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CREDIT-X1local:A reference dataset for machine learning seismology from ChinArray in Southwest China

earthquake Science 2024年第2期37卷 139-157页

作者： Lu Li Weitao Wang Ziye Yu Yini Chen Institute of Geophysics China Earthquake AdministrationBeijing 100081China Key Laboratory of earthquake Source Physics China Earthquake AdministrationBeijing 100081China zhejiang earthquake agency HangzhouZhejiang 310013China

High-quality datasets are critical for the development of advanced machine-learning algorithms in ***,we present an earthquake dataset based on the ChinArray Phase I records(X1).ChinArray Phase I was deployed in the southern north-south seismic zone(20°N-32°N,95°E-110°E)in 2011-2013 using 355 portable broadband seismic ***-X1local,the first release of the ChinArray Reference earthquake Dataset for Innovative Techniques(CREDIT),includes comprehensive information for the 105,455 local events that occurred in the southern north-south seismic zone during array observation,incorporating them into a single HDF5 *** 100-Hz sampled three-component waveforms are organized by event for stations within epicenter distances of 1,000 km,and records of≥200 s are included for each *** types of phase labels are *** first includes manually picked labels for 5,999 events with magnitudes≥2.0,providing 66,507 Pg,42,310 Sg,12,823 Pn,and 546 Sn *** second contains automatically labeled phases for 105,442 events with magnitudes of−1.6 to *** phases were picked using a recurrent neural network phase picker and screened using the corresponding travel time curves,resulting in 1,179,808 Pg,884,281 Sg,176,089 Pn,and 22,986 Sn ***,first-motion polarities are included for 31,273 Pg *** event and station locations are provided,so that deep learning networks for both conventional phase picking and phase association can be trained and *** CREDIT-X1local dataset is the first million-scale dataset constructed from a dense seismic array,which is designed to support various multi-station deep-learning methods,high-precision focal mechanism inversion,and seismic tomography ***,owing to the high seismicity in the southern north-south seismic zone in China,this dataset has great potential for future scientific discoveries.

关键词： earthquake dataset machine learning Pg/Sg/Pn/Sn phase picking P-wave first-motion polarity

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Evaluating human exposure to antibiotic resistance genes

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Biosafety and Health 2024年第2期6卷 98-100页

作者： Zhenchao Zhou Hong Chen College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China International Cooperation Base of Environmental Pollution and Ecological Health Science and Technology Agency of ZhejiangZhejiang UniversityHangzhou 310058China

Antibiotic resistance is an escalating global concern,leading to millions of annual *** resistance genes(ARGs)present in bacteria equip them to withstand the effects of ***-and interspecific ARGs transmission through horizontal gene transfer further exacerbates resistance *** presence of ARGs in the environment heightens the probability of human exposure via direct inhalation,ingestion,or contact with polluted air,food,or water,posing substantial biosafety and health ***,ARGs represent a critical focal point in public health and environmental safety and are classified as emerging *** perspective underscores the necessity to assess ARG exposure within the One Health framework and to accord greater attention to the mitigation strategies and tactics associated with ARGs.

关键词： Antibiotic resistance genes Human exposure Biosafety

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Point defects in 2-D liquid crystals with a singular potential:Profiles and stability

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Science China Mathematics 2024年第11期67卷 2515-2540页

作者： Zhiyuan Geng Wei Wang Basque Center for Applied Mathematics School of Mathematical Sciences Zhejiang University

We study radial symmetric point defects with degree k/2 in the 2-D disk or R2in the Q-tensor framework with a singular bulk energy,which is defined by Bingham ***,we obtain the existence of solutions for the profiles of radial symmetric point defects with degree k/2 in the 2-D disk or ***,we prove that the solution is stable for |k| = 1 and unstable for |k| > *** identities are derived and utilized throughout the proof of existence and stability/instability.

