[1] We collected and processed a large amount of high-quality broadband teleseismic waveform data recorded by the 48 Chinese National Digital Seismic Network stations to estimate large-scale lateral variations of crustal thickness and V p /V s ratio (hence Poisson's ratio) beneath China. A statistical method was used to select mutually coherent receiver functions at each station, which yielded over 200 traces for most of the stations. With the conventional HÀk (the crustal thickness and V p /V s ratio) approach, there is a large trade-off between H and k. Consequently, multiple maxima are frequently observed in the HÀk domain. We introduced a weight function that measures the coherence between the P-to-S conversion and the reverberation phases at each HÀk grid to reduce the trade-off. A 4th-root stacking method was further applied to reduce uncorrelated noise relative to the linear stack. These modifications turned out to be very effective in reducing the HÀk trade-off and yielded reliable estimates of crustal thickness and V p /V s ratio. The crust beneath eastern China is as thin as 31-33 km and the underlying Moho is relatively flat and sharp. In the western part of China, the crust is considerably thicker and shows large variations. The Moho is observed at about 51 km depth along the Tian Shan fold system and about 84 km deep beneath the central part of the Tibetan Plateau. The transition occurs at the so-called N-S belt between about 100°and 110°E, which is featured by unusually high seismicity and large gravity anomalies. The average V p /Vs ratio over the mainland China crust is about 1.730 (s = 0.249), significantly lower than the global average 1.78 (s = 0.27) of the continental crust. This lower V p /V s ratio may suggest a general absence of mafic lowermost crustal layer beneath China.
S U M M A R YWith a growing number of modern broad-band seismographic stations in Asia, the conditions have improved to allow higher resolution structural studies on regional scales. Here, we perform a receiver-based study of the lithosphere of southeast China using waveform records of excellent quality from 14 Chinese National Digital Seismic Network and four Global Seismic Network stations. Calculating the theoretical receiver functions (RFs) that match the observed RFs from teleseismic waveforms is an established technique for retrieving information about crustal and upper mantle structure beneath a seismic receiver. RFs, however, are predominantly sensitive to the gradients in the lithospheric elastic parameters, and it is impossible to determine a non-unique distribution of seismic parameters such as absolute shear wave speeds as a function of depth unless other geophysical data are combined with RFs. Thus, we combine RFs with independent information from shear and compressional wave speeds above and below the Mohorovičić discontinuity, available from the existing tomographic studies. We introduce a statistical approach for automatically selecting only mutually coherent RFs from a large set of observed waveforms. Furthermore, an interactive forward modelling software is introduced and applied to observed RFs to define a prior, physically acceptable range of elastic parameters in the lithosphere. This is followed by a grid-search for a simple crustal structure. An initial model for a linearized, iterative inversion is constructed from multiple constraints, including results from the grid-search for shear wave speed, the Moho depth versus v p /v s ratio domain search and tomography. The thickness of the crust constrained by our multistep approach appears to be more variable in comparison with tomographic studies, with the crust thinning significantly towards the east. We observe low values of v p /v s ratios across the entire region, which indicates the presence of a very silicic crust. We do not observe any correlation between the crustal thickness or age of the crust with v p /v s ratios, which argues against a notion that there is a simple relationship between mineralogical composition and crustal thickness and age on a global scale.
The aim of this study was to examine the anxiety status of the frontline clinical nurses in the designated hospitals for the treatment of coronavirus disease 2019 (COVID-19) in Wuhan and to analyze the influencing factors, to provide data for psychologic nursing. This study used a cross-sectional survey design and convenience sampling. The questionnaires were completed by 176 frontline clinical nurses. Anxiety was determined using the Hamilton anxiety scale. General data were collected using a survey. Correlation analyses were used. Among the 176 frontline nurses, 77.3% (136/176) had anxiety. The anxiety scores of the frontline clinical nurse fighting COVID-19 were 17.1 ± 8.1. Anxiety symptoms, mild to moderate anxiety symptoms, and severe anxiety symptoms were found in 27.3%, 25%, and 25% of the nurses, respectively. Sex, age, marital status, length of service, and clinical working time against COVID-19 were associated with anxiety ( P < .05). The frontline nurses working in the designated hospitals for the treatment of COVID-19 in Wuhan had serious anxiety. Sex, age, length of service, and clinical working time against COVID-19 were associated with anxiety in those nurses. Psychologic care guidance, counseling, and social support should be provided to the nurses to reduce their physical and mental burden. Nursing human resources in each province should be adjusted according to each province's reality.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.