Clustering and constrained inversion of seismic refraction and gravity data for overburden stripping: Application to uranium exploration in the Athabasca Basin, Canada

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. B133-B146 ◽  
Author(s):  
Mehrdad Darijani ◽  
Colin G. Farquharson ◽  
Peter G. Lelièvre
Keyword(s):  
Gravity Data ◽  
Seismic Refraction ◽  
Real Data ◽  
Drill Hole ◽  
Gravity Inversion ◽  
Glacial Sediments ◽  
Athabasca Basin ◽  
Overburden Thickness ◽  
Uranium Exploration ◽  
Fuzzy C Means Clustering

Gravity signatures from features associated with the footprints of uranium deposits within the sandstones and basement of the Athabasca Basin are masked in the measured gravity by the contribution from glacial sediments (overburden), in particular by the variable thickness of the overburden. The 2D inversions of seismic refraction and gravity data are assessed as a means of reliably mapping overburden thickness, enabling the contribution to gravity from the overburden to be taken into account and density anomalies associated with deeper mineralization and alteration to be reconstructed through further inversion. Results show that independent inversion of seismic refraction data using the fuzzy c-means clustering method is able to determine the base of overburden well. Subsequent gravity inversion constrained by the overburden thickness reveals possible subtle density variations at depth, which could be associated with alteration in the sandstones associated with the uranium mineralization. Application of the seismic clustering inversion followed by constrained gravity inversion to both representative synthetic scenarios and real data from the Athabasca Basin, Canada, are considered. Drill-hole data show that the inversion results can predict the base of the overburden well, and there is an acceptable match between geologic information and possible alteration zones suggested by the inversions.

A method for inversion of 1D vertical soundings of gravity anomalies

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. G15-G23
Author(s):  
Andrea Vitale ◽  
Domenico Di Massa ◽  
Maurizio Fedi ◽  
Giovanni Florio
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Finite Size ◽  
Gravity Anomalies ◽  
Real Data ◽  
Gravity Inversion ◽  
Chain Process ◽  
Potential Field Data ◽  
Gamma Density

We have developed a method to interpret potential fields, which obtains 1D models by inverting vertical soundings of potential field data. The vertical soundings are built through upward continuation of potential field data, measured on either a profile or a surface. The method assumes a forward problem consisting of a volume partitioned in layers, each of them homogeneous and horizontally finite, but with the density changing versus depth. The continuation errors, increasing with the altitude, are automatically handled by determining the coefficients of a third-order polynomial function of the altitude. Due to the finite size of the source volume, we need a priori information about the total horizontal extent of the volume, which is estimated by boundary analysis and optimized by a Markov chain process. For each sounding, a 1D inverse problem is independently solved by a nonnegative least-squares algorithm. Merging of the several inverted models finally yields approximate 2D or 3D models that are, however, shown to generate a good fit to the measured data. The method is applied to synthetic models, producing good results for either perfect or continued data. Even for real data, i.e., the gravity data of a sedimentary basin in Nevada, the results are interesting, and they are consistent with previous interpretation, based on 3D gravity inversion constrained by two gamma-gamma density logs.

Computer modeling of electromagnetic data for mineral exploration: Application to uranium exploration in the Athabasca Basin

2021 ◽  
Vol 40 (2) ◽  
pp. 139a1-139a10
Author(s):  
Xushan Lu ◽  
Colin Farquharson ◽  
Jean-Marc Miehé ◽  
Grant Harrison ◽  
Patrick Ledru
Keyword(s):  
Computer Modeling ◽  
Unstructured Grids ◽  
Mineral Exploration ◽  
Real Data ◽  
Data Interpretation ◽  
Error Modeling ◽  
Accurate Data ◽  
Trial And Error ◽  
Athabasca Basin ◽  
Uranium Exploration

