In order to quantify the uncertainty in the grade estimate for the Sadiola Deep Sulphide Prefeasibility Project a conditional simulation model was generated using Direct Block Simulation methodology. Compared to conventional Sequential Gaussian Simulation, the Direct Block Simulation algorithm produced a reliable model in significantly less time, lending its application to a production environment. Through application of a mining transfer function, risk pits were generated for comparison with the Deep Sulphide Prefeasibility pit. The results of this study revealed that the prefeasibility pit is optimal at the applied gold price and cost parameters, and that the risk of not achieving the project grade profile is low. Should the gold price increase, or the operating costs of the project decrease significantly, the Deep Sulphide reserve tonnage would realise significant upside potential. The potential for using the simulation model coefficient of variation to improve the classification of the resource has been highlighted. This exercise could allow significant saving of feasibility drilling capital.

Unlike the traditional two-stage methods, a conditional and inverse-conditional simulation approach may directly generate independent, identically distributed realizations to honor both static data and state data in one step. The Markov chain Monte Carlo (McMC) method was proved a powerful tool to perform such type of stochastic simulation. One of the main advantages of the McMC over the traditional sensitivity-based optimization methods to inverse problems is its power, flexibility and well-posedness in incorporating observation data from different sources. In this work, an improved version of the McMC method is presented to perform the stochastic simulation of reservoirs and aquifers in the framework of multi-Gaussian geostatistics. First, a blocking scheme is proposed to overcome the limitations of the classic single-component Metropolis-Hastings-type McMC. One of the main characteristics of the blocking McMC (BMcMC) scheme is that, depending on the inconsistence between the prior model and the reality, it can preserve the prior spatial structure and statistics as users specified. At the same time, it improves the mixing of the Markov chain and hence enhances the computational efficiency of the McMC. Furthermore, the exploration ability and the mixing speed of McMC are efficiently improved by coupling the multiscale proposals, i.e., the coupled multiscale McMC method. In order to make the BMcMC method capable of dealing with the high-dimensional cases, a multi-scale scheme is introduced to accelerate the computation of the likelihood which greatly improves the computational efficiency of the McMC due to the fact that most of the computational efforts are spent on the forward simulations. To this end, a flexible-grid full-tensor finite-difference simulator, which is widely compatible with the outputs from various upscaling subroutines, is developed to solve the flow equations and a constant-displacement random-walk particle-tracking method, which enhances the com / Fu, J. (2008). A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1969 / Palancia

Ce travail porte sur la reconnaissance de sols à faible profondeur grâce aux données de résistance de pointe recueillies à l'aide de l'essai de pénétration dynamique à énergie variable, Panda®. L'objectif principal est d'étudier et de proposer un ensemble d'approches dans le cadre d'une méthode globale permettant d'exploiter les mesures issues d'une campagne de sondages Panda afin de bâtir un modèle géotechnique du terrain.Ce manuscrit est structuré en quatre parties, chacune abordant un objectif spécifique :dans un premier temps, on rappelle les principaux moyens de reconnaissance des sols, notamment l'essai de pénétration dynamique Panda. Ensuite on réalise un bref aperçu sur le modèle géotechnique et les techniques mathématiques pour décrire l'incertitude dans la caractérisation des propriétés du sol;la deuxième partie porte sur l'identification automatique des unités homogènes du terrain, à partir du signal pénétrométrique Panda. Suite à l'étude réalisée sur l'identification "experte" des couches à partir du signal Panda, des approches statistiques basées sur une fenêtre glissante ont été proposées. Ces techniques ont été étudiées et validées sur la base d'un protocole d'essais en laboratoire et sur des essais effectués en sites naturels et en conditions réelles;la troisième partie porte sur l'identification automatique des matériaux composant les unités homogènes détectées dans le signal Panda à partir des méthodes proposées en partie II. Une méthode de classification automatique basée sur des réseaux de neurones artificiels a été proposée et appliquée aux deux cas d'étude : la caractérisation de sols naturels et la classification d'un matériau granulaire argileux industrialisé (bentonite) ; enfin, la dernière partie est consacrée à la production d'un modèle de terrain basé sur la modélisation et la simulation de la résistance de pointe dynamique au moyen de fonctions aléatoires de l'espace. Cette modélisation est basée sur une approche par champs aléatoires conditionnés par les sondages Panda du terrain. Sa mise en œuvre a été étudiée pour un terrain expérimental situé dans la plaine deltaïque méditerranéenne en Espagne. Des études complémentaires en vue de raffiner cette démarche ont été réalisées pour un deuxième site expérimental dans la plaine de la Limagne en France. / This research focuses on the site characterization of shallow soils using the dynamic cone penetrometer Panda® which uses variable energy. The main purpose is to study and propose several techniques as part of an overall method in order to obtain a ground model through a geotechnical campaign based on the Panda test.This work is divided into four parts, each of them it is focused on a specific topic :first of all, we introduce the main site characterization techniques, including the dynamic penetrometer Panda. Then, we present a brief overview of the geotechnical model and the mathematical methods for the characterization of uncertainties in soil properties;the second part deals with the automatic identification of physical homogeneous soil units based on penetration's mechanical response of the soil using the Panda test. Following a study about the soil layers identification based only on expert's judgment, we have proposed statistical moving window procedures for an objective assessment. The application of these statistical methods have been studied for the laboratory and in situ Panda test;the third part focuses on the automatic classification of the penetrations curves in the homogeneous soil units identified using the statistical techniques proposed in part II. An automatic methodology to predict the soil grading from the dynamic cone resistance using artificial neural networks has been proposed. The framework has been studied for two different research problems: the classification of natural soils and the classification of several crushed aggregate-bentonite mixtures;finally, the last chapter was devoted to model the spatial variability of the dynamic cone resistance qd based on random field theory and geostatistics. In order to reduce uncertainty in the field where Panda measurements are carried out, we have proposed the use of conditional simulation in a three dimensional space. This approach has been applied and studied to a real site investigation carried out in an alluvial mediterranean deltaic environment in Spain. Complementary studies in order to improve the proposed framework have been explored based on another geotechnical campaign conducted on a second experimental site in France.