Estimating the heterogeneity of base and precious metal mineralisation is a great challenge for mining engineers and geologists who undertake resource evaluation, grade control and reconciliation. The calculation of the minimum broken sample mass to represent a given lot of mineralisation at a given comminution size is based on the estimation of IH_L, the constant factor of constitution heterogeneity. IH_L can be derived by different heterogeneity testwork or calibration approaches. Three methodologies are well known in the mining industry: the standard heterogeneity test, the segregation free analysis, and the sampling tree experiment or duplicate sample analysis. However, the methodologies often show different results, especially when it comes to gold. These differences are due to many reasons. Assuming the variances added by sample preparation and analysis to be equivalent for all tests, the reasons for the differences may include the nugget effect (particularly the presence of coarse gold), the segregation effect and the procedure of collecting/splitting the samples when performing the tests. This paper analyses and compares two heterogeneity tests: the original heterogeneity test and the simplified segregation free analysis, both performed on mineralisation from different Brazilian operations. The results show clear differences between the tests, highlighting the complexity of estimating the heterogeneity of mineral deposits. The study reports the importance of using proper methodologies for constitution heterogeneity estimation so that minimum sample masses and relative standard deviations of the fundamental sampling error can be relied upon. It also provides recommendations for practitioners on the application of testwork/calibration studies.

The purpose of this article is to show the evolution of international reporting standards for the public reporting of exploration results, mineral resources, and reserves, from the first initiatives to the current days. Particularities, similarities, and components, which ensure that the information provided complies to various worldwide instruments, are discussed. We also examined the recent changes in the national/regional Codes and guides, including the CRIRSCO Template published in late 2019. The systematization promotes understanding of company risks from the capital market and other financing mechanisms, making it possible to compare projects and do benchmarking analysis, allowing mining companies to comply in a more efficient, transparent, and less costly way to the jurisdictions under which they operate. Finally, presented is a chronology and evolution of the standardization and the localization of similar topics within the Codes of the CRIRSCO Family. The international reporting standards are a relevant and consistent instrument with reliable statements, clear language, and are recognized from one jurisdiction to another by financial institutions, which helps to improve and increase confidence in the mining industry. The objective of the CRIRSCO members is to provide and promote the understanding of the practices for mining investment decisions, being successful around the world.

The fundamental sampling error is one of the sampling errors defined by Pierre Gy’s Theory of Sampling and is related to the constitution heterogeneity of the mineralisation. Even if a sampling procedure is considered ideal or perfect, this error will still exist and, therefore, cannot be eliminated. A key input into Gy’s fundamental sampling error equation is intrinsic heterogeneity. The intrinsic heterogeneity of a fragmented lot can be estimated by the “calibrated” formula of Gy, which can be written as a function of the sampling constants K and α. These constants can be calibrated by the standard heterogeneity test, originally developed by Pierre Gy and Francis Pitard. This method is based on the selection of rock fragments, individually and randomly, in an equiprobabilistic way from a lot of particulate material, aiming to estimate the intrinsic heterogeneity of the lot. This test, in addition to demanding time and space, can be influenced by human biases, and is difficult to quantify or measure. Aiming to simplify the test execution and eliminate the variance generated by human biases, a prototype called the intrinsic heterogeneity tester was developed as an automated alternative for heterogeneity testing. This prototype selects fragments from a falling stream, one by one, by means of a predefined laser count. To evaluate the prototype, a study was carried out, using painted chickpeas to simulate mineralisation grades and, sequentially, processing the same lot in the intrinsic heterogeneity tester prototype several times. The statistical and mineral content analysis, and comparisons between the intrinsic heterogeneity tester and the standard heterogeneity test sampling constants and constitution heterogeneities were undertaken. As a result, the authors conclude that the intrinsic heterogeneity tester prototype can be used as an alternative to the manual selection of individual fragments and for estimating the intrinsic heterogeneity of particulate material lots to support sampling protocol optimization.