Feature fusion-enhanced t-SNE image atlas for geophysical features discovery

The discovery and identification of geophysical features from diverse gridded datasets play a pivotal role in understanding geological phenomena. Traditional tools tailored to identify specific signatures, such as lineaments in magnetic data, do not account for (1) the naturally occurring complexity of the causative geology which results from competing and overprinting processes and (2) often overlook important related patterns present in other forms of data (e.g., gradients in the associated gravity data). To address those limitations, we propose a two-step data-driven approach that integrates diverse gridded datasets and autonomously reveals inherent patterns without predefined assumptions in terms of feature geometry. Utilizing Haralick texture descriptors, we encode data patches from different geophysical datasets as points in a high-dimensional unified representation space, facilitating seamless data fusion. This representation is then nonlinearly projected into a two-dimensional space using t-distributed stochastic neighbor embedding (t-SNE), forming an interactive "t-SNE Atlas". This atlas positions tiles from each dataset according to their position in the t-SNE while linking them back to their original geographical coordinates. Such a setup allows earth scientists to intuitively navigate through the data, exploring complex relationships between geophysical responses and geological structures, thus facilitating the discovery of new insights, the formulation of hypotheses, and the exploration of non-trivial connections. This is illustrated using magnetic and gravity data covering [Formula: see text] in the east Yilgarn Craton, Western Australia. Our methodology is adaptable and can be extended to include various other gridded datasets like gamma-ray spectrometry data, satellite imagery, and digital terrain models, thus broadening its applicability and enhancing geoscientific exploration. Our approach not only reveals subtle geophysical patterns but also offers practical benefits for exploration geologists. The atlas enables the identification of geological settings associated with mineral prospectivity and can serve as a pre-planning tool for pinpointing promising exploration sites, potentially accelerating discovery timelines.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-05-17
DOI
https://doi.org/10.1038/s41598-025-01333-3
Citations
3
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
3.41

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Feature fusion-enhanced t-SNE image atlas for geophysical features discovery

Mark Jessell, Mark Lindsay, Guillaume Pirot, Edward Cripps et al.
3 citations
Scientific Reports
Remote-Sensing Image Classification
3.41
article

Feature fusion-enhanced t-SNE image atlas for geophysical features discovery

Mark Jessell, Mark Lindsay, Guillaume Pirot, Edward Cripps, Jérémie Giraud, Leonardo Portes, Michel M Nzikou
article en
3 citations

Abstract

The discovery and identification of geophysical features from diverse gridded datasets play a pivotal role in understanding geological phenomena. Traditional tools tailored to identify specific signatures, such as lineaments in magnetic data, do not account for (1) the naturally occurring complexity of the causative geology which results from competing and overprinting processes and (2) often overlook important related patterns present in other forms of data (e.g., gradients in the associated gravity data). To address those limitations, we propose a two-step data-driven approach that integrates diverse gridded datasets and autonomously reveals inherent patterns without predefined assumptions in terms of feature geometry. Utilizing Haralick texture descriptors, we encode data patches from different geophysical datasets as points in a high-dimensional unified representation space, facilitating seamless data fusion. This representation is then nonlinearly projected into a two-dimensional space using t-distributed stochastic neighbor embedding (t-SNE), forming an interactive "t-SNE Atlas". This atlas positions tiles from each dataset according to their position in the t-SNE while linking them back to their original geographical coordinates. Such a setup allows earth scientists to intuitively navigate through the data, exploring complex relationships between geophysical responses and geological structures, thus facilitating the discovery of new insights, the formulation of hypotheses, and the exploration of non-trivial connections. This is illustrated using magnetic and gravity data covering [Formula: see text] in the east Yilgarn Craton, Western Australia. Our methodology is adaptable and can be extended to include various other gridded datasets like gamma-ray spectrometry data, satellite imagery, and digital terrain models, thus broadening its applicability and enhancing geoscientific exploration. Our approach not only reveals subtle geophysical patterns but also offers practical benefits for exploration geologists. The atlas enables the identification of geological settings associated with mineral prospectivity and can serve as a pre-planning tool for pinpointing promising exploration sites, potentially accelerating discovery timelines.

Scientific ReportsVol. 15(1)
Centre National de la Recherche Scientifique (FR), The University of Western Australia (AU), Health Sciences and Nutrition (AU), Australian Resources Research Centre (AU), Université de Lorraine (FR)
Australian Government, European Commission, H2020 Marie Skłodowska-Curie Actions
Life below water
Openalex Percentile: Top 7%
Remote-Sensing Image Classification
3.41
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.