Importance of Analytical Standards in Mining

Ensuring Data Integrity in Resource Estimation: A Competent Person's Responsibility Resource estimation forms the backbone of mining and geological projects, relying on precise data collection, validation, and interpretation. Adhering to Industry Best Practices (IBP) ensures data quality across all stages—from initial sampling to final reporting—providing a reliable basis for decision-making and fostering trust in resource evaluations. Data quality plays a critical role throughout the estimation process. Every step—sampling, geological logging, assay analysis, data validation, and interpretation—requires meticulous planning and execution. Inputs to resource estimation are gathered from multiple sources, including exploration geologists, assay laboratories, geotechnical teams, and modeling specialists. Despite this collaborative effort, the Competent Person(s) carries the ultimate responsibility for the entire process. They must verify the quality and reliability of the data and ensure compliance with reporting standards like JORC, NI 43-101, or SAMREC. The Competent Person’s accountability includes ensuring: Proper QA/QC protocols for sampling and assay data. Accurate geological modeling and block estimation. Transparency in assumptions and parameters used for classification. Ensuring rigorous data quality is fundamental to the integrity of resource estimation. By adhering to Industry Best Practices, implementing robust QA/QC protocols, and maintaining transparency in methodologies, we uphold the credibility of geological models. This commitment to precision and reliability not only supports informed decision-making but also aligns resource reporting with global standards, reinforcing confidence in project outcomes. #Geology #Mining #ResourceEstimation #DataQuality #CompetentPerson #QAQC #MiningStandards #JORC #NI43101 #SAMREC #Exploration #GeologicalModeling

👷♂️ QA/QC in mining samples. “The quality of the mineral resource estimate is dependent on the quality of the data collection and handling procedures used”. To ensure sample representativity, strict quality assurances and quality control programs should be put in place. If the samples are not representative, then there may be sample bias that will directly affect the final resource estimate. Several issues need to be considered in relation to sample collection, handling, preparation, and analysis. 💡 The main objective of the QA/AC program is to minimize errors introduced due to sampling, sample preparation, and sample assaying procedures, it´s a continuous process providing information necessary to correct defects in the shortest amount of time possible. 🎯 Accuracy is a measure of the degree of agreement of the assayed sample value to the true unknow value of that sample. Precision is a measure of the reproducibility of the sample value, which can be estimated by re assaying the same sample several times. The sampling QA/QC program should cover: ✅ Sampling conditions in the field. ✅ Sample preparation. ✅ Analytical accuracy and precision. ✅ Correctness of the laboratory reports. ✅ Transfer of the information to the database. 🔬 There are generally 2 or more laboratories involved that would include a primary or principal laboratory for routine work, and a secondary or check laboratory. Sampling and assaying protocols are established prior to processing the samples, these protocols should cover all aspects of sample processing and handling including chain of custody. The materials to be used in the program include: ✳ || Standards or reference material ||. Grade is known, check the accuracy of the laboratory. ✳ || Blanks ||. Samples with no grade, for review laboratory contamination and verify correct handling. ✳ || Field duplicates ||. Samples taken from the same point, for measure of the sample precision. ✳ || Pulp duplicates ||. They are taken at the final stage of the sample preparation, measure precision of the analytical procedures in the laboratory or between the two laboratories. 🚨 All data obtained of the QA/QC must be analyzed and monitoring for ensure a correct assay procedure. 📃Reference: Mario E. Rossi, Clayton V. Deutsch, Mineral Resource Estimation, Springer. #mining #geology #laboratory #assays #qaqc #ore #samples

Quality Assurance (QA) and Quality Control (QC) are critical components of any mineral exploration project to ensure the reliability and accuracy of the data collected, which ultimately affects the interpretation and decision-making processes. Here's a breakdown of how QA and QC are typically applied in mineral exploration: #Quality #Assurance (QA) QA is the overarching process that ensures that all procedures and practices in the exploration project are carried out systematically and meet predetermined standards. It includes: 1. #Standard #Operating #Procedures (SOPs): Establishing and following SOPs for sampling, sample handling, logging, and assaying to minimize errors and biases. 2. #Training: Ensuring all team members are properly trained and competent in their roles to maintain consistency in data collection and processing. 3. #Documentation: Keeping detailed records of procedures, equipment calibration, and maintenance logs to ensure traceability and transparency. 4. #Sample #Security: Implementing measures to protect the integrity of samples, such as proper labeling, secure storage, and chain-of-custody protocols. 5. #Audits: Regular internal and external audits to verify that QA protocols are being followed and to identify areas for improvement. #Quality #Control (QC) QC involves the specific measures taken to monitor and verify the accuracy and precision of data collected during the exploration process. It includes: 1. #Blanks: Using blank samples to detect contamination during sample preparation and analysis. This is something you're already familiar with and use to quickly identify contamination issues. 2. #Standards (Certified Reference Materials): Inserting standards into the sample stream to assess the accuracy of the analytical methods and to detect any systematic errors. 3. #Duplicates: Analyzing duplicate samples to check the precision of sampling and analytical processes. This can include field duplicates, coarse duplicates, and pulp duplicates. 4. #Control #Charts: Plotting results of standards and blanks on control charts to visually monitor data quality over time and quickly identify any deviations or trends. 5. #Data #Verification: Regularly reviewing and verifying data for any inconsistencies, outliers, or errors. This can include re-assaying or re-sampling in case of suspicious results. 6. #Cross-Lab #Checks: Sending a subset of samples to a secondary laboratory to verify the results from the primary lab, ensuring that the data is consistent and reliable. #Application #in #Exploration** - #Geochemical #Sampling: Implementing QC procedures in soil, rock, and stream sediment sampling to ensure representativeness and reliability of the geochemical data. - #Drilling #Programs: Incorporating QA/QC in core logging, sample splitting, and assaying to maintain the integrity of the geological data. - #Resource #Estimation: Using variograms and other geostatistical tools to evaluate the spatial variability. https://t.me/OreZone