Station Density Research Articles

Toroidal mantle flow beneath the NE termination of the Kuril–Kamchatka subduction zone from seismic anisotropy

SUMMARY This study presents the findings of a splitting analysis conducted on core-refracted teleseismic shear waves (SKS, SKKS and PKS, called together as XKS) and local shear waves, obtained from a dense seismological network spanning the Kamchatka Peninsula. The objective of the study is to examine the pattern of mantle flow beneath the study area through the investigation of seismic anisotropy. The peninsula is situated at the northeastern end of the Kuril–Kamchatka subduction zone, where the Kuril trench intersects with the western boundary of the Aleutian trench. The data set utilized in this study comprises waveform data from a dense network of seismic stations (99 broad-band and short-period stations for the local shear wave splitting analysis and 69 broad-band stations for the SKS splitting analysis). The seismograms were downloaded from publicly available data repositories including the IRIS Data Management Center and the GFZ Data Services (GEOFON program). The dense station coverage allows us to investigate the lateral variations in anisotropy, providing insights into the flow patterns within the mantle. The processing of the combined data sets of local shear wave and teleseismic XKS waves allowed us to partially decipher the source of anisotropy in the mantle. Small delay (splitting) times (∼0.35 s) observed from the local-S data suggest that anisotropy in the mantle wedge is relatively weak with lateral variations. Larger splitting times (∼1.1 s) observed for the XKS waves relative to local S suggest that the main part of splitting on the XKS waves occurs in the subslab mantle. On the other hand, the rotational pattern of seismic anisotropy observed by both the local S and XKS waves suggests the presence of a toroidal flow at the NE edge of the subducting slab, which affects both the mantle wedge and subslab mantle. For the regions away from the edge of the slab, the mantle flow seems to be governed mainly by the drag of the lithospheric plate over the underlying asthenosphere.

SItomo – A toolbox for splitting intensity tomography and application in the Eastern Alps

The tomographic inversion of shear wave splitting data for upper mantle anisotropy has been a longstanding challenge. This is due to the ray-based approximation of classical approaches and the near-vertical incidence of the core-mantle converted phases such as SKS that are often used. Recent developments include the calculation of finite-frequency sensitivity kernels for SKS splitting intensity observations, which allows us to accurately take into account the sensitivity to anisotropic structure with depth. A requirement of this tomographic technique is a dense station spacing, which results in overlapping sensitivity kernels at depth and allows for the localization of anisotropic structure. This is satisfied by a growing number of temporary seismic deployments, which motivates the desire to image anisotropic complexities with depth. Here, we introduce and make available a toolbox for the MATLAB environment that facilitates the application of finite-frequency splitting intensity tomography to dense seismic arrays. Our implementation includes several key features, including: 1) A forward calculation of splitting intensities and sensitivity kernels for a complex anisotropic model space. 2) Consideration of the dominant period of the wave, allowing for multiple-frequency analysis, as well as the incoming wave’s non-vertical incidence. 3) The inversion can be based on a classical gradient descent, on a form of the conjugate gradient method known as the BFGS algorithm, or on a gradient-informed stochastic reversible jump algorithm, allowing for a data-driven parametrization of the model space. 4) Importing splitting intensity measurements from waveforms processed in SplitRacer allows for fast pre-processing of large data sets due to its fully automatic design. To illustrate our method, we present both synthetic tests and an application to real data. We apply our inversion procedure to data from the Swath-D network, which densely covers the transition of the Central to the Eastern Alps. Previous studies showed evidence for an abrupt lateral change of layered seismic anisotropy that had been attributed to an opening for channeled asthenospheric flow. Using an SKS splitting intensity tomography approach, we can confirm previous inferences while providing additional constraints on the distribution of anisotropy laterally and with depth.

