Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Publicado
Open-source learning as a skill for geoelectrical data processing: the case of pyGIMLi
Aprendizaje de código abierto como habilidad para el procesamiento de datos geoeléctricos: caso pyGIMLi
DOI:
https://doi.org/10.15446/dyna.v89n222.101826Palabras clave:
learning in engineering; geosciences; open-source; pyGIMLi; geoelectrical data (en)aprendizaje en ingeniería; ciencias de la tierra; código abierto; pyGIMLi; datos geoeléctricos (es)
Descargas
It is important that new strategies are developed for the management of geoelectrical data produced from engineering and geoscience processing techniques. For this reason, the article demonstrates how pyGIMLi can be used for this purpose. pyGIMLi is an open-source library for the inversion of apparent resistivity array that are often obtained with different geoelectrical survey equipment. The aim is to be able to use this library unaided for various projects and/or to perform various data operations in which the results obtained are more specific and differentiated than those derived from other processing techniques, taking advantage of the fact that this tool is open-source.
Debido a la importancia de utilizar nuevas estrategias para el manejo de datos geoeléctricos a partir de las técnicas de procesamiento de la ingeniería y ciencias de la tierra, este artículo se centra en mostrar la estructura para el uso de pyGIMLi, una librería de código abierto para realizar la inversión de la matriz de resistividad aparente que se obtienen a menudo con diferentes equipos de prospección geoeléctrica, con la finalidad de adquirir la habilidad de usar esta librería de forma independiente en diversos proyectos y/o realizar varias operaciones con los datos en las se puedan obtener resultados más específicos y diferenciados de otras técnicas de procesamiento; gracias a las ventajas de usar código abierto.
Referencias
Ledo, A., Lourenço C.A., Laranjinha, J., Gerhardt, G.A. and Barbosa, R.M., Concurrent measurements of neurochemical and electrophysiological activity with microelectrode arrays: new perspectives for constant potential amperometry. Current Opinion in Electrochemistry, 12, pp. 129-140, 2018. DOI: https://doi.org/10.1016/j.coelec.2018.05.018
Seo, G., Lee, G., Kim, M.J., Baek, S.-H., Choi, M., Ku, K.B., Lee, C.-S., Jun, S., Park, D., Kim, H.G., Kim, S.-J., Lee, J.-O., Kim, B.T., Park, E.C. and Kim, S.I., Rapid detection of Covid-19 causative virus (SARS-CoV-2) in human nasopharyngeal swab specimens using field-effect transistor-based biosensor. ACS Nano, 14(4), pp. 5135-5142, 2020. DOI: https://doi.org/10.1021/acsnano.0c02823
Trillo-Montero, D., Santiago, I., Luna-Rodriguez, J.J. and Real-Calvo, R., Development of a software application to evaluate the performance and energy losses of grid-connected photovoltaic systems. Energy Conversion and Management, 81, pp. 144-159, 2014. DOI: https://doi.org/10.1016/j.enconman.2014.02.026
Guerrero-Alarcón, C.A. y Recaman-Chaux, H., Frameworks para el desarrollo de aplicaciones Web que utilizan código abierto. Unidades Tecnológicas de Santander, Bucaramanga, Colombia, 2009.
