Hendro Purnomo

Pemetaan Sebaran Kadar Besi Pada Endapan Laterit Nikel Menggunakan Metode Interpolasi Indikator Kriging dan Ordinary Kriging

Introduction

Pemetaan sebaran kadar besi pada endapan laterit nikel menggunakan metode interpolasi indikator kriging dan ordinary kriging. Peta sebaran kadar besi di endapan laterit nikel menggunakan metode indikator kriging dan ordinary kriging. Analisis probabilitas, variabilitas spasial, dan estimasi potensi sumber daya Fe.

Abstract

Beside containing nickel (Ni), nickel laterite deposits also contain other elements, including iron (Fe) which have varying levels in each layer. In this study, the distribution of Fe content in the limonite layers was carried out using the indicator kriging method to analyze the probability distribution of iron levels and ordinary kriging to analyze the variability of iron levels spatially. Fitting the variogram was undertaken by using spherical, exponential and gaussian models. The selection of the best variogram model was carried out based on the smallest root mean square error (RMSE) value, while the estimation of resource potential was calculated by the polygon extended area method. The results of the interpolation show that the distribution of iron anomaly occupies ± 83,3% of the research area with a potential resource of ±64.522.110 ton of iron. The evaluation of the interpolation results base on the root mean square standardized prediction error (RMSP) indicates that the estimation results of iron content using the ordinary kriging method are underestimated.

Review

This paper, titled "Pemetaan Sebaran Kadar Besi Pada Endapan Laterit Nikel Menggunakan Metode Interpolasi Indikator Kriging dan Ordinary Kriging," addresses a significant aspect of resource characterization within nickel laterite deposits. Recognizing that these deposits are not monolithic and contain varying levels of other elements like iron, the study sets out to map the spatial distribution of iron content specifically within the limonite layers. The application of geostatistical methods, specifically Indicator Kriging (IK) for probability distribution and Ordinary Kriging (OK) for spatial variability, is a sound approach to understanding such complex geological phenomena and holds direct relevance for resource evaluation and potential processing strategies. The methodological framework presented is commendably robust. The authors' decision to test multiple variogram models (spherical, exponential, and gaussian) and select the best fit based on the Root Mean Square Error (RMSE) reflects a rigorous application of geostatistical principles. The study successfully delineates a significant iron anomaly, occupying approximately 83.3% of the research area, and provides a quantifiable potential resource of roughly 64.5 million tons of iron. These findings offer valuable initial insights into the iron potential, which could be relevant for co-product recovery or as an impurity assessment in nickel processing. However, a key point for further elaboration and discussion in the full manuscript is the concluding statement that "the estimation results of iron content using the ordinary kriging method are underestimated" based on the Root Mean Square Standardized Prediction Error (RMSP). This finding is crucial and warrants a detailed explanation of its implications for resource confidence and subsequent decision-making. Future analysis could benefit from a deeper dive into the reasons for this underestimation (e.g., data quality, stationarity assumptions, influence of outliers, or presence of trends not captured by OK), and perhaps a discussion on whether alternative estimation techniques or sensitivity analyses were considered to mitigate this observed bias. A more thorough discussion of the "polygon extended area method" and its integration with the kriging results would also enhance clarity for the overall resource estimation workflow.

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