Service: 3D modeling of deposit footprints from geochem data
3D DEPOSIT footprint modeling is a way of establishing clear and testable exploration targets from routinely-obtainable lithogeochemical data. Targets are not only defined by latitude and longitude, but also by depth.
Deposit footprint modeling is a 3D mineral exploration method of mapping out where geochemical comparison testing returns stronger or weaker results. The technique is broadly analogous to the search-and-compare process adopted when one completes a child's activity worksheet involving 'find the hidden pictures'.
The footprint modeling method has been evaluated at Kiska Metal Corporation's then-owned Whistler property in Alaska. [1] [2]
Here, we present a case study involving First Quantum Minerals' Haquira porphyry copper deposits in Peru.**
Footprint model chosen
As is true for mineral exploration generally, most surface-expressed porphyry copper deposits have been found already. The goal now for the exploration industry is to be able to find mineralized porphyries when they're poorly exposed and their haloes are rather cryptic.
Our deposit footprint modeling method isn't fettered to any particular ore-genesis theory in favor at any given time, because it's based on empirically seen trace-element abundances at an exemplar site.
When it comes to examining the three-dimensional distribution of trace elements at Haquira, the model or exemplar footprint we employed (Figure 1) was based on research work done at Oregon State University on the Ann-Mason porphyry copper deposit in the Yerington district of Nevada. [3] The Ann-Mason deposit has been erosionally exposed in cross-section thanks to 90-degrees post-mineralization tilting of the deposit and its host rocks.
FIGURE 1: Deposit footprint model used for porphyry system targeting case study at Haquira, Peru. Diagram adapted from Figure 3.16 of Cohen (2011). To view a larger version, click on either the image or this text link.
The porphyry copper footprint modeling method we've developed is an algorithm that looks at spatial changes in the abundance of about a dozen chemical elements at once. The elements include copper, molybdenum, tungsten, tin, selenium, antimony, and bismuth, among others. Multivariate analysis such as this would be impossible for a single person to carry out by mentally keeping track of all of these simultaneously changing concentrations. This method also goes further than 2D alteration mapping, which is already widely used by mineral explorers, and which is a simple visualization of where various mineralogical zones are situated at the surface.
This method involves searching for the spatial chemical-variability footprint left behind by the formation of an ore deposit. [4] The spatial distribution of trace-element concentrations of samples from the area of interest is compared to the spatial distribution of trace-element concentrations typically found surrounding the deposit style being sought.
Our method recognizes any similarities between the 'ideal' chemical footprint of a deposit and the chemical layout of the ground being investigated — and assesses the strength of similiarity.
The better the agreement between the sampled area's footprint and the model footprint, the stronger and, all else being equal, the more encouraging the results.
Haquira East and Haquira West deposits, Peru
Located in the Andahuaylas-Yauri porphyry belt of southern Peru, the Haquira porphyry copper deposits (Figure 2) and their related granodioritic stock and porphyritic dikes intruded into Mesozoic-aged metasedimentary rocks about 34 million years ago over a period of about 170,000 years, at an original depth estimated to be around 10 kilometers. [5]
FIGURE 2: Local geology in the vicinity of the Haquira East and Haquira West deposits, Peru. Sources: Regional 1:50,000 map from INGEMMET; prospect detailed map from Phil Gans (2009, unpublished). To view a larger version, click on either the image or this text link.
The Haquira project was a grassroots discovery attributed to Phelps Dodge [6] [7], who established the presence of two main mineralized areas. [8] Haquira has since become a well-described system featuring quite extensive exploration drilling, making it a good location for a case study.
Porphyry copper deposits are thought to form as disseminated sulfides and stockwork veining in the hypogene environment when evolved subduction-related magmatic systems violently unbottle hydrothermal fluids enriched in volatile components and incompatible elements. [9] The fluids invade host rock, causing laterally extensive geochemical-alteration haloes.
A tale of two datasets
The entire Haquira case-study project area covers about 150 square kilometers.
Datasets used in our Haquira case-study analysis contain trace-element abundances, which were obtained from whole-rock samples.