关键词： radial symmetric point defects singular bulk potential Bingham closure stability

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Over the Moon

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Beijing Review 2024年第19期67卷 46-47页

作者： Xinhua News agency Xinhua News agency

The Yuehe historical area in Jiaxing,zhejiang Province,is a microcosm of riverside towns located south of the Yangtze *** rivers embrace the ancient town,which dates back to the Ming and Qing dynasties(1368-1911),like... 详细信息

关键词： hence translate Yangtze

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Uncharted effects of Fukushima Nuclear Plant Wastewater Discharge on marine life

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Journal of Plant Ecology 2024年第3期17卷 1-4页

作者： Xiaoqi Zhou zhejiang Tiantong Forest Ecosystem National Observation and Research Station School of Ecological and Environmental SciencesEastChina Normal UniversityShanghai 200241China

INTRODUCTION On 24 August 2023,the Japanese government insisted on initiating the discharge of Fukushima nuclearcontaminated water into the sea,causing widespread global concerns regarding marine biological safety,seafood and marine resources(TEPCO 2023).Although the Japanese government has consistently emphasized that the nuclear wastewater has been treated using an Advanced Liquid Processing System(ALPS),resulting in very low concentrations of various nuclear elements,and that the International Atomic Energy agency(IAEA)has conducted real-time monitoring of the treatment process and discharge fow(IAEA 2023),people around the world still harbor signifcant *** are a few reasons for this ongoing apprehension.

关键词： initiating harbor wastewater

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Quick Phase Identification for Dense Seismic Array with Aid from Long Term Phase Records of Co-located Sparse Permanent Stations

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earthquake Research in China 2020年第3期34卷 328-342页

作者： CHEN Yini LI Jun WANG Zhenjie ZHAO Mengqi YU Tiehong LI Danning YU Junyi WANG Weitao zhejiang earthquake agency Hangzhou 310013China Key Laboratory of earthquake Source Physics Institute of GeophysicsChina Earthquake AdministrationBeijing 100086China Yunnan earthquake agency Kunming 650224China

The phase identification and travel time picking are critical for seismic tomography,yet it will be challenging when the numbers of stations and earthquakes are *** here present a method to quickly obtain P and S travel times of pre-determined earthquakes from mobile dense array with the aid from long term phase records from co-located permanent *** records for 1768 M≥2.0 events from 2011 to 2013 recorded by 350 ChinArray stations deployed in Yunnan Province are processed with an improved AR-AIC method utilizing cumulative envelope and *** reference arrivals are predicted based on phase records from 88 permanent stations with similar spatial coverage,which are further refined with ***,718573 P picks and 512035 S picks are obtained from mobile stations,which are 28 and 22 times of those from permanent stations,*** comparing the automatic picks with manual picks from 88 permanent stations,for M≥3.0 events,81.5%of the P-pick errors are smaller than 0.5 second and 70.5%of S-pick errors are smaller than1 *** events with a lower magnitude,76.5%P-pick errors fall into 0.5 second and 69.5%S-pick errors are smaller than 1 ***,the Pn and Sn phases are easily discriminated from directly P/S,indicating the necessity of combining traditional auto picking and integrating machine learning method.

关键词： Phase picking Travel time Dense array Spatial overlap

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Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network

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earthquake Science 2023年第2期36卷 113-131页

作者： Ziye Yu Weitao Wang Yini Chen Institute of Geophysics China Earthquake AdministrationBeijing 100081China Key Laboratory of earthquake Source Physics China Earthquake AdministrationBeijing 100081China zhejiang earthquake agency Hangzhou 310013China

Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and ***,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore ***,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU *** based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive *** performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most *** RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and ***++requires the most computational effort among the *** all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their *** beginners,we provide a tutorial on training and validating the pickers using the DiTing *** also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by *** of our models are open-source and publicly accessible.

