Fracture network characterization using Gibbs marked point processes and Bayesian inference

@article{bonneau:hal-05184375,
 abstract = {Fracture networks are systems of complex mechanical discontinuities that significantly impact the physical behavior of rock masses. The mathematical framework of marked point processes approximates these networks in two dimensions as a collection of straightline segments. Unlike most approaches that model fractures as independent entities, a few models that include interactions between fractures have been proposed, but parameter inference has remained elusive. This paper proposes a new model that captures essential aspects of fracture network geometry and organization using pairwise geometric interactions between fractures, together with an Approxiame Bayesian methodology for estimating model parameters. The approach is demonstrated through the estimation of model parameters from a specific fracture network observed in the Oman Mountains. The development of this model combined with the parameter inference, paves the way for predictive stochastic simulations of fracture networks.},
 author = {Bonneau, Fran{\c c}ois and Stoica, Radu S. and Caumon, Guillaume},
 doi = {10.1007/s11004-026-10295-9},
 hal_id = {hal-05184375},
 hal_version = {v2},
 journal = {{Mathematical Geosciences}},
 pdf = {https://hal.univ-lorraine.fr/hal-05184375v2/file/DFNSim.pdf},
 publisher = {{Springer Verlag}},
 title = {{Fracture network characterization using Gibbs marked point processes and Bayesian inference}},
 url = {https://hal.univ-lorraine.fr/hal-05184375},
 year = {2026}
}