The Parker Challenge Results & Resource Disclosure Risks | K2fly
Part 1: Findings From The Parker Challenge and Risks For Resource Disclosure

Part 1: Findings From The Parker Challenge and Risks For Resource Disclosure

Given the same dataset, will geo-statisticians, geoscientists and geologists achieve the same estimation? What is the variability? 

This is what The Parker Challenge set out to uncover. 

The challenge, which has never been done before, generated some interesting results that miners should be aware of, particularly in the resource disclosure space.  

In this article, we will run through The Parker Challenge and the findings that were drawn from it, as well as the risks that were exposed for resource disclosure and how you can minimise your exposure.  

What is the Parker Challenge?  

The late Dr Harry Parker (1946-2019) contributed a considerable amount to standardising reconciliation nomenclature, as well as working on the CRIRSCO reporting code. The Parker Challenge culminates the work Dr Harry Parker achieved in his life to create something that he would have been equally fascinated and perturbed by.  

With over three decades of experience in the Resource sector, Scott Dunham, the founder of SD2, is now dedicating himself to imparting his knowledge and expertise to the industry.  

Committed to addressing the fundamental factors behind the intricate challenges faced by the industry on a daily and yearly basis, Scott searches to make the industry more efficient. 

The Parker Challenge called on mineral resource estimators to create a classified model from the same dataset as with the results to be presented at the AusIMM Mineral Resource Estimation Conference 2023.

The task they were given was:  

“Develop a classified and reportable mineral resource estimate from the supplied data. The estimate will be used to inform long-term mine planning and investment decisions.”  

The aim was to quantify the “between person variance” or “pattern noise” in mineral resource estimation between geologists and mineral resource estimation techniques. The estimation results from each participant were amalgamated and compared to work out just how large human difference can be despite being given the same data.  

The range was calculated on three differences:  

  • Difference in approaches  
  • The range of outcomes  
  • Difference in classification decisions  

Glossary of definitions  

If you’re an experienced geologist, geoscientist, or industry professional, you most likely already have a grasp of these terms. However, here are some basic definitions explaining The Parker Challenge. 

When is something reasonably inferred?  

An inferred mineral resource refers to a section of a mineral resource where the estimated quantity, grade, or quality is determined based on limited geological evidence and sampling. The available geological evidence is enough to suggest the presence of geological and grade or quality continuity, but it doesn’t provide conclusive verification.  

How precise is an indicated resource?  

Indicated mineral resources offer greater confidence in quantity, grade, and quality estimates than inferred resources. They stem from extensive geological evidence and sampling, enhancing assessments of continuity and deposit characteristics. Despite remaining uncertainties, indicated resources better inform decision-making and planning in mineral projects. 

What is a measured resource?  

A measured mineral resource is the utmost in precision and reliability. It reflects the highest confidence in quantity, grade, and quality estimates, grounded in thorough geological evidence, sampling, and analysis. This resource category entails meticulous assessment, encompassing exhaustive drilling and comprehensive geological modeling. It stands as a dependable foundation for project evaluation, mine planning, and feasibility studies. 

What is “pattern noise”?  

Pattern noise, or between person variance, is the difference measured between each person’s estimation. The larger the pattern noise that’s revealed, the bigger the problem that is discovered from The Parker Challenge.  

Factors that contribute to pattern noise include:  

  • How many decisions do you make when estimating a mineral resource.  
  • Which decisions are more critical, and which are trivial.  
  • The real range of reasonable outcomes.  

The burning question for The Parker Challenge, what drives the noise in the system?  

Results of The Parker Challenge 

Overall, the Parker Challenge was a success and there were some interesting findings, so let’s run through them:  

The participants  

The majority of participants were geologists, which was desired, however there were more academics than expected. The breakdown of participants is shown below.  

Difference in resource classifications  

When it comes to resource classifications, the difference between participants was less than expected, at least for some classifications. As you can see from the graph below, indicated resources were quite similar across the board, while inferred resources had a lot of variability.  

Funnily enough, the more experience someone had, the less likely they were to classify a resource as measured, while the less experience someone had, the more likely they are to classify an inferred resource.   

Additionally, resource estimates were more similar when the resource classification was higher, which is surprising? Probably not. It makes more sense that when a resource is more certain, there would be less guess work, and with less guess work, comes less personal bias and as such less variability.  

