采矿扰动下层析成像及微震多参数特征实例研究

采矿活动诱发的岩爆可引起矿区内强烈的振动和严重的岩石破坏。结合微震多参数、大震级事件分布与层析成像特征综合井下分析,综合为一种较好的预警方法。首先,在中国陕西某地下矿山建立了自主研发的微震监测系统。然后,根据层析成像计算结果得出了波速场异常与采矿活动区的关联模型,初步确定了可能存在风险的区域与采矿活动的特征。此外,对b值、S值、成像结果、大震级微震事件分布及陕西矿区795和860水平现场调查情况等多个因素进行了分析。根据该矿山6月7日岩爆案例,将795至860水平现场的多个区域进行对比分析,从工程角度发现了潜在的不安全区域,对安全施工管理提供了参考。微震监测系统可作为一个危险预警的手段,具有广泛的应用前景。

安全智能采矿:大数据时代的若干探索与挑战

为进一步完善采矿学学科体系,针对传统采矿学理论与方法上存在的不足,立足于学科建设高度,积极推进采矿学学科理论的研究与课程内容体系的基本建设,本文开展了安全智能采矿学的建立研究。首先,根据采矿学发展进程以及国内外智能采矿的发展现状,提出了安全智能采矿学的概念,并分析了其学科内涵与学科属性。其次,从学科建设角度提出安全智能采矿学学科基础与学科任务,并构建了学科内容体系。最后,本文提出了安全智能采矿学面临的十大挑战,讨论了安全智能采矿学的未来研究任务与应用前景。安全智能采矿学的创建,是大数据时代下信息化采矿工程学科发展的必然趋势,可为安全智能采矿学学科的发展注入生机与活力。

硬岩矿山开采方式变革与智能化绿色矿山构建——以开阳磷矿为例

在阐述矿山开采模式历程、非爆连续开采和智能化开采现状的基础上,探讨硬岩矿山开采方法变革与智能化矿山构建。基于深部开采矿岩受力特征、秉承"变害为利"的思想,构建了深地资源安全高效开采发展框架和矿山循环经济模式,并以开阳磷矿为例,初步实践了矿山深部硬岩开挖与灾害控制互换的非爆连续开采与资源开采无害化循环利用新模式;构建和完善了智能化矿山理论框架和方法体系,在资源与开采环境可视化、生产过程与设备智能化、生产信息与决策管理科学化的基础上,借助数字化建模软件、信息采集系统、光纤环网,人机交互系统和数据传输系统等,架构了开阳磷矿三维可视化集成平台,实现了设计智能化、监测可视化、设备自动化、生产系统无人化和管控一体化,为深部固体资源安全高效智能化开采与循环利用实践提供参考。

未来有色金属采矿可持续发展实践与思考

我国深地、深海、深空战略的布局与实施对未来有色金属采矿可持续发展提出了更高要求。本文在简述贵重有色金属分布及其特点的基础上,就有色金属的深地、深海、深空采矿现状和开采前景进行了总结和展望。结合有色金属深地开采现状和存在问题,提出了实现有色金属开采可持续发展必须走精准、连续、智能与绿色开采之路。本文介绍了作者为实现有色金属采矿可持续发展所做的工作,并给出了对未来有色金属采矿可持续发展的一些思考。

用沙坝断层滑移型微震事件群活动特征分析

提出利用空间聚类算法DBSCAN与S波、P波能量比（ES/EP）相结合的方法,识别得到开磷用沙坝矿2014年3—5月IMS微震监测系统数据库中F310断层附近的断层滑移型微震事件群。对微震事件群区域的微震事件活动率与时间分布特征、视体积与施密特数、b等特征进行分析。结果表明：微震事件主要集中在每天的12时到14时,震级大于-0.5级的微震事件主要发生在每天的11时到16时;4月3—10日、5月2—9日和5月16—23日3个时间段内微震事件活动频繁,而且都具有施密特数时间序列曲线斜率增大、累积视体积曲线急剧下降、b出现下降的特性,表明这3个时间段断层区域岩体不稳定,发生危险事故的可能性较大。相比于同等能量的爆破事件,断层滑移型微震事件更能诱发顶板冒落和矿柱坍塌甚至岩爆等灾害,分析其活动特征可为分析断层活动趋势提供有效的预判信息。

岩石破坏声发射b值的误差分析

岩石声发射b值变化是岩石破坏的重要前兆之一,由于b值大小受到样本数、起算幅值和幅值间隔等人为设定的影响,开展b值误差量化分析意义重大。采用不同的样本数、起算幅值和幅值间隔,使用最大似然法和最小二乘法计算由随机数组成的岩石声发射幅值目录的b值,将结果中分布在误差以内的数据记为有效数据,使用有效数据占比分析了b值误差大小,并使用室内花岗岩三轴压缩的声发射幅值目录对其进行了验证。结果表明:使用最大似然法计算岩石声发射b值时,有效数据占比随着样本数的增加而减少,随着起算幅值的增大而增加。使用最小二乘法估计b值时,随着样本数的增加,有效数据占比先增后减,样本数为2000个左右时可得到最多的有效数据;幅值间隔一定时,起算幅值越大,有效数据占比越小;随着起算幅值的增加,对应的取得最多有效数据的幅值间隔从22 dB减少到18 dB。研究成果可为岩石破坏声发射b值的合理计算提供参考。

Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform

Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring.The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass.The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology.An image identification model based on Convolutional Neural Network(CNN)is established in this paper for the seismic waveforms of microseismic events and blasts.Firstly,the training set,test set,and validation set are collected,which are composed of 5250,1500,and 750 seismic waveforms of microseismic events and blasts,respectively.The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training.Results show that the accuracies of microseismic events and blasts are 99.46%and 99.33%in the test set,respectively.The accuracies of microseismic events and blasts are 100%and 98.13%in the validation set,respectively.The proposed method gives superior performance when compared with existed methods.The accuracies of models using logistic regression and artificial neural network(ANN)based on the same data set are 54.43%and 67.9%in the test set,respectively.Then,the ROC curves of the three models are obtained and compared,which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model.It not only decreases the influence of individual differences in experience,but also removes the errors induced by source and waveform parameters.It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.

Conventional triaxial compression on hollow cylinders of sandstone with various fillings: Relationship of surrounding rock with support

The interaction of surrounding rock with a support system in deep underground tunnels has attracted extensive interest from researchers.However,the effect of high axial stress on tunnel stability has not been fully considered.In this study,compression tests with and without confining pressure were conducted on solid specimens and hollow cylinder specimens filled with aluminium,lead,and polymethyl methacrylate(PMMA)to investigate the strength,deformation and failure characteristics of circular roadways subjected to high axial stress.The influence of the three-dimensional stress on the surrounding rock supported with different stiffness was studied.The results indicate that the strength and peak strain of hollow cylinders filled with PMMA are higher than those of hollow cylinders filled with aluminium or lead,indicating that flexible retaining is beneficial for roadway stability.The results obtained in this paper can contribute to better understanding the support failure of a buried roadway subjected to high axial stress and thus to analyzing and evaluating roadway stability.

Evaluation methods of man-machine-environment system for clean and safe production in phosphorus mines: A case study

Phosphorus is an essential element in agricultural production and chemical industry. However, since the risk of casualties and economic loss by mining accidents, the application of clean and safe production in phosphorus mines encounters great challenges. For this purpose, a man-machine-environment system composed of evaluation indexes was established, and the grading standards of indexes were defined. Firstly, the measurements of 39 qualitative indexes were obtained through the survey data. According to the measured values of 31 quantitative indexes, the measurements of quantitative indexes were calculated by linear measurement function(LM) and other three functions. Then the singleindex measurement evaluation matrixes were established. Secondly, the entropy weight method was used to determine the weights of each index directly. The analytic hierarchy process(AHP) was also applied to calculate the weights of index and index factor hierarchies after the established hierarchical model. The weights of system hierarchies were given by the grid-based fuzzy Borda method(GFB). The comprehensive weights were determined by the combination method of AHP and GFB(CAG). Furthermore, the multi-index comprehensive measurement evaluation vectors were obtained.Thirdly, the vectors were evaluated by the credible degree recognition(CDR) and the maximum membership(TMM)criteria. Based on the above functions, methods, and criteria, 16 combination evaluation methods were recommended.Finally, the clean and safe production grade of Kaiyang phosphate mine in China was evaluated. The results show that the LM-CAG-CDR is the most reasonable method, which can not only determine the clean and safe production grade of phosphorus mines, but also improve the development level of clean and safe mining of phosphorus mines for guidance.In addition, some beneficial suggestions and measures were also proposed to advance the clean and safe production grade of Kaiyang phosphorus mine.

Cloud model-clustering analysis based evaluation for ventilation system of underground metal mine in alpine region

Ventilation system is significant in underground metal mine of alpine region.Reasonable evaluation of ventilation effectiveness will lead to a practical improvement for the maintenance and management of ventilation system.However,it is difficult to make an effective evaluation of ventilation system due to the lack of classification criteria with respect to underground metal mine in alpine region.This paper proposes a novel evaluation method called the cloud model-clustering analysis(CMCA).Cloud model(CM)is utilized to process collected data of ventilation system,and they are converted into cloud descriptors by CM.Cloud similarity(CS)based Euclidean distance(ED)is proposed to make clustering analysis of assessed samples.Then the classification of assessed samples will be identified by clustering analysis results.A case study is developed based on CMCA.Evaluation results show that ventilation effectiveness can be well classified.Moreover,CM is used alone to make comparison of evaluation results obtained by CMCA.Then the availability and validity of CMCA is verified.Meanwhile,difference of CS based ED and classical ED is analyzed.Two new clustering analysis methods are introduced to make comparison with CMCA.Then the ability of proposed CMCA to meet evaluation requirements of ventilation system is verified.