Vertical Electrical Sounding Inversion Models Trained from Dataset using Synthetic Data and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Aristotle-De-Leon:2022:HNICEM,

• author = "Joseph {Aristotle De Leon} and Mike {Louie Enriquez} and Ronnie Concepcion and Ira Valenzuela and Ryan {Rhay Vicerra} and Homer Co and Argel Bandala and Elmer Dadios",
• booktitle = "2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)",
• title = "Vertical Electrical Sounding Inversion Models Trained from Dataset using Synthetic Data and Genetic Programming",
• year = "2022",
• abstract = "The inversion process of Vertical Electrical Sounding (VES) is an important step in 1-D subsurface surface surveys to determine the true resistivities and heights of different layers of soil or rocks underground which is beneficial in geological and hydrological applications like locating potential areas for aquifers. Machine learning based algorithms is currently a trend in the inversion of vertical electrical sounding (VES) data to address the issues of the conventional methods. However, most models trained are being limited to one electrode half spacing configuration, and not being able to explain the underlying relationships of the model. Hence, the present study seeks to address these by obtaining VES inversion models for four-layer earth models through genetic programming and a synthetic dataset. The synthetic dataset covering different electrode half spacing configurations and VES curve types was generated and used to train the genetic programming model through GPTIPS software. By testing the best models on the synthetic dataset, it offered good metrics on the true resistivities of each layer, but performed poorly on estimating the layers' heights. Regardless, the models obtained can be symbolically expressed and be interpreted which has not been done in other machine learning inversion models for VES. While this study's implementation of genetic programming is not yet satisfactory, obtaining the symbolic expressions can allow future works to systematically improve the worst performing models.",
• keywords = "genetic algorithms, genetic programming, Measurement, Electrodes, Earth, Machine learning, Conductivity, Soil, Vertical Electrical Sounding, Inversion, Underground Imaging, Resistivity Imaging",
• DOI = "doi:10.1109/HNICEM57413.2022.10109565",
• ISSN = "2770-0682",
• month = dec,
• notes = "Also known as \cite{10109565}",

}

Genetic Programming entries for Joseph Aristotle R De Leon Mike Louie Enriquez Ronnie S Concepcion II Ira Valenzuela Ryan Rhay P Vicerra Homer Co Argel A Bandala Elmer Jose P Dadios

Citations