Variation in volume

When it comes to the difference in volume estimated amongst participants, variation was large. Even with the two top and two bottom results removed from the data, the range is still near on 10,000Kt Cu metal (measured and indicated). That’s a difference of -66% to +91% of metal in the measured and indicated estimates. 

There’s even variance in time used for the challenge 

Sometimes a quick estimate is just as good as a large effort – overthinking can have you chasing after shadows which won’t move you forward. Sometimes you should embrace clarity and trust your instincts because quite often you can find the best decisions come from simplicity. However, that isn’t to say you shouldn’t be thorough. Here’s the results that were yielded along with the time that each participant spent on their estimate. The key takeaway from this is that a longer estimate might make you succumb to your own biases, so sometimes the best thing to do is keep it simple, and not overthink your estimates.

Three main takeaways from The Parker Challenge  

Classification and estimation are very different  

The magnitude of variation caused by different geological domains and classification is significantly higher compared to the variation in grade estimation on a global scale. Surprisingly, many individuals tend to solely concentrate on the grade estimation and geostatistics aspects, even within the classification process. 

Estimation and classification are distinct processes that require different skills, knowledge, and experience. It involves making the best possible forecast for a particular purpose, aiming to provide an accurate assessment. On the other hand, classification focuses on understanding and evaluating the associated risks.  

The JORC Code, and other codes, as a guideline for reporting mineral resources, should ideally reflect these fundamental differences by providing clear guidance and considerations for both estimation and classification methodologies. 

Mineral resource estimation software does not replace expertise 

Mineral resource estimation software has a significant impact on various aspects of our work. The workflows and training provided by vendors often promote specific approaches and can hinder critical thinking by neglecting to address unknown or unexpected situations. 

In order to avoid fundamental errors, machine learning and artificial intelligence (ML/AI) approaches in resource estimation benefit from the guidance and expertise of experienced practitioners in the field. This requirement is not unique to ML/AI; it is also applicable to industry-standard kriging practices. Both methodologies rely on the involvement of skilled professionals to ensure accurate and reliable resource estimations. 

Evidence based geology is important 

It is essential to ensure that geology is valid, verified, and plausible, meaning it should be supported by evidence and align with accepted geological principles and knowledge. 

The winning entry adopted an integrated approach, seamlessly connecting various stages from data review to final classification. The result was a cohesive “story” that flowed smoothly and coherently throughout the process. 

There exists a considerable amount of variability among individuals in resource estimation. This variability arises from interactions between volume, tonnes, and geological domains, as well as the chosen estimation method and approach. Multiple sources contribute to parametric uncertainty, further complicating the process. Experience plays a significant role in finding the optimal balance, commonly referred to as the “sweet spot,” where expertise and knowledge align to achieve reliable and accurate estimations. 

What this means for resource disclosure  

So then, what does this mean for resource disclosure? The Parker Challenge has expressly shown that governance is key.  As we can see, when it comes to project resource estimation methods and results, it is very easy to arrive at different opinions.  

If your team is not on the same page about your resource disclosure and reconciliation, it can be very easy to land yourself in hot water and waste a significant amount of time and money.  

You can limit variability in your team by limiting the chance for people to have different estimations which can be done with a resource disclosure and mine reconciliation platform to assist with your governance.  

Which platform should you trust for your governance?  

If you’re looking for the right Resource Disclosure and Mine Reconciliation governance solutions, you can be sure to remove subjectivity over your mineral estimates and public reporting with K2fly. K2fly allows you to coordinate your reporting process on a single standardised platform so you will never have to experience the difficulty of trying to find or follow up information from your team – you will already know exactly where to find it. 

K2fly’s solutions let you:  

  • Maintain your social license by limiting mistakes and protect your reputation.  
  • Automate your disclosure process to decrease time wasted as well was human-made errors.  
  • Give yourself a gold star for governance with the highest standard in platform readability and reliability.  
  • Remove animosity with disclosure by having all of your data in one place.  

K2fly’s proprietary software services some of the largest tier one miners in the world, helping them achieve their ESG goals through governance platforms that provide a companywide single source of truth. Spanning over 500 sites in more than 62 countries, we’re sure our solution will be able to service your needs. Get in contact with our team and find out how we can solve the issue you’re having today.   

Posts relacionados