Contact Profile Analysis of Resource Estimation Domains: A Case Study on a Laterite Nickel Deposit

Resource estimation is commonly performed in separate domains that are defined using different criteria depending on the type and geometry of the deposit, the mining method used, and the estimation method applied. The validity of estimation domains can be critical to the quality of produced resource estimates as they control various steps of the estimation process, including sample and block selection. Estimation domains also affect statistical and geostatistical analyses because they define what estimation practitioners will consider as statistically separate distributions of data. Sometimes, samples from different estimation domains share similar grade properties close to the contact between domains, a situation known as a soft boundary. In such cases, it can be useful to include samples from different domains at short distances from the boundary. Contact profile analysis is a technique that allows for the measurement of the relationship between grades on either side of the contact between two estimation domains. As discussed in the study presented in this paper, contact profile analysis can help validate the defined estimation domains and control the application depth of any soft boundaries found between domains.

Hydrothermal and Metasomatic Kaolin Resource Estimation in the Quang Ninh area, Northeastern Vietnam

In northeastern Vietnam, hydrothermal‐metasomatic kaolinite-pyrophyllite from the secondary quartzite origin has been found in many places, including Pin Ho, Ban Ngai, Khe Khoai, Pec Sec Leng, Tan Mai ore occurrences, etc. They are exploited together with pyrophyllite, alunite, and high‐alumina quartzite as a byproduct. There were 810 chemical and mineral samples in the Quang Ninh area collected to investigate hydrothermal‐metasomatic kaolin resources. The ore minerals consist of kaolin-group minerals (kaolinite, dickite), pyrophyllite, quartz with minor sericite, alunite, diaspora, etc. They were identified by X-ray diffraction, and microscope and scanning electron microscope coupled with energy-dispersive X-ray spectroscopy analyses. Chemical analyses of major oxides were carried out on clays and parent rock samples by X-ray fluorescence spectrometry. The similarity-analogy in ore geology and mineral resource estimation based on statistical methods are employed to estimate hydrothermal‐metasomatic kaolin resources from the Quang Ninh area in northeastern Vietnam. The mineral resource estimation based on statistical methods shows 2.21 million tons of kaolin obtained by the content of aluminum oxide over 28% of the Pec Sec Leng mine, accounting for 14.3% in total. The similarity-analogy in ore geology indicates 158.16 million tons of kaolinite-pyrophyllite ores, of which, 22.0 million tons are kaolin. These methods display that the Quang Ninh area contains mainly pyrophyllite rather than kaolin resources. Our study suggests that the Quang Ninh area can be considered as a potential pyrophyllite resource in northeastern Vietnam for future exploration. Furthermore, the one resource estimation based on similarity-analogy in ore geology method shows an overview of the prospect on kaolinite-pyrophyllite resources in the Quang Ninh area, northeastern Vietnam.

Review of the distribution of granite using geomagnetic methods at Bukit Nunggal Air Mesu Village, Pangkalan Baru Sub-District, Bangka Tengah District

Exploration is activities happens before the mining activity. It has purposes to know, predict and obtain the dimension in quality and quantity from a reserve that has economic value. From surveying at bukit Nunggal, Mesu Village, Pangkalan Baru Sub-district, Bangka Tengah District showed that there are outcrops indicating the potential for becoming a reserve for granite. Measurement is conducted to know the resource estimation and it is done by an approach of geomagnetic method that is environment friendly. Geomagnetic is passive method to measure the magnetism level or susceptibility from the measuring point. The total of the line to know the dispersion pattern and the depth of the resource is 13 lines with the space between measurement is 10m. From the geomagnetic measurement obtained IGRF value is 42969, inclined degree 0.424 declined degree – 21.157, susceptibility value in between -54.2nT to 56.1 nT, and the granite susceptibility value is in between 21 nT to 50nT. The anomalies map showed that there is a resource potential, and the dispersion is from east to west. Viewed from the topography and the residential areas, mining activity is recommended to be done in the west area.

Machine Learning—A Review of Applications in Mineral Resource Estimation

Mineral resource estimation involves the determination of the grade and tonnage of a mineral deposit based on its geological characteristics using various estimation methods. Conventional estimation methods, such as geometric and geostatistical techniques, remain the most widely used methods for resource estimation. However, recent advances in computer algorithms have allowed researchers to explore the potential of machine learning techniques in mineral resource estimation. This study presents a comprehensive review of papers that have employed machine learning to estimate mineral resources. The review covers popular machine learning techniques and their implementation and limitations. Papers that performed a comparative analysis of both conventional and machine learning techniques were also considered. The literature shows that the machine learning models can accommodate several geological parameters and effectively approximate complex nonlinear relationships among them, exhibiting superior performance over the conventional techniques.

Assessment of Ore Grade Estimation Methods for Structurally Controlled Vein Deposits - A Review

Resource estimation techniques have upgraded over the past couple of years, thereby improving resource estimates. The classical method of estimation is less used in ore grade estimation than geostatistics (kriging) which proved to provide more accurate estimates by its ability to account for the geology of the deposit and assess error. Geostatistics has therefore been said to be superior over the classical methods of estimation. However, due to the complexity of using geostatistics in resource estimation, its time-consuming nature, the susceptibility to errors due to human interference, the difficulty in applying it to deposits with few data points and the difficulty in using it to estimate complicated deposits paved the way for the application of Artificial Intelligence (AI) techniques to be applied in ore grade estimation. AI techniques have been employed in diverse ore deposit types for the past two decades and have proven to provide comparable or better results than those estimated with kriging. This research aimed to review and compare the most commonly used kriging methods and AI techniques in ore grade estimation of complex structurally controlled vein deposits. The review showed that AI techniques outperformed kriging methods in ore grade estimation of vein deposits. Keywords: Artificial Intelligence, Neural Networks, Geostatistics, Kriging, Mineral Resource, Grade

Integration of Dual Border Effects in Resource Estimation: A Cokriging Practice on a Copper Porphyry Deposit

Hierarchical or cascade resource estimation is a very common practice when building a geological block model in metalliferous deposits. One option for this is to model the geological domains by indicator kriging and then to estimate (by kriging) the grade of interest within the built geodomains. There are three problems regarding this. The first is that sometimes the molded geological domains are spotty and fragmented and, thus, far from the geological interpretation. The second is that the resulting estimated grades highly suffer from a smoothing effect. The third is related to the border effect of the continuous variable across the boundary of geological domains. The latter means that the final block model of the grade shows a very abrupt transition when crossing the border of two adjacent geological domains. This characteristic of the border effect may not be always true, and it is plausible that some of the variables show smooth or soft boundaries. The case is even more complicated when there is a mixture of hard and soft boundaries. A solution is provided in this paper to employ a cokriging paradigm for jointly modeling grade and geological domains. The results of modeling the copper in an Iranian copper porphyry deposit through the proposed approach illustrates that the method is not only capable of handling the mixture of hard and soft boundaries, but it also produces models that are less influenced by the smoothing effect. These results are compared to an independent kriging, where each variable is modeled separately, irrespective of the influence of geological domains.