Description of codes to image processing and data classification (Python and MATLAB)

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Vásconez García, Richard Guillermo; Basilici, Giorgio, 2025, "Description of codes to image processing and data classification (Python and MATLAB)", https://doi.org/10.25824/redu/VO4MMU, Repositório de Dados de Pesquisa da Unicamp, V1
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Description
This dataset contains the open-source Python and MATLAB® codes developed for my doctoral research. The materials correspond to the workflows used for image processing, pore characterization, and automatic classification of well-log data through Self-Organizing Maps (SOM). No raw data, images, or laboratory results are included—only the reproducible code used in the thesis. The Python scripts implement image enhancement (MSRCP), segmentation, contour detection, and quantitative measurement of pore attributes, generating tabulated outputs and visualizations. The MATLAB® scripts use the SOM Toolbox to load LAS files, integrate the derived vug_phi curve with conventional logs, standardize data, classify it using SOM combined with k-means and the Davies–Bouldin criterion, and produce SOM visualizations. These codes were created as part of the final research work, based on bibliographic review, iterative testing, and computational analysis. The dataset enables other researchers to reproduce or adapt the methodologies for geological image analysis and unsupervised classification of well logs. Description of codes to perform an image segmentation, automathic classification of data, and image processing. (2025-07-24)
Subject Earth and Environmental Sciences; Engineering
Keyword Facies classification
Related Publication Vásconez, R.G., Mohammadizadeh, S., Avansi, M.C.K., Basilici, G., Da Silva Bomfim, L., Cunha, O.R., Soares, M.V.T., Mesquita, Á.F., Mahjour, S.K., y Vidal, A.C., 2024, Geological Insights from Porosity Analysis for Sustainable Development of Santos Basin’s Presalt Carbonate Reservoir: Sustainability, v. 16, p. 5730, doi:10.3390/su16135730. doi: 10.3390/su16135730
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Description of codes.pdf
Description of codes.pdf
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Published Dec 5, 2025
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This dataset contains the open-source Python and MATLAB® codes developed for my doctoral research. The materials correspond to the workflows used for image processing, pore characterization, and automatic classification of well-log data through Self-Organizing Maps (SOM). No raw data, images, or laboratory results are included—only the reproducible code used in the thesis. The Python scripts implement image enhancement (MSRCP), segmentation, contour detection, and quantitative measurement of pore attributes, generating tabulated outputs and visualizations. The MATLAB® scripts use the SOM Toolbox to load LAS files, integrate the derived vug_phi curve with conventional logs, standardize data, classify it using SOM combined with k-means and the Davies–Bouldin criterion, and produce SOM visualizations. These codes were created as part of the final research work, based on bibliographic review, iterative testing, and computational analysis. The dataset enables other researchers to reproduce or adapt the methodologies for geological image analysis and unsupervised classification of well logs.
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