Clark, Alice

O'Sullivan, Rhonda

Valenta, Rick

English

University of Queensland eSpace

  
Prediction of reactive ground using geoscientific 
datasets 
Rick Valenta1*, Rhonda O’Sullivan1  and Alice Clark2, 
1. WH Bryan Mining and Geology Research Centre – Sustainable Minerals Institute, The 
University of Queensland, Australia 
2. Julius Kruttschnitt Mineral Research Centre – Sustainable Minerals Institute, The University of 
Queensland, Australia 
ABSTRACT 
The presence of reactive ground – rocks which undergo a spontaneous exothermic reaction on contact 
with nitrates, represents a significant risk in many mines, and requires the use of costly alternatives 
to traditional ammonium nitrate explosives.  Prediction of the likelihood of occurrence of reactive 
ground using typical mine datasets has the potential to reduce costs for explosives and increase mine 
safety. The standard test for reactive ground according to Australian codes of practice is the 
Isothermal Reactive Ground Test, in which a mixture of rock, ammonium nitrate and an acidic 
“weathering by-products” solution are heated in a sealed vessel.   
In this contribution we present a case study in which a large dataset of reactive ground tests was 
assessed against geological and geochemical drill datasets in order to develop criteria for prediction 
of reactive ground.  Testing of geological criteria such as pyrite content, presence of carbonaceous 
material and evidence of leaching showed little correlation with test results, and the control of 
variation in rock type also appeared to be unclear.  A number of geochemical proxies were tested 
which were successfully able to predict strongly reactive ground with relatively low rates of false 
positives and false negatives.   In this case study, the presence of carbonate appears to suppress 
reactivity, and it is important to consider the degree to which code of practice test is valid in 
carbonate-bearing potentially-reactive materials.   
 
*Corresponding author: Director - WH Bryan Mining and Geology Research Centre, Sustainable 
Minerals Institute, 40 Isles Rd, Indooroopilly, 4068 Australia. Phone: +61 7 3365 5881. Email: 
r.valenta@uq.edu.au 
 
INTRODUCTION 
Reactive ground relates to the situation where certain rocks spontaneously undergo exothermic 
reactions on contact with nitrites. Reactive ground of this nature requires the development of 
particular risk approaches as spontaneous combustion in underground mines is difficult to control 
  
once the reaction starts and often results in ores that are difficult to mine and may introduce 
metallurgical challenges. 
This study was undertaken in response to the need for an improved ability to predict the location of 
reactive ground in the Mount Isa Copper orebodies. The focusing questions were “Could a more 
accurate predictive tool be developed using existing mine datasets?” and “If existing mine datasets 
are not applicable, can a new effective test be developed that is both cost effective and able to 
produce results in time for production requirements?” 
The study area was limited to the Glencore Plc owned Mount Isa 500 copper orebody in northwest 
Queensland Australia. The copper orebodies of the Mount Isa deposit are hosted in a brecciated 
variably calcified and silicified package of altered mid-Proterozoic siltstones and shales of the 
Urquhart Shales within the Mount Isa Group (Perkins, 1990).  
A thorough compilation of the history of reactive pyrite incidents and assessments (Campbell and 
Bunker, 2017) captures the current understanding and knowledge gaps regarding causes and 
prediction of reactivity (eg Smith, 1963; Hewitt, 1968; Leahy, 1988; Campbell and Bunker, 2017).  As 
well as detection and risk assessment approaches on the basis of knowledge relating to reactive 
ground (eg Bellairs, 2006; Faulkner and Davis, 2008; O’Sullivan, 2017).  However; none of these 
studies have attempted to integrate reactive ground datasets with mine geological drill datasets, 
geotechnical information and underground mapping to look for correlations that could assist in the 
development of methods for prediction of reactive ground risk. 
Challenges 
The prediction of where reactive ground will be encountered is governed to a degree by oxidation 
processes which are due to the localised structural weakness on the ground surrounding the 500 
orebody (Smith 1963). Smith refers to the spontaneous combustion of parts of the orebody as “hot” 
ground and observes that due to the high variability of the oxidised rock it is very difficult to predict 
where the reactive ground will be concentrated enough to cause a risk to mining.   
Subsequent work has focused more on risk recognition and mitigation (eg Bellairs, 2006) as well as 
mapping of the boundaries of the areas of reactive ground risk (eg David and Dare, 2008, Wong, 
2010).  More recently, Connell (2016) carried out a program of sampling, isothermal reactive ground 
testing, and geochemical analysis with samples mainly derived from the deeper Enterprise Copper 
Mine at Mount Isa. 
METHODOLOGY 
Data integration and database validation 
A variety of information was provided for the study from different areas including; a reactive ground 
register containing 462 drill samples and 155 development samples; dxf surface files relating to 
important controlling faults and contacts; geological and geotechnical logs; assays; a partial set of 
sample photographs and additional reports relating to reactive ground in the 500 orebody and 
elsewhere in the copper mine. 
  
