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News

AZoMining
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AI Model Cuts Copper Concentrate Weighing Error by 64.6%

53+ min ago   (670+ words) Aiming to improve measurement accuracy and support sustainable mining, researchers have developed and validated an intelligent error compensator based on long short-term memory (LSTM) recurrent neural networks for dynamic weighing systems on copper concentrate belt conveyors. They published their findings…...

AZoMining
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New Algorithm Automates Roof-Weighting Analysis in Underground Coal Mines

2+ week, 5+ day ago   (609+ words) The approach successfully identified high-resistance regions associated with roof weighting and produced a mean periodic weighting interval close to the field reference value. The study provides a practical approach for automating mine-pressure analysis and supporting intelligent ground-control systems. Roof weighting…...

AZoMining
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Machine Learning in Geology: Key Data and Modeling Challenges

1+ mon, 2+ week ago   (981+ words) Geological data breaks many of the assumptions built into machine-learning systems. Rock formations differ significantly across meters, not miles, and that kind of variability leaves a wide gap between what an algorithm learns in training and what it actually meets…...

AZoMining
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How Machine Learning Helps Geologists Discover Hidden Ore Deposits

1+ mon, 3+ week ago   (1134+ words) Finding ore bodies buried hundreds of meters beneath Earth's surface remains one of geology's most prominent challenges. Artificial intelligence (AI) is changing this by simultaneously processing enormous volumes of geophysical, geochemical, and geological data to identify mineral deposits that conventional…...

AZoMining
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Transformer Network Enhances Underground Mining Image Resolution

4+ mon, 1+ week ago   (262+ words) Extensive experiments demonstrate that the BDL network significantly outperforms state-of-the-art super-resolution methods, including SRCNN, VDSR, EDSR, SwinIR, and DAT, on underground coal mine image datasets. In the ×2 upscaling task, the proposed method achieved a PSNR of 32.07 dB and SSIM of…...