3D inversions of magnetic and gravity data for the Yaouré region, Ivory Coast: a greenstone belt characterization
Abstract
We carried out 3D inverse modeling of full tensor gravity gradiometry data, its derived products (Tz and Tz residual), and magnetic data to recover the density and magnetic susceptibility distributions of the Yaouré Greenstone Belt in Ivory Coast, West Africa. The study area presents significant challenges: it lacks well-defined stratigraphy and structural measurements, and it lies close to the magnetic equator, where the magnetic inclination is low. Inverted models were analyzed using isosurfaces and histogram curves to characterize physical properties distribution and highlight geophysical sources. We extracted vertical slices through the 3D inverted models across significant mapped structures and lithotypes, and measured magnetic anomaly dips within these sections to create an artificial structural map. We identified positive and negative density contrasts as well as magnetic susceptibility sources in the region that correlated with mapped surface geology. Our results revealed that sedimentary and volcano-sedimentary rocks in the region present higher susceptibility than some intrusions, contrasting with their comparatively lower density. The 3D inverted models provided insights of some mapped structures, such as major faults, that may act as physical property boundaries. Ranking magnetic susceptibility and density contrast of geological units alongside the structural map offers a systematic first approach to maximizing the value of geophysical inversions. This integration may serve as a petrophysical or geological constraint when prior information is limited for subsequent inversions, acting as useful information for starting or reference models.
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BibTeX
@misc{antunes2026inversions,
title = {{3D inversions of magnetic and gravity data for the Yaouré region, Ivory Coast: a greenstone belt characterization}},
author = {Vinicius Antunes and Vitaliy Ogarko and Mark Jessell and Jérémie Giraud},
howpublished = {EarthArXiv},
year = {2026},
month = oct,
doi = {10.31223/x54f85},
url = {https://eartharxiv.org/repository/view/15673/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - 3D inversions of magnetic and gravity data for the Yaouré region, Ivory Coast: a greenstone belt characterization
AU - Vinicius Antunes
AU - Vitaliy Ogarko
AU - Mark Jessell
AU - Jérémie Giraud
PY - 2026
DA - 2026/10/21
PB - EarthArXiv
DO - 10.31223/x54f85
UR - https://eartharxiv.org/repository/view/15673/
AB - We carried out 3D inverse modeling of full tensor gravity gradiometry data, its derived products (Tz and Tz residual), and magnetic data to recover the density and magnetic susceptibility distributions of the Yaouré Greenstone Belt in Ivory Coast, West Africa. The study area presents significant challenges: it lacks well-defined stratigraphy and structural measurements, and it lies close to the magnetic equator, where the magnetic inclination is low. Inverted models were analyzed using isosurfaces and histogram curves to characterize physical properties distribution and highlight geophysical sources. We extracted vertical slices through the 3D inverted models across significant mapped structures and lithotypes, and measured magnetic anomaly dips within these sections to create an artificial structural map. We identified positive and negative density contrasts as well as magnetic susceptibility sources in the region that correlated with mapped surface geology. Our results revealed that sedimentary and volcano-sedimentary rocks in the region present higher susceptibility than some intrusions, contrasting with their comparatively lower density. The 3D inverted models provided insights of some mapped structures, such as major faults, that may act as physical property boundaries. Ranking magnetic susceptibility and density contrast of geological units alongside the structural map offers a systematic first approach to maximizing the value of geophysical inversions. This integration may serve as a petrophysical or geological constraint when prior information is limited for subsequent inversions, acting as useful information for starting or reference models.
ER -