The data points were initially classified on the basis of reactivity derived from the isothermal tests 
carried out at the Ernest Henry laboratory.  Samples which were not reactive were assigned a 
reactivity number of 1; those reactive at 105°C were assigned a reactivity number of 2; those 
reactive at 65°C were assigned a reactivity number of 3; and those reactive at 55°C were assigned a 
reactivity number of 4.  In the database, 229 samples (48%) were non-reactive; 120 samples were 
weakly reactive (reactivity number 2); 59 were strongly reactive (reactivity number 3); and 67 were 
very strongly reactive (reactivity number 4) (Figure 1). 
 
Assessment of established criteria 
Assessment criteria for the recognition of reactive ground risk (Bellairs, 2006; as summarised in 
Campbell and Bunker 2017) are presented in Table 1.  Assessment of these variables was carried out 
as part of the sampling program and included in the reactive ground register independently of the 
test data and the other external datasets. None of the samples in the database were noted as having 
displayed the heating criteria, so that characteristic was not included in further analysis. 
 
Table 1  Reactive ground risk criteria from the sample register, along with an explanation and total 
number of samples displaying each criteria. 
Criteria Explanation Number of samples in 
register 
Sulph >1% The presence of sulphides estimated to be greater than 1% 265 
Mixed Sulp 
The presence of multiple types of sulphides (eg coarse and fine pyrite, or 
pyrrhotite) 184 
FG Sulp The presence of fine-grained pyrite 161 
Graphic SH The presence of graphite or carbonaceous material 27 
LCH GRO The noted presence of leaching 254 
Corrosion Evidence of oxidation of iron sulphide minerals 138 
Salts The presence of salt precipitates on the sample 78 
SO2 Smell A sulphurous smell noted to be associated with the sample 4 
Heating Heating of the sample noted, indicating an exothermic reaction 0 
 
  
Figure 1  Summary figure of the proportion of samples of varying reactivity classified on the basis of the presence of 
identified reactivity risk factors. 
Pie charts of the mix of sample reactivities for each of the risk criteria (Figure 1) show some minor 
variations in the ratio of reactivities, though for most criteria the relative proportion of non-reactive 
and moderately reactive samples remained relatively constant.  The presence of carbonaceous 
material and/or graphite showed the greatest association with more reactive samples, though this 
characteristic was noted in relatively few samples. Each of the selected reactive ground indicators 
was assessed to determine its relationship to reactive ground and use as a predictive indicator. 
Fine grained sulphides: There does not appear to be a strong relationship between the quantitative 
estimates of fine-grained pyrite and reactivity. 
Leaching.  Leaching at moderate or greater level is noted in a relatively large number of samples.  
The ratio of reactive to non-reactive samples is slightly greater in the samples in which leaching is 
noted. 
Corrosion.  The noted presence of corrosion does not appear to modify the proportions of sample 
reactivities to any great extent. 
Number of Indicators.  Analysis of sample reactivity divided on the basis of the number of risk 
indicators predicts that the likelihood of samples being strongly reactive increases as the number of 
risk indicators increases. However, even samples with a large number of risk indicators showed a 
relatively strong likelihood of being non-reactive or only moderately reactive (Figure 2). 
  
