The role of artificial intelligence and IoT in prediction of earthquakes: Review
December 2024
Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment, lives, and properties. There has been an increasing interest in the...
Cellular automata models for simulation and prediction of urban land use change: Development and prospects
December 2025
Rapid urbanization and land-use changes are placing immense pressure on resources, infrastructure, and environmental sustainability. To address these, accurate urban simulation models are essential...
Scalable variational Gaussian process framework for implicit geological modelling and compositional grade interpolation
June 2026
Geological modelling and estimation of polymetallic ore grades require methods that simultaneously honour spatial heterogeneity, compositional constraints, and predictive uncertainty. We present a scalable...
Unlocking the potential of legacy data for future geoenergy and storage applications: Porosity and permeability prediction based on machine learning applied to petrographic data
June 2026
Machine learning techniques are increasingly applied in geological research and widely adopted in industry. However, one commonly available dataset remains underutilized: petrographic data from classical...
Optimizing zero-shot text-based segmentation of remote sensing imagery using SAM and Grounding DINO
June 2025
The use of AI technologies in remote sensing (RS) tasks has been the focus of many individuals in both the professional and academic domains. Having more accessible interfaces and tools that allow people...
Automatic description of rock thin sections: A web application
June 2025
The identification and characterization of rock types is a core activity in geology and related fields, including mining, petroleum, environmental science, industry, and construction. Traditionally,...
SeisReconNO: Leveraging a U-Net-Enhanced Fourier neural operator for 3D seismic reconstruction
June 2026
Missing traces in 3D seismic data are a recurring challenge caused by receiver malfunctions, acquisition limitations, and geological or environmental constraints. These gaps hinder accurate interpretation...
Seismic facies characterization: Integrated subsurface-outcrop analysis for complex depositional systems in northeast India
March 2026
Seismic facies analysis involves the interpretation of reflection patterns from seismic data to provide insights into subsurface sedimentary environments, depositional processes, and lithological variations,...
AI-based approaches for wetland mapping and classification: A review of current practices and future perspectives
December 2025
Wetlands are critical ecosystems that provide essential ecological, hydrological, and socio-economic services, such as water purification, climate regulation, and biodiversity conservation. However,...
Reservoir evaluation using petrophysics informed machine learning: A case study
December 2024
We propose a novel machine learning approach to improve the formation evaluation from logs by integrating petrophysical information with neural networks using a loss function. The petrophysical information...
Recent advances and challenges of cement bond evaluation based on ultrasonic measurements in cased holes
March 2026
Cement bond quality evaluations are essential for assessing zonal isolation between formation strata, providing crucial information for ensuring environmental and ecological safety in oil and gas exploitation,...
Application of machine learning for permeability prediction in heterogeneous carbonate reservoirs
March 2026
Accurate prediction of reservoir permeability based on geostatistical modeling and history matching is often limited by spatial resolution and computational efficiency. To address this limitation, we...
Explainable flood damage assessment using multi-atrous self-attention and vision-language integration
March 2026
Flood disasters triggered by excessive rainfall cause severe damage to infrastructure and pose significant risks to human life. Within the context of disaster management, accurately identifying affected...
Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines
December 2023
Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource...
Spatial mapping and modelling of soil organic carbon using random forest and remote sensing variables in part of Kaduna, Northern Nigeria
March 2026
Reliable and up-to-date digital soil data is crucial for achieving Sustainable Development Goal 13 (Climate Action) by enabling improved monitoring of soil carbon and land degradation, thereby supporting...
Prediction of the soil–water retention curve of compacted clays using PSO–GA XGBoost
March 2026
Soil–water retention (SWR) is fundamental for understanding the hydro-mechanical behavior of unsaturated clay soils. The soil–water retention curve is typically obtained through extensive and costly...
Application of YOLOv11 deep learning model for classification and counting ice-rafted debris (IRD) in core sediments in the Arctic Ocean
March 2026
The classification and quantification of ice-rafted debris (IRD) in marine sediments are key to reconstructing glacial-interglacial dynamics and sediment provenance. However, traditional IRD analysis,...
Quantification of greenhouse gas emissions from livestock using remote sensing & artificial intelligence
December 2025
Greenhouse gases (GHGs) from agriculture in Africa are among the world's fastest-growing emissions, with the livestock sector as the primary contributor. However, the methods for quantifying these emissions...
Water resource forecasting with machine learning and deep learning: A scientometric analysis
December 2024
Water prediction plays a crucial role in modern-day water resource management, encompassing both hydrological patterns and demand forecasts. To gain insights into its current focus, status, and emerging...
Machine learning in petrophysics: Advantages and limitations
December 2022
Machine learning provides a powerful alternative data-driven approach to accomplish many petrophysical tasks from subsurface data. It can assimilate information from large and rich data bases and infer...
Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces
June 2025
This study employed convolutional neural networks (CNNs) for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces. The image dataset...
Machine learning-driven permeability prediction in carbonates and sandstones using NMR relaxation data
March 2026
Nuclear Magnetic Resonance (NMR) has proven to be a powerful tool for in-situ permeability quantification however, it typically requires laboratory calibration, and its accuracy is strongly influenced...
Remote sensing estimation of rice chlorophyll content based on UAV image feature selection and PSO-optimized ensemble learning
March 2026
Chlorophyll content is one crucial indicator of evaluating crop growth and physiological status. Rapid, accurate, and large-scale monitoring of chlorophyll content is vital for the precise diagnosis...
Hierarchical machine learning for the automatic classification of surface deformation from SAR observations
March 2026
Ground deformation processes, such as landslides and subsidence, cause significant social, economic, and environmental impacts. This study aims to automatically classify ground deformation processes...
LatentPINNs: Generative physics-informed neural networks via a latent representation learning
June 2025
Physics-informed neural networks (PINNs) are promising to replace conventional mesh-based partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. However, PINNs...