Improved domaining for geostatistical modeling

改进了地质统计建模的域划分

• 批准号: RGPIN-2017-06155
• 负责人: Boisvert, Jeff
• 金额: $ 1.75万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679780
• 关键词: Improved; domaining; geostatistical; modeling

Geostatistics uses statistical techniques to assess resources/reserves for geological deposits. These techniques have become increasingly popular for understanding uncertainty in sparsely sampled mineral deposits, petroleum reservoirs and other spatially distributed phenomenon. This work relates to the development of techniques to better model deposits that exhibit complex geological features; these features are modeled using rock types that define a particular rock type (such as sand stone, shale, granite, etc). Usually a geologist interprets drill hole data/images to determine rock types but numerical modeling requires statistical assumptions about each rock type that may not be correct depending on the geological definition. ******The first aspect of the planned research is to explore the modeling implications of the definition of rock types from a statistical point of view. Each sample taken from an ore deposit will be assigned a rock type based on the quantitative data available (such as mineral grade, geophysical surveys, contaminate levels) and the qualitative data available (such as the geological interpretation). This will ensure that rock types meet all necessary modeling assumptions but will also maintain the benefit of geological knowledge from qualitative data.******The second aspect of this research is to improve large scale ore body limits modeling. This involves interpreting the available data for a deposit and determining the mineralization extents. Normally this is done using the geological knowledge of the deposit; however, an automated method is proposed as a starting point for geological interpretation to improve models of mineralization.***This research is generally directed towards all disciplines where spatial modeling is required, including but not limited to: mineral resource/reserve modeling; mine planning; contaminate modeling; petroleum resource modeling. However, the research will be demonstrated on mineral deposits. The anticipated outcomes of this work are methodologies, computational programs and modeling recommendations for the assignment of categories (i.e. rock types, facies, etc) to sample data as well as automatic large scale mineralization extents modeling.******Engineering decisions are made based on these numerical models, including: mine plans; environmental footprints of mines; stockpiling decisions; plant processing input feeds. The benefits of the proposed work are to account for known uncertainties and increase the accuracy of numerical models, resulting in improved engineering decision making. The benefits of this research to Canada will be the increased competitive advantage for Canadian mining companies due to better modeling of ore bodies. More accurate models of ore deposits will be constructed, resulting in better mine plans with increased profits and sustainability of mining in Canada.**

地质统计学使用统计技术来评估地质矿床的资源/储量。这些技术已成为越来越受欢迎的了解稀疏采样的矿床，石油储层和其他空间分布现象的不确定性。这项工作涉及开发技术，以更好地模拟具有复杂地质特征的矿床;这些特征使用定义特定岩石类型（如砂岩，页岩，花岗岩等）的岩石类型进行建模。通常，地质学家解释钻孔数据/图像以确定岩石类型，但数值建模需要关于每种岩石类型的统计假设，根据地质定义，这些假设可能不正确。****** 计划研究的第一个方面是从统计学角度探讨岩石类型定义的建模含义。从矿石存款采集的每个样品将根据可用的定量数据（如矿物品位、地球物理调查、污染水平）和可用的定性数据（如地质解释）指定岩石类型。这将确保岩石类型满足所有必要的建模假设，但也将保持定性数据的地质知识的优势。第二方面是大比例尺矿体边界模拟的改进。这包括解释存款的可用数据和确定矿化程度。通常情况下，这是利用存款的地质知识完成的;然而，提出了一种自动化方法，作为地质解释的起点，以改进矿化模型。这项研究一般是针对所有学科的空间建模是必需的，包括但不限于：矿产资源/储量建模;矿山规划;污染建模;石油资源建模。然而，这项研究将在矿藏上得到证明。这项工作的预期成果是为样本数据分配类别（即岩石类型、相等）的方法、计算程序和建模建议，以及自动大规模矿化范围建模。工程决策是根据这些数字模型，包括：采矿计划;矿山的环境足迹;库存决策;工厂加工输入饲料。所提出的工作的好处是考虑到已知的不确定性，提高数值模型的准确性，从而改善工程决策。这项研究对加拿大的好处是，由于更好的矿体建模，加拿大矿业公司的竞争优势将增加。将建立更准确的矿床模型，从而制定更好的采矿计划，增加加拿大采矿的利润和可持续性。