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.

中文翻译：

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训练数据集形成过程中 MWD 数据收集和验证的细节

本文介绍了随钻测量 (MWD) 数据处理和验证方法领域的结构化分析，并描述了“干净”数据选择的主要细微差别和某些细节，以构建“父”训练数据库以供后续使用。用于机器学习算法。作者的主要目的是创建一个可训练的机器学习算法，该算法基于与特定条件相关的可用“干净”输入数据，可以关联、处理和选择从钻机获得的参数，并将它们用于进一步估计各种岩石特征，最佳钻孔和爆破参数的预测以及爆破结果。