A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations

Land surface temperature (LST) is a crucial parameter in the circulation of water, exchange of land-atmosphere energy, and turbulence. Currently, most LST products rely heavily on thermal infrared remote sensing, which is susceptible to cloud and rain interference, leading to inferior temporal continuity. Microwave remote sensing has the advantage of being available “all-weather” due to strong penetration capability, which provides the possibility to simulate time-continuous LST data. In addition, the continuous increase in high-density station observations (>10,000 stations) provides reliable measured data for the remote sensing monitoring of LST in China. This study aims to adopt the “Earth big data” generated from high-density station observation and microwave remote sensing data to monitor LST based on deep learning (U-Net family) for the first time. Given the significant spatial and temporal variability of LST and its sensitivity to various factors according to radiation transmission equations, this study incorporated climatic, anthropogenic, geographical, and vegetation datasets to facilitate a multi-source data fusion approach for LST estimation. The results showed that the U-Net++ model with modified skip connections better minimized the semantic discrepancy between the feature maps of the encoder and decoder subnetworks for 0.1° daily LST mapping across China than the U-Net and U2-Net deep learning models. The accuracy of the LST simulation exhibited favorable outcomes in the spatial and temporal dimensions. The station density met the requirements of monitoring air-ground integration monitoring in China. Additionally, the temporal change in the simulation accuracy fluctuated in a W-shape owing to the limited simulation capability of deep learning in extreme scenarios. Anthropogenic factors had the largest influence on LST changes in China, followed by climate, geography, and vegetation. This study highlighted the application of deep learning in remote sensing monitoring against the background of “big data” and provided a scientific foundation for the response of climate change to human activities, ecological environmental protection, and sustainable social and economic development.

Review on the concentration of police stations in the Accra Metropolitan Area and its implications on security and social control

The increasing urban crime rate in Ghana is causing the development of police stations in the Accra metropolitan area. The location of a police station depends on factors influencing crime-prone areas. In Accra, 16 departments report crime situations to the metro coordinator director, who then organizes meetings with the presiding member. Crime rates are typically high in densely populated areas. This study aims to reveal the density of police stations and their impact on security and social control in the Accra metropolitan area. GIS spatial data and tools were used to observe the concentration of police stations, while Arc Software was used to determine the level of concentration and its impact on crime response and social control. Hotspots were also used to collect crime data for better monitoring of police station concentration in Accra and number of crime cases observed in each police station and more police stations presents with its impact on response to crime and social control.

High-density analysis of surface wave profile imaging based on a multiple coverage common-midpoint signal-couple array

Surface wave exploration technology has been extensively used in the inspection of construction engineering quality and shallow surface surveys. To enhance the efficiency of surface wave exploration field acquisition and achieve high-precision and high-density surface wave profile imaging, a wireless distributed seismic surface wave signal acquisition system has been developed based on the principles of active source transient surface wave signal acquisition and dispersion curve calculation methods. For the purpose of achieving rapid multiple coverage signal acquisition and enhancing fieldwork efficiency, a method for rapidly configuring common-midpoint signal couples (CMCs) for multiple coverage common-shot signal acquisition has been devised, and a high-precision visualization method for dispersion curve calculation based on the CMC array has been formulated. When compared with the multichannel analysis of the surface wave method under identical conditions, the CMC array can effectively enhance surface wave dispersion curve survey station density and lateral resolution, thereby enabling high-density analysis of surface wave profile imaging. Through model analysis and field examples related to construction quality detection, including foundation compactness and earth and rock mixture compactness, it has been demonstrated that this method offers significant advantages in terms of high accuracy, high density, and a wide application range. These advantages greatly enhance the efficiency of surface wave exploration and the accuracy of profile imaging for the construction of engineering projects.

Using geodetic measurements derived terrestrial water storage to investigate the characteristics of drought in Yunnan, China

The use of the global navigation satellite system (GNSS) for monitoring changes in terrestrial water storage (TWS) is growing. However, the density of GNSS stations is sparse in most areas, and the widely used Green’s function (GF) method cannot provide a satisfactory resolution for inversion problems. As the Slepian basis function (SBF) method has been successfully used for gravity inversions, GNSS geodesists have recently applied the SBF method to GNSS displacements. However, the evaluation of TWS differences inferred from GNSS using the SBF and the GF has been rarely assessed. In this study, we use both the GF and the SBF methods to investigate the TWS in Yunnan by using more than ten years (2010–2021) of GNSS observations. We observe a remarkable consistency between the two inversion methods employed for GNSS data, as well as a strong agreement with equivalent water heights (EWH) inferred from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO), hydrological model (GLDAS-NOAH), and precipitation data, despite variations in the amplitude. Furthermore, affected by monsoon climate and topography, Yunnan is prone to drought. We analyze the main nine drought events, evaluate the connection between GNSS-inverted TWS during 2010–2021, and find that almost all droughts in Yunnan occurred during the "trough period" within the GNSS-DSI.

