摘要
引入并介绍6种新型群体智能优化算法(灰狼算法、鲸鱼优化算法、蝗虫优化算法、麻雀搜索算法、蚁狮算法、蜻蜓算法)的仿生原理、核心计算公式及优化特性,在经典到时差模型基础上设计一种新型微震震源反演数学模型,利用仿真的矿山微震震源正反演数据对比分析6种方法的性能差异。结合实际矿山人工爆破数据,通过6个统计指标从精度、收敛速度、稳定性等多个角度测试这6种新型群体智能优化算法在微震震源定位中的有效性和可靠程度。
We introduce and describe the bionic principle,core formulae and optimization characteristics of six novel swarm intelligence optimization algorithms(gray wolf optimization,whale optimization algorithm,grasshopper optimization algorithm,sparrow search algorithm,ant lion optimization and dragonfly algorithm).Based on the classical arrival time difference model,we design a new mathematical model of micro-seismic source inversion,and use the forward modeling and inversion data of simulated mine micro-seismic sources to compare and analyze the performance differences of six methods.We test the effectiveness and reliability of these six novel swarm intelligence optimization algorithms in micro-seismic source localization from multiple perspectives,such as accuracy,convergence speed and stability,by combining the actual mine manual blasting data with six statistical indicators.
作者
庞聪
马武刚
李查玮
江勇
廖成旺
陈国庆
PANG Cong;MA Wugang;LI Chawei;JIANG Yong;LIAO Chengwang;CHEN Guoqing(Institute of Seismology,CEA,Wuhan 430071,China;Wuhan Gravitation and Solid Earth Tides,National Observation and Research Station,Wuhan 430071,China;Mathematical Modeling Research Center,Chengdu Jincheng College,Chengdu 611731,China)
出处
《大地测量与地球动力学》
CSCD
北大核心
2023年第7期708-714,共7页
Journal of Geodesy and Geodynamics
基金
中国地震局地震研究所和应急管理部国家自然灾害防治研究院基本科研业务费专项(IS202236328,IS202226321)
湖北省自然科学基金(2019CFB768)
四川省软科学研究项目(2019JDR0084)
武汉引力与固体潮国家野外科学观测研究站开放基金(WHYWZ202208)。
关键词
微震震源定位
到时差模型
灰狼算法
鲸鱼优化算法
蝗虫优化算法
麻雀搜索算法
蚁狮算法
蜻蜓算法
micro-seismic source localization
arrival time difference model
gray wolf optimization
whale optimization algorithm
grasshopper optimization algorithm
sparrow search algorithm
ant lion optimization
dragonfly algorithm
