Specifics of MWD Data Collection and Verification during Formation of Training Datasets

This paper presents a structured analysis in the area of measurement while drilling (MWD) data processing and verification methods, as well as describes the main nuances and certain specifics of clean data selection in order to build a parent training database for subsequent use in machine learning algorithms. The main purpose of the authors is to create a trainable machine learning algorithm, which, based on the available clean input data associated with specific conditions, could correlate, process and select parameters obtained from the drilling rig and use them for further estimation of various rock characteristics, prediction of optimal drilling and blasting parameters, and blasting results. The paper is a continuation of a series of publications devoted to the prospects of using MWD technology for the quality management of drilling and blasting operations at mining enterprises.

Automated summary

This paper provides a structured analysis of measurement while drilling (MWD) data processing and verification methods, focusing on clean data selection to build a parent training database for machine learning algorithms. The main goal is to create a trainable machine learning algorithm to estimate rock characteristics, predict optimal drilling and blasting parameters, and blasting results. This research is part of a series on using MWD technology for quality management in mining drilling and blasting operations.