An initial 'reconnaissance' dataset was supplied to Fathom Geophysics for use in a first round of footprint modeling. A 'follow-up' dataset involving about 300 additional rock samples was then supplied to Fathom Geophysics for use in further footprint modeling (Figure 3).
FIGURE 3: Diagram showing the the Haquira deposits, the location of 'reconnaissance' surface samples versus the location of 'follow-up' surface samples, and the locations of deposit-related intrusives as determined from drilling. Sample locations are color-coded according to the dataset they belong to. Blue dots belong to the 'reconnaissance' dataset, while red dots belong to the 'follow-up' dataset. The 'reconnaissance' dataset involves sampling locales selected by First Quantum Minerals to mimic a first-pass whole-rock sampling campaign dataset. The 'follow-up' dataset involves sampling locales on hand (including sites bearing some of the higher-grade elemental abundances) that were held back from the 'reconnaissance' dataset. The splitting of the data into two separate datasets permitted sample-density testing of this deposit footprint modeling method. The volumes shown in pale red represent the envelopes of the Haquira East and Haquira West mineralization and associated intrusives. View involves looking to the southwest. To view a larger version, click on either the image or this text link.
The 'reconnaissance' dataset contained chemical analysis results for about 300 rock samples, which equates to an average sampling density of about 2 rock samples collected per square kilometer. The rock samples included in this dataset were selected by First Quantum Minerals with the aim of constructing a dataset that closely resembled a first-pass ground-based exploration sampling campaign.
'Follow-up' rock samples included anomalously high copper or molybdenum abundances, so that the dataset resembled a follow-up ground-based sampling campaign that had homed in on higher-grade rock samples.
By supplying first the 'reconnaissance' data and then the 'follow-up' data, First Quantum Minerals was able to conduct a relatively controlled, single-blind test to help determine as objectively as possible what the surface-sampling density would need to be to obtain meaningful results from deposit footprint modeling.
When combined to create a single, pooled dataset, the 'reconnaissance' plus the 'follow-up' data together represented an average sampling density of about 4 rock samples per square kilometer.
Targeting results from 'reconnaissance' data
Porphyry deposit footprint modeling using only the 'reconnaissance' dataset generated targets whose cores correctly coincide with drilling-determined locations of the intrusive centers at Haquira East and Haquira West (Figure 4). The beauty of this method is that the locations, sizes and shapes of targets are defined in the depth dimension as an integral part of the comparison-mapping results.
FIGURE 4: Cores of exploration targets (dark blue volumes) and the outer reaches of exploration targets (pale blue volumes) generated by porphyry deposit footprint modeling that employs only the 'reconnaissance' surface-based whole-rock sampling dataset for the Haquira project area. Note that this image shows modeling results only for a subarea immediately surrounding the Haquira deposits. Blue dots denote sampling sites. The volumes shown in pale red represent the envelopes of the Haquira East and Haquira West mineralization and associated intrusives. View involves looking to the southwest. To view a larger version, click on either the image or this text link.
The cores of targets are locales where an ore-deposit system has the highest probability of being situated, if a system is in fact present. In this case study, cores are defined as volumes existing interior to a minimum probability threshold (also known as a cutoff) of 0.4.
We've also defined a second minimum probability threshold of 0.25, which lets us visualize additional volumes that surround the targets' cores, and that are helpful in showing where 'things are getting warmer' when it comes to the likelihood of the presence of an ore deposit.
Note that most of our figures show modeling results only for a subarea immediately surrounding the Haquira deposits. Modeling results for the entire case-study project area are shown in this write-up's final figure.
A couple of additional testable targets were also generated with this 'reconnaissance'-data-only modeling run, and their centers are situated adjacent to the two known porphyry occurrences (Figure 4). If our case-study client were truly at the reconnaissance stage of exploration, these additional targets would be candidates for possible drill-testing or other forms of ground-truthing.
Not only did 'reconnaissance' data-only modeling generate target cores coinciding with the intrusive centers at Haquira East and Haquira West, but also the method managed to achieve this result from this dataset even though surface-rock samples directly above the two deposits possessed copper abundances that were quite unremarkable (Figure 5).
