Taking Better Advantage of Fold Axis Data to Characterize Anisotropy of Complex Folded Structures in the Implicit Modeling Framework

Laure Pizzella (1, 2) , Robin Alais (3) , Simon Lopez (4) , Xavier Freulon (2, 1) , Jacques Rivoirard (1, 2)

1 Équipe Géostatistique
2 GEOSCIENCES - Centre de Géosciences
3 CMM - Centre de Morphologie Mathématique
4 BRGM - Bureau de Recherches Géologiques et Minières (BRGM)

When too few field measurements are available for the geological modeling of complex folded structures, the results of implicit methods typically exhibit an unsatisfactory bubbly aspect. However, in such cases, anisotropy data are often readily available but not fully exploited. Among them, fold axis data are a straightforward indicator of this local anisotropy direction. Focusing on the so-called potential field method, this work aims to evaluate the effect of the incorporation of such data into the modeling process. Given locally sampled fold axis data, this paper proposes to use the second-order derivatives of the scalar field in addition to the existing first-order ones. The mathematical foundation of the approach is developed, and the respective efficiencies of both kinds of constraints are tested. Their integration and impact are discussed based on a synthetic case study, thereby providing practical guidelines to geomodeling tool users on the parsimonious use of data for the geological modeling of complex folded structures.

Domaines: Tectonique; Statistiques [math.ST]; Géométrie différentielle [math.DG]

Laure Pizzella, Robin Alais, Simon Lopez, Xavier Freulon, Jacques Rivoirard. Taking Better Advantage of Fold Axis Data to Characterize Anisotropy of Complex Folded Structures in the Implicit Modeling Framework. Mathematical Geosciences, 2021, ⟨10.1007/s11004-021-09950-0⟩. ⟨hal-03278336⟩