Fast Automatic Segmentation of Microseismic and Blasting Events without Labeled Data Using Sparse Graph Spectral Clustering

dc.contributor.author	Sharma, Atma
dc.contributor.author	Zhang, Jie
dc.contributor.author	Wu, Shuangyi
dc.contributor.author	Lu, Meng
dc.contributor.author	Huang, Zhong-Kai
dc.date.accessioned	2026-08-20T21:22:24Z
dc.date.available	2026-08-20T21:22:24Z
dc.date.copyright	2026/04/21
dc.date.issued	2026
dc.identifier.other	AJRUA6.RUENG-1732.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314353
dc.description.abstract	AbstractDifferentiating between microseismic and blasting events is a classical task in underground excavation monitoring. Most of the machine learning methods utilized for this task are supervised and require labeled data. To this aspect, this study ...
dc.publisher	American Society of Civil Engineers
dc.title	Fast Automatic Segmentation of Microseismic and Blasting Events without Labeled Data Using Sparse Graph Spectral Clustering
dc.type	Journal Article
dc.journal.volume	12
dc.journal.issue	3
dc.journal.title	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
dc.identifier.doi	10.1061/AJRUA6.RUENG-1732
dc.journal.fristpage	04026022-1
dc.journal.lastpage	04026022-10
dc.page	10
dc.tree	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 003
dc.contenttype	Fulltext