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Seismic damping of near-surface deposits is an important input to site-response analysis for seismic hazard assessment. In Groningen, the Netherlands, gas production from a reservoir at 3 km depth causes seismicity. Above the gas field, an 800 m thick layer of unconsolidated sediments exist, which consists of a mixture of sand, gravel, clay and peat strata. Shear waves induced at 3 km depth experience most of their anelastic attenuation in these loose sediments. A good estimate of damping is therefore crucial for modelling realistic ground-motion levels. In Groningen, we take advantage of a large network of 200 m deep vertical arrays to estimate damping from recordings of the induced events. As a first step, we apply seismic interferometry by deconvolution to estimate local transfer functions over these vertical arrays. Subsequently, two different methods are employed. The first is the 'upgoing' method, where the amplitude decay of the retrieved upgoing wave is used. The second is the 'up-down' method, where the amplitude difference between retrieved up- and downgoing waves is utilized. For the upgoing method, the amplitude of the upgoing direct wave is affected by both elastic and anelastic effects. In order to estimate the anelastic attenuation, it is necessary to remove the elastic amplification first. Despite the fact that elastic compensation could be determined quite accurately, non-physical damping values were estimated for a number of boreholes. Likely, the underlying cause was small differences in effective response functions of geophones at different depths. It was found that the up-down method is more robust. With this method, elastic propagation corrections are not needed. In addition, small differences in in situ geophone response are irrelevant because the up- and downgoing waves retrieved at the same geophone are used. For the 1-D case, we showed that for estimating the local transfer function, the complex reverberations need to be included in the interferometric process. Only when this is done, the transfer function does not contain elastic transmission loss and Q estimation can be made without knowing the soil profile in detail. Uncertainty in the estimated damping was found from the signal-to-noise ratio of the estimated transfer function. The Q profiles estimated with the up-down method were used to derive a damping model for the top 200 m of the entire Groningen field. A scaling relation was derived by comparing estimated Q profiles with low-strain damping profiles that were constructed using published models for low-strain damping linked to soil properties. This scaling relation, together with the soil-property-based damping model, allowed up-scaling of the model to each grid-cell in the Groningen field. For depths below 200 m, damping was derived from the attenuation of the microseism over Groningen. The mean damping model, over a frequency band between 2 and 20 Hz, was estimated to be 2.0 per cent (0-50 m depth), 1.3 per cent (50-100 m), 0.66 per cent (100-150 m), 0.57 per cent (150-200 m) and 0.5 per cent (200-580 m). TURNkey (Towards more Earthquake-resilient Urban Societies through a Multi-sensor-based Information System enabling Earthquake Forecasting, Early Warning and Rapid Response actions); EU [H2020 - SC5 - 2018 - 2] Published version The work of ER has partly been funded by TURNkey (Towards more Earthquake-resilient Urban Societies through a Multi-sensor-based Information System enabling Earthquake Forecasting, Early Warning and Rapid Response actions), which is a project funded by the EU within the H2020 - SC5 - 2018 - 2 call. We would like to thank Jun Matsushima and an anonymous reviewer for a detailed and critical assessment.

Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of hydrothermal alteration minerals using hyperspectral remote sensing imagery. In this research, the Dirichlet Process based on Stick-Breaking (DPSB) model-based clustering algorithm was implemented to hyperion remote sensing imagery to discriminate HAZs associated with the Kuh-Panj porphyry copper deposit, south, Iran. The DPSB clustering algorithm was implemented and subsequently compared with the k-means algorithm, CLARA clustering, hierarchical clustering, Gaussian finite mixture model (GFMM), Gaussian model for high-dimensional (GMHD) and spectral clustering as well as spectral angle mapping (SAM). Results derived from the DPSB model-based clustering algorithm show 88.6% accuracy in distinguishing propylitic, argillic, advanced argillic, propylitic-argillic and phyllic alteration zones. The DPSB algorithm can be broadly implemented to hyperspectral remote sensing imagery for detecting alteration zones associated with porphyry systems.