Electromagnetic (EM) methods are important geophysical tools for mineral exploration. Forward and inverse computer modeling are commonly used to interpret EM data. Real-life geology can be complex, and our computer modeling tools need to faithfully represent subsurface features to achieve accurate data interpretation. Traditional rectilinear meshes are less flexible and have difficulty conforming to the complex geometries of realistic geologic models, resulting in large numbers of mesh cells. In contrast, unstructured grids can represent complex geologic structures efficiently and accurately. However, building realistic geologic models and discretizing these models with unstructured grids suitable for EM modeling can be difficult and requires significant effort and specialized computer software tools. Therefore, it is important to develop workflows that can be used to facilitate model building and mesh generation. We have developed a procedure that can be used to build arbitrarily complex geologic models with topography using unstructured grids and a finite-volume time-domain code to calculate EM responses. We present an example of a trial-and-error modeling approach applied to a real data set collected at a uranium exploration project in the Athabasca Basin in Canada. The uranium mineralization is closely related to graphitic fault conductors in the basement. The deep burial depth and small thickness of the graphitic fault conductors demand accurate data interpretation results to guide subsequent drill testing. Our trial-and-error modeling approach builds initial realistic geologic models based on known geology and downhole data and creates initial geoelectrical models based on physical property measurements. Then, the initial model is iteratively refined based on the match between modeled and real data. We show that the modeling method can obtain 3D geoelectrical models that conform to known geology while achieving a good match between modeled and real data. The method can also provide guidance of where future drill holes should be directed.

Integration of geophysical constraints for multilayer geometry refinements in 2.5D gravity inversion

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. G95-G106 ◽  
Author(s):  
Jian Xing ◽  
Tianyao Hao ◽  
Ya Xu ◽  
Zhiwei Li
Keyword(s):  
Gravity Data ◽  
Inequality Constraints ◽  
Real Data ◽  
Gravity Inversion ◽  
Inversion Method ◽  
Sharp Condition ◽  
Estimated Parameters ◽  
Multiple Interfaces ◽  
Variation Function ◽  
Different Depths

We have explored the feasibility of estimating depths of multiple interfaces from gravity data. The strata are simulated by an aggregate of 3D rectangular prisms whose bottom depths are parameters to be estimated. In the inversion process, we have integrated geophysical constraints including the borehole information and the sharp condition described by the total variation function. The iterative residual function is also introduced to adjust the weighting of the estimated parameters so that layers of different depths have nearly equal likelihood for deviation. The inversion is processed by minimizing the Tikhonov parametric functional by the reweighted regularized conjugate gradient method. Inequality constraints are adopted to deal with the coupling of the interfaces. Synthetic tests show that such integration is conducive to restoring the multilayer depth distribution. Real data applications in Mariana confirm that the inversion method is effective in complex geologic settings in practice. We have also evaluated several issues that specifically deserve attention for obtaining satisfactory results in multilayer gravity inversion.

scholarly journals 3D Gravity Inversion on Unstructured Grids

2021 ◽  
Vol 11 (2) ◽  
pp. 722
Author(s):  
Siyuan Sun ◽  
Changchun Yin ◽  
Xiuhe Gao
Keyword(s):  
Objective Function ◽  
Unstructured Grids ◽  
Gravity Data ◽  
Depth Resolution ◽  
Continuous Model ◽  
Gravity Inversion ◽  
Inversion Method ◽  
Model Parameters ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Compared with structured grids, unstructured grids are more flexible to model arbitrarily shaped structures. However, based on unstructured grids, gravity inversion results would be discontinuous and hollow because of cell volume and depth variations. To solve this problem, we first analyzed the gradient of objective function in gradient-based inversion methods, and a new gradient scheme of objective function is developed, which is a derivative with respect to weighted model parameters. The new gradient scheme can more effectively solve the problem with lacking depth resolution than the traditional inversions, and the improvement is not affected by the regularization parameters. Besides, an improved fuzzy c-means clustering combined with spatial constraints is developed to measure property distribution of inverted models in both spatial domain and parameter domain simultaneously. The new inversion method can yield a more internal continuous model, as it encourages cells and their adjacent cells to tend to the same property value. At last, the smooth constraint inversion, the focusing inversion, and the improved fuzzy c-means clustering inversion on unstructured grids are tested on synthetic and measured gravity data to compare and demonstrate the algorithms proposed in this paper.