FIGURE 5: This image differs from Figure 4 in that it shows sampling sites colored according to their respective whole-rock copper abundance in parts per million (red and orange denotes higher abundances, yellow and green denotes moderate abundances, blue denotes lower abundances). As shown above in Figure 4, here we see the cores of exploration targets (dark blue volumes) and the outer reaches of exploration targets (pale blue volumes) generated by porphyry deposit footprint modeling that employs only the 'reconnaissance' surface-based whole-rock sampling dataset for the Haquira project area. Note that this image shows modeling results only for a subarea immediately surrounding the Haquira deposits. The volumes shown in pale red represent the envelopes of the Haquira East and Haquira West mineralization and associated intrusives. View involves looking to the southwest. To view a larger version, click on either the image or this text link.
And despite the presence of relatively high molybdenum abundances in whole-rock surface samples within nearby (barren) carbonaceous sedimentary lithologies, our footprint modeling method successfully avoided being led astray by this spurious molybdenum anomalism (Figure 6).
FIGURE 6: This image differs from Figures 4 and 5 in that it shows sampling sites colored according to their respective whole-rock molybdenum abundance in parts per million (red and orange denotes higher abundances, yellow and green denotes moderate abundances, blue denotes lower abundances). As shown above in Figures 4 and 5, here we see the cores of exploration targets (dark blue volumes) and the outer reaches of exploration targets (pale blue volumes) generated by porphyry deposit footprint modeling that employs only the 'reconnaissance' surface-based whole-rock sampling dataset for the Haquira project area. Note that this image shows modeling results only for a subarea immediately surrounding the Haquira deposits. The volumes shown in pale red represent the envelopes of the Haquira East and Haquira West mineralization and associated intrusives. View involves looking to the southwest. To view a larger version, click on either the image or this text link.
A further encouraging result from this case study was that even at the 'reconnaissance' stage of footprint modeling, our method produced targets that successfully captured the relative depth differences between Haquira East, which is seated more shallowly, and Haquira West, which is seated more deeply (Figure 7).
FIGURE 7: This image differs from Figures 4, 5, and 6 in that it shows results as they look from the south. As shown above in Figures 4, 5, and 6, here we see the cores of exploration targets (dark blue volumes) and the outer reaches of exploration targets (pale blue volumes) generated by porphyry deposit footprint modeling that employs only the 'reconnaissance' surface-based whole-rock sampling dataset for the Haquira project area. Note that image shows only a subarea in the immediate vicinity of the Haquira deposits. Blue dots denote sampling sites. The volumes shown in pale red represent the envelopes of the Haquira East and Haquira West mineralization and associated intrusives. View involves looking to the north. To view a larger version, click on either the image or this text link.
Targeting refinements from pooled data
Targets generated by footprint modeling carried out on pooled 'reconnaissance' and 'follow-up' datasets are better-constrained compared to the targets generated in the 'reconnaissance'-only modeling run, particularly at Haquira East (Figure 8).
In other words, the higher sampling density of the pooled data led to a strengthened footprint-modeling signal at Haquira East and a weakened signal in the area east of Haquira East.
This progressive refinement toward truer targets is what one wants to see when progressing from a lower sampling density scenario to a higher sampling density scenario.
FIGURE 8: Cores of exploration targets (dark orange volumes) and the outer reaches of exploration targets (yellow volumes) generated by porphyry deposit footprint modeling that employs a pooled surface-based whole-rock sampling dataset (that is, 'reconnaissance' sampling data combined with 'follow-up' sampling data) for the Haquira project area. Note that image shows only a subarea in the immediate vicinity of the Haquira deposits. Blue dots denote 'reconnaissance' sampling sites, while red dots denote 'follow-up' sampling sites. The volumes shown in pale red represent the envelopes of the Haquira East and Haquira West mineralization and associated intrusives. View involves looking to the north. To view a larger version, click on either the image or this text link.
Target quality and morphology captured
A detailed cross section looking to the north shows that even using the relatively basic sampling data contained in the 'reconnaissance' dataset, footprint modeling successfully captures the fact that Haquira West is the higher-quality exploration target (due to its preservation below the modern-day erosional surface), even though at face value the surface elemental abundances suggest otherwise (Figure 9).