Morphology and growth of convective cold pools observed by a dense station network in Germany

AbstractThis study explores the morphology of convective cold pools, that is, their size, shape, and structure, as well as factors controlling their growth using surface‐based observations of the Field Experiment on Sub‐mesoscale Spatio Temporal Variability in Lindenberg (FESSTVaL). FESSTVaL featured a dense network of 99 custom‐built, low‐cost measurement stations covering a circular area of 30 km in diameter at sub‐mesoscale resolution (distances between 0.1 and 4.8 km) and was held at the Lindenberg Observatory near Berlin (Germany) from May–August 2021. The station network sampled 42 cold‐pool events during the 103‐day measurement period. The morphological properties of cold pools are derived by interpolating the temperature observations spatially to a Cartesian grid and defining cold pools as individual objects at a given time with a temperature perturbation stronger than K. The sample of 1232 cold‐pool objects with extents sufficiently captured by the network has a median equivalent diameter of 8.5 km. The objects exhibit aspect ratios between 1.5 and 1.6 independent of their size and strength, meaning they are generally not circularly shaped. On average, is strongest at the cold‐pool center and decreases linearly towards the edge. For the growth phase of four selected events, the cold‐pool object area scales linearly with the radar‐observed, area‐integrated rainfall accumulation, while the object‐mean temperature perturbation strengthens most efficiently early in the life cycle. The global, radial expansion velocity decreases as the cold pool gets stronger and larger, in contradiction of density‐current theory. Instead, is a better predictor of the expansion rate. These findings identify cold‐air import by precipitation, through both evaporative cooling and convective downdrafts, as the dominant driver of the observed growth.

Policy sequencing can increase public support for ambitious climate policy

Abstract Public support for ambitious climate policies and carbon prices that have direct costs for voters may depend on policy sequencing. Policy sequencing theory suggests that the strategic ordering of policies into sequences that initially create benefits can subsequently increase support for higher carbon prices. However, systematic quantitative evidence about the effects of sequencing on public support is lacking. We provide novel theoretical and empirical insights on the mechanisms through which strategic policy sequencing affects public support for climate policies. We generated these insights using geospatial data and a representative conjoint experiment with Swiss voters conducted just before the popular vote on an amendment to the Federal Act on the Reduction of Greenhouse Gas Emissions in June 2021. Our evidence shows that the perceived effectiveness of prior policy-induced benefits is related to more public support for higher carbon prices across sectors. Moreover, we find that more opportunity structures for low-emission alternatives—like higher EV charging station density—are associated with increased public support for carbon prices in the sector where the former material benefits occur. Our results also imply that positive policy perceptions of prior climate policies are related to increased support, particularly among conservative voters and those who do not regard climate change as a salient issue. Thus, strategic policy sequencing could be an effective strategy for broadening public support for ambitious climate policies.

Design of Base Station Density for Maximizing Coverage Probability in Multicell Networks With LoS/NLoS Channels

Design of Base Station Density for Maximizing Coverage Probability in Multicell Networks With LoS/NLoS Channels

