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What are the benefits and drawbacks of using machine learning algorithms for geological field mapping?

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Geological field mapping is the process of collecting and interpreting spatial data on the rocks, structures, and landforms of a given area. It is a fundamental skill for geologists, as it helps them understand the history, evolution, and potential of the Earth. However, field mapping can also be challenging, time-consuming, and expensive, especially in remote or inaccessible regions. This is where machine learning algorithms can offer some advantages and disadvantages for geologists.

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Benedict Odhiambo

Assistant Surveyor at REM Consultants
Chinonso Onwuegbucha

E&P Data Administrator |Data Scientist
Guettal Adel

Geologist sedimentologist at governmental agency

1 What is machine learning?

Machine learning is a branch of artificial intelligence that involves teaching computers to learn from data and perform tasks without explicit programming. Machine learning algorithms can analyze large and complex datasets, identify patterns and trends, and make predictions or classifications based on the data. Machine learning can be applied to various domains, such as image recognition, natural language processing, and recommendation systems.

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2 How can machine learning help with field mapping?

Machine learning can offer geologists a number of advantages when it comes to field mapping. For instance, it can improve the quality and resolution of remote sensing images, like satellite or aerial photos, to provide useful insights into the surface geology and topography of an area. Additionally, machine learning can automate the extraction and interpretation of geological features from remote sensing images or other data sources, such as lidar or radar. Furthermore, it can integrate and synthesize different types of data, including geophysical, geochemical, and geochronological data, to create more precise and comprehensive geological maps and models. Finally, machine learning can assist with field data collection and validation by using mobile devices or drones that are equipped with sensors and cameras that communicate with algorithms and give feedback or suggestions to the geologist.

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3 What are the benefits of using machine learning for field mapping?

Using machine learning for field mapping can be beneficial to geologists in several ways, such as saving time and resources, by cutting down on manual data processing and analysis, as well as improving accuracy and efficiency. Additionally, machine learning can expand the scope and scale of field mapping by making it possible to explore and map areas that may be hard to access with conventional methods. Finally, machine learning can generate new insights and discoveries by uncovering unknown relationships and patterns in the data which could lead to new hypotheses or interpretations.

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Chinonso Onwuegbucha

E&P Data Administrator |Data Scientist
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Some of the benefits: Efficiency: Machine learning algorithms can process and analyze large datasets quickly, allowing geologists to map and interpret geological features more efficiently. It can provide highly accurate and consistent results, minimizing human errors in geological mapping. Machine learning algorithms can integrate various data sources, such as remote sensing imagery, geophysical data, and geochemical data, to create comprehensive geological maps. Machine learning can identify complex geological patterns and anomalies that may not be obvious to geologists. It can be trained to perform repetitive or time-consuming tasks, such as image analysis or data classification. Geologists can focus on more advanced tasks.

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4 What are the drawbacks of using machine learning for field mapping?

Using machine learning for field mapping can also have some drawbacks for geologists, such as losing control and transparency by relying on black-box algorithms, facing ethical and legal issues, encountering technical and practical challenges, and lacking human intuition and creativity. These algorithms may not explain how they arrived at their outputs or how confident they are in their predictions or classifications, potentially violating the privacy or ownership of the data or the results, requiring high-quality and relevant data, adequate computational power and storage, and appropriate training and validation of the algorithms. Additionally, they may miss the nuances and complexities of the geological context and the field experience and judgment of the geologist.

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Benedict Odhiambo

Assistant Surveyor at REM Consultants
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There are some instances where the data used in the creation of the models may be inaccurate and may not represent true happenings on the ground

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Guettal Adel

Geologist sedimentologist at governmental agency
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Geology is a based on observation and analysis . AI may analyse images and numeric data calculate reserves and make estimations but can' t make observations!

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Chinonso Onwuegbucha

E&P Data Administrator |Data Scientist
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Yes, Machine Learning model may not consider the broader regional knowledge which can be essential for accurate mapping and also noisy, incomplete or biased data can lead to inaccurate results. There is always need for the human geologists to bring in their domain knowledge for accurate interpretation.

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5 How to use machine learning for field mapping effectively?

Machine learning is not a magical solution for field mapping, but rather a powerful tool that can complement and enhance the geologist's skills and knowledge. To maximize its potential, geologists should familiarize themselves with the basics and limitations of machine learning, such as the principles and methods of the algorithms, the types and sources of data, and the metrics for evaluating performance. Additionally, it is important to choose the right machine learning approach and application for the data and the problem. Furthermore, geologists should collaborate with machine learning experts and stakeholders to ensure clear roles and responsibilities are established when it comes to sharing data and results. Lastly, it is important to keep learning and improving by updating data, refining algorithms, testing results, validating findings, and incorporating new developments.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the benefits and drawbacks of using machine learning algorithms for geological field mapping?

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1 What is machine learning?

2 How can machine learning help with field mapping?

3 What are the benefits of using machine learning for field mapping?

4 What are the drawbacks of using machine learning for field mapping?

5 How to use machine learning for field mapping effectively?

6 Here’s what else to consider

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