46 research outputs found
Retrieving 2-D laterally varying structures from multistation surface wave dispersion curves using multiscale window analysis
No full textThe analysis of multistation surface wave records is of increasing popularity in imaging the structure of the Earth due to its robustness on dispersion measurement. Since the representation of multistation surface wave dispersion curves (DCs) is uncertain in laterally varying media, average information beneath the receiver array is assumed to be obtained by inverting the dispersion curves with a horizontally layered model. To retrieve a more realistic 2-D laterally varying structure, we present a multiscale window analysis of surface waves (MWASW) method for analysing 2-D active-source surface wave data. The MWASW method is based on the use of a forward algorithm for calculating the theoretical DCs over 2-D models and multisize spatial windows for estimating the dispersion data. The forward algorithm calculates the theoretical dispersion considering the lateral variation beneath the receiver array; hence, the estimated DC is not treated as representative of the average properties but as data containing the lateral variation information. By inverting the dispersion data extracted from different spatial windows, the subsurface information at different depth ranges and lateral extensions are integrated to produce a shear wave velocity model. The dispersion curves analysed from smaller spatial windows retrieve the shallow structure with a higher lateral resolution, whereas the phase velocity data from larger spatial windows provide average information with a greater depth. We test the effectiveness of the MWASW method using three synthetic examples and two field data sets. Both results show the improved lateral resolution of the S-wave velocity structure retrieved with the MWASW method compared to the traditional multistation method in which the local horizontally layered model is adopted
Institutional, social, and household determinants of reproduction in Northeast China, 1789-1906
No full textThis study examined the effects of institutional, social, and household contexts on reproductive outcomes in rural east Liaoning (Liaodong) and Shuangcheng in northeast China from the late 18th to the early 20th century. This study used the China Multi-Generational Panel Dataset for Liaoning and Shuangcheng (CMGPD-LN and CMGPD-SC), which was constructed based on the household registers of residents of Liaodong and Shuangcheng between 1749 and 1913. The subjects of the study, who were mostly farmers, had an institutional affiliation with the Eight Banners. In Liaodong, they were categorized according to their economic obligations, and in Shuangcheng, they were categorized according to their entitlements. Overall, the differences in institutional backgrounds, entitlements, and duties meant that Shuangcheng bannermen had more surviving sons and higher marital fertility rates and total fertility rates than those from Liaodong. A further discrete-time event-history analysis of marital fertility, total fertility, and the number of surviving sons by age 45 showed that official employment played a crucial role in Liaodong, whereas the within-household hierarchy was of great significance in Shuangcheng. Within Liaodong and Shuangcheng, bannermen with distinct institutional affiliations had different fertility rates and reproductive responses to official employment and within-household hierarchy. Differences in reproduction persisted even after accounting for differences in access to marriage and survivorship of births. Institutions, characterized by membership of an institutional category and local policies that created inequality between categories, were found to play a more important role in reproduction than inter- and intra-household inequalities, as institutional differences impacted patterns of reproduction. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
Mutually Constrained Inversion of Common Offset GPR Reflection and Surface Wave Dispersion Data in Layered Media
No full textThe effects of back scattering from a defect zone on guided wave dispersion in coast dykes
No full textDetecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression
No full textLandscape fragmentation is usually caused by many different anthropogenic influences and landscape elements Scientifically revealing the spatial relationships between landscape fragmentation and related factors is highly significant for land management and urban planning The former studies on statistical relationships between landscape fragmentation and related factors were almost global and single-scaled In fact, landscape fragmentations and their causal factors are usually location-dependent and scale-dependent Therefore we used geographically Weighted Regression (GWR) with a case study in Shenzhen City Guangdong Province China to examine spatially varying and scale-dependent relationships between effective mesh size an indicator of landscape fragmentation and related factors We employed the distance to main roads as a direct influencing factor and slope and the distance to district centers as indirect influencing factors which affect landscape fragmentation through their impacts on land use and urbanization respectively The results show that these relationships are spatially non-stationary and scale-dependent, indicated by clear spatial patterns of parameter estimates obtained from GWR models and the curves with a characteristic scale of 12 km for three explanatory variables respectively Moreover GWR models have better model performance than OLS models with the same Independent variable as is indicated by lower AICc values higher Adjusted R(2) values from GWR and the reduction of the spatial autocorrelation of residuals GWR models can reveal detailed site information on the different roles of related factors in different parts of the study area Therefore this finding can provide a scientific basis for policy-making to mitigate the negative effects of landscape fragmentation (C) 2010 Elsevier Ltd All rights reservedhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000285661100029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701GeographySSCI50ARTICLE1,SI292-3023
Simulating multiple class urban land-use/cover changes by RBFN-based CA model
No full textLand use systems are complex adaptive systems, and they are characterized by emergence, nonlinearity, feedbacks, self organization, path dependence, adaptation, and multiple-scale characteristics. Land use/cover change has been recognized as one of the major drivers of global environmental change. This paper presents a coupled Cellular Automata (CA) and Radial Basis Function Neural (RBFN) Network model, which combines Geographic Information Systems (GIS) to contribute to the understanding of the complex land use/cover change process. In this model. GIS analysis is used to generate spatial drivers of land use/cover changes, and RBFN is trained to extract model parameters. Through the RBFN-CA model, the conversion probabilities of each cell from its initial land use state to the target type can be generated automatically. Future land use/cover scenarios are projected by using generated parameters in the model training process. This RBFN-CA model is tested based on the comparison of model output and the real data. A BPN-CA model is also built and compared with the RBFN-CA model by using a variety of calibration metrics, including confusion matrix, figure of merit, and landscape metrics. Both the location and landscape metrics based assessment for model simulation indicate that the RBFN-CA model performs better than the BPN-CA model for simulating land use changes in the study area. Therefore the RBFN-CA model is capable of simulating multiple classes of land use/cover changes and can be used as a useful communication environment for stakeholders involved in land use decision-making. (C) 2010 Elsevier Ltd. All rights reserved.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000287290200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Interdisciplinary ApplicationsGeosciences, MultidisciplinarySCI(E)EISSCI8ARTICLE2111-1213
Investigating spatial variation in the relationships between NDVI and environmental factors at multi-scales: a case study of Guizhou Karst Plateau, China
No full textKnowing the spatial relationships between the normalized difference vegetation index (NDVI) and environmental variables is of great importance for monitoring rocky desertification. This article investigated the spatially non-stationary relationships between NDVI and environmental factors using geographically weighted regression (GWR) atmulti-scales. The spatial scale-dependency of the relationships between NDVI and environmental factors was identified by scaling the bandwidth of the GWR model, and the appropriate bandwidth of the GWR model for each variable was determined. All GWR models represented significant improvements of model performance over their corresponding ordinary least squares (OLS) models. GWR models also successfully reduced the spatial autocorrelations of residuals. The spatial relationships between NDVI and environmental factors significantly varied over space, and clear spatial patterns of slope parameters and local coefficient of determination (R-2) were found from the results of the GWR models. The study revealed detailed site information on the different roles of related factors in different parts of the study area, and thus improved the model ability to explain the local situation of NDVI.Remote SensingImaging Science & Photographic TechnologySCI(E)EI6ARTICLE72112-21293
Analysis of hollow area beneath concrete slab of seawall by means of ground penetration radar
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