Three-dimensional gravity inversion using graph theory to delineate the skeleton of homogeneous sources

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. G53-G66 ◽  
Author(s):  
Rodrigo Bijani ◽  
Cosme F. Ponte-Neto ◽  
Dionisio U. Carlos ◽  
Fernando J. S. Silva Dias
Keyword(s):  
Genetic Algorithm ◽  
Graph Theory ◽  
Minimum Spanning Tree ◽  
Gravity Data ◽  
A Priori ◽  
Three Dimensional ◽  
Real Data ◽  
Gravity Inversion ◽  
Inversion Method ◽  
Dimensional Gravity

We developed a new strategy, based on graph theory concepts, to invert gravity data using an ensemble of simple point masses. Our method consisted of a genetic algorithm with elitism to generate a set of possible solutions. Each estimate was associated to a graph to solve the minimum spanning tree (MST) problem. To produce unique and stable estimates, we restricted the position of the point masses by minimizing the statistical variance of the distances of an MST jointly with the data-misfit function during the iterations of the genetic algorithm. Hence, the 3D spatial distribution of the point masses identified the skeleton of homogeneous gravity sources. In addition, our method also gave an estimation of the anomalous mass of the source. So, together with the anomalous mass, the skeleton could aid other 3D methods with promising geometric a priori parameters. Several tests with different values of regularizing parameter were made to bespeak this new regularizing strategy. The inversion results applied to noise-corrupted synthetic gravity data revealed that, regardless of promising starting models, the estimated distribution of point masses and the anomalous mass offered valuable information about the homogeneous sources in the subsurface. Tests on real data from a portion of Quadrilátero Ferrífero, Minas Gerais state, Brazil, were performed for complementary analysis of the proposed inversion method.

scholarly journals Characterization of geological boundaries using 1‐D wavelet transform on gravity data: Theory and application to the Himalayas

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 1116-1129 ◽  
Author(s):  
G. Martelet ◽  
P. Sailhac ◽  
F. Moreau ◽  
M. Diament
Keyword(s):  
Wavelet Transform ◽  
Gravity Data ◽  
Real Data ◽  
Source Analysis ◽  
Gravity Inversion ◽  
Analysis Tool ◽  
Geometric Constructions ◽  
Complex Wavelets ◽  
Inversion Procedure ◽  
Geological Boundaries

We investigate the use of the continuous wavelet transform for gravity inversion. The wavelet transform operator has recently been introduced in the domain of potential fields both as a filtering and a source‐analysis tool. Here we develop an inverse scheme in the wavelet domain, designed to recover the geometric characteristics of density heterogeneities described by simple‐shaped sources. The 1‐D analyzing wavelet we use associates the upward continuation operator and linear combinations of derivatives of any order. In the gravity case, we first demonstrate how to localize causative sources using simple geometric constructions. Both the upper part of the source and the whole source can be studied when considering low or high altitudes, respectively. The homogeneity degree of the source is deduced without prior information and allows us to infer its shape. Introducing complex wavelets, we derive analytically the scaling behavior of the wavelet coefficients for the dyke and the step sources. The modulus term is used in an inversion procedure to recover the thickness of the source. The phase term provides its dip. This analysis is performed on gravity data we measured along a profile across the Himalayas in Nepal. Good agreement of our results with well‐documented thrusting structures demonstrates the applicability of the method to real data. Also, deeper, less constrained structures are characterized.

