Beholder’s Post

This week at the Mineral-X Symposium, experts explored critical minerals, efficient exploration, and responsible mining. Our Head of Machine Learning, Jack Muir, took to the stage at Stanford University to discuss Ambient Noise Tomography as a scalable data platform for ML-driven mineral discovery. Jack also delved into the innovative work we are doing at Fleet Space to combine real-time multiphysics and AI to improve mineral exploration efficiency. #FutureofExploration #CriticalMinerals #AI #MiningInnovation

For more than 2 decades, Mira Geoscience has consistently pushed the boundaries of technology, pioneering the application of integrated interpretation in mining and mineral exploration, among other significant geoscientific innovations. Integrated interpretation is defined as the interpretive process by which a ‘Common Earth Model’, consistent with all data and geological reasoning, is created. Although the value is broadly acknowledged, quantitative integration of geological and geophysical data in mineral exploration often remains an afterthought because it can be technically demanding. In the era of AI and machine learning, integrated interpretation is (or should be!) a prerequisite to the employment of these new methods of data and model analysis. At Mira Geoscience, we believe in the power of knowledge, data, technology, and collaboration to address the most complex geoscience-based challenges, and we will continue to drive technological innovation and excellence in the realm of geoscience. Thank you for being part of this amazing journey! 👉Learn more about the ‘Common Earth Model’ on this technical paper, published by John McGaughey in 2006: https://lnkd.in/eNzWjnbi #MiraGeoscienceturns25 #25yearsmodellingtheearthtogether #geoscience #ourpassion #commonearthmodel #integratedinterpretation #3Dmodelling #technology #innovation #pioneering #AI #machinelearning #mining #mineralexploration

🚀 Transforming Mineral Exploration with Satellite Imagery 🚀 Satellite imagery is revolutionizing the mining industry, providing high-res data for identifying and assessing mineral potential. 🌍 Key Benefits: 🔍 Comprehensive Surveys: Cover large, remote areas efficiently. 🌟 Advanced Imaging: Reveal detailed mineralogical data. 📊 Data Integration: Combine imagery with geological and geochemical data for precise exploration. 🤖 AI & Machine Learning: Predict high-potential zones. Success Stories: 🌟 Discoveries in the Andes and Australian Outback through integrated satellite and field data. Ready to revolutionize your exploration projects? 🚀 🔗 Read More: https://lnkd.in/de4wdsPA 🛰

Today unfolded as an adventure led by curiosity, untouched by the constraints of a predetermined schedule. My journey began with delving into AI applications for industrial data, only to find the quest somewhat thwarted by less-than-impressive search results—a testament to the challenges of navigating through the deluge of marketing material online. The exploration then veered towards the realm of copper mining, where I uncovered the fascinating process of bioleaching. This technique employs bacteria to extract metals from ores, a sustainable alternative to traditional mining methods. The discovery that bioleaching was successfully conducted in the microgravity of the International Space Station in 2020 sparked visions of future asteroid mining— as I imagine, bacteria would be much easier to transport than bulky mining equipment. Space continued to captivate my attention as I witnessed a SpaceX launch. The spectacle of the stage 1 rocket gracefully returning to land at the launch site is truly amazing. The day's exploration concluded with a review of APIs offering real-time data on natural disasters. I explored offerings from Ambee, PredictHQ, the US Geological Survey (USGS), the Global Disaster Alert and Coordination System, and Kontur's Event Feed. These tools promise some interesting information for my upcoming project around demonstrating the modeling capabilities with Irys Frame, and I'm eager to examine them more closely in the days ahead. Today was a reminder of the power of curiosity to transform an open schedule into a day filled with learning and discovery.

This month, we signed another partnership agreement to revolutionize mineral discovery using AI/ML technology to generate high-probability targets and directly identify economic mineral deposits, making the process better, quicker, and more cost effective. We partnered with US Critical Materials to deploy our AI-powered mineral targeting platform to detect rare earth elements in Montana, USA. This agreement continues our momentum in leading a paradigm shift in the industry and systematically de-risking the economics of discovering concealed mineral deposits. We work across multiple jurisdictions and commodities to maximize the potential of mineral exploration. Let's discuss how we can work together! #mineralexploration #investing #partnership

@Atomionics is transforming the resource exploration landscape with Gravio. This quantum sensor heralds a new era of precision and environmental consciousness in exploration. Read the full story here on @reuters: https://lnkd.in/etJuc4-v

AI's transformative role in geology unveiled! Did you miss our recent #AI seminar in Perth? No problem! Dive into our latest blog for a comprehensive recap of 'The Dawn of AI for Geology'. Gain insights from industry leaders on how AI is reshaping #geology and #mining. Read the full recap at https://lnkd.in/ePZVKBwv #NextGenTech #Technology

Machine learning has proven a successful tool in expanding the exploration search space into new frontiers and refining areas for follow-up exploration. SRK Consulting's Ben Jupp and Stephen Johnson tell us more. #machinelearning #exploration

In this article you will find a list of key reasons why your resource model may not behave and consistently disrupt your reconciliation results. ZVENIA.COM => https://lnkd.in/dYUfAJ66 All credits to Aleksandr Mitrofanov, PhD, PGeo

8 practical tips to check why your resource model does not fit your production