Figure 2  Raw and normalised totals of samples of varying reactivity as a function of the number of indicators noted in 
the sample 
Geochemical correlations: A set of log-log plots for the assay values associated with the reactive 
ground sample datasets is shown in Figure 3, with samples colour-coded by reactivity.  CaO shows 
the clearest relationship with reactivity, so was used as the Y-axis in each plot. 
In terms of the relationship between assay values and reactivity there is a relatively strong 
correlation between higher reactivities and low CaO values. Al2O3 and SiO2 show similar patterns 
to each other, in that samples with low Al2O3 and SiO2 tend to have lower reactivities, though 
samples with higher Al2O3 and SiO2 show no preferential pattern of reactivity. There appears to be 
no correlation between Cu content and reactivity. Similarly, there does not appear to be any notable 
correlation between Fe and S values and reactivity. MgO and CaO show a distinctive pattern, with 
high values linearly correlated and probably reflecting the presence of dolomite.  Importantly, 
samples with higher Mg/Ca ratios tend to be more reactive, suggesting that this ratio may be a proxy 
for leaching. 
  
 
Figure 3  Log-log plots of element abundances from assays, with samples colour coded on the basis 
of reactivity. 
Development of Proxies 
The assessment of logging and geochemical data has shown a number of trends which could 
potentially be applied as proxies for assessment of reactive ground risk in untested drill holes.  Some 
of the criteria and potential proxies are assessed in Table 2. 
Table 2 Assessment of criteria and potential proxies for estimation of reactive sample risk. 
Criteria Effectiveness as a potential proxy 
Potential proxy 
Sulp >1% Very little correlation between reactivity and sulphide content Fe or S 
Mixed Sulp Very little correlation between this characteristic and reactivity  
FG Sulp Weak correlation with logged sulphidic rocktypes Logged percentage 
Graphic SH 
Graphitic rocks preferentially reactive, but 
graphitic/carbonaceous material noted in very few samples  
LCH GRO 
Presence and intensity of leaching do correlate with increased 
reactivity. 
Inverse CaO; 
CaO/(CaO+MgO) 
Corrosion 
Increased proportion of reactive samples show evidence, but 
not a strong correlation in geochemical datasets (Fe+Cu+Pb+Zn)/S 
Salts Only very minor evidence of a link with reactivity in the dataset  
SO2 Smell Not assessed  
Heating Not assessed  
 
Figure 4 plots sample reactivity vs logCaO and shows a distinctive pattern in which low values of CaO 
have a much higher proportion of strongly reactive samples.  This pattern is by far the strongest of 
  
any of the correlations between raw assay values and reactivity (eg Figure 3).  The logCaO values, 
however, do not appear to differentiate mildly reactive samples to any great extent, with a broadly 
equal proportion of mildly reactive samples occurring at both lower and higher values. 
 
Figure 4  Abundance of samples of varying reactivity as a function of log CaO.  See figure 3 for legend. 
RESULTS AND DISCUSSION 
The aim of development of a proxy for prediction of reactivity risk is to be able to develop a more 
effective means of identifying areas within the orebody which are subject to higher risk of  the 
occurrence of reactive ground.  Consideration of any proxy of this type requires assessment of 
thresholds for identification of non-reactive, moderately reactive and strongly reactive ground, as 
well as assessment of the prevalence of “false positives” and “false negatives” in the data. 
Three separate approaches to reactive ground risk prediction were tested: (a) a simple CaO 
threshold; (b) a MgO/(MgO + CaO) ratio threshold; and (c) a robust linear regression based on 11 
explanatory variables.  A number of observations can be made regarding the various proxies tested: 
• All of the approaches are moderately effective at separating strongly reactive samples 
from non-reactive samples. 
• When considering the thresholds that yield a strongly reactive false negative rate of 
approximately 15-16%, all three measures result in a correct negative rate of 
approximately 70%, and a strongly reactive positive rate of approximately 85%. 
• The use of a more complex multi-variable regression did not produce an improvement in 
the ability to predict the degree of reactivity, as a simple threshold based on CaO assay 
produces comparable results. 
• None of the measures are effective at predicting the occurrence of samples which are 
reactive at 105° 
There are at least two possibilities which may explain the relatively strong correlation between CaO 
values and sample reactivity; firstly this pattern may reflect the distribution of zones of leaching 
  