FIGURE 9: Detailed section showing the cores of exploration targets (dark blue volumes) and the outer reaches of exploration targets (pale blue volumes) generated by porphyry deposit footprint modeling that employs only the 'reconnaissance' surface-based whole-rock sampling dataset for the Haquira project area. The ground surface has been colored (using interpolation between sampling sites) according to whole-rock molybdenum abundance. Drilling traces (angled 'spines' pendant from the ground surface) have been colored by total copper results (note that this subsurface dataset wasn't used in footprint modeling in any way). Note that image shows only a subarea in the immediate vicinity of the Haquira deposits. The volumes shown in pale red represent the envelopes of the Haquira East and Haquira West mineralization and associated intrusives. View involves looking to the north. To view a larger version, click on either the image or this text link.
The quality distinction between Haquira East and Haquira West is successfully maintained in the better-constrained targets returned from modeling carried out on the higher sampling density dataset obtained from pooling the 'reconnaissance' and 'follow-up' datasets (Figure 10).
In addition, the carrot-like plunging morphology of the core of the target volume modeled at Haquira East (Figure 10) appears to be in fairly good agreement with the vertically elongated copper-ore 'half-shell' mapped there, a phenomenon thought to be due to the abrupt disappearance of precipitated copper-iron sulfides wherever the granodioritic stock is in contact with iron-poor meta-sandstones. [10]
FIGURE 10: Detailed section showing the cores of exploration targets (dark orange volumes) and the outer reaches of exploration targets (yellow volumes) generated by porphyry deposit footprint modeling that employs a pooled surface-based whole-rock sampling dataset (that is, 'reconnaissance' sampling data combined with 'follow-up' sampling data) for the Haquira project area. The ground surface has been colored (using interpolation between sampling sites) according to whole-rock molybdenum abundance. Drilling traces (angled 'spines' pendant from the ground surface) have been colored by total copper results (note that this subsurface dataset wasn't used in footprint modeling in any way). Note that image shows only a subarea in the immediate vicinity of the Haquira deposits. The volumes shown in pale red represent the envelopes of the Haquira East and Haquira West mineralization and associated intrusives. To view a larger version, click on either the image or this text link.
The big picture at Haquira
Absolute-elevation depth slices containing targets returned for the entire project area (based on modeling carried out on pooled 'reconnaissance' and 'follow-up' datasets) show that when sampling data from the entire project area are considered as part of modeling, Haquira East and Haquira West are still returned as the highest-quality exploration targets in the area (Figure 11).
If all that was known exploration-wise about the Haquira case study were solely encapsulated by what's contained in the 'reconnaissance' and 'follow-up' datasets (which together represent an average sampling density of about 4 surface-rock samples per square kilometer), the overall conclusion stemming from this footprint modeling work would be to investigate the Haquira West locale followed by the Haquira East locale.
The overall conclusion would be the same even if the only dataset available was a first-pass campaign (representing an average sampling density of about 2 surface-rock samples per square kilometer). Based on this case study, it appears that even reconnaissance-level data sufficed in the sense that it produced the same overall targeting recommendation.
FIGURE 11: Perspective-view depth slices (with large bold-font numbers showing absolute elevation in meters) of the entire Haquira case-study project area. Shown are the cores of exploration targets (dark orange volumes) and the outer reaches of exploration targets (yellow volumes) generated by porphyry deposit footprint modeling that employs a pooled surface-based whole-rock sampling dataset (that is, 'reconnaissance' sampling data combined with 'follow-up' sampling data). Black dots denote all sampling sites. The volumes shown in green represent the envelopes of the Haquira East and Haquira West mineralization and associated intrusives. View involves looking to the southwest. To view a larger version, click on either the image or this text link.
We are pleased to note that a significant advantage to using this footprint modeling method is that explorers can take advantage of the low-cost rock-chemistry data readily obtainable from widely available, routinely-used, high-throughput assay techniques, such as inductively coupled plasma spectrometry and x-ray fluorescence. It means explorers can avoid having to obtain analyses involving 'big ticket' analytical equipment — such as isotope, microprobe, laser ablation, and fluid-inclusion studies — which so far are relatively laborious, relatively expensive and aren't yet amenable to batch or bulk processing.
