OOIR: Observatory of International Research

Papers

(The H4-Index of Mathematical Geosciences is 17. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-09-01 to 2024-09-01.)

Article	Citations
Graph Deep Learning Model for Mapping Mineral Prospectivity	51
Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder	45
GANSim: Conditional Facies Simulation Using an Improved Progressive Growing of Generative Adversarial Networks (GANs)	44
A Physically Constrained Variational Autoencoder for Geochemical Pattern Recognition	34
Fusion of Geochemical and Remote-Sensing Data for Lithological Mapping Using Random Forest Metric Learning	29
Contaminant Source Identification in Aquifers: A Critical View	28
A Truly Spatial Random Forests Algorithm for Geoscience Data Analysis and Modelling	28
Quantifying the Tetrad Effect, Shape Components, and Ce–Eu–Gd Anomalies in Rare Earth Element Patterns	27
Classical and Robust Regression Analysis with Compositional Data	26
Three-Dimensional Structural Geological Modeling Using Graph Neural Networks	24
MIN3P-HPC: A High-Performance Unstructured Grid Code for Subsurface Flow and Reactive Transport Simulation	24
Stochastic Modelling of Mineral Exploration Targets	21
Statistical Interpolation of Spatially Varying but Sparsely Measured 3D Geo-Data Using Compressive Sensing and Variational Bayesian Inference	19
One Step at a Time: The Origins of Sequential Simulation and Beyond	19
Variational Autoencoder or Generative Adversarial Networks? A Comparison of Two Deep Learning Methods for Flow and Transport Data Assimilation	19
Analysing Pairwise Logratios Revisited	18
Automated Machine Learning-Based Landslide Susceptibility Mapping for the Three Gorges Reservoir Area, China	17
Finite Difference Implicit Structural Modeling of Geological Structures	17
Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information	17