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首页> 外文会议>International Conference on Signal Processing, Communication, Power and Embedded System >Adapting pattern recognition approach for uncertainty assessment in the geologic resource estimation for Indian iron ore mines
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Adapting pattern recognition approach for uncertainty assessment in the geologic resource estimation for Indian iron ore mines

机译：适应模式识别方法在印度铁矿山地质资源估算中的不确定性评估

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The Geologic resource estimation requires the accurate prediction of the regionalized variables such as ore grade at an un-sampled location with the knowledge of sparse borehole information. It plays prominent role in the decision-making process for investment and development of various mining projects and hence judicious selection of the assessment method is essential for making profitable investment. The grade and quantity of the ore varies with spatial coordinates and directions and thus spatial uncertainty has to be taken into consideration for improvement in estimation of minerals deposits. Traditional geostatistical approaches such as ordinary kriging (OK), Inverse Distance weighing (IDW) and object based approach are still being used for the purpose, but because of its limited capability of honoring the statistics up to the second order, they cannot faithfully represent the complex spatial variability of minerals deposit. The remarkable feature of various pattern recognition techniques to capture the inherent patterns in the complex data made them suitable for the geologic resource estimation. This paper describes the use of two distinct pattern recognition techniques: support vector regression and Gaussian process regression to assess the mineral grade of one of the Indian iron ore mines from eastern region of the country. The spatial coordinates and multiple types of lithology were taken as input variables and iron ore grade as an output variable. The comparative analysis of these models was carried out, and the results obtained were validated with traditional geostatistical method: Ordinary Kriging (OK). The various performance measures such as root mean square error (RMSE), and coefficient of determination (R) were used to evaluate the performance of the different models. It is found that SVR and GPR provide significant improvement in resource estimation.
机译：地质资源估算需要准确的预测区域变量，例如在稀疏钻孔信息的情况下在未采样位置的矿石品位。它在各种采矿项目的投资和开发的决策过程中起着重要作用，因此，明智地选择评估方法对于进行可盈利的投资至关重要。矿石的品位和数量随空间坐标和方向的不同而变化，因此必须考虑空间不确定性以改善矿床的估算。传统的地统计方法，例如普通克里金法（OK），反距离权重（IDW）和基于对象的方法仍被用于此目的，但是由于其遵循二阶统计的能力有限，因此无法如实地表示矿床复杂的空间变异性。多种模式识别技术的显着特征是可以捕获复杂数据中的固有模式，因此它们很适合进行地质资源估算。本文介绍了两种不同模式识别技术的使用：支持向量回归和高斯过程回归，以评估该国东部地区的印度铁矿之一的矿物等级。将空间坐标和多种岩性作为输入变量，将铁矿石品位作为输出变量。对这些模型进行了比较分析，并使用传统的地统计方法：普通克里金法（OK）验证了所获得的结果。各种性能指标（例如均方根误差（RMSE）和确定系数（R））用于评估不同模型的性能。发现SVR和GPR在资源估计方面提供了显着的改进。

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