Cristina holds a Ph.D. and a Master’s degree in Geostatistics from the Federal University of Rio Grande do Sul (UFRGS) and a Post-doctorate degree in Oil Reservoir Engineering specializing in secondary information integration, reservoir modeling, characterization, and simulation. She gained international experience through academic mobility to the Instituto Superior Técnico in Lisbon, Portugal, and participated in the Real-Time Mining Horizon 2020 project.

Her area of expertise includes the development of methodologies for the integration of imprecise and biased secondary information (soft data) in short-term mine planning.

She is experienced in the exploratory analysis of multivariate data to construct geostatistics-based models describing the spatial variability and correlation of grades in mineral deposits.

She has also developed and analyzed good practices in Resource and Reserve Estimations in line with the CBRR and international codes (JORC 2012, SEC, NI-403-101) and technical notes of the Brazilian Resource and Reserve Mineral System from the Brazilian National Mining Agency (ANM).

Yvan is a mining engineer who graduated from the School of Mines and Geology in Yamoussoukroin, Ivory Coast, specializing in exploration methods and mineral resource exploitation techniques. He then obtained a master’s in geostatistics at the Center of Geostatistics of MINES ParisTech in Fontainebleau (CFSG).

Throughout his career, Yvan has extensively applied geostatistics in various geological modeling issues, including 3D or layer-cake models involving hydrodynamic parameters, seismic velocities, and ore grades. He has also worked in the contaminated sites sector, characterizing distributions with extreme values.

Before joining Geovariances in 2022, Yvan worked at the French National Geological Survey, developing geomodeling tools and geostatistical concepts and methods. At Geovariances, Yvan is currently involved in consulting and training services to the Mining Industry worldwide.

David joined the team in February 2009 as a specialist Mathematical Analyst. David obtained a BSc with Honours at the University of Queensland, achieving First Class Honours and being awarded a University Medal. On completing this degree, he undertook a Master of Philosophy degree in Physics.

At Geovariances, he has led or been a part of geostatistical consulting studies with linear and nonlinear geostatistics (Kriging, Uniform Conditioning, Conditional Simulation, drill-hole spacing analysis) in a range of commodities, including gold, iron ore, lateritic nickel, and coal. He has run many training courses in both geostatistical theory and the use of Geovariances software and has lectured the Geostatistics course at the University of Queensland. David is also active in software development and testing for Isatis.neo.

Hélène Binet is a geostatistician with a background in geophysics, specializing in spatial data analysis, filtering, and time-to-depth conversion. She has over a decade of international experience, including working in Brazil and Norway in the Oil&Gas industry. Hélène also has experience in geostatistics applied to chemical and radiological soil contamination.

As a geostatistician and engineering consultant, she applies her experience to conduct exploratory data analysis, sampling optimization, geostatistical estimations and simulations to build cartography, quantify uncertainties, and delineate contaminated surfaces and volumes.

Moreover, her in-depth knowledge has allowed her to become a successful product manager and an integral part of our training team.

Luigi is a geologist who graduated from the University of São Paulo in 2017 with a Bachelor’s degree in Geology and is currently pursuing a master’s degree in geostatistics. He has expertise in geostatistics, data mining, and geological modeling. He gains solid experience as a geostatistician, mainly in ore uncertainty analysis, drill hole spatial analysis, and profit uncertainty analysis using conditional simulations. Luigi joined Geovariances in 2020 and is involved in consulting and training services to the mining industry in Latin America and the Caribbean. He is also responsible for developing the company’s mining business in Latin America.

Yvon Desnoyers now has 15 years of experience in consulting, mentoring, and training as a geostatistician expert at Geovariances. His main proficiency fields are sampling optimization and radiological characterization of nuclear facilities and contaminated sites to drive decommissioning & dismantling, and remediation projects. In addition, Yvon is actively involved in numerous working groups and R&D projects (France, Europe, and international organizations).

Giulia holds a bachelor’s degree in Geology from the Federal University of Minas Gerais in Brazil and a specialization in mining from the Gorceix Foundation. She is currently pursuing a master’s degree focusing on geostatistical simulations at the University of São Paulo.

Giulia has experience in geological modeling, database validation, mineral resource estimation, geometallurgy, mine reconciliation, and mine geology. She worked at Datamine as a geological modeling intern and at Vale SA (Iron Ore) as a mine geologist for a couple of years. She now works as a mineral resources consultant at Geovariances Latin America.