Gauthier, A., Palabras de instalación. Foro Innovación en los sectores de la minería y la energía, 26 de Julio de 2011. Revista de Ingeniería, 34, pp. 43, 2011. DOI: https://doi.org/10.16924/revinge.34.7
Mariño-Drews, O., Fortalecimiento de la enseñanza de la ingeniería con las tecnologías de información y comunicaciones. Revista de Ingeniería, 39, pp. 46-49, 2013. DOI: https://doi.org/10.16924/riua.v0i39.376
van der Linden, D., De Bruyn, P., Kastner, W. and Mannaert, H., Deriving guidelines for cross-vendor reuse of IEC 61131-3 modules based on Normalized Systems theorems. Computer Standards & Interfaces, 36(3), pp. 598-603, 2014. DOI: https://doi.org/10.1016/j.csi.2013.10.001
Jing, S., Li, B. and Yao, R., Exploring the “black box” of thermal adaptation using information entropy. Building and Environment, 146, pp. 166-176, 2018. DOI: https://doi.org/10.1016/j.buildenv.2018.09.038
Patel, A.M. and Li, J.K.-J., Validation of a novel nonlinear black box Wiener System model for arterial pulse transmission. Computers in Biology and Medicine, 88, pp. 11-17, 2017. DOI: https://doi.org/10.1016/j.compbiomed.2017.06.020
Petch, J., Di, S. and Nelson, W., Opening the black box: the promise and limitations of explainable machine learning in cardiology. Canadian Journal of Cardiology, 38(2), pp. 204-213, 2021. DOI: https://doi.org/10.1016/j.cjca.2021.09.004
Rücker, C., Günther, T. and Wagner, F.M., pyGIMLi: an open-source library for modelling and inversion in geophysics. Computers and Geosciences, 109, pp. 106-123, 2017. DOI: https://doi.org/10.1016/j.cageo.2017.07.011
Anaconda Documentation. Anaconda individual edition. [online]. 2022. [date of reference February 21st of 2022]. Available at: https://docs.anaconda.com/anaconda/
Rücker, M., Advanced electrical resistivity modelling and inversion using unstructured discretization, PhD Dissertation, Fakultät für Physik und Geowissenschaften, University of Leipzig, Leipzig, Sachsen, Deutschland, 2011.
Hansen, P.C. and O’Leary, D.P., The use of the L-curve in the regularization of discrete Ill-Posed problems. SIAM Journal on Scientific Computing, 14(6), pp. 1487-1503, 1993. DOI: https://doi.org/10.1137/0914086
Vogel, C.R., Non-convergence of the L-curve regularization parameter selection method. Inverse Problems, 12(4), pp. 535-547, 1996. DOI: https://doi.org/10.1088/0266-5611/12/4/013
Boundless Electrical Resistivity (BERT). The unified data format. [online]. 2017. [date of reference February 22nd of 2022]. Available at: http://resistivity.net/bert/data_format.html
Maurya, P.K., Fiandaca, G., Christiansen, A.V. and Auken, E., Field-scale comparison of frequency- and time-domain spectral induced polarization. Geophysical Journal International, 214(2), pp. 1441-1466, 2018. DOI: https://doi.org/10.1093/gji/ggy218
Yi, M.J., Kim, J.H., Park, S.G. and Son, J.S., Investigation of ground condition changes due to cryogenicconditions in an underground LNG storage plant. Exploration Geophysics, 36(1), pp. 67-72, 2005. DOI: https://doi.org/10.1071/eg05067
Lee, Y., Deusner, C., Kossel, E., Choi, W., Seo, Y. and Haeckel, M., Influence of CH4 hydrate exploitation using depressurization and replacement methods on mechanical strength of hydrate-bearing sediment. Applied Energy, 277, art. 115569, 2020. DOI: https://doi.org/10.1016/j.apenergy.2020.115569
Gebbers, R., Lück, E., Dabas, M. and Domsch, H., Comparison of instruments for geoelectrical soil mapping at the field scale. Near Surface Geophysics, 7(3), pp. 179-190, 2009. DOI: https://doi.org/10.3997/1873-0604.2009011
Alshehri, F. and Abdelrahman, K., Groundwater resourses exploration of Harrat Khaybar area, northwest Saudi Arabia, using electrical resistivity tomography. Journal of King Saud University – Science, 33(5), art. 101468, 2021. DOI: https://doi.org/10.1016/j.jksus.2021.101468
Müller, K., Polom, U., Winsemann, J., Steffen, H., Tsukamoto, S., Günther, T., Igel, J., Spies, T., Lege, T., Frechen, M., Franzke, H.J. and Brandes, C., Structural style and neotectonic activity along the Harz Boundary Fault, northern Germany: a multimethod approach integrating geophysics, outcrop data and numerical simulations. International Journal of Earth Sciences, 109(5), pp. 1811-1835, 2020. DOI: https://doi.org/10.1007/s00531-020-01874-0
Aizebeokhai, A.P., Ogungbade, O. and Oyeyemi, K.D., Geoelectrical resistivity data set for characterising crystalline basement aquifers in Basiri, Ado-Ekiti, southwestern Nigeria. Data in Brief, 19, pp. 810-816, 2018. DOI: https://doi.org/10.1016/j.dib.2018.05.091
Anaconda.org. Boundless Electrical Resistivity Tomography. [online]. 2022. [date of reference March 1st of 2022]. Available at: https://anaconda.org/gimli/pybert
Lenzen, F. and Scherzer, O., Tikhonov type regularization methods: history and recent progress, European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS), pp. 1-21, 2004.
