High quality kriging-based geochem heatmaps in seconds

Geochemical heatmaps

A notable example of the effectiveness of geochemical mapping is the comprehensive study conducted by the China Geological Survey in the Mina Pirquitas region of Argentina. In this extensive 1:250,000 geochemical mapping survey, researchers analyzed 39 elements from over 2,470 samples, covering nearly 10,000 km². The data revealed significant geochemical anomalies, including 487 single-element anomalies and 52 comprehensive anomalies. This dataset provided vital information for identifying regions with high mineral resource potential, particularly for copper, gold, and other metals​.

These geochem maps are of high value, but require some efforts, knowledge and infrastructure to build. The data needs to be made compatible with scripting (whether it's in a GIS application or in python), and it needs to be stored, etc.

A dynamic use of the MinersAI platform

In this example, based on the geochem sample data that we digitized and standardized, we wanted to replicate the geochem maps produced by the Geological Survey of China. While technical details about the generation of the map are not made available in the study, we assumed that kriging, with a standard implementation and rasonable parameters can replicate results that can provide valuable insights to highlight anomalies and opportunities in the dataset in question.

The geochem sample dataset was processed and made available on the MinersAI platform, fully interoperatble with all other datasets, whether public or private (user uploaded). Running our geochem feature allowed obtaining in less than a minute a geochem heatmap that shows comparable features and values to the high quality map produced by Segemar and the Chinese Geological Survey.

All of this, in less than a minute from login to the platform, to obtaining the heatmap. This shows how such heatmaps can be used easily, in any location, on any dataset available on the platform, or private ones added on the fly.

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The complete workflow can be seen below:

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All of this is truly dynamic, flexible, and puts the user in control of what datasets such heatmaps should be generated on. Whether it be regional public datasets or high-resolution private ones on a property, the tool works seamlessly.

Do you want to try on your own? Request a demo!