Evidence map›Paper›PMID 39363057›Full record

ArticleScientific reports2024

Synergistic application of digital outcrop characterization techniques and deep learning algorithms in geological exploration.

Zhicheng Dong, Pan Tang, Gongyang Chen, Senlin Yin

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Zhicheng DongResearch Institute of Mud Logging Technology and Engineering, Yangtze University, Jingzhou, 434023, China.
Pan TangResearch Institute of Mud Logging Technology and Engineering, Yangtze University, Jingzhou, 434023, China. tangpan2013@126.com.
Gongyang ChenResearch Institute of Mud Logging Technology and Engineering, Yangtze University, Jingzhou, 434023, China.
Senlin YinResearch Institute of Mud Logging Technology and Engineering, Yangtze University, Jingzhou, 434023, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to meet the needs of geologists for the analysis of data characterizing field outcrops (rock sections or formations exposed on the ground surface), this study developed a field digital outcrop visualization platform based on Cesium (a 3D geospatial visualization technology) digital outcrop characterization technology. The platform was developed based on WebGL (a protocol for rendering interactions on web pages), which overcame the shortcomings of traditional software in terms of visualization, cross-device, cross-platform, and ease of use. Firstly, UAV inclined photography is used for data collection, which transforms a large amount of geological data into an intuitive 3D geological model, while the visualization platform provides rich measurement and mapping tools for the identified features, which more intuitively displays the outcrop information, helps geological explorers to understand the geological conditions in the field more quickly and comprehensively, and improves the analysis efficiency and ease-of-use of outcrop characterization data. Combined with the improved VGG19 (a deep convolutional neural network architecture) algorithm model, it has excellent performance in dealing with the fine texture and complex structure of rocks, which significantly improves the accuracy of lithology identification. The synergistic application of this technology provides geologists with a faster and more comprehensive means to understand the geological conditions in the field. The reliability of combining the Cesium digital outcrop characterization technology with the VGG19 lithology identification algorithm in geological exploration is verified through case studies. The synergistic application of this technology will greatly enhance the efficiency and ease of analysis of outcrop characterization in the field, and provide new perspectives for future research in geosciences.

Indexed as

Cesium2Digital Outcrop1Geological Exploration Technology4Oblique Photography5VGG193

Identifiers

PMID39363057
PMCPMC11450144

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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.