计算机科学
工作流程
分布式声传感
实时计算
自动化
重新调整用途
被动地震
事件(粒子物理)
地震学
地质学
光纤
工程类
光纤传感器
电信
机械工程
物理
数据库
量子力学
废物管理
作者
I. Lim Chen Ning,L. Swafford,M. Craven,K. Davies
标识
DOI:10.3997/2214-4609.202310393
摘要
Summary As the need for continuous passive seismic monitoring grows, we continue to seek sensors that can provide a large aperture and are highly repeatable. Distributed acoustic sensing provides a unique opportunity to deploy optical fibres as a complementary distributed sensing instrument for seismic monitoring that is repeatable once permanently deployed (i.e., trenched or cemented) and with minimal maintenance effort when installed correctly. Additional cost savings can be achieved when repurposing the existing network of telecommunication optical fibres. However, the data rates, both temporally and spatially, can be challenging for near-real-time seismic monitoring solutions. We propose using a machine learning-enabled seismic phase picker (i.e., PhaseNet) and an automated pick corrections procedure in preparation for seismic event location. We demonstrate our workflow on synthetic data from a modified SEAM II Barrett unconventional model.
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