Geophysical Exploration

AI /ML & Marine Geophysical 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.

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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

Overview of the Course

Objective
Different Applied domains of Geoscience
Marine Geophysics and Seafloor topography
What Role AI/ML to play
Role of AI/ML in real world Solutions and impact, some examples
AI/ML contribution in Marine Geophysics

Introduction of AI and ML

How it works and what exactly it is expected do
Difference between AI and ML
What are the major tasks (detection, classification, prediction)
What the are the applied area
Results Vs Data quality,
What is python and How it is useful
Popular architectures of AI and ML Algorithms and their significance
Criterion for selection of
Supervised networks (Regression, Decision Trees, SVM, Random Forest) and Examples
Deep learning Neural Networks (CNN, GNN) & Examples
Timeseries networks (LSTM) (Examples
Unsupervised Networks (clustering) & Examples

Marine Geophysical Instruments

Basic principle, technique and significance
1. Marine Gravity
2. Marine Magnetic

Multichannel 2D/3D Seismic reflection arrays

Shallow seismics- Sparker-Streamer
High resolution Sub-bottom profilers
Single beam Echosounder

Multibeam Echosounder
Side Scan Sonar

Offshore Hydrocarbons Exploration

Sedimentary basins
Geological/Geophysical tools
Seismic Prospecting /Seismic reflection – Multi channel seismic prospecting
Data acquisition/processing/Analysis
Identification of potential zones

Borehole logging studies

Core sample analysis
Geophysical logging and significance
Correlation of geophysical log and core data
subsurface lithology classification
AI/ML in lithology classification-Advantages

Marine /Geological/Geophysical Investigations

Marine Geophysical Techniques and instruments
Sea floor Topography-Swath Bathymetry, DEM
Seafloor Imaging _Side Scan Sonar imaging
Sub bottom Information-Subbottom profilers
High Resolution Shallow Seismic investigations
Bottom properties Cone penetrometer
Sediment sampling- Grab, Core

AI applications in Marine Sediment Classification from

Echosounder data
MBES scattering
Sonar imagery
High resolution SBP data

Offshore Geoengineering Applications

Ports development, offshore structures
Channel Clearance
Cable/pipe laying
u/w Search operations
Selection of cable/pipeline roots
Search operations
u/w cab
u/w awards, eg. gas leaks

Summary

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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