Joint inversion of potential-fields data over the DO-27 kimberlite pipe using a Gaussian mixture model prior

We have carried out petrophysically and geologically guided inversions (PGIs) to jointly invert airborne and ground-based gravity data and airborne magnetic data to recover a quasi-geology model of the DO-27 kimberlite pipe in the Tli Kwi Cho (also referred to as TKC) cluster. DO-27 is composed of three main kimberlite rock types in contact with each other and embedded in a granitic host rock covered by a thin layer of glacial till. The pyroclastic kimberlite (PK), which is diamondiferous, and the volcanoclastic kimberlite (VK) have anomalously low density, due to their high porosity, and weak magnetic susceptibility. They are indistinguishable from each other based upon their potential-field responses. The hypabyssal kimberlite (HK), which is not diamondiferous, has been identified as highly magnetic and remanent. Quantitative petrophysical signatures for each rock unit are obtained from sample measurements, such as the increasing density of the PK/VK unit with depth and the remanent magnetization of the HK unit, and are represented as a Gaussian mixture model (GMM). This GMM guides the PGI toward generating a 3D quasi-geology model with physical properties that satisfies the geophysical data sets and the petrophysical signatures. Density and magnetization models recovered individually yield volumes that have physical property combinations that do not conform to any known petrophysical characteristics of the rocks in the area. A multiphysics PGI addresses this problem by using the GMM as a coupling term, but it puts a volume of the PK/VK unit at a location that is incompatible with geologic information from drillholes. To conform to that geologic knowledge, a fourth unit is introduced, PK-minor, which is petrophysically and geographically distinct from the main PK/VK unit. This inversion produces a quasi-geology model that presents good structural locations of the diamondiferous PK unit and can be used to provide a resource estimate or decide the locations of future drillholes.

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Petrophysically and geologically guided multi-physics inversion using a dynamic Gaussian mixture model

Geophysical Journal International ◽

10.1093/gji/ggaa378 ◽

2020 ◽

Vol 224 (1) ◽

pp. 40-68 ◽

Cited By ~ 1

Author(s):

Thibaut Astic ◽

Lindsey J Heagy ◽

Douglas W Oldenburg

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Quantitative Information ◽

Geophysical Data ◽

Magnetic Data ◽

Data Sets ◽

Data Set ◽

Geophysical Surveys ◽

Geological Information

SUMMARY In a previous paper, we introduced a framework for carrying out petrophysically and geologically guided geophysical inversions. In that framework, petrophysical and geological information is modelled with a Gaussian mixture model (GMM). In the inversion, the GMM serves as a prior for the geophysical model. The formulation and applications were confined to problems in which a single physical property model was sought, and a single geophysical data set was available. In this paper, we extend that framework to jointly invert multiple geophysical data sets that depend on multiple physical properties. The petrophysical and geological information is used to couple geophysical surveys that, otherwise, rely on independent physics. This requires advancements in two areas. First, an extension from a univariate to a multivariate analysis of the petrophysical data, and their inclusion within the inverse problem, is necessary. Secondly, we address the practical issues of simultaneously inverting data from multiple surveys and finding a solution that acceptably reproduces each one, along with the petrophysical and geological information. To illustrate the efficacy of our approach and the advantages of carrying out multi-physics inversions coupled with petrophysical and geological information, we invert synthetic gravity and magnetic data associated with a kimberlite deposit. The kimberlite pipe contains two distinct facies embedded in a host rock. Inverting the data sets individually, even with petrophysical information, leads to a binary geological model: background or undetermined kimberlite. A multi-physics inversion, with petrophysical information, differentiates between the two main kimberlite facies of the pipe. Through this example, we also highlight the capabilities of our framework to work with interpretive geological assumptions when minimal quantitative information is available. In those cases, the dynamic updates of the GMM allow us to perform multi-physics inversions by learning a petrophysical model.

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Multiple level-set joint inversion of traveltime and gravity data with application to ore delineation: A synthetic study

Geophysics ◽

10.1190/geo2016-0675.1 ◽

2018 ◽

Vol 83 (1) ◽

pp. R13-R30 ◽

Cited By ~ 9

Author(s):

Polina Zheglova ◽

Peter G. Lelièvre ◽

Colin G. Farquharson

Keyword(s):

Physical Properties ◽

Level Set Method ◽

Level Set ◽

Joint Inversion ◽

Gravity Data ◽

Level Set Methods ◽

Physical Property ◽

Magnetic Data ◽

Inversion Method ◽

Multiple Level

We have developed a multiple level-set method for simultaneous inversion of gravity and seismic traveltime data. The method recovers the boundaries between regions with distinct physical properties assumed constant and known, creating structurally consistent models of two subsurface properties: P-wave velocity and density. In single level-set methods, only two rock units can be considered: background and inclusion. Such methods have been applied to examples representing various geophysical scenarios, including in the context of joint inversion. In multiple level-set methods, several units can be considered, which make them far more applicable to real earth scenarios. Recently, a multiple level-set method has been proposed for inversion of magnetic data. We extend the multiple level-set formulation to joint inversion of gravity and traveltime data, improving upon previous work, and we investigate applicability of such an inversion method in ore delineation. In mineral exploration environments, traditional seismic imaging and inversion methods are challenging because of the small target size and the specific physical property contrasts involved. First-arrival seismic traveltime and gravity data complement each other, and we found that joint multiple level-set inversion is more beneficial than separate inversions, especially with limited data and slow targets. Our method is more robust than the joint inversion method based on clustering of physical properties in recovery of piecewise homogeneous models not well-constrained by the data. To justify the known property assumption, we found the degree of robustness of the multiple level-set joint inversion to uncertainties arising from incomplete knowledge of small-scale subsurface physical property variations and target composition.

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