Research Interests: Geostatistics, Data Analysis, Estimation, Modeling and Simulation of Natural Resources, Industrial Minerals, Geo-metallurgy, Minerals Economy
Professional & Scientific Membership: IAMG, LASIM, IRSME
Awards & Patents: Top ranked student in the PhD study exam, First student in rank M.Sc. examination (1998), Rewarded a scholarship to study for a Ph.D. degree and Technology of Iran, Mention de These: Tres Honorable, Exempt from military service (Elite privilege),
Biography
Mohammad Koneshloo has received the B.Sc. degree from University of Tehran in 1997, He achieved the first rank in national MSc entrance exam and finished his MSc degree in Mineral Exploration-Mining Engineering in 2000. He worked as a researcher in Iranian Minerals Research and Application Institute (IMRA) for two years, then he rewarded a Ph.D. study scholarship from Iranian Ministry of sciences, Research and Technology (top ranked in another national exam). He learned Geostatistics in Centre de Geostatistique in Paris School of Mines (Ecole des Mines de Paris), during geostatistical formation of CFSG equivalent of Master of Engineering. He has passed several geostatistical course in CFSG 2005-2006; Linear and Multivariate Geostatistics (taught by Gaël Le Loch), Simulation (taught by Hans Wackernagel, Christian Lantuejoul), Non-stationary Geostatistics and SKI-MAT (by Serge Seguret) and Project (under supervision of Jean-Paul Chiles). Mohammad has finished his PhD Thesis in Ecole des Mines de Paris in 2006 and worked after for 4 years and half as an assistant professor in Shahrood University of Technology. He taught several courses in this university (Geostatistics for Mining Engineers, Industrial Minerals and Rocks, Mine Economics, Modeling and Simulation, Non-Metallic Deposit Exploration, Geostatistics for Petroleum Reservoir Engineers, Principles of mineral exploration and resource evaluation, Mineral Resources Estimation and Evaluation, Mathematical Geosciences (Statistics and Probability), Seminar) and also in University of Tehran (Petroleum Geostatistics and Mineral Resources Estimation and Evaluation) and Tarbiat Modares University (Geostatistics for mining engineering) . Actually, he is a post doctorate researcher at Department of Geological Sciences in University of Delaware. His current research interests include geostatistical modeling of aquifer systems. The project will involve geostatistical modeling of the Hawaiian Islands and the Bengal Basin. The goal of the project, undertaken in collaboration with other researchers, is to understand controls of geologic heterogeneity on land-sea fluxes.
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Stochastic Environmental Research and Risk Assessment, February 2014
Multiple-point geostatistical simulation of dykes: application at Sungun porphyry copper system, Iran
Hassan Rezaee, Omid Asghari, Mohammad Koneshloo, Julián M. Ortiz
Abstract
Variogram-based methods are not capable of capturing high (>2) order statistics since the variogram measures the relationship between two points at a time only. Multiple-point geostatistics (MPS) has brought new insights into many geological modeling problems. The application of MPS methods has been well documented in realizing complex geological patterns. These methods have often been used in reservoir characterization since their advent in recent decades. The frequent non-linear behaviors of geologic continuity are not limited to reservoirs, but mineral deposits bear complicated formations in many cases. Relying on the power of MPS methods and considering the complexity of geological scenarios in mineral deposits, we have applied MPS in the modeling of mineral deposits. A training image (TI) is produced using geological data from upper horizons of a porphyry copper ore deposit which have been mined out during the previous mining operations. In this study, the SNESIM algorithm has been used. A number of realizations are produced using this multiple-point geostatistical method. Extensive validation steps are performed considering the TI as the reference model. These validations first show that the TI is representative for the domain under study and also illustrates some degrees of similarity between the TI and the realizations. Despite simplifications made to the problem, the application of MPS in mineral deposit modeling still faces many challenges.
http://link.springer.com/article/10.1007/s00477-014-0857-8
Computers & Geosciences,Volume 54, April 2013, Pages 293–308
Multiple-point geostatistical simulation using the bunch-pasting direct sampling method
Hassan Rezaeea, Gregoire Mariethoz, Mohammad Koneshloo , Omid Asghari
Abstract
Multiple-point geostatistics has opened a new field of methodologies by which complex geological phenomena have been modeled efficiently. In this study, a modified form of direct sampling (DS) method is introduced which not only keeps the strength of DS simulation technique but also speeds it up by one or two orders of magnitude. While previous methods are based on pasting only one point at a time, here the simulation is done by pasting a bunch of nodes at a time, effectively combining the flexibility of DS with the computational advantages of patch-based methods. This bears the potential of significantly speeding up the DS method. The proposed simulation method can be used with unilateral or random simulation paths. No overlap occurs in the simulation procedure because the bunch takes the shape of the empty space around the simulated nodes. Systematic tests are carried on different training images including both categorical and continuous variables, showing that the realizations preserve the patterns existent in the training image. To illustrate the method, a Matlab implementation of the method is attached to the paper.
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