Results 11 to 20 of about 313,735 (187)
DTPP:An efficient depthwise separable TCN for seismic phase picking
Artificial Intelligence in GeosciencesWith the rapid development of artificial intelligence in seismology, various deep learning-based seismic phase picking models have emerged in recent years.
Shuai Lv, Yuxiang Peng
doaj +2 more sources
Picking Regional Seismic Phase Arrival Times with Deep Learning
SeismicaSparse instrumental coverage for much of the Earth requires working with regional seismic phases for effective seismic monitoring. Machine learning phase pickers to date have focused on local earthquake recordings.
Albert Leonardo Aguilar Suarez +1 more
doaj +2 more sources
EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking
Frontiers in Earth Science, 2022 Earthquake detection and seismic phase picking play a crucial role in the travel-time estimation of P and S waves, which is an important step in locating the hypocenter of an event. The phase-arrival time is usually picked manually. However, its capacity
Wei Li +17 more
doaj +1 more source
USTC-Pickers: a Unified Set of seismic phase pickers Transfer learned for ChinaKey points
Earthquake Science, 2023 Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China. To mitigate this problem, we build a unified set of customized seismic phase pickers for different levels of use in China. We first
Jun Zhu, Zefeng Li, Lihua Fang
doaj +1 more source
A Lightweight Network for Seismic Phase Picking on Embedded Systems
IEEE AccessPhase picking is a critical task in seismic data processing, where deep learning methods have been applied to enhance its accuracy. While lightweight deep learning networks have been optimized for edge computing devices, there is a lack of networks ...
Yadongyang Zhu +4 more
doaj +2 more sources
Dizhen xuebao, 2020 为了快速、高效地从地震数据中识别地震事件和拾取震相，本文利用基于样本增强的卷积神经网络自动震相拾取方法，将西藏林芝地区L0230台站3个月数据作为训练集，该区内另外6个台站连续1个月的波形数据作为测试集，采用高斯噪声、随机噪声拼接、随机挑选噪声、随机截取地震事件等4种样本增强的方法扩增训练集，以提高自动震相拾取技术的准确率。结果显示：样本增强前模型在测试集上的地震事件识别准确率为80%，样本增强后提升至97%，表明样本增强有效地提高了模型的泛化性能和抗干扰能力；在0.5 s误差范围内 ...
An Li +4 more
doaj +1 more source
Frontiers in Earth Science, 2023 Accurate detection of P-wave arrivals has important applications in real-time seismic data processing, such as earthquake monitoring and earthquake early warning. The Sichuan and Yunnan regions, where the China Seismic Experimental Site (CSES) is located,
Boren Li +6 more
doaj +1 more source
Entropy, 2023 The underground pressure disaster caused by the exploitation of deep mineral resources has become a major hidden danger restricting the safe production of mines. Microseismic monitoring technology is a universally recognized means of underground pressure
Rui Dai, Yibo Wang, Da Zhang, Hu Ji
doaj +1 more source
Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network
IEEE Access, 2020 High-accuracy microseismic phase picking is fundamental to microseismic signal processing. Phase picking methods based on deep learning show great potential dealing with low signal to noise ratio (SNR) data but need enormous training data.
Tianqi Jiang, Jing Zheng
doaj +1 more source
Seismicity‐Scanning Based on Navigated Automatic Phase‐Picking [PDF]
Journal of Geophysical Research: Solid Earth, 2019 AbstractWe propose a new method, named Seismicity‐Scanning based on Navigated Automatic Phase‐picking (S‐SNAP), that is capable of delineating complex spatiotemporal distributions of seismicity. This novel algorithm takes a cocktail approach that combines source scanning, kurtosis‐based phasepicking, and the maximum intersection location technique into
Fengzhou Tan +3 more
openaire +1 more source