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Pirrie D: Applications of automated
mineral analysis

Applications of automated mineral analysis

Geologie und Metallogenie der Lagerstätten im Erzgebirge - Krušné hory

Earth Observation

Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development. Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning.

Deep Learning for Multi-Sensor Earth Observation

Deep Learning for Earth Observation and Climate Monitoring bridges the gap between deep learning and the Earth sciences, offering cutting-edge techniques and applications that are transforming our understanding of the environment. With a focus on practical scenarios, this book introduces readers to the fundamental concepts of deep learning, from classification and image segmentation to anomaly detection and domain adaptability. The book includes practical discussion on regression, parameter retrieval, forecasting, and interpolation, among other topics. With a solid foundational theory, real-world examples, and example codes, it provides a full understanding of how intelligent systems can be applied to enhance Earth observation and especially climate monitoring. This book allows readers to apply learning representations, unsupervised deep learning, and physics-aware models to Earth observation data, enabling them to leverage the power of deep learning to fully utilize the wealth of environmental data from satellite technologies.

Deep Learning for Earth Observation and Climate Monitoring

Nature wants to heal, and we are finally understanding the best ways to help. We know that when Earth's ecosystems fall out of harmony, the damage can spiral out of control. But what if we could help nature to regain its balance?As a leading ecologist, Professor Thomas Crowther studies not just how species work individually but how whole complex ecosystems work to regulate themselves. When we set the right conditions for nature to thrive, each species helps to support the life of every other species. This means not just planting trees, but taking into account the fascinating role of fungi and soil bacteria, as well as the free movement of wild animals. By revealing how the feedback loops that generate sustainable ecosystems give rise to the stars, planets, and life; economic inequality and privilege; and the way we see the world, Restore shows how we can do our part so that nature can begin the vital process of healing itself.

The Power of Feedback Loops to Make a Better World

Nature's Echo

Basin Analysis

Mining has evolved in the 21st century into an indispensable industry that provides a variety of mineral products needed to sustain a modern society. To achieve efficiency, it has also grown into an integrated enterprise of enormous size, wielding substantial economic power. Tailings is essentially a fine-particle mine-waste byproduct. Its management has gone through major transformations in the last century. In early years tailings was disposed of out of sight and literally out of mind. However, due to the physical and chemical characteristics of tailings, this laissez-faire approach in dealing with its disposal led to significant environmental degradation such as pollution and blockage of watercourses, etc. With the rising environmental protection movement, the mining operation is under increasing regulatory constraints and governmental monitoring. Modern tailings dams have become major hydraulic structures designed, constructed, and monitored until they are properly closed and reclaimed after the completion of mining tenure. These dams are often higher than 100 m, with a storage capacity of over hundreds of million cubic metres, serving a vital industrial function for the public. Unfortunately, when such a dam fails, it unleashes tremendous destructive power causing inundation of water and tailings mud over the downstream area resulting in fatalities, loss of property and infrastructure, and lasting environmental degradation. This book presents a condensed monograph on forensic investigations of select case histories of failed tailings dams. The selection tends to emphasize recent cases that have representative characteristics including those affecting the evolution of the current practice of tailings dam engineering. Efforts are made to facilitate readers’ understanding of important factors involved by using plain language. Technical terms are clearly explained before their usage. The objective of the monograph is to provide a neutral reference on tailings dam failure.The author hopes ongoing dialogues by all stakeholders on the important topics of dam safety will raise the safety standard of tailings dams to a new level compatible with public expectations of the sustainable mining industry.&nbsp; &nbsp; &nbsp;&nbsp;

Socio-Economic-Environmental Impacts

Tailings Dam Failure

Multimodal Remote Sensing Data Fusion for Classification: Algorithms and Applications offers a comprehensive overview of Earth observation data fusion, focusing on multimodal remote sensing. It presents state-of-the-art algorithms and practical applications that enhance understanding of Earth's dynamic processes. Through detailed analysis, case studies, and practical examples, this book equips readers with the necessary tools to effectively utilize multimodal data fusion for land cover and land use classification, as well as environmental monitoring, making it an invaluable resource for those in remote sensing and Earth sciences. Furthermore, the book is tailored for Masters and Doctorate students, scientists, and professionals in remote sensing, geography, and Earth sciences. It delves into the integration and analysis of multimodal remote sensing data, offering insights into sustainable solutions for environmental challenges. This comprehensive coverage ensures readers are well-versed in the cutting-edge techniques and methodologies required for advanced Earth observation and classification tasks.

Applications of automated mineral analysis

Informations bibliographiques

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