AI is utilized in geology for tasks such as geological mapping, mineral exploration, and geophysical data analysis. It employs machine learning algorithms and pattern recognition to interpret complex geological data, identify anomalies, and aid in the discovery of mineral resources, enhancing geological surveying and exploration efforts.
Researchers developed the SACA-StyleGAN method to generate and semi-automatically annotate cast thin section images of tight oil reservoirs. This approach significantly improves data diversity, image quality, and annotation efficiency, offering a promising solution for geological analysis and exploration.
Researchers propose a novel deep learning (DL) method utilizing convolutional neural networks (CNNs) for automatic sediment core analysis. The DL-based approach employs semantic segmentation on digital images of sediment cores, demonstrating high accuracy in interpreting sedimentary facies, offering a precise, efficient tool for subsurface stratigraphic modeling in geoscience applications.
This comprehensive review explores the growing use of machine learning and satellite data in water quality monitoring, emphasizing the importance of proper data analysis techniques and highlighting the potential for advancements in environmental understanding.
The study explores the potential of using visual ChatGPT, a visual language model, in remote sensing tasks. It highlights the model's capabilities in image analysis and manipulation, including scene classification, edge detection, and image segmentation.
UCLA geologists have devised an innovative method employing artificial intelligence to enhance the prediction of potential landslide locations, a development that could significantly boost safety measures in regions frequently afflicted by natural calamities.
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