Article(id=1321895029673677205, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321894989857141221, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1768752000000, receivedDateStr=2026-01-19, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093934695, onlineDateStr=2026-10-27, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093934695, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093934695, creator=13701087609, updateTime=1793093934695, updator=13701087609, issue=Issue{id=1321894989857141221, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='8', pageStart='1', pageEnd='324', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093925203, creator='13701087609', updateTime=1793094606751, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897848535372687, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321894989857141221, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897848535372688, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321894989857141221, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=67, endPage=74, ext={EN=ArticleExt(id=1321895029937918358, articleId=1321895029673677205, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=A Particle Swarm Optimization Algorithm Integrating Chaotic Mapping and Opposition-Based Learning for Microseismic Localization in Deep Rock Masses, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issues of a complex solution space, strong nonlinearity, and easy entrapment in local optima in microseismic localization, the application of swarm intelligence optimization algorithms in microseismic localization was systematically investigated. By comparing the performance of the sparrow search algorithm (SSA), differential evolution (DE) algorithm, and particle swarm optimization (PSO) algorithm in single P-wave localization, it was found that PSO algorithm exhibited the best performance in terms of solution stability. To further increase the localization accuracy and enhance algorithm robustness, S-waves were introduced to construct a dual-wave joint localization model, reducing the inversion space from four dimensions to three dimensions. The experimental results indicate that the dual-wave joint localization significantly outperformed the single P-wave localization, where PSO algorithm achieves the lowest average localization error in the dual-wave joint localization and demonstrated good adaptability. To overcome the shortcomings of the PSO algorithm, such as premature convergence and sensitivity to initial values, an adaptive improved particle swarm optimization (APSO) algorithm integrating opposition-based learning and chaotic mapping was proposed. Opposition-based learning was utilized to generate a high-quality initial population, while chaotic mapping achieved the dynamic adjustment of the inertia weight to balance the global exploration and local exploitation capabilities. The numerical experimental results indicate that the average localization error of APSO algorithm at multiple typical source points is lower than that of the standard PSO algorithm, with the average localization error reduced by approximately 62.4%, and it exhibits stronger stability and convergence efficiency. This method provides an effective tool for high-precision microseismic localization in deep rock mass engineering and has promising prospects for engineering application.

, authors=Yang Wang¹, Heping Xiao², Yanhui Liu³, Bin Gan², Yue Huang², Shibin Tang³, authorsList=Yang Wang, Heping Xiao, Yanhui Liu, Bin Gan, Yue Huang, Shibin Tang, authorCompany=null, correspAuthors=Shibin Tang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321895030340571543, articleId=1321895029673677205, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=融合混沌映射与反向学习的深部岩体微震定位粒子群优化算法, columnId=1226462294582936453, journalTitle=矿业研究与开发, columnName=深部、深海矿产资源勘探与开发, runingTitle=null, highlight=null, articleAbstract=

针对微震定位解空间复杂、非线性强、易陷入局部最优等问题，系统研究了群智能优化算法在微震定位中的应用。通过对比麻雀搜索算法（SSA）、差分进化（DE）算法和粒子群优化（PSO）算法在单P波定位中的性能，发现PSO算法在求解稳定性方面表现最佳。为进一步提高定位精度并增强算法鲁棒性，引入S波构建双波联合定位模型，将反演空间由四维降至三维。试验结果表明，双波联合定位显著优于单P波定位，其中PSO算法在双波联合定位中的平均定位误差最低，展现出良好的适应性。为克服PSO算法早熟收敛、初值敏感等不足，提出了一种融合反向学习与混沌映射的自适应改进粒子群优化（APSO）算法。反向学习用于生成高质量初始种群，混沌映射实现惯性权重的动态调节，以平衡全局探索与局部开发能力。数值试验结果表明，APSO算法在多个典型震源点上的平均定位误差均低于标准PSO算法，平均定位误差降低约62.4%，且表现出更强的稳定性与收敛效率。该方法为深部岩体工程微震高精度定位提供了有效工具，具有良好的工程应用前景。

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王洋（1972—），男，河南南阳人，高级工程师，主要从事工程建设管理及运行工作。E-mail:4491506@qq.com

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王洋（1972—），男，河南南阳人，高级工程师，主要从事工程建设管理及运行工作。E-mail:4491506@qq.com

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