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In the mining industry, adopting and developing new technologies, including deep learning methods, has much potential. Deep learning has been used to handle various mining, mineral and metal extraction, and recovery challenges. The rising acceptance of automation in mining opens the door to a broader application of deep learning as a component of a mine automation system.
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As the world's need for natural resources rises due to population increase, urbanization, and industrial development, the mining industry has to discover additional resources in the earth's crust to supply the demand for metals and natural resources.
After the mineral deposit is discovered, exploration is necessary to reduce risks at each subsequent step of the mining cycle, from feasibility study to growth, extraction, closure, and recovery. Exploration provides information for the requirements needed to evaluate whether or not the deposit could produce economic value at the feasibility research stage. Exploration also provides data on the quantity and grade of ore for ensuring the growth and functioning of the mine from the mine planning to mine operation stages.
Deep learning is a form of machine learning that is essentially a three- or more-layered neural network. These neural networks seek to imitate the activity of the human brain with limited success, allowing it to "learn" from massive amounts of data. While a single-layer neural network can still generate approximate predictions, more hidden layers can assist in optimizing and tuning for accuracy.
In simple words, Deep learning is an artificial intelligence subfield that incorporates feature engineering and categorization into a single technique. It is a data-driven approach for optimizing prediction models by learning from huge data sets.
Deep learning assists in mineral exploration by offering robust and flexible tools for handling large and complicated data sets, improving data quality and dependability, lowering exploration risk and expense, and improving decision-making and management of resources.
Deep learning could benefit mineral exploration by utilizing various data sources, including geology, geological chemistry, geophysics, and satellite imagery, to construct prediction models that rank and categorize prospects based on mineralization probability.
Deep learning can help discover anomalies, which are characteristics or patterns that depart from the typical or anticipated background. Anomaly detection can aid in discovering new targets or refining current ones by revealing areas of interest that would otherwise go undetected.
Integrating deep learning into mineral exploration has opened new frontiers, enhancing the industry's ability to interpret complex datasets and make informed decisions. Some of the critical applications include:
Deep learning algorithms excel in analyzing geological data and identifying mineral signatures. These algorithms can autonomously recognize patterns indicative of potential deposits by training neural networks on extensive datasets of geological features and mineral occurrences.
Satellite imagery provides a wealth of information for mineral exploration. Deep learning algorithms can analyze vast amounts of satellite and aerial imagery to identify geological structures, alterations, and anomalies. This expedites the identification of exploration targets and enables monitoring and assessing environmental impacts caused by mining activities.
Deep learning enhances the interpretation of geophysical and geochemical data, leading to more precise targeting of mineral deposits. By combining different data types, neural networks can uncover subtle patterns that traditional methods may overlook. This multidimensional analysis improves the reliability of exploration models, reducing the risk of false positives and negatives.
Goldcorp (now Newmont) implemented deep learning algorithms to analyze geological and geochemical data at its Red Lake Mine in Canada. The models identified previously unrecognized patterns, leading to the discovery of new gold deposits. This success underscored the potential of deep learning in unlocking hidden mineralization opportunities.
Vale, a major mining company, leverages deep learning algorithms to analyze satellite imagery for environmental monitoring and exploration. The integration of these technologies has enhanced their exploration efficiency and demonstrated a commitment to sustainable and responsible mining practices.
While integrating deep learning in mineral exploration holds immense promise, it is not without challenges and considerations.
The effectiveness of deep learning models is highly dependent on the quality and quantity of training data. In mineral exploration, obtaining comprehensive datasets that accurately represent the diversity of geological conditions can be challenging. Addressing data quality issues and ensuring diverse and representative datasets are essential for the reliability of deep learning models.
Deep learning models are also often regarded as "black boxes" due to their complex architectures. Understanding how these models arrive at specific conclusions is crucial for gaining trust in their predictions.
Enhancing the interpretability and transparency of deep learning models is an ongoing area of research to address concerns related to accountability and decision-making.
As deep learning continues to evolve, emerging trends such as the integration of real-time data, the use of artificial intelligence (AI), and advancements in unsupervised learning are poised to enhance the capabilities of mineral exploration technologies further.
Collaborative research and development efforts between academia, industry, and technology providers will drive innovation. The collective expertise of diverse stakeholders can address current challenges and uncover new opportunities for deep learning in mineral exploration.
Integrating deep learning into mineral exploration represents a paradigm shift, offering unprecedented capabilities to decipher the Earth's geological complexities. From automating geological mapping to optimizing the analysis of satellite imagery and enhancing the interpretation of geophysical and geochemical data, deep learning is reshaping the landscape of mineral exploration.
Shirmard, H., Farahbakhsh, E., Müller, R. D., & Chandra, R. (2022). A review of machine learning in processing remote sensing data for mineral exploration. Remote Sensing of Environment, 268, 112750. https://www.sciencedirect.com/science/article/abs/pii/S0034425721004703
Okada, K. (2022). Breakthrough technologies for mineral exploration. Mineral Economics, 35(3-4), 429-454. https://link.springer.com/article/10.1007/s13563-022-00317-3
Latif, G., Bouchard, K., Maitre, J., Back, A., & Bédard, L. P. (2022). Deep-learning-based automatic mineral grain segmentation and recognition. Minerals, 12(4), 455. https://www.mdpi.com/2075-163X/12/4/455
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Written by
Usman holds a master's degree in Material Science and Engineering from Xian Jiaotong University, China. He worked on various research projects involving Aerospace Materials, Nanocomposite coatings, Solar Cells, and Nano-technology during his studies. He has been working as a freelance Material Engineering consultant since graduating. He has also published high-quality research papers in international journals with a high impact factor. He enjoys reading books, watching movies, and playing football in his spare time.
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