AIMC Topic: Geologic Sediments

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Automated identification of sedimentary structures in core images using object detection algorithms.

PloS one Jul 18, 2025
Manual interpretation of sedimentary structures in core-based analyses is critical for understanding subsurface geology but remains time-intensive, expert-dependent, and susceptible to bias. This study investigates the use of convolutional neural net...
Neural Networks, Computer Geologic Sediments Detection Algorithms Image Processing, Computer-Assisted Algorithms
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Automatic identification and characteristics analysis of crack tips in rocks with prefabricated defects based on deep learning methods.

PloS one Jul 15, 2025
In complex geological environments, the morphology, orientation and distribution characteristics of cracks in the rock directly affect the stability assessment for rock masses and engineering safety decisions. However, the traditional manual interpre...
Geological Phenomena Algorithms Geologic Sediments Image Processing, Computer-Assisted Deep Learning
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Event-Driven Taxonomy (EDT) Screening: Leveraging Effect-Based Spectral Libraries to Accelerate Semiquantitative Nontarget Analysis of AhR Agonists in Sediment in the Era of Big Data.

Environmental science & technology Jul 8, 2025
Sediments contain complex chemical mixtures. While effect-directed analysis (EDA) combined with nontarget screening (NTS) is promising, its large-scale application has been limited by time-consuming workflows. Here, we developed an event-driven taxon...
Geologic Sediments Receptors, Aryl Hydrocarbon Big Data Water Pollutants, Chemical
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Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm.

Environmental monitoring and assessment Jun 25, 2025
Estimation of sediment concentration (SC) is of vital importance in terms of siltation and economic life of dams, lakes and aqueducts, reservoir operations, design of water resource structures, monitoring and control of water pollution, and flood man...
Algorithms Environmental Monitoring Neural Networks, Computer Rivers Geologic Sediments Turkey Water Pollutants, Chemical
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Artificial Intelligence-Enhanced Detection of Biogenicity Using Laboratory Specimens of Biologically and Microbially Induced Sedimentary Structures in a Controlled Experiment.

Astrobiology May 30, 2025
The search for traces of life can be based on the detection of specific signatures produced by microorganisms on sedimentary rocks. Microbially induced sedimentary structures (MISSs) develop under specific physicochemical conditions that are likely t...
Geologic Sediments Artificial Intelligence Exobiology Mars Neural Networks, Computer
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LucaPCycle: Illuminating microbial phosphorus cycling in deep-sea cold seep sediments using protein language models.

Nature communications May 26, 2025
Phosphorus is essential for life and critically influences marine productivity. Despite geochemical evidence of active phosphorus cycling in deep-sea cold seeps, the microbial processes involved remain poorly understood. Traditional sequence-based se...
Archaea Geologic Sediments Alkaline Phosphatase Seawater Phylogeny Phosphorus
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Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

Environmental monitoring and assessment Apr 26, 2025
The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analyzes historical changes in SSL and evaluates machine learning (ML) models for SSL prediction in the G...
Humans Environmental Monitoring Machine Learning Rivers Geologic Sediments Bayes Theorem
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Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach.

Journal of environmental management Mar 17, 2025
Suspended sediment load (SSL) refers to sediment particles, such as silt and clay, that are suspended in water. It plays a critical role in hydrology and water quality management, influencing factors such as water quality, river erosion, sedimentatio...
Extreme Learning Machines Machine Learning Rivers Geologic Sediments Water Quality Environmental Monitoring
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Machine learning-based prediction of unconfined compressive strength and contaminant leachability in dredged contaminated sediments for land reclamation projects.

Environmental science and pollution research international Mar 8, 2025
This research investigates the application of machine learning techniques for predicting unconfined compressive strength (UCS) and contaminant leachability in dredged contaminated sediments (DCS) with implications for land reclamation projects. Tradi...
Compressive Strength Machine Learning Geologic Sediments
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Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone.

Journal of the American Society for Mass Spectrometry Dec 19, 2024
The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mix...
Organic Chemicals Spectrometry, Mass, Secondary Ion Algorithms Geologic Sediments Machine Learning Biomarkers Spectrometry, X-Ray Emission Microscopy, Electron, Scanning
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