This document discusses artificial intelligence (AI) and its applications in geoscience. It defines AI and machine learning, noting that machine learning is a subset of AI. It then outlines several applications of AI in geoscience fields like petrophysics, geology, geophysics, and remote sensing. These include using AI to build predictive models of soil behavior, analyze geological data to understand formations, and assist with mineral exploration. The document also notes limitations of AI, such as potential bias in models, and discusses the future potential of AI in geoscience, including transforming scientific methods and taking guesswork out of mineral exploration.

1. UNIVERSITY OF MINES AND TECHNOLOGY, TARKWA UMaT
ARTIFICIAL INTELLIGENCE IN
GEOSCIENCE
GEOLOGICAL ENGINEERING DEPARTMENT
ARTIFICIAL INTELLIGENCE IN GEOLOGY(GL276)

2. CONTENT
 Introduction
 The difference between AI and Machine Learning
 Application of AI in Geoscience
 Limitations of AI in Geoscience
 Future perspective
 Conclusion
 References
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3. INTRODUCTION
 Artificial intelligence is a branch of computer science
dealing with the reproduction of human-level
intelligence, self-awareness, knowledge, conscience,
thought in computer programs.
 Artificial intelligence (AI) is one of the fastest growing
disciplines in modern technology.
 AI has found numerous applications in the various
sciences including Geoscience
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4. 4

5. THE DIFFERENCE BETWEEN AI AND ML
 AI is as explained in the introductory slide
 Machine learning on the other hand is a subset of AI.
 Machine learning (ML) is the process of using
mathematical models of data to help a computer learn
without direct instruction.
 Good examples of AI are Siri, Google Assistant and
Amazon Alexa.
 Examples of ML are Google search engines, Twitter
sentiment analysis and stock prediction.
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6. APPLICATIONS OF AI IN GEOSCIENCE
 AI and ML have become very useful many areas of
Geoscience mainly because of their capability to
interpret humongous data collected by various sensors.
 AI is applicable in the following fields of geoscience and
more:
 Petrophysics/Formation elevation
 Geography, Geodesy/Topography
 Geology/Geophysics
 Remote sensing and photogeology.
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7. APPLICATIONS OF AI IN GEOSCIENCE
 AI is used to build predictive models of soil behavior
based on data from laboratory tests and field
measurements
 It is applied in the analysis of geological data to help
geologists understand the deposit model, formation
process; and discover the metallogenic rule and assist
mineral exploration and development.
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8. LIMITATIONS OF AI IN GEOSCIENCE
 AI has proven to be ubiquitous in the field of geoscience
however a few limitations of AI in the discipline include
 The fact that AI models can be biased if they are trained
on incomplete or in accurate data.
 AI cannot replace human intuition and decision-making
skills.
 The vulnerability and uncertainty of AI models demand
further attention, considering the fact that many
geoscience and remote sensing tasks are highly safety-
critical.
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9. FUTURE PERSPECTIVE OF AI
 In general AI have the following potentials:
 AI will transform the scientific method
 It will become a pillar of foreign policy
 AI will enable next generation consumer experiences
 Addressing the climate crisis will require AI
 AI will enable truly personalized medicine
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10. FUTURE PERSPECTIVE
 In geology AI the future of AI may be realized in the
exploration of minerals, geological mapping and
geohazard assessment
 AI will take out the guesswork and minimize the risk
inherit in the search for new deposits.
 AI can also help geologists to identify patterns in data
that may not be visible to the human eye.
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11. CONCLUSION
 In conclusion, AI has the potential to help geosciences
move from qualitative to quantitative analysis. AI-
enabled technologies such as image processing, smart
sensors, and intelligent inversion are being tested by
researchers in a wide variety of geosciencces domains.
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12. CONCLUSION cont’d
 A novel approach to representing geosystem dynamics
via a recurrent neural network within deep learning
architectures has been proposed. The physics-aware AI
system exhibits robust transferability and good
intelligence for inferring unobserved processes in runoff
modeling
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13. REFERENCES
 https://www.sciencedaily.com
 https://www.ai.engineering.columia.edu
 https://www.sciencedirect.com
THANK YOU
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