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Mineral Exploration

AI & ML Applications in Mineral Exploration

Unlock the potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing mineral exploration with our specialized course. In this cutting-edge program, participants will delve into the intersection of AI, ML, and geoscience to discover how these technologies are reshaping the future of mineral exploration.
4.7 (23 user ratings)
Rated 4.7 out of 5
  • Offline Coaching
  • Batch Starts on 15/04/2024

Descriptions

Throughout the course, participants will:

  • Gain a comprehensive understanding of AI and ML concepts and their applications in the mineral exploration industry.
  • Explore real-world case studies and examples showcasing the successful integration of AI and ML techniques in geological data analysis, interpretation, and decision-making.
  • Learn how to leverage AI algorithms for predictive modeling, target identification, and resource estimation, enhancing exploration efficiency and success rates.
  • Acquire practical skills in data preprocessing, feature engineering, model selection, and evaluation, tailored specifically for mineral exploration datasets.
  • Collaborate with industry experts and fellow participants to tackle complex exploration challenges and develop innovative AI-driven solutions.

 

By the end of the course, participants will be equipped with the knowledge and skills needed to harness the power of AI and ML to drive advancements in mineral exploration, paving the way for more efficient, cost-effective, and sustainable resource discovery.

Course Curriculum

Part - 1
  • Introduction
  • Need and significance of AL/ML in Mineral Exploration (upskilling/updating)
  • Role of AI in Geosciences and relevance to Industry needs
  • Activities in Applied Geosciences (Mineral/coal/Geotech/GW/Logging/ hydrocarbons/geohazards/etc)
  • Mineral deposits in India
  • Geological, Geochemical, and Geophysical and RS Exploration
  • Challenges and uncertainties (sampling), large data
  • Significance of AI in mineral Exploration (base and critical Minerals)
  • Architecture of the Course
  • Linear Algebra, Vectors operations
  • Matrix Operations, Eigen Vectors
  • Basics of Calculus
  • Derivatives & their geometry
  • Partial Derivatives
  • Chain Rule
  • Visualization of Derivatives & Product Rule
  • Basics of Probability
  • Rules, Conditional Probability
  • Bayes theorem
  • Statistics: Different measures, Distributions, linear regression
  • Dictionaries
  • Scikit-learn’s datasets
  • Data for Regression, clustering & classification
  • Adding And Subtracting Matrices
  • Apply Operations To Elements Dot Product Vectors
  • Average, Variance, And Standard Deviation, Determinant
  • Converting A Dictionary Into A Matrix
  • Maximum And Minimum, Rank, Flatten Diagonal A Matrix
  • Invert & Reshaping matrix operations
  • Classical Algorithms
  • NumPy & SciPy
  • Pandas, Matplotlib
  • Deeper Look at ML
  • Features, their selection and extraction
  • Classification and Regression Problems
  • Intuitive and Simple Algorithms
  • Dimensionality Reduction
  • Sampling Method: Bootstrap and Bagging
  • Manifold Learning & Unsupervised Learning
  • Performance Metrics
  • Non-Linear solutions and MLP
  • Gradient Descent and Back Propagation
  • PCA with Eigen Faces
  • Support Vector Machines and Kernels
  • Natural Language Processing
  • Features for Perception (Image and Speech)
  • Pytorch
  • CNN, RNN,GAN, Transformers
  • Overfitting and Generalization
  • Tensorflow and Keras
  • Time Series Application
  • Decision Tree, Ensemble Methods and Random Forest
  • Saving model
Part - 2
  • Significance of AI/ML in Mineral Exploration
  • Mineral Identification, Prospecting Mapping
  • Mineral deposits in India
  • Geological, Geochemical, Geophysical and RS Exploration
  • Challenges and uncertainties (sampling), large data
  • Architecture of the Course
  • Significance Mineral exploration and Prospecting
  • Mineral deposits and potential resources in India
  • Ore minerology
  • The four-stage architecture: reconnaissance, detailed survey, target identification, exploratory drilling.
  • Geological Exploration (sampling, morphological, structural, lineaments etc)
  • Geochemical Exploration
  • Importance of
  • Geophysical data in Mineral Exploration
  • Geophysical Exploration Methods
    Gravity methods
  • Magnetic methods
  • Electrical methods
  • EM methods
  • Geophysical borehole logging, etc
  • Principles of Remote
  • Sensing.
    Satellite sensors and data acquisition.
  • Image processing techniques in mineral exploration
  • Case studies and practical applications
  • Fundamentals of Geographic Information Systems (GIS).
  • Spatial data representation and analysis.
  • GIS software tools and applications
  • Project on GIS and RS in mineral exploration
  • Hands-on exercises
  • Introduction to AL/ML Architecture/ Models
  • Model Applications and Comparison
  • Building Concept Model of target commodity
  • Translating the Conceptual Model into a practical exploration targeting model
  • Work flow
  • Integration of prior Geological/ Geophysical knowledge
  • Organize relevant geological/geochemical/geophysical and satellite imagery and historical exploration data
  • Data Preprocessing: Clean and preprocess the data
  • Construction of Comprehensive high quality Geodatabase
  • Identifying Targeting Parameters
  • Selection of Model
  • Supervised / Unsupervised Network
  • Extraction of feature Vectors, Data Dimension Reduction etc
  • Select AI/ML based on your specific needs and the nature of your data.
  • Selection of Network parameters and Training the model
  • Model Evaluation and Optimization-
  • Use Appropriate metrics-Evaluate the performance
  • Optimize by tuning its parameters, selecting different features
  • Testing the Model with untrained Data
  • Comparison of Prediction results with Ground truth
  • Transform the Model output to targeted information
  • Integration AI model with Exploration Workflow
  • Augmentation -Model’s predictions to guide drilling/mineral identification
  • Continuous Learning and Updating retrain the model as new data to be accurate and relevant as conditions change

 

  • Case studies AI in mineral Exploration
  • Hands-on Projects (Supervised & unsupervised)
  • Project 1 :
  • Project 2 :
  • Project 3 :
  • Discussion and conclusions
This course includes:
  • Duration:
  • Enroll Fee:
  • Total Fee:
  • Language:
  • Accomodation Details:
  • 03 Months
  • ₹19,999/-
  • ₹59,999/-
  • English
  • Click Here
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Meet our team

Course Experts

Dr. Yegireddi Satyanarayana

Director (R&D and AI Applications in GeoSciences) Former Associate Director & Scientist G, Def.Res.&Dev. Org. (DRDO), India. Former Geophysicist, Geological Survey of India

S. Uma Maheswara Rao

Director (GIS & RS), CMT GSC, TRP -IITH, M.Sc Geology
Former Director PT.PMCR, Jakarta, Indonesia
Former Manager (Geology) MECL, Nagpur

S Rama Murthy

Technical Area Expert (GIS & RS), CMT GSC, TRP-IITH
M.Sc Geology
Former Director (GSI), Hyderabad

Vikram K.Y

DIRECTOR - OPERATIONS Operations Management (IIT - Delhi) Exports Management (IIEM, Bangalore)
Advisory Council To ITPC

P. Prashanth Kumar

DIRECTOR - PLANNING

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About Critical Mineral Trackers
Critical Mineral Trackers is dedicated to empowering individuals and organisations through our expert-led education and upskilling programs. We bridge the gap between discovery and exploration, offering comprehensive geology, geophysics, and geochemistry expertise.
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