煤层微断层参数的人工智能预测

Kipko Oleksandr, Efremov Igor, Levit Victor, Gogo Volodymyr, Merzlikina Yelyzaveta
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摘要

目的。基于人工智能方法的煤层微断层裂缝预测方法研究与开发。方法学。为了进行研究,我们建立了一个基于神经网络的单结构神经网络。网络的训练是基于自然选择的原则,自然选择是最强的。该原理的实现旨在支持遗传算法。每个网络都被测试了适合度,与它的预测能力成正比,最好的一个被用于预测。预测是通过这样一种方式进行的,即从基点对整个项目现场进行预测。研究了一种利用神经网络和遗传算法预测低振幅扰动坐标和振幅的方法。该方法是基于采矿计划的可靠数据训练神经网络,经过一定次数的训练,预测工程现场的微故障不连续扰动。以隧道开挖和清扫工程所揭示的违例参数作为训练数据。利用人工智能预测微断层不连续扰动的方法可以预测扰动在预测区域的存在和分布概率。该方法的主要优点是成本最小且具有足够的可靠性。这与已知的方法有明显的区别。这种方法的应用不需要在矿井条件下使用特殊设备,这大大降低了预测的劳动强度。这种方法的效率使您能够在短时间内快速地对给定地点进行预测,从而有助于最快地调试新矿区。实用价值。该方法可用于预测乌克兰顿巴斯矿区开采场的微断层不连续扰动,以评估开采柱的可靠性,并保证开采柱的合理位置对准备工作的稳定性。同时,考虑到微断层不连续扰动发生的物理过程的主要特点,预测技术也在不断改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FORECASTING OF THE PARAMETERS OF MICRO-FAULT OF COAL SEAMS USING ARTIFICIAL INTELLIGENCE
Purpose. Research and development of a method for predicting micro-fault fractures in coal seams using artificial intelligence methods. Methodology. To carry out the study, a neural network of the sing structure was formed. The training of the network was based on the principles of natural selection, which is the strongest one. The implementation of this principle was designed to support the genetic algorithm. Each network was tested for fitness, proportional to its ability to predict, and the best one was used for forecasting. The forecast was made in such a way that from the base point the forecast was made for the entire project site. Results. The research was conducted to develop a method for predicting the coordinates and amplitudes of low-amplitude disturbances using neural networks and genetic algorithms.The method consists in training a neural network based on reliable data taken from mining plans and, after a certain number of trainings, forecasting micro-fault discontinuous disturbances on the project site. The parameters of the violations revealed by tunnelling and cleaning works are used as training data. Originality. The method of forecasting micro-fault discontinuous disturbances using artificial intelligence allows predicting the presence and probability of distribution of disturbances in the predicted area. The main advantage of this method is the minimal cost with sufficient reliability. This favourably distinguishes it from the known methods. The application of this method does not require the use of special equipment in mine conditions, which significantly reduces the labour-intensiveness of the forecast. The efficiency of this method allows you to quickly and in a short time make a forecast on a given site, which contributes to the fastest possible commissioning of new mining fields. Practical value. The proposed method can be used to predict micro-fault discontinuous disturbances in the extraction fields of mines in the Ukrainian Donbas to assess the reliability of working out the extraction pillars and ensure the stability of the preparatory works due to their rational location. At the same time, the forecasting technique is being improved, taking into account the main characteristics of the physical process of the genesis of micro-fault discontinuous disturbances.
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