Searching for excess density via inversion of 3D geology and gravity data – a brownfields exploration method

Hellen Gibson

Rod Paterson

Des Fitzgerald

Introduction

Excess density evaluations from detailed gravity methods can be successfully applied in both greenfields and brownfields exploration. Here we focus on brownfields, where investigating the extension of orebodies near to existing operational mine sites (brownfields) is the goal for prolonging mine life. The technique is very suitable because of the large amount of geologic control and geophysical data (e.g., ground gravity data) which is typically available or can be readily acquired.Here we outline a brownfields exploration workflow which utilises a geophysical inversion approach for usefully identifying zones of excess density. That is, zones where best-known geology constraints, and a justifiable 3D geological interpretation still cannot explain the observed gravity response. As such, the driven inversion outcome in terms of most-probable density can indicate clusters of high-density cells which may in turn indicate significant, and unaccounted for, massive sulphide mineralisation which would become drilling targets, especially if supported by other key indicators (for example, consistent with a SEDEX geological setting).

Employing forward modelling and inversion

Figure 1 above shows a schematic of integrated 3D geological and density property modelling.

Forward modelling: From a 3D geology model and associated densities, one can calculate a geophysical potential field response (gravity) and compare it with observed gravity data (Figure 1, right).

If the calculated and observed responses don’t agree, we can try to infer geology from geophysics observations i.e. invert the above process, but this process is not unique.

Methodology

This workflow is best applied using high quality ground gravity surveys in combination with well constrained, prospect-scale 3D geology models (for example Figure 2).

We also advocate that ‘searching for excess density’ employs litho-constrained stochastic geophysical inversion, for example, based on a Markov Chain Monte Carlo formulation (Figure 3). So that rather than iterations ceasing when misfits reach a specified low limit (a deterministic approach), inversion continues and model perturbations (in geometries and densities), progressively explore potentially millions of possible models within an overlap zone representing all models supported within set-tolerances of our two independent data sets: the geology constraints and the gravity survey.

By this approach, millions of retained models are then analysed through statistical distillation and hence the inversion outcomes can be reported in terms of probabilities for geology-geometry and density in the initial geology model.

Inversion Workflow

Our recommended inversion workflow also includes some or all of the following preliminary stages:• A property optimization routine to determine optimum starting density values per lithology.• Prior only inversion runs to check the effective set-tolerances in the movable nature of geology boundaries within the initial or reference model.• Calibration using different discretisation schemes reflecting variable resolutions for the input geology and density models.

• Sensitivity testing, using multiple inversion runs commencing from different initial models, for example, with and without mapped gossans and their predicted depth-extensions into mineralized zones.

• Several forward modelling runs throughout preliminary stages to compute the gravity response directly from the evolving but pre-built 3D geology model and coupled density model.

Figure 4 shows more detail of the inversion workflow steps.

After preliminary stages, and when a fairly close match is achieved between the modelled and observed gravity – then preparations for a litho-constrained stochastic geophysical inversion are complete. Note that property optimization, forward modelling and inversion are all performed in GeoModeller on a discretised version (Figure 5) of a smooth 3D geology model (Figure 2). This facilitates model perturbations, computations, and statistical distillation of the inversion outcomes.

In the concluding stages, our workflow accesses masked 3D grids of most-probably density in the posterior models to reveal clusters

of high density in 3D, together with their volumes and locations as optimum drilling targets (Figure 6). Our inversion results also indicate volume changes between reference and final geology models – thus quantified indications of whether the ore-bearing geology units and their associated densities may have been over-estimated or under-estimated in the starting models. (Hopefully it’s the latter).

Least unambiguous results can be expected when there is a high density contrast between host rocks and mineralised lithologies.

Inversion Quality Control Checks

Inversion workflow and posterior model QC checks include the following:

• How were density values pushed around during inversion, as driven by the observed ground gravity? • How were geology volumes pushed around, especially those of the gossans and sulphides?

Final QC checks and questioning of the results includes:

• Checking misfits of final gravity grids • Analysing the most probable mean densities, statistically-distilled from all the kept-models of converged solutions. Are they higher than initial mean densities? • Analysing the most probable geology-geometries, from converged solutions. Are there any positive volume changes to the ore-bearing units?

Drilling targets

Finally drilling targets (Figure 6) can be identified (Figure 5) by: -Identifying the nature and size of significant clusters of high density – Use prior geological knowledge and inversion results to assess the possibility they indicate disseminated to massive sulphides.

Retrieve the geo-locations of high density clusters and design exploration drilling of the targets– Assess likelihood statistics of the volumes and densities of the clusters & set up the masked views of the posterior 3D grids/voxets.

Conclusions

We have outlined an exploration workflow using GeoModeller software which may usefully identify zones of excess density. That is, zones where best-known geology constraints, and a justifiable 3D geology interpretation still cannot explain the observed gravity data, such that the driven inversion outcome in terms of most-probable density, indicates clusters of high-density cells which may in turn suggest unaccounted for massive sulphide mineralisation given other key indicators are present, for example for a potential SEDEX deposit setting.We have: -Presented a brownfields exploration workflow for SEDEX deposits Illustrated principles of a Bayesian, integrated 3D geological modelling and gravity density inversion (litho-constrained stochastic geophysical inversion, based on a Markov Chain Monte Carlo formulation, implemented in GeoModeller software) – A superior modelling approach compared with conventional gravity modelling Demonstrated how posterior models of 3D density can be evaluated for their validity under quantitative methods founded in statisticsShown how excess high density clusters can be identified and rigorously characterised in 3D, and geo-located as direct targets for prospect-scale exploration drillingHighlighted the importance of this methodology, along with detailed ground gravity survey data, as a rigorous technique for exploration of massive sulphide mineralisation, e.g., SEDEX deposits in either brownfields or greenfields settings.

Author Bios

---

---

---

---