A Three-Dimensional Geological Structure Modeling Framework and Its Application in Machine Learning

Wang, Shenghou; Cai, Zhongxian; Si, Xu; Cui, Yatong

doi:10.1007/s11004-022-10027-9

Cited by 9 publications

(6 citation statements)

References 137 publications

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“…We also know exactly what geological processes are or are not simulated in a synthetic dataset and therefore what kinds of geological settings a neural network trained on that dataset may be suited to interpreting. Similar considerations to these have led to the use of synthetic geological models in training a neural network for identification of faults in seismic data by Wu et al (2020) and Wang et al (2023), of structures in magnetic data by Guo et al (2021), and in developing a large dataset of 3D models for machine learning applications by Jessell et al (2022).…”

Section: Use Of Synthetic Training Modelsmentioning

confidence: 93%

“…In the geosciences, it has been used previously in automated seismic interpretation applications (e.g. Muller et al, 2012;Wu et al, 2019;Wang et al, 2023).…”

Section: Supervised Learningmentioning

confidence: 99%

“…Another problem that has received greater attention is automatic interpretation of seismic reflection data, including the identification of faults (e.g. Wu et al, 2019Wu et al, , 2022Cunha et al, 2020;An et al, 2021An et al, , 2023Gao et al, 2022;Wang et al, 2023) and salt structures (e.g. Shi et al, 2019.…”

mentioning

confidence: 99%

“…problem of identifying which portions of the map are folded and is similar to U-NET-based seismic interpretation methods, which also focus on semantic segmentation (e.g. Wu et al, 2019Wu et al, , 2020Wang et al, 2023). Overall, the use of raster datasets does add some limitations to the supervised learning approach as opposed to the unsupervised approach, but still provides meaningful and useful results.…”

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confidence: 99%

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GEOMAPLEARN 1.0: Detecting geological structures from geological maps with machine learning

Oakley,

Loiselet,

Coowar

et al. 2024

Preprint

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Abstract. The increasing availability of large geological datasets together with modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. Such methods offer the potential for faster and less subjective interpretations of geological data than are possible from a human interpreter, but translating the understanding of a trained geologist to an algorithm is not straightforward. In this paper, we present automated workflows for detecting geological folds from map data using both unsupervised and supervised machine learning. For the unsupervised case, we use regular expression matching to identify map patterns suggestive of folds along lines crossing the map. We then use the hdbscan clustering algorithm to cluster these possible fold identifications into a smaller number of distinct folds, the number of which is not known a priori. For the supervised learning case, we use synthetic models of folds to train a convolutional neural network to identify folds using map and topographic data. We test both methods on synthetic and real datasets.

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Section: Use Of Synthetic Training Modelsmentioning

confidence: 93%

“…In the geosciences, it has been used previously in automated seismic interpretation applications (e.g. Muller et al, 2012;Wu et al, 2019;Wang et al, 2023).…”

Section: Supervised Learningmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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GEOMAPLEARN 1.0: Detecting geological structures from geological maps with machine learning

Oakley,

Loiselet,

Coowar

et al. 2024

Preprint

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“…There are three key dimensions to consider when analyzing the impact of sustainability practices: vertical integration inside the organization, horizontal integration within supplier chains, and end-to-end value creation over the whole life cycle of the final product. In addition to using the three-dimensional framework, we have chosen to use the three-layer structure proposed by Wang, Cai, Si, and Cui [22]. The first layer includes the particular local setting where work is done, namely the production facilities.…”

Section: Organisational Capabilities Required To the Maturitymentioning

confidence: 99%

Analysis of Human Performance in Manufacturing Framework of Industry 4.0

Chekalina

2024

JEBI

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The phrase "Industry 4.0" refers to the fourth wave of industrial change, which includes areas like smart cities that aren't often thought of as stand-alone industrial applications. Water power, steam power, and mechanization were all introduced during the start of the first industrial revolution. The second industrial revolution that followed was marked by the expansion of mass production and assembly lines made possible by the use of electricity. The third industrial revolution was sparked by the development of electronics, information technology, and automation. This was followed by the fourth industrial revolution, which was defined by the appearance of systems of cyber-physical. The goal of the scientific discipline of human performance is to increase a system's overall performance as well as the wellbeing of the people who are a part of it. A comprehensive search yielded a total of 336 scholarly papers, out of which 37 were examined using a human-centered system of work paradigm as described in the body of HFE literature. Within the frames of the macro- and microergonomics work system paradigms, difficulties related to technological growth were analyzed. We outline the essential components of an organizational maturity model using the study that was done. Within the unique context of the manufacturing industry's fast technological improvements, this model seeks to improve the overall performance of sociotechnical work system.

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Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities

Lin,

Zhong,

et al. 2024

Earth-Science Reviews

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A Three-Dimensional Geological Structure Modeling Framework and Its Application in Machine Learning

Cited by 9 publications

References 137 publications

GEOMAPLEARN 1.0: Detecting geological structures from geological maps with machine learning

GEOMAPLEARN 1.0: Detecting geological structures from geological maps with machine learning

Analysis of Human Performance in Manufacturing Framework of Industry 4.0

Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities

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