Vertical Electrical Sounding Inversion Models Trained from Dataset using Synthetic Data and Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Aristotle-De-Leon:2022:HNICEM,
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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)",
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title = "Vertical Electrical Sounding Inversion Models Trained
from Dataset using Synthetic Data and Genetic
Programming",
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year = "2022",
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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.",
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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",
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ISSN = "2770-0682",
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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