Yapay Zeka (AI) Jeolojiyle Buluşuyor: Yeni Yapay Zeka Modeli Madencilik Doğruluğu İçin Tüm Jeolojik Süreçleri Simüle Ediyor

Çin'in kuzeybatısında yapay zeka destekli mineral arama alanında önemli bir adım atılıyor. Bir araştırma ekibi, bölgenin madencilik sektörüne özel olarak tasarlanmış ilk büyük dil modeli olan "Congling Zhixun"u piyasaya sürdü.

16. Kaşgar Orta ve Güney Asya Emtia Fuarı'nda tanıtılan bu model, güney Sincan ve Orta Asya'yı kapsayan metalojenik kuşak için tasarlandı. Genel amaçlı yapay zekadan farklı olarak, özel yerel jeolojik veri kümeleri üzerinde eğitildi ve başlangıç ​​örneği olarak Tacikistan'ın Taxkorgan özerk bölgesindeki yüksek kaliteli demir cevheri yatakları kullanıldı. Bu yerelleştirilmiş yaklaşım, veri paylaşımı ve yapay zeka için yüksek kaliteli eğitim verilerinin yetersizliği ile ilgili uzun süredir devam eden endüstri zorluklarını doğrudan ele alıyor.


Artificial Intelligence (AI) Meets Geology: New AI Model Simulates Entire Geological Processes For Mining Accuracy

A major step forward in AI-driven mineral exploration is underway in Northwest China. A research team has launched "Congling Zhixun," the region's first large language model dedicated specifically to the mining sector.

Unveiled at the 16th Kashgar Central and South Asia Commodity Fair, this model is designed for the metallogenic belt spanning southern Xinjiang and Central Asia. Unlike general-purpose AI, it is trained on specialized local geological datasets, utilizing high-grade iron ore deposits in the Taxkorgan Tajik autonomous county as an initial sample. This localized approach directly addresses longstanding industry challenges regarding data sharing and the shortage of high-quality training data for AI.

According to Ding Haifeng, head of the project, the model incorporates a "world model" of geological mineralization. This means it goes beyond conventional prospecting by inferring the entire geological process, from the sources of mineral-forming materials to their migration, accumulation, and subsequent alteration. This expert-level reasoning makes the system's predictions explainable, traceable, and verifiable.

Notably, the project is backed by sustainable infrastructure. It leverages Kashi's new 4,000 petaflops computing center alongside a 10-million-kilowatt photovoltaic renewable energy base. This combination provides the substantial computing power required for continuous AI training and updates while maintaining a low-carbon and cost-effective footprint.

For global buisness leaders:

AI and machine learning have been used in geoscience and mineral exploration for years. Companies and research institutions globally already use AI to process satellite imagery, analyze geophysical data, and identify predictive mineral patterns.

The "Congling Zhixun" model represents a meaningful step forward in explainable, localized, and sustainably powered AI. Its claim to being a "first" applies specifically to its regional deployment in southern Xinjiang and its specific architectural goal of simulating the complete geological mineralization process, rather than being the first AI ever applied to mining.


https://www.linkedin.com/posts/jens-weschta-900767277_ai-mineralexploration-geoscience-share-7497889762142294017-HDEz/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAN3ABkBQ2I-SBIH7ihP83N8sjUAb2RsseQ


Yorumlar

Bu blogdaki popüler yayınlar

Geological Methods in Mineral Exploration and Mining / Roger Marjoribanks

Baz metal yataklarının uzaktan algılama ile belirlenmesine bir örnek: Hakkari güneyi…

Çatalçam (Soma-Manisa) Au-Pb-Zn-Cu cevherleşmesinin jeolojik, mineralojikpetrografik ve sıvı kapanım özellikleri

ALACAKAYA (ELAZIĞ) MERMERİNDE GULEMAN OFİYOLİTİNİN MUCİZESİ

Tectonic Triggers for Postsubduction Magmatic-Hydrothermal Gold Metallogeny in the Late Cenozoic Anatolian Metallogenic Trend, Türkiye