However, having said that, the modeling setup employed in the Haquira case-study could nevertheless be adapted to suit more esoteric or later-stage data types, should they be available already. This may give explorers an edge if their competitors have so far overlooked the information gleanable from these later-generation data types.
For instance, footprint modeling could, instead of employing elemental concentrations, employ data from 3D alteration mapping in the form of remote-sensed mineralogical data (namely short-wave infrared spectroscopic ASTER data and various hyperspectral data).
The method could also be extended to incorporate fieldwork-based data obtained from portable mineral-analyzer devices that measure hyperspectral mineral-chemistry properties, such as the ASD Terra Spec instrument.
And the method could harness geochemical data from any subsurface drill-sampling campaigns done.
Acknowledgements
** Fathom Geophysics gratefully acknowledges Tim Ireland and Mike Christie of First Quantum Minerals for permission to discuss the Haquira case study and to present some imaged results of data-led footprint modeling on the area. Fathom Geophysics also gratefully acknowledges Mike Roberts of Kiska Metals Corporation for the time he generously gave during the write-up of this article.
References
[1] Kiska Metals Corporation (18 Feb 2015) "Lithogeochemical targeting for porphyry exploration", 2 pages, www.kiskametals.com. (Note that Kiska had acquired exclusive rights to the method worldwide except for Chile and Peru until April 2017.)
[2] M. Roberts "Lithogeochemical modeling", Kiska Metals Corporation, 10 pages, www.kiskametals.com.
[3] J.F. Cohen (2011) "Mineralogy and geochemistry of hydrothermal alteration at the Ann-Mason porphyry copper deposit, Nevada: Comparison of large-scale ore exploration techiques to mineral chemistry", M.S. Thesis, Oregon State University, 580 pages.
[4] At the time of this writing (December 2016), calc-alkaline porphyry copper footprint modeling is the only type of deposit footprint modeling that's functional. Conceivably, deposit types that should be amenable to this type of footprint modeling include alkalic porphyry copper, volcanic-associated massive sulfides, sediment-hosted lead-zinc (also known as sedex lead-zinc), skarns, iron-oxide-copper-gold (IOCG), and any other deposit type possessing chemical zonations that are distinct and readily definable in all three dimensions. In other words, before doing any modeling for these other deposit types, you'd first need to establish an appropriate exemplar footprint. By employing empirical ore-model footprints quantitatively, this method ensures geoscientists state explicit depths, elemental abundances, and lateral distances. The scale at which the modeling can generally be done is mining camp-scale. Modeling could be done on a more regional scale than that, as long as a regional sampling campaign has been done and was sufficiently closely-spaced.
[5] F. Cernuschi Rodilosso (2015) "The geology and geochemistry of the Haquira East porphyry copper deposit of southern Peru: Insights on the timing, temperature and lifespan of the magmatic-hydrothermal alteration and mineralization", Oregon State University PhD dissertation.
[6] E. Russell (17 March 2005) "Antares takes out option on PD's Haquira", BNamericas Wire.
[7] K.B. Heather, H.G. Rondon, J.E. Black and W.C. Williams (2006) "The Haquira SX-EW copper deposit, Las Bambas district, south-central Peru", 11th Chilean Geological Congress.
[8] J.E. Black, W.C. Williams and C.R. Frio (2006) "The Haquira SX-EW copper deposit, Las Bambas district, south-central Peru", Denver Region Exploration Geologists' Society, technical presentation abstract.
[9] L. Greenlaw (2014) "Surface lithogeochemistry of the Relincho porphyry copper-molybdenum deposit, Atacama region, Chile", M.S. Thesis, University of British Columbia, 121 pages.
[10] F. Cernuschi, M. Einaudi, J. Dilles, K. Heather and N. Barr (2012) "Hydrothermal veins, porphyry geochemistry and mineralization zonation of the Haquira East porphyry Cu-Mo deposit, Peru", poster for Society of Economic Geologists 2012 Conference on Integrated Exploration and Ore Deposits, Lima, Peru.