A High-Resolution Site Amplification Map for Wellington, New Zealand

Abstract Although earthquake site effects play a crucial role in the evaluation of local seismic hazard and associated risk, their quantification over the frequency range of interest for engineering applications still remains challenging. Mapping the local amplification at high resolution is difficult even in seismically active cities such as Wellington, New Zealand. Employing traditional methods to map amplification, such as the standard spectral ratio (SSR), is realistic only with sufficient density of strong-motion stations (SMS) across the city and the presence of a suitable rock reference station. Recently, hybrid standard spectral ratio methodologies (SSRh) have been proposed to fill in the gaps and provide estimates at much finer spatial resolution. SSRh combines traditional SSR, calculated on earthquake data between a soil reference and a rock station, with SSR computed from simultaneous ambient vibration recordings (SSRn) at a temporary location and the soil reference site within the sedimentary basin. In the last decade, over 450 single-station ambient noise measurements were undertaken across Wellington, and no collocated soil reference station is available, making the SSRh method as it stands impossible to apply. To overcome this limitation, we propose an adaptation of SSRh to capture the same basin response between a soil site and soil reference station as in the case of the synchronous ambient vibration data. We employ an additional interim step that uses the traditional SSRn between each of the soil sites and a rock reference broadband station recording synchronous long-term ambient vibration. The resulting empirical amplification model using the SSRh adaptation is in good agreement with the available SSR at SMS. Amplification factors up to 10 are present along the Centreport area, where significant damage was observed during the Mw 7.8 Kaikōura earthquake. By employing the adjusted SSRh methodology, we were able to develop a first-level high-resolution empirical site amplification model for Wellington. The approach provides an attractive solution for the evaluation of site effects across regions where a significant number of unsynchronized ambient vibration measurements are available.

The Relationship between Land Surface Temperature and Air Temperature in the Douro Demarcated Region, Portugal

Climatic studies of agricultural regions normally use gauge-based air temperature datasets, which are produced with interpolation methods. The informative quality of these datasets varies depending on the density of the weather stations in a particular region. A way to overcome this limitation is to use the land surface temperature calculated from satellite imagery. To show this, the MODIS land surface temperature was compared with the PTHRES gridded dataset for air temperature in the Douro Demarcated Region (Portugal) between the years 2002 and 2020. The MODIS land surface temperature was able to detect a more pronounced maritime–continental gradient, a higher lapse rate, and thermal inversions in valley areas in winter. This information could prove to be crucial for farmers looking to adapt their practices and crops to extreme events, such as heat waves or heavy frost. However, the use of land surface temperature in climate studies should consider the differences in air temperature, which, on some occasions and locations, can be up to ten degrees in the summer.

Difference in seasonal shift of spatial homogenization between taxonomic and functional structure in demersal fish communities

Under climate change and anthropogenic activities, the homogenization of fish communities shows spatial and temporal shifts and threatens biodiversity and ecosystem functioning, but little effort has been made to assess the seasonal shift of spatial homogenization in marine demersal fish, which hinders effective management. In the present study, we investigated the spatial patterns of demersal fish communities and their seasonal changes in the shelf sea of Shandong Peninsula in northeast China based on the four seasonal surveys. Our findings showed that both season and region were important factors in the distributions of biodiversity and the community structure of demersal fish. Spatial homogenization of taxonomic structure and functional structure exhibited inconsistent changes with season. Compared with taxonomic structure, functional structure exhibited relatively lower spatial homogenization in all seasons. Our results suggest that the density of survey stations for demersal fish resource investigation in the study area should be conducted in at least four regions and considering seasonality. This study highlights a need to understand spatial homogenization and its seasonal changes in demersal fish in both taxonomic and functional approaches, which could give insight into biodiversity and resource state and be conducive to improving the efficiency of resource investigation and management.

High-resolution seismic velocity structure using induced inter-event interferometry at the geothermal sites, Geysers, Eastern California

The possibility of retrieving Rayleigh wave empirical Green's functions (EGFs) using virtual seismometers provides an opportunity to calculate high-resolution velocity models without a set of dense stations and/or expensive seismic imaging/monitoring. The high-resolution tomographic map can give an overview of the fracturing within the saturated rock. Theoretically, the cross-correlations of earthquakes contain significant information about the EGFs between event-pairs. In this work, we developed an application of virtual seismometers on a very small scale in superficial layers where anthropogenic seismicity occurred. To test this method's capabilities, we applied it to investigate the NW-part of The Geysers (TG) geothermal field. We used 1276 micro-earthquakes, occurred in a cuboid with the horizontal side of ∼1 × ∼2 km and the vertical edge at a depth from 0.9 to 2.2 km, to obtain the high-resolution (100 × 100 m) velocity structure. A group of strict criteria was imposed separately for single event waveform and event pairs to stabilize the tomographic results. After retrieving the inter-event EGF signal and calculating the Rayleigh wave group velocity dispersion curves, we obtained the group velocity maps for each horizontal layer. The tomographic maps as a function of depth indicated a low-velocity anomaly related to existing inactive faults, topography variation of the top of the steam zone, and low-velocity anomalies possibly stimulated by fluid injection. The topography of the top of the steam varies from ∼1.0 km to 1.3 km of maps, and also anomalies connected with fluid migration appear at the depth of injection wells (1.5–1.9 km).