Arthur has a degree in Geology from the University of São Paulo (2015) and a Master’s in Geotectonics from the same university (2017). He has experience in process automation, data science, database management, geostatistics, and machine learning. He is currently a consultant in geostatistics and a senior software developer at Geovariances.

Claire Faucheux joined Geovariances in 2010 as an engineering consultant applying geostatistics to environmental fields. After graduating with a diploma of Ingénieur Agronome (equivalent to MSc.) from the Ecole Nationale Supérieure d’Agronomie de Rennes, she gained experience in sampling for water quality assessment at IRSTEA as well as in geostatistics for soil pollution and air quality at MinesParisTech.

Her geostatistical and statistical expertise allows her to conduct exploratory data analysis, sampling optimization, geostatistical estimations and simulations to build cartography, quantify uncertainties and delineate contaminated surfaces and volumes. Claire mainly applies these techniques to soil contamination and air quality while responding to client-centered needs. Her scientific software skills include Isatis, Kartotrak, and R.

As part of our experienced training team, Claire can offer her expertise in English and French.

João Pedro is a geologist from the Federal University of Rio Grande do Sul (UFRGS), where he acquired experience in geological modeling, geostatistics, and data science. He has participated in projects related to the implementation of machine learning for domain and predictive modeling. Currently, he is developing projects related to geostatistics, machine learning, and automation of mineral resource estimation routines at Geovariances.

Pedram Masoudi joined Geovariances in 2019 as a consultant and trainer following his postdoctoral project on the geostatistical characterization of contaminated soil by Cs-137. With his expertise in Isatis.neo geostatistical software and Python programming language, he is interested in developing and testing new methods and algorithms for integrating and interpreting geophysical and geological data. Pedram has a wide range of skills, including mineral resource estimation compliant with the JORC standards, petroleum reservoir characterization involving time-depth conversion and petrophysics, facies modeling, and geotechnical studies related to liquefaction (CPT, SPT, and EM). He also specializes in cartography of contaminated soil with a focus on radioprotection.

Graduated in Geology from the University Centre of Belo Horizonte, Jéssica started her career 10 years ago at the Geophysics Laboratory of the Federal University of Minas Gerais, where she was involved in the geological mapping of the Codemig (Companhia de Desenvolvimento Econômico de Minas Gerais) Fronteira de Minas project. Then, she worked a couple of years for Vale, where she gained experience in QA/QC, database management, and geological modeling that she applied to the company’s ferrous mineral deposits. After that, she spent some time at Hidrovia Hidrogeologia e Meio Ambiente conducting geological modeling. Finally, she joined Geovariances as a mining geostatistician in 2020. Today, she is also responsible for developing the company’s mining business in Brazil.

Silvia holds a Bachelor’s degree in Geology from the Federal University of Minas Gerais in Brazil and gets international experience at the Queensland University of Technology (Australia). She joined Geovariances in 2017, where she is involved in consulting and training services to the mining industry in Latin America and the Caribbean area. She has solid experience gained from several consulting and training projects for mining companies in various commodities, including applying advanced geostatistics for drill hole mesh analysis and mineral resource estimation. Silvia is currently pursuing a master’s degree in geostatistics at the Federal University of Rio Grande do Sul.

Gabriel is a geologist who graduated from the Federal University of Minas Gerais (UFMG) in Brazil and holds a master’s degree in engineering from the Federal University of Rio Grande do Sul (UFRGS). His MSc thesis involved geostatistics, geodata analysis, and machine learning with Python, his areas of expertise.

He has solid experience as a mine geologist, handling long, medium, and short-term modeling and covering some aspects of the ore production chain.

Gabriel joined Geovariances in 2020, acting as a consultant in mineral resources. In 2022 he served as the technical leader of projects applied to mining in the Americas. Currently, he is the Country Manager in Brazil and conducts his Ph.D. research in geometallurgical modeling at UFRGS.

Roberto holds a Mining Engineer Bachelor’s degree from the School of Mines of the Federal University of Ouro Preto (UFOP) and a Master’s and a Ph.D. degree in geostatistics from the Federal University Rio Grande do Sul (UFRGS). His expertise is in implicit geological modeling, uncertainty assessment in geological models, ore grades estimation, and simulation workflows according to the international mineral reporting codes. He is also strongly proficient in Python and machine-learning techniques applied to geosciences. Along with his career as a geostatistician, he has developed custom geostatistical and machine learning-based solutions and Python codes for the mining industry. He currently works as a mineral resources consultant at Geovariances Latin America.