Benning, M. and Burger, M., Modern regularization methods for inverse problems. Acta Numerica, 27, pp. 1-111, 2018. DOI: https://doi.org/10.1017/S0962492918000016
Jordi, C., Doetsch, J., Günther, T., Schmelzbach, C. and Robertsson, J.O., Geostatistical regularization operators for geophysical inverse problems on irregular meshes. Geophysical Journal International, 213(2), pp. 1374-1386, 2018. DOI: https://doi.org/10.1093/gji/ggy055
Inman, J.R., Ryu, J. and Ward, S.H., Resistivity inversion. Geophysics, 38(6), pp. 1088-1108, 1973. DOI: https://doi.org/10.1190/1.1440398
Loke, M.H. and Barker, R.D., Rapid least-squares inversion of apparent resistivity pseudosections by a quasi-Newton method. Geophysical Prospecting, 44(1), pp. 131-152, 1996. DOI: https://doi.org/10.1111/j.1365-2478.1996.tb00142.x
Liu, B., Guo, Q., Li, S., Liu, B., Ren, Y., Pang, Y., Guo, X., Liu, L. and Jiang, P., Deep learning inversion of electrical resistivity data. IEEE Transactions on Geoscience and Remote Sensing, 58(8), pp. 5715-5728, 2020. DOI: https://doi.org/10.1109/TGRS.2020.2969040
Aleardi, M., Vinciguerra, A. and Hojat, A., A convolutional neural network approach to electrical resistivity tomography. Journal of Applied Geophysics, 193, art. 104434, 2021. DOI: https://doi.org/10.1016/j.jappgeo.2021.104434
Dong, L., Jiang, F., Wang, M. and Li, X., Fuzzy deep wavelet neural network with hybrid learning algorithm: application to electrical resistivity imaging inversion. Knowledge-Based Systems, 242, art. 108164, 2022. DOI: https://doi.org/10.1016/j.knosys.2022.108164
Cómo citar
IEEE
ACM
ACS
APA
ABNT
Chicago
Harvard
MLA
Turabian
Vancouver
Descargar cita
CrossRef Cited-by
1. Mauren E. Gaspar, Gustavo Heck, Fernando Hamerski, João Pedro T. Zielinski, Clarissa L. Melo. (2025). Advancements in electroresistivity processing and data inversion for monitoring CO2 leakage: Insights from a field experiment and empirical data analysis using the pyGIMLi library. Journal of South American Earth Sciences, 159, p.105512. https://doi.org/10.1016/j.jsames.2025.105512.
Dimensions
PlumX
Visitas a la página del resumen del artículo
Descargas
Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
El autor o autores de un artículo aceptado para publicación en cualquiera de las revistas editadas por la facultad de Minas cederán la totalidad de los derechos patrimoniales a la Universidad Nacional de Colombia de manera gratuita, dentro de los cuáles se incluyen: el derecho a editar, publicar, reproducir y distribuir tanto en medios impresos como digitales, además de incluir en artículo en índices internacionales y/o bases de datos, de igual manera, se faculta a la editorial para utilizar las imágenes, tablas y/o cualquier material gráfico presentado en el artículo para el diseño de carátulas o posters de la misma revista.