Revisiting HISTALP precipitation dataset

AbstractThe article presents a recent update of a comprehensive dataset of long‐term series of precipitation data from instrumental observations in the Greater Alpine Region (GAR), that is, the region of Europe including the Alpine mountain range and their nearer surroundings (4°–19° E in longitude and 43°–49° N in latitude). A comparison to different national homogenized datasets is also presented. Results show that in the national homogenized datasets more breaks have been detected due to higher station density. They also demonstrate the necessity of constant exchange with data providers. The resulting trends in all datasets are mainly weak and only a minority of them is statistically significant. In most cases the similarity of statistical index numbers are promising, with, for example, small RMSE between the presented new HISTALP homogenization and the time series of the national homogenized datasets. Nevertheless, for some stations higher differences occur and break signals are not what would be expected due to possible causes in the station history. The differences between the national and the HISTALP new homogenization—due to, for example, different methods used, different points in time when the homogenization took place, different options of data handling (combination of station data, gap filling routines, …) and different reference stations—illustrate the inherent uncertainty unavoidably associated to homogenization and point out the need of careful communication and use of the data. On the other hand, the results highlight the advantage of consistently homogenized datasets, versus the risks associated with mixing results from different homogenizations.

Determining the Electromagnetic Power Density of GSM Base Stations by Using Lagrange Interpolation Approach

Mobile communication systems are playing an important role in human life and the importance of this role depends on the increase in the number of people. Reports show that the numbers of base stations are getting increasing in parallel to use of mobile communication systems and population growth. Determining the base station strength is very important especially for the crowded cities. Because of the possible medical effects of base stations, people have the right to know how much is affected by base stations. Therefore, radiation level of 120 base stations have been measured 20, 40 and 60 meters distances from base stations at the frequency of 900 MHz. In this study, the lagrange interpolation method is applied to estimate the strength of electromagnetic level for random intermediate distances and, above 0.99 correlation was achieved for each distance.

Revealing the Spatio-Temporal Heterogeneity of the Association between the Built Environment and Urban Vitality in Shenzhen

Sensing urban vitality is a useful method for understanding urban development. However, the spatio-temporal characteristics of the association between the built environment and urban vitality in Shenzhen, the youngest mega-city in China, have not yet been explored. In this paper, we examined the effects of built environment indicators on urban vitality by using spatial regression models and multi-source geospatial data. The main research findings were as follows. Firstly, urban vitality displayed a consistent high–low pattern during both weekdays and weekends. Differences in the distribution of urban vitality with time between weekdays and weekends were more significant. Secondly, the effects of various built environment indicators on urban vitality exhibited significant temporal disparities. Within a day, population density, building density, bus station density, and distance to metro stations all exhibited positive effects, while distance to the central business district (CBD) exhibited negative effects, with pronounced diurnal differences. Moreover, the effects of road network density and functional mix on urban vitality were both positive and negative throughout the day. Thirdly, besides population density and building density, road network density, functional mix, bus stop density, and distance from metro stations exhibited positive and negative disparities within the study area. Overall, distance to the CBD had a negative effect on urban vitality. This concludes that planning for urban vitality should consider the spatio-temporal heterogeneity of the association between the built environment and urban vitality.

Nuclear explosion monitoring network design considerations

Design of an efficient monitoring network requires information on the type and size of releases to be detected, the accuracy and reliability of the measuring equipment, and the desired network performance. This work provides a scientific basis for optimizing or minimizing networks of 133Xe samplers to achieve a desired performance level for different levels of release. The approach of this work varies the density of sampling locations to find optimal location subsets, and to explore the properties of variations of those subsets – how crucial is a specific subset; are substitutions problematic? The choice of possible station locations is arbitrary but constrained to some extent by the location of islands, land masses, difficult topography (mountains, etc.) and the places where infrastructure exists to run and support a sampler. Performance is evaluated using hypothetical releases and atmospheric transport models that cover an entire year.Three network performance metrics are calculated: the probability of detecting the releases, the expected number of stations to detect the releases, and the expected number of samples that detect the releases. The quantitative measures support picking optimal or near-optimal network of a specific station density. If a detection probability of 90% (high) was desired for a design basis release of 1014 Bq (1% of 133Xe production from a 1 kt explosion), then a very high density would be required using today's sampling and measurement technology. If the design basis release were raised to 1015 Bq, then the station density could be lowered by a factor of 3. To achieve a location goal of three station detections on average, posited here for the first time, would also require very high station density for a release of 1014 Bq.