with low CaO values reflecting areas with permeability to allow the ingress of fluid and oxygen to 
promote the heat-producing sulphide breakdown reaction; and/or secondly the areas of low CaO 
represent areas where the ability of carbonate in the rock to suppress the reaction <Iron sulphides 
+ Oxygen + water  →  Ferrous ions + Sulphuric Acid>  no longer exists. 
A plot of the <2% and <4% CaO shells generated in Leapfrog (Figure 5) shows that these shells 
encompass the main area of highly reactive samples within the 500 orebody, though they also 
enclose a number of non-reactive samples which would be modelled as false.  The zone of low CaO 
is similar to, but not exactly coincident with the Base of Moderate Leaching (BOML). 
 
Figure 5  Plan slice through the sample and drill dataset at 2993 RL showing the <4% and <2%CaO 
shells calculated isotropically within Leapfrog, along with the geometry of the BOML. 
 
CONCLUSIONS 
Consideration of all relevant geoscientific data in the combined database for the 500 orebody at 
Mount Isa enabled the development of a reactive ground prediction methodology that improved on 
traditional methodologies. It was determined that the previously established criteria for prediction 
of reactive ground such as sulphide content and composition, noted presence of leaching/corrosion, 
presence of carbonaceous material, salts and other indicators do not provide an effective means to 
differentiate between reactive and non-reactive ground in drill datasets with any degree of 
confidence. Reactive samples show a notable association with low CaO values and high MgO/(MgO 
  
+ CaO) ratios, as well as a weak positive correlation with Al2O3 and SiO2.  There is very little 
correlation with other elements such as Fe, S, and Cu. The three separate approaches to reactive 
ground risk prediction produced comparable outcomes, successfully predicting 85% of highly 
reactive samples and 70% of non-reactive samples. On this basis, the simple threshold of 4% CaO 
was deemed to be the most easily applied to the 500 orebody, though the extent to which it can be 
applied elsewhere in the mine is yet to be established. A simple shell model of CaO differentiates 
higher-risk and lower-risk areas on both sides of the BOML boundary, providing an additional 
constraint for prediction of areas of high reactive ground risk which could easily be applied to a 
larger drill dataset. 
The results of the study suggest that the key factors in predicting reactive ground areas/zones are 
the presence of fracture-related permeability and the lack of acid-buffering carbonates, which can 
be due to either due to original rock composition or leaching-related depletion.  The lack of 
correlation with iron sulphide content is explained by the observation that pyrite is essentially 
ubiquitous in the mine environment, and exists in sufficient quantities to produce reactive ground 
in the presence of the other necessary facilitating factors. In this case study, the presence of 
carbonate appears to suppress reactivity, and it is important to consider the degree to which the 
code of practice test is valid in carbonate-bearing potentially-reactive materials.   
ACKNOWLEDGEMENTS 
The authors would like to acknowledge the support and permission from Mount Isa Mines Ltd (a 
subsidiary of Glencore Plc) for the publication of this contribution REFERENCES 
Australian Explosives Industry and Safety Group (AEISG) Code of Practice. 4th edition. (2017). 
Bellairs, P. (2006)  Summary orebody 500 Visit Report.  Consultant report to Mount Isa Mines. 8 
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Campbell, A. and Bunker, K. (2017) A review of reactive ground in the 500OB in preparation for 
additional testing and explosive trials.  Mount Isa Mines Internal Memorandum. 21 Pages. 
Connell, S. (2016)  Reactive Ground Investigation.  Memorandum to Mount Isa Mines.  Appendix 6 
of the University of Adelaide Honours Thesis – Fine-grained pyrite within the Mount Isa (Enterprise) 
copper system, NW Queensland: geological relationships, modelled distribution and links to reactive 
ground. 199 pages. 
Davis, T. and Dare, E.  (2008)  Delineation of reactive ground.  Mount Isa Mines Internal 
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Faulkner, J. and Davis, T. (2008)  ‘Development in elevated temperature ground of the 500 Orebody 
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Leahy, F.J., (1988)  ‘Mining in hot ground in the 500 Orebody at Mount Isa’.  The AUSIMM Bulletin 
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Prediction of reactive ground using geoscientific datasets

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Prediction of reactive ground using geoscientific datasets

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