Multiple point fault observation association using random forest from representative structural models
in: Mathematical Geosciences
Abstract
The association of incomplete fault observations is a complex task for which several solutions exist. This problem has already been addressed using a probabilistic approach, where pairwise expert rules are considered. An approach considering multiple-point interactions could better represent geological knowledge, but defining such rules from expert knowledge is challenging. To address this limitation, a Random Forest is used to estimate, from representative three-dimensional models, the potential that a triplet or pair of sparse observations belongs to the same fault. In addition, a new analytical model expresses the probability density function on the graph that combines the triplet and pair potentials previously described. Two random forest learners are trained, one from pairs of fault features and the second from triplets of fault features. The features are computed from fault sticks extracted from a known three-dimensional geological model (e.g. the length of the fault stick, the throw value, etc.). The learners are tested on fault sticks extracted on a contiguous sector of the same model. The resulting three-point potentials are different from combinations of pair potentials. Also, the combination of triplet and pair potentials shows better results than considering pair potentials alone.
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BibTeX Reference
@article{fratani:hal-05032543,
abstract = {The association of incomplete fault observations is a complex task for which several solutions exist. This problem has already been addressed using a probabilistic approach, where pairwise expert rules are considered. An approach considering multiple-point interactions could better represent geological knowledge, but defining such rules from expert knowledge is challenging. To address this limitation, a Random Forest is used to estimate, from representative three-dimensional models, the potential that a triplet or pair of sparse observations belongs to the same fault. In addition, a new analytical model expresses the probability density function on the graph that combines the triplet and pair potentials previously described. Two random forest learners are trained, one from pairs of fault features and the second from triplets of fault features. The features are computed from fault sticks extracted from a known three-dimensional geological model (e.g. the length of the fault stick, the throw value, etc.). The learners are tested on fault sticks extracted on a contiguous sector of the same model. The resulting three-point potentials are different from combinations of pair potentials. Also, the combination of triplet and pair potentials shows better results than considering pair potentials alone.},
author = {Fratani, Amandine and Stoica, Radu S. and Caumon, Guillaume and Giraud, J{\'e}r{\'e}mie},
doi = {10.1007/s11004-026-10327-4},
hal_id = {hal-05032543},
hal_version = {v1},
journal = {{Mathematical Geosciences}},
keywords = {Inference ; Graph reconstruction ; Machine Learning ; Fault network ; Structural interpretation},
pdf = {https://hal.science/hal-05032543v1/file/Fratani_et_al.pdf},
publisher = {{Springer Verlag}},
title = {{Multiple point fault observation association using random forest from representative structural models}},
url = {https://hal.science/hal-05032543},
year = {2026}
}