Identification of sustainable urban settlement sites using interrelationship based multi-influencing factor technique and GIS

Evaluation of a site suitability for sustainable urban settlement growth is crucial. A system for evaluating an interaction between the factors must be established to assure that critical site selection interrelationships are not neglected. To solve this problem, the proposed methodology was evaluated to identify a suitable site for sustainable residential development in Nashik, India. The Geographic information system (GIS)-based multi-influence factor (MIF) approach is used in this study to find ideal locations for future urban settlement. The assessment was based on 11 factors including vegetation, elevation, land use, industry, drainage network, slope, water bodies, road, health services, railway station and population density. The sustainable site suitability was delineated by aggregating all considered factors and their associated MIF weights using the interrelationship between the factors. The results showed that 27.26% of research area was not ideal for settlement development, 16.82% was low suitable, 30.65% was moderately suitable, 16.48% was highly suitable and 8.77% was very highly suitable. Most of the suitable sites were located near the existing habitant area, and major roads. The suitability sites were validated using receiver-operating characteristic (ROC), and area under curve (AUC) value of 0.895 indicated that the model was effective. Sensitivity analysis revealed that the distance from road, drainage network, and health services are the dominant for selecting the optimal location of settlement development in research area. The findings of the delineated ideal areas for the intensive urban settlement development will be helpful to planners and policy makers for sustainable urban growth in the near future.

Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm

Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. Although the current rainfall monitoring system in China has a dense station network and high-precision rainfall data, the time series is short. In contrast, historical rainfall data have a longer sample time series but lower precision. This study introduced a PCA algorithm to reconstruct historical rainfall data. Based on the temporal and spatial characteristics of rainfall extracted from high-resolution rainfall data over the past decade, historical (6 h intervals) rainfall spatial data were reconstructed into high-resolution (1 h intervals) spatial data to satisfy the requirements of the urban flood risk analysis. The results showed that the average error between the reconstructed data and measured values in the high-value area was within 15% and in the low-value area was within 20%, representing decreases of approximately 65% and 40%, respectively, compared to traditional interpolation data. The reconstructed historical spatial rainfall data conformed to the temporal and spatial distribution characteristics of rainfall, improved the granularity of rainfall spatial data, and enabled the effective and reasonable extraction and summary of the fine temporal and spatial distribution characteristics of rainfall.

Derivation of long duration geographically-centred areal reduction factors for catchment design rainfall estimation in South Africa

Study regionSouth Africa. Study focusThe use of Areal Reduction Factors (ARFs) to convert average design point rainfall into areal catchment design rainfall is regarded as fundamental input to event-based deterministic design flood estimation methods. The ARF methods currently used in South Africa are outdated and were neither developed nor verified using local data. This paper presents the development and assessment of a regionalised approach to estimate long duration (≥ 1-day) geographically-centred ARFs using daily rainfall data representative of the 78 homogeneous long duration rainfall clusters associated with the Regional Linear Moment Algorithm and Scale Invariance (RLMA&SI) regionalisation scheme in South Africa. In order to meet the recommended World Meteorological Organisation rainfall station density criteria, the latter clusters were merged into 46 clusters. Subsequently, long duration geographically-centred sample ARFs were estimated using 2 053 circular catchments and 1 779 daily rainfall stations. New hydrological insights for the regionFive ARF regions were deduced from the 46 clusters and a single regional empirical ARF equation, with unique regional calibration coefficients, was derived. The ARF methodology developed and the subsequent findings are not only new to the South African flood hydrology community and practice, but are also probabilistically correct given that the ARF estimates decrease with an increasing catchment area and decrease with a decreasing storm duration and return period, unlike many other methods used internationally.