HyLogger 4

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赋能勘探,安百拓全力支持中国地质找矿——安百拓隆重推出HyLogger 4高光谱数字化
Zhong Guo Chan Ye Jing Ji Xin Xi Wang· 2025-07-18 07:00
Core Insights - The article emphasizes the importance of mineral resources as the foundation for national development and highlights the strategic actions being taken to enhance exploration and resource security during the "14th Five-Year Plan" period [1] - Advanced geological exploration technologies are identified as key to overcoming challenges in resource exploration, with the HyLogger 4 being a significant innovation in this field [1][4] Exploration Technology - The HyLogger 4, introduced during the recent Anbato China Innovation Day, features revolutionary capabilities such as "nano-level resolution + AI mineral prediction," which has sparked interest among mining companies and research institutions [1] - The HyLogger 4 has been officially launched at the Perth Core Library, enhancing the capabilities of geological analysis and reducing costs for exploration projects [2] Investment and Efficiency - The HyLogger 4 is projected to reduce exploration costs by 15-20% and shorten resource discovery cycles by 30%, potentially unlocking billions of Australian dollars in new mining investments [3] - Case studies demonstrate the effectiveness of HyLogger 4 in identifying mineralization targets and improving resource prediction accuracy, thereby lowering drilling costs [3] Advanced Features - The HyLogger 4 integrates full-spectrum, high-precision spectral technology, enabling micron/nanometer-level identification of mineral components and rapid geological modeling [5] - The system operates at a scanning speed of up to 50mm/s, processing core lengths of up to kilometers in a single day, significantly faster than traditional laboratory analyses [6] AI Integration - The AI-driven mineral prediction module in HyLogger 4 enhances the ability to learn regional mineralization patterns and predict the potential of unexplored areas, transforming data collection into intelligent decision-making [8] - The implementation of machine learning and real-time data feedback allows for continuous improvement in the identification of complex minerals, streamlining the exploration process [8]