Publications
My name appears in bold. An asterisk (*) marks a student under my supervision.
Journal articles (13) · Conference contributions (18) · Workshops and training (1) · Theses (1) · Educational resources (1)
Peer-reviewed journal articles
2026
Lauzon, D., Hörning, S., & Bàrdossy, A. (2026). A generalized FFTMA approach for simulating multivariate non-Gaussian random fields with spatial asymmetry. Computers & Geosciences, 212, 106157 (18 pages). DOI
Pugliese, E., Lauzon, D., Dematteis, A., Rodani, S., Benedetti, G., & Gargini, A. (2026). Risk assessment of tunnelling-induced hydrogeological interference on springs using a machine learning approach. Hydrogeology Journal. DOI
Liang, X. X., Lauzon, D., Gloaguen, E., Straubhaar, J., & Renard, P. (2026). Evaluating the influence of temporal data resolution on the performance of deep neural networks for Karst groundwater forecasting. Applied Computing and Geosciences, 100393. DOI
2025
Lauzon, D., Straubhaar, J., & Renard, P. (2025). A Deep Generative Model for the Simulation of Discrete Karst Networks. Earth and Space Science, 12(10). DOI
Lauzon, D., & Hörning, S. (2025). Efficient computation on large regular grids of higher-order spatial statistics via fast Fourier transform. Computers & Geosciences, 105878 (38 pages). DOI
Liang, X. X., Gloaguen, E., Claprood, M., Paradis, D., & Lauzon, D. (2025). Graph Neural Network Framework for Spatiotemporal Groundwater Level Forecasting. Mathematical Geosciences, 23 pages. DOI
2024
Lauzon, D. (2024). A U-Net architecture as a surrogate model combined with a geostatistical spectral algorithm for transient groundwater flow inverse problems. Advances in Water Resources, 189, 104726 (14 pages). DOI
Lauzon, D., & Gloaguen, E. (2024). Quantifying uncertainty and improving prospectivity mapping in mineral belts using transfer learning and Random Forest. Ore Geology Reviews, 166, 105918 (16 pages). DOI
2023
Lauzon, D., & Marcotte, D. (2023). Joint hydrofacies-hydraulic conductivity modeling based on a constructive spectral algorithm constrained by transient head data. Hydrogeology Journal, 31(6), 1647-1664. DOI
2022
Lauzon, D., & Marcotte, D. (2022). Statistical comparison of variogram-based inversion methods for conditioning to indirect data. Computers & Geosciences, 160, 105032 (15 pages). DOI
2020
Lauzon, D., & Marcotte, D. (2020). Calibration of random fields by a sequential spectral turning bands method. Computers & Geosciences, 135, 104390 (13 pages). DOI
Lauzon, D., & Marcotte, D. (2020). The sequential spectral turning band simulator as an alternative to Gibbs sampler in large truncated- or pluri-Gaussian simulations. Stochastic Environmental Research and Risk Assessment, 34(11), 1939-1951. DOI
2019
Lauzon, D., & Marcotte, D. (2019). Calibration of random fields by FFTMA-SA. Computers & Geosciences, 127, 99-110. DOI
Conferences
Peer-reviewed conference papers
Straubhaar, J., Lauzon, D., & Renard, P. (2026). Deep learning karst network generator. EuroKarst 2026.
Dion, G., Pasquier, P., & Lauzon, D. (2026). Stochastic approach to ground heat exchanger sizing accounting for weather and ground property variability. 15th IEA Heat Pump Conference (HPC 2026), Vienna, Austria. link
Brisebois, O.*, Lauzon, D., & Pasquier, P. (2026). Investigation of a constructive geostatistical method to assimilate unused conventional data during geothermal drilling for hydraulic conductivity profiling. 15th IEA Heat Pump Conference (HPC 2026), Vienna, Austria. link
Lauzon, D. (2026). From Data to Decisions: How Geostatistics Supports Geotechnical Engineering. GeoQuébec 2026.
Tagne Nkounga, I. B.*, & Lauzon, D. (2026). Automatic Spatial Inference of Stratigraphic Piles under Non-Stationary Conditions from Borehole Data. GeoQuébec 2026.
Kfoury, M., Roy, S., Langevin, M., Lauzon, D., & Gosselin, Y. (2024). Geostatistical analysis of geotechnical uncertainty: risk assessment in engineering projects. 77th Canadian Geotechnical Conference (GéoMontréal 2024), Montréal, Canada. link
Lauzon, D., & Marcotte, D. (2022). On a constructive spectral method for conditioning pluriGaussian simulations to boreholes observations and indirect data. GeoEnv 2022, Parma, Italia. link
Oral presentations
Renard, P., Straubhaar, J., Lauzon, D., & Trunz, C. (2026). Karst network simulation with statistical learning. EGU General Assembly 2026, Vienna, Austria. DOI
Burgoa Tanaka, A. P., Renard, P., Liang, X. X., Straubhaar, J., & Lauzon, D. (2026). Fracture network modeling with graph deep learning. EGU General Assembly 2026, Vienna, Austria. DOI
Liang, X. X., Lauzon, D., Gloaguen, E., Claprood, M., Straubhaar, J., & Renard, P. (2026). Evaluating the influence of temporal data resolution on the performance of deep neural networks for karst groundwater forecasting. IAMG 2026, Montréal, Canada.
de Magalhães, G., Cacciari, P., & Lauzon, D. (2026). A Bayesian–Geostatistical Framework for Inferring Fracture Volumetric Intensity in Rock Masses from Multiple Data Sources. IAMG 2026, Montréal, Canada.
Lauzon, D., Hörning, S., & Bárdossy, A. (2025). A novel framework for stochastic simulation of multivariate non-Gaussian random fields. EGU General Assembly 2025, Vienna, Austria. link
Hörning, S., Lauzon, D., & Bárdossy, A. (2025). Spectral methods for non-linear co-regionalization. EGU General Assembly 2025, Vienna, Austria. link
Lauzon, D. (2024). Deep neural networks in surrogate hydrogeological modeling. GeoEnv 2024, Chania, Greece. link
Straubhaar, J., Lauzon, D., & Renard, P. (2024). Graph recurrent neural networks for stochastic simulation of Karst network topology and properties. GeoEnv 2024, Chania, Greece. link
Posters
Arega, K. A.*, Benoit, N., Lauzon, D., & Bédard, K. (2026). Stochastic groundwater recharge modelling in the Harricana River watershed, Québec, Canada. CGU and IAH-CNC Annual Meeting 2026, Halifax, Nova Scotia. link
Lauzon, D., & Marcotte, D. (2022). On a constructive spectral method for conditioning pluriGaussian simulations (poster). GeoEnv 2022, Parma, Italia. link
Convened sessions
Dimitrakopoulos, R., & Lauzon, D. (2026). Multi-point and High-order simulations. IAMG 2026, Montréal, Canada.
Workshops and training
2026
Liang, X. X., Wen, T., & Lauzon, D. (2026). Fundamental Deep Learning Concepts for Applied Geoscientists. IAMG 2026, Montréal, Canada.
Theses
2022
Lauzon, D. (2022). Développement d’algorithmes pour le calage de modèles géologiques [Thèse de doctorat, Polytechnique Montréal]. link
Books and educational resources
2025
Lauzon, D. (2025). Géostatistique et géologie minière [Notes de cours]. link
Publications: 34 · Last updated: August 2026