Lithology data
- Are there implicit or explicit intervals missing?
- Are there overlaps between intervals? Some software programs accept them, but it is recommended not to have them in the same lithology table.
- Have the lithology names been standardized? The stage of homogenizing the lithological data between the various periods of exploration/operation, or between the various project owners, for example, can save you a lot of time in terms of interpretation and modelling.
Assay data
- Are there empty fields in the assay table (except for pending assays)?
- Are there values of 0? If so, confirm that they are not “false” zeros (not sampled, pending assays, etc.).
- Are the highest grades justified by mineralization observation?
- What are the minimum and maximum interval lengths? Confirm that they are not errors.
- Are there overlaps between intervals?
- Is each assay properly identified with a unique sample number?
- If several analysis methods are used, is the final value systematically calculated using the same formula and does it take all methods into consideration?
- Have values below the detection limit received the same treatment? Although there can be exceptions (mainly in the case of historical assays), it is good practice to assign them half the value of the detection limit.
Other questions to ask
- Is the drillhole length consistent with the maximum depths of lithology, deviation and assay?
- Do all drillholes contain the information in the deviation, lithologies and assay tables? It is important to indicate if a drillhole was deliberately not assayed.
If you have other tables in your database, it is important to validate them as well.
Finally, if the deviation measures, coordinates and data were not automatically entered in the database, it is necessary to compare the database with the original data to ensure there are no typing errors.
If you have any questions or want to learn more on the subject, contact us. Our team of experts can support you throughout all your exploration and mining projects, from data acquisition, interpretation and modelling to estimation and optimization of resources and reserves.