Role of Machine Learning in Mineral Exploration

1. Introduction

There are many different approaches that can be used in mineral exploration. Machine learning, which processes data from a variety of sources, is an example of such a tool. It is used in geology and mineral exploration software to compare the results of various analyses and to make decisions about where to drill further.
In this blog we discuss how machine learning can contribute to the exploration process by providing a more accurate prediction of mineral deposits or areas that have been previously explored. We also discuss how machine learning can be applied in geological software by processing precise geological data sets with little or no human intervention, thus reducing processing time and increasing the accuracy of predictions.

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2. The role of the machine learning in stratigraphic modeling

 Machine learning is a set of techniques for doing math that can be applied to any type of problem. Some of the most common ways you may use machine learning include:
Geological modeling :
The advent of computational geology and the development of software tools such as GIS has made it possible for researchers to perform geological mapping in a systematic manner. In the field, geological mapping is performed by using a handheld GPS unit, which generates maps that are then downloaded onto a computer. Once these maps are printed out, geological exploration can be performed using software developed for this task. This software allows for extracting information from the maps. Some examples of such software include ArcGIS Desktop and ProPilot; both are PC-based programs available on many computer stores and online.
Three-dimensional point cloud of slope failures in the Sensuikyo Valley (Aso City, Kumamoto, Japan) obtained by UAS-based SfM-MVS photogrammetry
Three-dimensional point cloud of slope failures in the Sensuikyo Valley (Aso City, Kumamoto, Japan) obtained by UAS-based SfM-MVS photogrammetry

3. Significance of machine learning in mineral exploration

Minerals are everywhere, and geologists use computers to explore the earth’s crust. How do they do it? Using machines.
Computer-aided geological modeling (CAG) was developed in the 1980s by a team of researchers at the University of California, Santa Barbara (UC-Santa Barbara). In the early 2000s, computer scientists at Google created an artificial intelligence program called DeepMind to help them mine minerals.
By using CAG software and DeepMind’s algorithm, Google has discovered two rare minerals that have never before been found. This discovery is significant in that it shows that deep learning can be used for mining mineral exploration; new techniques are needed to extract information from large datasets that can be difficult to fit into a few minutes.
4. Developing Geological Machine Learning Models.
In order to develop an effective geological exploration program, one must first understand the data that is being collected in this process and how this data is being analyzed in order to determine the best technique to extract useful information from it. The major advantage associated with using these software programs is that they make it possible for an individual to manage his or her own geological exploration project without having to have access to specialized training or financial resources.  This makes their use much more accessible than when one uses traditional training methods like classroom instruction or laboratory experiments.
5. Some Disadvantages Related to Machine Learning Software.
However, there are some drawbacks associated with using this type of software as well:
Software tools do not always work correctly The limitations inherent in any software tool prevent them from working correctly on all datasets A common problem with statistical analysis is that it tends not to take into consideration how small samples are The exact circumstances under which it will work accurately will vary depending on the dataset One must understand how each tool works before using them.
Besides these problems, another drawback associated with using any type of artificial intelligence-based program (generally known as machine learning) is its inability to accurately evaluate data sets consisting only of simple observations i.e., those made by humans or other non-digital entities like other animals or plants as well as those made only by digital entities (like humans) such as text or images; i.e., those generated by computers and/or other digital devices instead of physical objects inside our world (like rocks). All digital entities have different properties and characteristics; thus, one cannot expect all digital entities (or human ones) in their entirety may be evaluated exactly the same way by all machine learning programs.  Accordingly, machine learning solutions will often exhibit.

6. Challenges with Machine learning.

Machine learning is a new and promising technology that has the potential to revolutionize our field of geological exploration. According to a recent survey, 53% of people believe that machine learning will be part of the digital age within the next five years Despite its potential, however, there are still many challenges and hurdles in trying to draw large-scale applications from this technology. One challenge with machine learning is the lack of transparency in how it works and how it is used. Another problem is the difficulty in applying it accurately to real-world geology problems. And lastly, there are concerns over privacy issues – despite advances in artificial intelligence systems, they have not yet been able to provide information on user data or their activities. As a result, there remains an urgent need for more research and development on these issues. 


Mineral exploration is a grinding, backbreaking, uphill task that requires a high degree of technical expertise and knowledge. With the advent of digital geoscience techniques like GIS, mobile computing and self-learning algorithms, it’s important to recognize the role of machine learning in mineral exploration. The use of machine learning can provide cost-effective solutions for geological applications.

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