【摘 要】
:
Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines. Although machine learning has been widely applied in seismic data processing, feasibility and reliability of applying this technique to
【机 构】
:
CSRIO Mineral Resources,Brisbane,QLD,4069,Australia;School of Minerals and Energy Resources Engineer
论文部分内容阅读
Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines. Although machine learning has been widely applied in seismic data processing, feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated. In this research, two groups of seismic events with a minimum local magnitude (ML) of -3 were observed in an underground coal mine. They were respectively located around a dyke and the longwall face. Additionally, two types of undesired signals were also recorded. Four machine learning methods, i.e. random forest (RF), support vector machine (SVM), deep convolu-tional neural network (DCNN), and residual neural network (ResNN), were used for classifying these signals. The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91%accuracy. The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy. As mining is a dynamic progress which could change the characteristics of seismic signals, the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining. A cascaded workflow consisting of database update, model training, signal prediction, and results review was established. By progressively calibrating the DCNN model, it achieved up to 99% prediction accuracy. The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.
其他文献
针对经典卷积神经网络难以有效分类全极化SAR数据中复杂的海岛地物的问题,为满足日益精细化的监测需求、充分发挥SAR在海岛监测中的作用,文章对经典的AlexNet改进,提出了一种应用于全极化SAR数据海岛地物分类的卷积神经网络模型.该模型是在AlexNet基础上调整卷积核大小及全连接层,减少参数,加入池化层,降低维度,减少计算复杂度.利用高分三号卫星对南日岛进行观测获取的全极化SAR图像进行实验和分析,表明该方法能够对全极化SAR图像中海岛的多类地物进行有效区分,与AlexNet的分类结果相比,精度提升5.
针对既有地铁遗留横通道隧道结构的特点,采用双轮铣槽机研磨、切割工艺方式可实现清障和地下连续墙成槽的统一.该工艺利用双轮铣槽机功率大、铣轮研磨、切割能力强的特征,实现清障与成槽施工同步,铣轮研磨、切割过程易控且对隧道结构扰动低,社会经济效益显著,值得推广.
地质环境是城市建设的基础,存在的隐伏断裂是城市建设中不可忽略的潜在危害之一.如何对隐伏断裂进行快速有效精确定位,一直是工程地球物理勘查的重要研究课题.本文利用瞬变电磁和微动探测等物探方法,对南京市秦淮新河梅山段地质情况进行了勘查,经过数据处理及综合分析,有效查明了区内地表以下300m深度内隐伏断层的位置,共推断断层破碎带2条,其中F2断层经钻孔验证误差仅1m.为南京市城市道路规划设计提供了可靠的地球物理依据.
为了克服SRTM和ASTER各自缺陷,充分结合二者优势得到更高质量的DEM,提出了一种基于神经网络模型的加权融合方法.首先,统一两种DEM坐标系和高程基准;其次,借助后向传播神经网络分别建立SRTM与ASTER高程误差和地形因子的非线性关系模型;然后,利用此模型估计各DEM的误差分布;最后,根据该误差计算SRTM和ASTER融合权重,并实现SRTM和ASTER加权融合.以董志塬为研究区进行分析.结果表明:融合后DEM精度有明显提高,相比于原始SRTM和ASTER,平均绝对误差分别降低了1.29 m和3.6
旅游城市因其性质、职能与发展演化历程的特殊性,其滨水空间人地关系变迁显现出独有特质.研究以典型的旅游城市滨水空间——黄山市中心城区屯溪区为例,通过结合滨水地域特性构建人地关系地域系统理论框架,系统分析其滨水空间人地关系的变迁历程.研究发现,黄山市中心城区滨水空间人地关系演化经历了3个主要阶段:在古、近代屯溪因水而兴,滨水空间主要发挥着交通商贸的功能,人地关系呈现自发依存的特征;改革开放以来产业粗放发展,人类对滨水空间进行了较为强烈的干预破坏,人地关系逐渐失衡;而随着生态文明建设及生态补偿机制的实施,旅游城
云南是一个高原多山、多民族的特殊地理区,复杂的人地关系、丰富的山地资源、独特的地理环境为地理科技工作者提供了丰富的研究对象.1987 ~1996年郭来喜在担任云南省地理研究所所长期间,发展云南地理科学,为民族、边疆、高原人地关系研究和扶贫、边疆口岸开放等社会经济发展做出了突出贡献、在深切怀念郭先生为地理科研事业建立丰功伟绩的同时,也回顾了发展云南省地理科学研究的历程.
环滇池地区是云南省经济发展最具活力、高原湖泊生态脆弱和民族文化多元融合的典型区域.运用主成分分析、标准差椭圆和地理加权回归分析法以揭示该区域县域经济空间格局演变特征及影响因素.研究表明:1995~2018年,该地区县域经济发展水平大幅提高且区域差异逐渐缩小;总体上由以翠湖为核心向以滇池为核心演变,五华、盘龙、西山和官渡区在经济发展中仍占主导地位,但拉动作用逐渐减弱,呈贡区成为滇池东岸经济新高地;行政力、工业化水平和市场化程度先后深刻塑造着该地区县域经济格局,行政力、市场化程度对经济发展具有正向作用,工业增
利用红河州新一代天气雷达观测数据,结合高空、地面观测资料,对2020年4月22日凌晨发生在滇南地区的一次极端强对流天气过程进行分析.结果 表明,受南支槽东移、高低空冷暖平流和高低空急流影响,触发了此次强对流天气.分析雷达回波发现,极端天气过程为弓形回波中靠南端的单体风暴在东移过程中发展增强为超级单体风暴并造成严重的风雹灾害.超级单体风暴雷达特征维持时间久,三体散射长钉回波长,VIL值大且大于50 (kg·m-2);径向速度图上有逆风区、中尺度辐合和中气旋等特征;另外,从6.0°仰角上看,大冰雹和雨粒子的Z
Data related to the pre-grouting work of a large underground project are systematically analyzed to reveal the mechanism behind, to shed some light on the execution of practical grouting, and to enrich the theory of engineering geology. Grouting is genera
The spatial information of rockhead is crucial for the design and construction of tunneling or under-ground excavation. Although the conventional site investigation methods (i.e. borehole drilling) could provide local engineering geological information, t