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ISSN: 2666-5441

The role of artificial intelligence and IoT in prediction of earthquakes: Review

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...

Optimizing zero-shot text-based segmentation of remote sensing imagery using SAM and Grounding DINO

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...

Cellular automata models for simulation and prediction of urban land use change: Development and prospects

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...

AI-based approaches for wetland mapping and classification: A review of current practices and future perspectives

Wetlands are critical ecosystems that provide essential ecological, hydrological, and socio-economic services, such as water purification, climate regulation, and biodiversity conservation. However,...

Recent advances and challenges of cement bond evaluation based on ultrasonic measurements in cased holes

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,...

On the application of machine learning algorithms in predicting the permeability of oil reservoirs

Permeability is one of the main oil reservoir characteristics. It affects potential oil production, well-completion technologies, the choice of enhanced oil recovery methods, and more. The methods used...

Generating high-resolution climate data in the Andes using artificial intelligence: A lightweight alternative to the WRF model

In weather forecasting, generating atmospheric variables for regions with complex topography, such as the Andean regions with peaks reaching 6500 m above sea level, poses significant challenges. Traditional...

Automatic description of rock thin sections: A web application

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,...

Quantification of greenhouse gas emissions from livestock using remote sensing & artificial intelligence

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...

Reservoir evaluation using petrophysics informed machine learning: A case study

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...

Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines

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...

Machine learning assisted estimation of total solids content of drilling fluids

Characterization and optimization of physical and chemical properties of drilling fluids are critical for the efficiency and success of drilling operations. In particular, maintaining the optimal levels...

Intelligent identification of fractures and holes in ultrasonic logging images based on the improved YOLOv8 model

Aiming to address the demand for intelligent recognition of geological features in whole-wellbore ultrasonic images, this paper integrates the YOLOv8 model with the Convolution Block Attention Module...

LatentPINNs: Generative physics-informed neural networks via a latent representation learning

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...

Constructing regional mineral prospecting knowledge graph from GIS maps

Geographic Information System (GIS) layers contain both spatial precision and domain knowledge, making them valuable for mineral prospectivity analysis. This study proposes a task-oriented methodology...

ASTER data processing and fusion for alteration minerals and silicification detection: Implications for cupriferous mineralization exploration in the western Anti-Atlas, Morocco

Alteration minerals and silicification are typically associated with a variety of ore mineralizations and could be detected using multispectral remote sensing sensors as indicators for mineral exploration....

Water resource forecasting with machine learning and deep learning: A scientometric analysis

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...

Prediction of the soil–water retention curve of compacted clays using PSO–GA XGBoost

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...

Unveiling climate-driven water surface dynamics in the largest tropical lake in Borneo: A machine learning approach using multi-source satellite imagery

Tropical lakes such as Lake Sentarum in Kalimantan, Indonesia, represent ecologically rich ecosystems with high biodiversity and constitute the largest lake on the island of Kalimantan. This lake serves...

Soil liquefaction assessment using machine learning

Liquefaction is one of the prominent factors leading to damage to soil and structures. In this study, the relationship between liquefaction potential and soil parameters is determined by applying feature...

Quantifying uncertainty of mineral prediction using a novel Bayesian deep learning framework

Mineral resource exploration increasingly demands not only accurate prospectivity maps but also reliable measures of confidence to guide high-stakes decisions. In this study, a novel Bayesian deep learning...

Hierarchical machine learning for the automatic classification of surface deformation from SAR observations

Ground deformation processes, such as landslides and subsidence, cause significant social, economic, and environmental impacts. This study aims to automatically classify ground deformation processes...

Pore size classification and prediction based on distribution of reservoir fluid volumes utilizing well logs and deep learning algorithm in a complex lithology

Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids. Traditional methods for predicting pore size distribution (PSD), relying...

Development of a reliable rock slope stability model utilizing field and analytical data – An integration of FE-ML approaches

Slope instability in hilly regions is a highly complex phenomenon, with triggering factors ranging from natural events to anthropogenic activities. Such failures hit disastrous losses both in terms...

Self-supervised multi-stage deep learning network for seismic data denoising

Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow, as our ability to mitigate noise in seismic data affects the quality of our subsequent...

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