digital core logging data; and to investigate the influence of data quality and quantity on geotechnical models used for mine design. The predictive geomechanical models from the digital core logging system will be compared to the models developed based on manual core logging data. The results of the research are expected to improve rock characterization and classification; allow informed decision making in mine planning and optimization; reduce the time and cost associated with unpredicted ground conditions; and reduce the risk of mine excavation failure and its associated costs.

地质和岩土岩质量特性的详细建模对于具有成本效益且安全的采矿操作至关重要。尽管矿山计划和设计的几乎所有技术方面都有改进，但缺乏足够且一致测量的数据来开发详细的地质和岩土技术模型仍然是一个挑战。需要更高质量和数量的地质和岩土数据来建模岩体质量特性的空间变化，并降低矿山计划和设计中的不确定性。核心记录是获取地质和岩土数据的基本方法。但是，手动核心记录方法是主观的，耗时且不一致的，可以显着降低所得地质和岩土技术模型的可靠性。为了克服这些局限性，需要新的岩石测量和分析技术来自动化数据的收集和分析。该研究计划旨在使用来自多传感器核心记录系统的数据来推动岩石地质和地质力学特性的预测。这将通过非破坏性测试的多变量岩石物理，机械，矿物学和结构特性以及核心样品的高分辨率图像以及高分辨率图像来实现。然后，数据将用于开发机器学习模型，以预测地质和地质力学岩石特性。使用手册和多传感器核心记录系统将记录来自岩土技术钻孔的核心样品。将处理核心样品的高质量图像以及多种核心记录数据以开发多模式数据集。该数据集将用于地质和地质力学岩石特性的预测模型。监督的机器学习技术将用于使用多模式混合数据来训练预测模型。这些模型将用于自动：预测和分类岩性岩石单元及其机械性能；使用图像分析检测并表征自然不连续性的几何属性；使用数字核心记录数据预测不连续性的剪切行为；基于多参数数字核心记录数据对地质力学岩体质量特性进行分类；并研究数据质量和数量对用于矿山设计的岩土技术模型的影响。将数字核心记录系统的预测地质力学模型与基于手动核心记录数据开发的模型进行比较。该研究的结果有望改善岩石的特征和分类。允许矿山计划和优化中明智的决策；减少与不可预测的地面条件相关的时间和成本；并降低我的发掘失败及其相关成本的风险。