Machine learning results and code

Name: Machine learning results and code
Published: Tue Oct 17 00:00:00 EDT 2023

Dataset · Tue Oct 17 00:00:00 EDT 2023

DOI:https://doi.org/10.6084/m9.figshare.24328870.v1· OSTI ID:2520488

Jamil, Ahsan ^[1]

New Mexico State Univ., Las Cruces, NM (United States); New Mexico State Univ., Las Cruces, NM (United States)

Results for all the Four MLP-NN, RF, XGBR, and RF algorithms for each of geophysical array`s and machine learning python code is provided.

Research Organization:: New Mexico State University, Las Cruces, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Cited By (1)

Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets Jamil, Ahsan; Rucker, Dale F.; Lu, Dan Journal of Applied Geophysics, Vol. 229 https://doi.org/10.1016/j.jappgeo.2024.105493	journal	October 2024

Implementation of MLP-NN on Field data for dipole dipole and Alt3 Wenner configurations

Dataset · Wed Oct 18 00:00:00 EDT 2023 · OSTI ID:2520476

Forward Modeling Dataset, protocol files, and forward modeling code (ResIPy)

Dataset · Tue Oct 17 00:00:00 EDT 2023 · OSTI ID:2520489

58 GEOSCIENCES
Boosting
Electrical resistivity
Geophysics
Machine learning
Neural networks
Random forests