Advanced Resource Mapping in Coal Exploration using IoT Sensor-Based Drilling Systems with SVM Integration
N. Ganesh Kumar, P. Rajalakshmi, K. Dhivya, Balachandra Pattanaik, G. Suresh, S. Srinivasan · 2024
To improve resource mapping in coal exploration, this research introduces a new method that combines Internet of Things (IoT) sensor-based drilling devices with Support Vector Machine (SVM) algorithms. Coal exploration techniques of yesteryear often included labor-intensive, time-consuming manual sampling and analysis, which had restricted geographical coverage. The proposed approach makes use of IoT drilling equipment that is fitted with a variety of sensors to gather geological data in real-time while the drilling is underway. With the data collected by these sensors, a wealth of information may be analyzed, including lithology, density, and coal seam characteristics. It creates prediction models for resource mapping by combining this sensor data with SVM, which allows for the efficient identification and delineation of coal seams across exploration locations. For precise coal deposit location and classification, the SVM framework excels at dealing with the complicated, high-dimensional character of geological data. The proposed system can transform the way coal is explored in various geological environments. The coal mining sector stands to gain a great deal from our technique, which allows for data-driven decision-making and improves resource mapping procedures. This, in turn, leads to cost reductions, productivity increases, and environmental sustainability.