Linear Algebra
Applied Matrices and Vector Spaces in Mining Engineering
In the modern landscape of mining engineering, linear algebra serves as a powerful engine behind data-driven decisions and innovation. From modeling mineral reserves through complex systems of equations, to applying eigenvalue techniques for assessing slope stability, and using matrix factorizations to structure and interpret vast datasets, linear algebra transforms abstract mathematics into practical solutions. By mastering these tools, students and professionals gain the ability to decode complex mining challenges, streamline engineering processes, and construct reliable models that drive efficiency, safety, and technological advancement in the mining industry.
This book offers a practical and application-driven introduction to linear algebra, designed specifically for mining contexts. Key topics include matrices and systems of linear equations, determinants, matrix inverses, matrix factorizations, vector spaces, inner product spaces, orthogonality, linear transformations, and eigenvalues. Each chapter bridges theory with hands-on applications—showing how abstract concepts translate into tools for mineral reserve planning, mine design, and operational optimization.
Beyond the fundamentals, the book highlights real-world case studies where linear algebra directly powers mining innovation: ore grade modeling, slope stability analysis, ventilation network design, and optimization of equipment usage. By blending solid mathematical foundations with cutting-edge engineering challenges, this book equips readers with both the technical depth and the practical mindset to apply linear algebra as a driver of efficiency, safety, and innovation in mining engineering.