Pejman Shamsipour

3D stochastic inversion of gravity data using cokriging and cosimulation

A new application has been developed, based on geostatistical techniques of cokriging and conditional simulation, for the 3D inversion of gravity data including geologic constraints. The necessary gravity, density, and gravity-density covariance matrices are estimated using the observed gravity data. Then the densities are cokriged or simulated using the gravity data as the secondary variable. The model allows noise to be included in the observations. The method is applied to two synthetic models: a short dipping dike and a stochastic distribution of densities. Then some geologic information is added as constraints to the cokriging system. The results show the ability of the method to integrate complex a priori information. The survey data of the Matagami mining camp are considered as a case study. The inversion method based on cokriging is applied to the residual anomaly to map the geology through the estimation of the density distribution in this region. The results of the inversion and simulation methods are in good agreement with the surface geology of the survey region.

Geologically driven 3D modelling of physical rock properties in support of interpreting the seismic response of the Lalor volcanogenic massive sulphide deposit, Snow Lake, Manitoba, Canada

Abstract 3D lithofacies and physical rock property models were generated to interpret 3D seismic data acquired over the Lalor volcanogenic massive sulphide deposit, Manitoba, Canada. The lithofacies model revealed that strong seismic reflectivity is associated with ore–host rock and mafic–felsic lithofacies contacts, including their hydrothermally altered equivalents. Different physical rock property models were subjected to 3D seismic forward modelling using the SOFI3D finite difference code. Seismic synthetics from discrete and interpolated models in which kriging of P-wave velocity and density was conditioned by curvilinear grids conformable to the 3D-modelled geological structure showed a much better match to the seismic data in comparison with those generated by kriging in Cartesian space. Synthetics from these curvilinear grid models corroborate the origin of seismic reflectors, as qualitatively inferred from the lithofacies model. Seismic synthetics generated from physical rock property models in which physical rock properties were augmented by densely sampled secondary variables, such as FeO percentage, enhanced lateral continuity of seismic reflectivity, although these co-kriged petrophysical models were not more accurate than their kriged equivalents. The physical rock property modelling methodology was also useful for testing the utility of passive interferometric seismic surveys, as this highlighted the limitations of the discrete physical rock property model.

Bayesian inference of spectral induced polarization parameters for laboratory complex resistivity measurements of rocks and soils

Abstract Spectral induced polarization (SIP) measurements are now widely used to infer mineralogical or hydrogeological properties from the low-frequency electrical properties of the subsurface in both mineral exploration and environmental sciences. We present an open-source program that performs fast multi-model inversion of laboratory complex resistivity measurements using Markov-chain Monte Carlo simulation. Using this stochastic method, SIP parameters and their uncertainties may be obtained from the Cole-Cole and Dias models, or from the Debye and Warburg decomposition approaches. The program is tested on synthetic and laboratory data to show that the posterior distribution of a multiple Cole-Cole model is multimodal in particular cases. The Warburg and Debye decomposition approaches yield unique solutions in all cases. It is shown that an adaptive Metropolis algorithm performs faster and is less dependent on the initial parameter values than the Metropolis-Hastings step method when inverting SIP data through the decomposition schemes. There are no advantages in using an adaptive step method for well-defined Cole-Cole inversion. Finally, the influence of measurement noise on the recovered relaxation time distribution is explored. We provide the geophysics community with a open-source platform that can serve as a base for further developments in stochastic SIP data inversion and that may be used to perform parameter analysis with various SIP models.

SPECTRAL INDUCED POLARIZATION SIGNATURES OF ALTERED METASEDIMENTARY ROCKS FROM THE CANADIAN MALARTIC GOLD DEPOSIT BRAVO ZONE, QUÉBEC, CANADA

At the Canadian Malartic deposit, gold mainly occurs as fine inclusions in 1 μm to 1 mm pyrite grains hosted in altered metagreywacke. Previous time-domain induced polarization surveys conducted over this deposit have, however, failed to delineate known areas of pyrite alteration in the metasedimentary host rocks. To define the petrophysical footprint of the gold mineralization using an alternative approach, spectral induced polarization (SIP) measurements were conducted both on drill core samples in the laboratory and in situ, at a key outcrop zone. Three groups of SIP spectra are identified and interpreted using Mineral Liberation Analysis of polished thin sections and by performing Bayesian inference of SIP parameters using a Debye decomposition model. Values of Debye mean relaxation time (τ) and total chargeability (Σm) are used to characterize the respective SIP signatures of ilmenite-rich mudstones and monzodiorite (τ = 0.10 s and Σm = 17%), mineralized metagreywacke (τ = 0.05 s and Σm < 10%), and barren metagreywacke (τ < 0.03 s with variable Σm).

3D STOCHASTIC GRAVITY INVERSION ON UNSTRUCTURED MESHES

The stochastic gravity inversion on unstructured meshes is presented. The unstructured meshes are used because they provide the flexibility required to closely approximate complicated geological structures. Sharp topographic relief and geological bodies of complex shapes are usually more accurately described by unstructured meshes rather than with regular grids. The forward method has a closed form solution and gives accurate results. A geostatistical method is applied to invert the gravity data.