Prospectivity Mapping Primer
Prospectivity mapping is a means to evaluate the potential for mineralisation of a particular
deposit type or commodity. Four main things to consider when planning a prospecitity mapping analysis include
the scale of observation, the kinds of data available for processing, whether an interpolative or extrapolative approach is used
and the ultimate desired product, be it a non-spatial estimate of reserves, a map, or an animation showing the location of potential metallogenic
provices throughout geologic time.
Factors to consider when planning a prospecivity mapping programme.
Global scale analysis
Prospectivity analysis can be conducted at a local, regional, or even global scale. Prospectivity analysis at a global scale
is normally reserved the identification of potentially new unexplored and untapped metallogenic provinces for spatial products, and the estimation
of remaining resources for non-spatial products. Global scale analysis involves the large scale analysis of known mineral provinces, their
geological makeup and tectonic setting and the identification of analagous areas that are yet to receive much scrutiny.
Regional scale analysis
The majority of prospectivity maps are generated at a regional scale, and as such have received the most attention. Theese incorporate data collected at
scales between 1:50K and 1:2M (with scales between 1:100K and 1:500K being optimal) and can be categorised into two broad types: conceptual (knowledge-driven)
and empirical (data-driven). A knowledge-driven approach attempts to break down the key factors that lead to the formation of an ore body and the representation
of these as mappable criteria. Multiple factors are ultimately combined to rank the relative prospectivity throughout the region.
The mineral systems concept that is the foundation of the knowledge-driven approach. Image by L. Wyborn
A data-driven approach,
on the other hand, examines the location of known deposits in relation to their geological surroundings in a bid to identify those factors that considered
important for mineralisation. Some common relationships that are examined for include:
- Association relationship - where deposits are spatially constrained by a particular feature (e.g. lithological or stratigraphic control).
- Proximity relationship - where known deposits occur closer to a particular feature (e.g. faults) than otherwise expected.
- Strike proximity relationship - an extension of a proximity relationship where the strike of the feature is also found to be important. This, for example
may highlight the location of dilational jogs along faults that tend to be more conducive to mineralisation.
- Abundance relationship - where a high abundance of a particular feature controls the location of deposits (e.g. strongly faulted areas).
Once identified, each important factor needs to be spatially quantified. Utlimately multiple quantified spatial relationships are combined using one of a
number of different techniques, including:
- Simple Boolean,
- Index Overlay,
- Weights of Evidence (Bayesian) combination,
- Algebraic Combination - Developed by Us
- Adaptive "Fuzzy" Logic,
- Vectorial Adaptive "Fuzzy" Logic - Developed by Us
A knowledge-driven approach works well in greenfields areas where there are few known deposits to work with. However a data-driven approach allows us to
identify spatial relationships that control the location of known, and hence unknown deposits. Spatial relationships empirically identified a mature area
have been applied successfully to analogous greenfield regions.
Local scale analysis
At the most detailed level are local-scale prospectivity analyses. These often focus on the identification of extensions to existing ore bodies.
At such a small scale the importance of information in the third dimensions becomes ever more important. As such, local scale prospecitivity mapping is conducted
using three dimensional GIS and specialised mining software and involves the examination of drill hole data.
assess your data and needs and advise you on the method that will provide you
with the best possible results!
Fixing a common misconception
With the advent of user friendly GIS, more powerful computers, the
availability of good datasets, and the existence of prospecitivty mapping software modules many are trying their hand at prospectivity
mapping. However, the tendency to treat the process as a black-box one is a recipe for disaster. At every step, processes
conducted on your data needs to be evaluated to ensure that it makes sense geologically and metallogenically. It is very easy to
inadvertantly bias your results and produce an essentially useless product if one is not careful. Prospecitivity mapping is an art as much
as it is a science. Dr Carl Knox-Robinson has over 21 years
experience in all facets of prospectivity mapping and understands what can and cannot be done.
Parametric fingerprint maps
Another common misconception regarding propspectivity maps is that they are an
"X-marks-the-spot" product. Nothing can be further from the truth. Prospectivity
maps are complex products that require careful interpretation in their own
right. To this aim, prospectivitymapping.com has developed the concept of parametric fingerprint maps which allows a better interpretation of a region's mineralisation potential relative to a particular known deposit (reference location). Other techniques, such as dendrogram analysis are also used in the interpretation of prospectivity maps.
Want to learn more?
Below is a selection of prospectivity mapping and exploration-related books available from the Amazon.com website.