Evidence map›Paper›PMID 40382353›Full record

ArticleScientific reports2025

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

Leonardo Portes, Guillaume Pirot, Michel M Nzikou, Jeremie Giraud, Mark Lindsay, Mark Jessell, Edward Cripps

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Leonardo PortesDepartment of Mathematics and Statistics, The University of Western Australia, 35 Stirling Highway, Crawley, 6009, Australia. ll.portes@gmail.com.
Guillaume PirotCentre for Exploration Targeting, School of Earth and Oceans, The University of Western Australia, 35 Stirling Highway, Crawley, 6009, Australia.
Michel M NzikouCentre for Exploration Targeting, School of Earth and Oceans, The University of Western Australia, 35 Stirling Highway, Crawley, 6009, Australia.
Jeremie GiraudCentre for Exploration Targeting, School of Earth and Oceans, The University of Western Australia, 35 Stirling Highway, Crawley, 6009, Australia.
Mark LindsayARC Centre for Data Analytics for Resources and Environments (DARE), Perth and Sydney, Australia.
Mark JessellARC Centre for Data Analytics for Resources and Environments (DARE), Perth and Sydney, Australia.
Edward CrippsDepartment of Mathematics and Statistics, The University of Western Australia, 35 Stirling Highway, Crawley, 6009, Australia.

Funding

Australian Research Council Training Centre in Data Analytics for Resources and Environment IC19010031European Union's Horizon 2020 101032994Loop consortium LP210301239MinEx CRC LP170100985
6 · The paper itself

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.

Identifiers

PMID40382353
PMCPMC12085580

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.