Posts

February 2021

Adel presented his work titled “A Novel Pixel-based Multiple-Point Geostatistical Simulation Method for Stochastic Modeling of Earth Resources” on 2/19/2021 at the Tufts CEE department weekly seminar.

Uncertainty is an integral part of modeling Earth resources and environmental processes. Geostatistical simulation technique is a well-established tool for uncertainty quantification of earth systems modeling. Multiple-point statistical (MPS) algorithms are specifically advantageous when dealing with the complexity and heterogeneity of geological data. MPS algorithms take advantage of using training images to mimic physical reality. This research presents a novel and efficient pixel-based multiple-point geostatistical simulation method for stochastic simulation. Pixel-based simulation implies the sequential modeling of individual points on the simulation grid by borrowing spatial information from the training image and honoring conditioning data points. The method is developed by integrating advanced machine learning algorithms for different purposes including dimensionality reduction and clustering. For the purpose of automation, to ensure high-quality realizations, and to maintain reasonable computational time, multiple optimization and parameter tuning strategies were introduced. The model is validated by simulating a variety of categorical and continuous variables for both two and three-dimensional cases and conditional and unconditional simulations. The proposed algorithm can be applied in a variety of contexts, including but not limited to petroleum reservoir forecasting, seismic inversion, mineral resources modeling, gap-filling in remote sensing, and climate modeling. The developed model can be extended for spatio-temporal modeling, multivariate simulation, non-stationary modeling, and super-resolution realizations.

Publications

Rashidian, V., Baise, L., and Koch, M. (2020). Using high resolution optical imagery to detect earthquake-induced liquefaction: the 2011 Christchurch earthquake. Remote Sensing. 12, 377; doi:10.3390/rs12030377.

Xie, P., Wen, H., Ma, C., Baise, L.G., and J. Zhang (2019). Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China. Geomatics, Natural Hazards and Risk 9 (1), 501-523.

Oommen, T., Baise L.G., Gens R., Prakash A., and Gupta R.P. (2013). Documenting Earthquake-Induced Liquefaction using Satellite Remote Sensing Image Transformations. Environmental and Engineering Geosciences, Vol. XIX (4), 303-318.