Discussion on dynamic orebody modeling with geological science big data
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摘要: 三维地学建模的理论与方法在大数据时代该如何发展，是当下这一领域研究人员非常关注的问题。从现代三维地学建模的重要方法——隐式建模的角度，对三维地学建模方法与地质大数据系统的有机集成、以及如何利用大数据技术提高地学建模的效率和质量等问题进行了探讨。初步提出了一套基于地质科学大数据的三维地学建模方法和流程，包括：地质大数据的搜集、主题大数据系统的搭建、地质特征要素的深度挖掘和三维地学模型的动态构建。同时，也指出了大数据背景下高质效三维地学建模的关键在于：研究实现充分顾及地质对象和地质科学大数据特点的地学人工智能、机器学习、数据挖掘及空间推断方法。通过一个典型矿体建模的应用实例对所提方法的可行性进行了验证。Abstract: As a key technology of geological information systems and an important method for geological information science, three dimensional geoscience modelling (3DGM) faces many opportunities and challenges in the age of big data. Many experts in this field are discussing ways to improve 3DGM in this context. From the point view of 3D implicit modeling, an up-to-date 3DGM method, this paper discusses several issues on this point such as the integration of a geological big data system and 3DGM, and the possible ways to enhance the quality and efficiency of 3DGM based on big data technologies. The paper proposes an initial integration framework of big data based 3DGM consisting of data collection, construction of subject-oriented big data system, geological feature mining and dynamic orebody modeling. This research also suggests that the approaches of geological artificial intelligence, machine learning, data mining, and predication should be designed and applied with full consideration of essential characteristics of the geological objects under study, which is crucial to improve the modeling result. The feasibility of the proposed method is illustrated by a case study on orebody modeling.
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图 1 面向地学大数据的三维地学建模方法框架
Figure 1. A geological big data based 3D geological modeling framework
图 2 研究区钻孔样品硫品位直方图及基本统计值
Figure 2. Basic statistics and histogram of the drill-hole samples in the study area
图 3 地学大数据系统中的矿石品位均值与标准差之间的关系
Figure 3. Relationship between means and standard variances of samples from the geological big data system
图 4 地学大数据系统中的矿石品位信息挖掘感知结果
Figure 4. Perception results from mining ore-grades of the geological big data system
图 6 带不同属性信息的矿体隐式建模结果
a.硫品位值；b.条件方差；c.硫品位值大于8%的条件概率；d.硫品位值置信区间[8%, 14%]对应的置信水平
Figure 6. Ore body models from implicit modeling methods with different properties
图 7 不同约束条件下的动态建模结果
a.硫品位值大于8%；b.品位值大于14%；c.品位值位于8%和14%之间
Figure 7. Ore body modeling results with ore-grades
图 8 矿体模型的多个随机实现
a.实现1；b.实现2；c.实现3；d.实现4
Figure 8. Ore body models corresponding to the simulated realization
表 1 三维地学显式建模与隐式建模的主要实施过程及方法原理对比
Table 1. Comparison between basic principles and methods of three dimensional explicit and implicit geological modeling
建模方法类型 所依赖的方法原理及基础 输入数据的基本类型和特征 关键算法 计算结果的常见体现形式 显式建模 建模人员的主观认识 类型和尺度相对一致的勘探工程及剖面分析数据等 交互式的几何图形绘制算法 仅能展示地质对象的空间形态 隐式建模 建模对象的数学模型 多类型、多尺度的勘探工程及剖面分析数据等 空间插值、模拟和等值面生成算法 可同时展示地质对象的空间形态、精细地质属性及其不确定性 
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表 2 三维地学显式建模与隐式建模的性能和特征对比
Table 2. Comparison between basic characteristics and performances of three dimensional explicit and implicit geological modeling
建模方法类型 是否能融入地质认识 是否能服从取样数据 是否需要大量人机交互 建模速度 是否可以重现建模结果 动态更新的难易程度 能否同时生成多个模型 显式建模 是 是 是 慢 几乎不能 困难 不能 隐式建模 是 是 否 快 可以 简单 能
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