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Artículo científico (JournalArticle). 2026

Predictive modelling of seismic properties in single-foliated slates

DIGITAL.CSIC. Repositorio Institucional del CSIC
  • López-Sánchez, Marco A.
  • Cárdenes, Víctor
  • Barou, Fabrice
  • Llana-Fúnez, Sergio
Slates play a key role in understanding the seismic anisotropy of the continental crust, a crucial aspect of
geophysical interpretation. Using a comprehensive set of high-quality single-foliated chlorite-bearing roofing
slates, we determined their typical seismic properties via mineral fractions and orientation distribution functions
using the geometric mean averaging method. Our study focused on identifying an optimal transverse isotropy
(polar) model, assess correlations between elastic constants, and explore the feasibility of predicting intrinsic
maximum anisotropy from a single proxy. We demonstrate that maximum axial and polarization anisotropy in
single-foliated slates can be accurately estimated with ~10 % error using a single proxy, termed the S-norm,
which integrates the ODF strength and volumetric fraction of phyllosilicates. Additionally, we found that a polar
parameterization combining elastic tensor decomposition and the Anderson equations yields seismic anisotropy
predictions similar to the Christoffel equation, with errors below 2.8 % (better than 0.2 % for Vp anisotropy).
Lastly, our findings suggest that it is feasible to estimate the seismic properties of transversely isotropic slates
from only two elastic measurements: a diagonal component and the non-diagonal C13 component. These models
are applicable for investigating slate belts at various depths, enabling the calculation of the minimum expected
seismic anisotropy from intrinsic properties., This work was supported by the Government of the Principality of Asturias, the European Union (Marie-Curie COFUND) and the Foundation for the Promotion of Applied Scientific Research and Technology in Asturias (FICYT) (Spain) (Grant numbers: SV-PA-21-AYUD/2021/57163, PA-18-ACB17-11) under the “Plan de Ciencia, Tecnología e Innovación 2018–2022” of Asturias (PCTI-Asturias).