Efficacy of Ensemble Machine Learning Techniques for Predicting Ultrasonic Shear Wave Velocity with Core Data

This publication has 17 references indexed in Scilit:

• Impact of data size on ML-based prediction of shear and compressional slowness
  Carbonates and Evaporites, 2025
• Improved model development and feature ranking for rock permeability prediction by coupling petrophysical log data and ensemble machine learning techniques
  Earth Science Informatics, 2024
• Shear wave velocity prediction based on bayesian-optimized multi-head attention mechanism and CNN-BiLSTM
  Computers & Geosciences, 2024
• Determination and investigation of shear wave velocity based on one deep/machine learning technique
  Alexandria Engineering Journal, 2024
• Physics informed machine learning: Seismic wave equation
  Geoscience Frontiers, 2020
• Predictive models and feature ranking in reservoir geomechanics: A critical review and research guidelines
  Journal of Natural Gas Science and Engineering, 2020
• A comparative analysis of gradient boosting algorithms
  Artificial Intelligence Review, 2020
• Machine Learning Approach to Model Rock Strength: Prediction and Variable Selection with Aid of Log Data
  Rock Mechanics and Rock Engineering, 2020
• The shear-wave velocity prediction for sedimentary rocks
  Journal of Natural Gas Science and Engineering, 2020
• Support Vector Machines – An Introduction
  2005