Fracture {Network} {Characterization} {Using} {Stochastic} {Simulations} of {Marked} {Point} {Process} {And} {Bayesian} {Inference}.

@inproceedings{bonneau_fracture_RM2023,
 abstract = {Fractures from systems of complex mechanical discontinuities which dramatically impact the physical behavior of rock masses. In this work, we use the mathematical framework of marked point process to approximate fracture networks in two dimensions with a collection of straight-line segments. Whereas most fracture characterization and modeling focuses on first order statistics (density and mark distribution), and assume independent fractures, we focus on fracture interactions. For this, we propose stochastic mathematical models involving simple pairwise interactions between fractures in order to capture key aspect of fracture network geometry and organization. We introduce a methodology to infer and calibrate model parameters using a maximum likelihood estimator. We demonstrate and illustrate the capability of the presented workflow by assessing model parameters from a particular fracture network observed in the Oman mountains. Such a model with calibrated parameters opens the path to predictive stochastic simulation of fracture networks.},
 author = {Bonneau, Francois and Caumon, Guillaume and Stoica, Radu Stefan},
 booktitle = {2023 {RING} meeting},
 language = {en},
 pages = {35},
 publisher = {ASGA},
 title = {Fracture {Network} {Characterization} {Using} {Stochastic} {Simulations} of {Marked} {Point} {Process} {And} {Bayesian} {Inference}.},
 year = {2023}
}