关键词： neural network deep learning seismic phase picking earthquake detection open-source science

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Seismic monitoring network based on MEMS sensors

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earthquake Science 2019年第3期32卷 179-185页

作者： Zhen-xuan Zou Ming Zhang Xu-dong He Sheng-fa Lin Zheng-yao Dong Kan Sun zhejiang earthquake agency Hangzhou 310013China Zhoushan Science and Technology Bureau Zhoushan 316000China

This paper provides a brief introduction to the application of the sensor monitoring network of micro-electro-mechanical systems(MEMS)to zhejiang *** the Wenzhou Shanxi reservoir and other areas,MEMS and traditional intensity-monitoring instruments have been deployed with complementary functions to implement hybrid *** low-cost MEMS network can continuously monitor areas at high risk of earthquakes at a high ***,it can quickly collect the parameters of earthquakes and records of the near-field acceleration of strong *** can be also used to rapidly generate earthquake intensity reports and provide early warning of *** used the MEMS sensors for the first time in 2016,and it has helped promote the development and application of seismic intensity instruments since then.

关键词： MEMS sensor seismic intensity instrument hybrid networking rapid intensity reports early warning

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A palaeoearthquake event and its age revealed by the travertine layer along the Litang fault in the southeastern margin of the Qinghai-Tibetan plateau

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earthquake Research Advances 2023年第3期3卷 53-62页

作者： Yao Yang Mingjian Liang Chao Ma Jun Li Hualiang Shen Fang Du Song Luo Shao Liu Xuelian Rui Sichuan earthquake agency Chengdu610041China College of Earth Sciences Chengdu University of TechnologyChengdu610059China zhejiang earthquake agency Hangzhou310013China Chengdu Geological Survey Center China Geological SurveyChengdu610081China

The Litang fault(LTF),located in the southeast of the Qinghai-Tibetan Plateau,is known for its high level of present-day seismicity,whereas its Pleistocene activity has been scarcely *** study focused on a tract of banded travertine deposits precipitated from thermal waters along the NW–SE-trending LTF *** role of travertine deposits in recording neotectonic activity has been studied by identifying their internal *** soft-sediment deformation structures observed within the banded travertines include micro folds,liquefied breccia,and liquefied *** deformed structures,which are restricted to a single unit separated unconformably by undeformed layers,can be traced for tens of meters,indicating that they were formed by seismic shaking triggered by LTF *** deformation of the banded travertine layers is attributed to the combined effects of seismic shaking,liquefaction,and fluidization,and it can be related to a paleo earthquake event with a magnitude of MS>*** U-series ages obtained from the banded travertine deposits perturbed by the earthquakes are in the range of 130.59–112.94 ka,indicating an important fault-assisted neotectonic activity that occurred during the Middle–Late *** of such structures,in combination with the use of U-series dating methods,can yield a reliable timing of neotectonic activity and provide new evidence for under-standing the seismotectonic setting of the Litang area.

关键词： Travertine Paleoearthquake Soft-sediment deformation U-series dating Litang fault

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SeisGuard: A Software Platform to Establish Automatically an earthquake Forecasting Model

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Open Journal of earthquake Research 2023年第4期12卷 177-197页

作者： Xiliang Liu Yajing Gao Mei Li zhejiang earthquake agency Hangzhou China Institute of earthquake Forecasting China Earthquake Administration Beijing China

SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an earthquake forecasting model. The main function of this system is to analyze and process the deformation, fluid, electromagnetic and other geophysical field observing data from ground-based observation, as well as space-based observation. Combined station and earthquake distributions, geological structure and other information, this system can provide a basic software platform for earthquake forecasting research based on spatiotemporal fusion. The hierarchical station tree for data sifting and the interaction mode have been innovatively developed in this SeisGuard system to improve users’ working efficiency. The data storage framework designed according to the characteristics of different time series can unify the interfaces of different data sources, provide the support of data flow, simplify the management and usage of data, and provide foundation for analysis of big data. The final aim of this development is to establish an effective earthquake forecasting model combined all available information from ground-based observations to space-based observations.

关键词： SeisGuard Platform Geophysical Observing Data Electromagnetic Emission Time Series Database Spatiotemporal Fusion earthquake Forecasting Model

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