Crustal structure of the Reykjanes Ridge near 62°N, on the basis of seismic refraction and gravity data

2007 ◽  
Vol 43 (1) ◽  
pp. 55-72 ◽  
Author(s):  
Wolfgang R. Jacoby ◽  
Wilfred Weigel ◽  
Tanya Fedorova
Keyword(s):  
Crustal Structure ◽  
Gravity Data ◽  
Seismic Refraction ◽  
Reykjanes Ridge

Assessing Model Uncertainty for the Scaling Function Inversion of Potential Fields

Geophysics ◽  
2021 ◽  
pp. 1-39
Author(s):  
Mahak Singh Chauhan ◽  
Ivano Pierri ◽  
Mrinal K. Sen ◽  
Maurizio FEDI
Keyword(s):  
Simulated Annealing Algorithm ◽  
Scaling Function ◽  
Vertical Section ◽  
Gravity Data ◽  
Geometric Model ◽  
Salt Dome ◽  
The Body ◽  
Gravity Inversion ◽  
Geometrical Parameters ◽  
The Scaling Function

We use the very fast simulated annealing algorithm to invert the scaling function along selected ridges, lying in a vertical section formed by upward continuing gravity data to a set of altitudes. The scaling function is formed by the ratio of the field derivative by the field itself and it is evaluated along the lines formed by the zeroes of the horizontal field derivative at a set of altitudes. We also use the same algorithm to invert gravity anomalies only at the measurement altitude. Our goal is analyzing the different models obtained through the two different inversions and evaluating the relative uncertainties. One main difference is that the scaling function inversion is independent on density and the unknowns are the geometrical parameters of the source. The gravity data are instead inverted for the source geometry and the density simultaneously. A priori information used for both the inversions is that the source has a known depth to the top. We examine the results over the synthetic examples of a salt dome structure generated by Talwani’s approach and real gravity datasets over the Mors salt dome and the Decorah (USA) basin. For all these cases, the scaling function inversion yielded models with a better sensitivity to specific features of the sources, such as the tilt of the body, and reduced uncertainty. We finally analyzed the density, which is one of the unknowns for the gravity inversion and it is estimated from the geometric model for the scaling function inversion. The histograms over the density estimated at many iterations show a very concentrated distribution for the scaling function, while the density contrast retrieved by the gravity inversion, according to the fundamental ambiguity density/volume, is widely dispersed, this making difficult to assess its best estimate.

Paradigmatic Shifts in the Uranium Exploration Process: Knowledge Brokers and the Athabasca Basin Learning Curve

SEG Discovery ◽  
2011 ◽  
pp. 1-23
Author(s):  
James L. Marlatt ◽  
T. Kurt Kyser
Keyword(s):  
Research And Development ◽  
Technology Development ◽  
Current Model ◽  
Development Effort ◽  
Organizational System ◽  
Exploration Process ◽  
Athabasca Basin ◽  
Uranium Exploration ◽  
The Status ◽  
Knowledge Brokers

ABSTRACT Uranium exploration increased over the past decade in response to an increase in the price of uranium, with more than 900 companies engaged in the global exploration on over 3,000 projects. Major economic discoveries of new uranium orebodies have been elusive despite global exploration expenditures of $3.2 billion USD, with most of the effort in historical uranium districts. The increased effort in exploration with minimal return can be described through the example of a cyclical model based on exploration and discovery in the prolific Athabasca Basin, Saskatchewan. The model incorporates exploration expenditure, quantities of discovered uranium, and the sequence of uranium deposit discoveries to reveal that discovery cycles are epochal in nature and that they are also intimately related to the development and deployment of new exploration technologies. Exploration in the Athabasca Basin can be divided into an early “prospector” phase and the current “model-driven”phase. The future of successful uranium exploration is envisaged as the “innovation exploration” stage in which a paradigmatic shift in the exploration approach will take the industry towards new discoveries by leveraging research and technology development. Effective engagement within the “innovation exploration” paradigm requires that exploration organizations recognize knowledge brokers, and adopt research, development, and technology transfer as a long-term, systematic strategy, including critical definition of exploration targets, identification of innovation frontiers needed, enhanced leadership to accurately portray the research and development imperative and elevation of the status of the research and development effort within the organizational system.

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