OOIR: Observatory of International Research

Papers

(The H4-Index of Computers & Geosciences is 35. 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 2020-11-01 to 2024-11-01.)

Article	Citations
A review of Earth Artificial Intelligence	98
PINNeik: Eikonal solution using physics-informed neural networks	82
A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet	81
Strip pooling channel spatial attention network for the segmentation of cloud and cloud shadow	68
Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+	68
Deep learning of rock images for intelligent lithology identification	62
Imputation of missing well log data by random forest and its uncertainty analysis	61
Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China	61
A comparative study of machine learning and Fuzzy-AHP technique to groundwater potential mapping in the data-scarce region	61
A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach	60
Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack	58
Knowledge graph construction and application in geosciences: A review	57
Machine learning in ground motion prediction	57
The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset	55
Deep convolutional neural network for automatic fault recognition from 3D seismic datasets	51
Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks	48
Numerical modelling of deep coaxial borehole heat exchangers in the Cheshire Basin, UK	48
Deep generative models in inversion: The impact of the generator's nonlinearity and development of a new approach based on a variational autoencoder	47
Landslide susceptibility prediction based on image semantic segmentation	45
A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China)	45
3D geological structure inversion from Noddy-generated magnetic data using deep learning methods	43
Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification	43
Spatiotemporal causal convolutional network for forecasting hourly PM2.5 concentrations in Beijing, China	42
Flood monitoring by integration of Remote Sensing technique and Multi-Criteria Decision Making method	42
Learning 3D mineral prospectivity from 3D geological models using convolutional neural networks: Application to a structure-controlled hydrothermal gold deposit	41

3D CNN-PCA: A deep-learning-based parameterization for complex geomodels	41
A positive and unlabeled learning algorithm for mineral prospectivity mapping	40
EMagPy: Open-source standalone software for processing, forward modeling and inversion of electromagnetic induction data	40
Real-time water level monitoring using live cameras and computer vision techniques	40
Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling	39
Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model	39
Multimodal imaging and machine learning to enhance microscope images of shale	38
A new structure for representing and tracking version information in a deep time knowledge graph	37
A general approach to seismic inversion with automatic differentiation	36
Characterization of pore and grain size distributions in porous geological samples – An image processing workflow	35
Application of deep learning for semantic segmentation of sandstone thin sections	35