This paper proposes a cloud-based mangrove monitoring framework that uses Google Collaboratory and Google Earth Engine to classify mangroves in Southeast Asia (SEA) using satellite remote sensing imagery (SRSI). Three multi-class classification convolutional neural network (CNN) models were generated, showing F1-score values as high as 0.9 in only six epochs of training. Mangrove forests are tropical and subtropical environments that provide essential ecosystem services to local biota and coastal communities and are considered the most efficient vegetative carbon stock globally. Despite their importance, mangrove forest cover continues to decline worldwide, especially in SEA. Scientists have produced monitoring tools based on SRSI and CNNs to identify deforestation hotspots and drive targeted interventions. Nevertheless, although CNNs excel in distinguishing between different landcover types, their greatest limitation remains the need for significant computing power to operate. This may not always be feasible, especially in developing countries. The proposed framework is believed to provide a robust, low-cost, cloud-based, near-real-time monitoring tool that could serve governments, environmental agencies, and researchers, to help map mangroves in SEA.

One of the most important considerations in urban environments is the extraction of urban objects, with a high automation level. This study aims to present a new method which uses aerial images and LiDAR data to extract buildings and trees footprint in urban areas. In this study, high-elevation objects were extracted from the LiDAR data using the developed scan labeling method, and then the classification methods of Neural Networks (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Based K-Means algorithm (GBKMs) were used to separate buildings and trees and with the purpose of evaluating their performance. The features used for the classification were extracted from aerial images and LiDAR data, and the training data for the classification were selected automatically. Mathematical morphology functions were also used to process the classification results. The results show that NN and the ANFIS are more effective than the genetic-based K-Means algorithm in detecting small and large buildings.

SUMMARY Greigite is a sensitive environmental indicator and occurs commonly in nature as magnetostatically interacting framboids. Until now only the magnetic response of isolated non-interacting greigite particles have been modelled micromagnetically. We present here hysteresis and first-order reversal curve (FORC) simulations for framboidal greigite (Fe3S4), and compare results to those for isolated particles of a similar size. We demonstrate that these magnetostatic interactions alter significantly the framboid FORC response compared to isolated particles, which makes the magnetic response similar to that of much larger (multidomain) grains. We also demonstrate that framboidal signals plot in different regions of a FORC diagram, which facilitates differentiation between framboidal and isolated grain signals. Given that large greigite crystals are rarely observed in microscopy studies of natural samples, we suggest that identification of multidomain-like FORC signals in samples known to contain abundant greigite could be interpreted as evidence for framboidal greigite.

Based on theory and climate model experiments, previous studies suggest most of the uncertainties in projected future changes in meridional energy transport and zonal mean surface temperature can be attributed to cloud feedback. To investigate how radiative and dynamical adjustments modify the influence of cloud-radiative changes on energy transport, this study applies a cloud-locking technique in a fully-coupled climate model, CESM. Under global warming, the impacts of cloud-radiative changes on the meridional energy transport are asymmetric in the two hemispheres. In the Northern Hemisphere, the cloud-radiative changes have little impact on energy transport, because 89% of the cloud-induced heating is balanced locally by increasing outgoing longwave radiation. In the Southern Hemisphere, on the other hand, cloud-induced dynamical changes in the atmosphere and the ocean cause enhanced poleward energy transport, accounting for most of the increase in energy transport under warming. Our experiments highlight that the local longwave radiation adjustment induced by temperature variation can partially offset the impacts of cloud-radiative changes on energy transport, making the estimated impacts smaller than those obtained from directly integrating cloud-radiative changes in previous studies. It is also demonstrated that the cloud-radiative impacts on temperature and energy transport can be significantly modulated by the oceanic circulation, suggesting the necessity of considering atmospheric-oceanic coupling when estimating the impacts of cloud-radiative changes on the climate system.

Summary Magnetic minerals form or alter in the presence of hydrocarbons, making them a potential magnetic proxy for identifying hydrocarbon migration pathways. In this paper we test this idea by magnetically measuring core samples from the Tay Fan in the Western Central Graben in the Central North Sea. In a companion paper, 3D petroleum systems modelling has been carried out to forward model migration pathways within the Tay Fan. Rock magnetic experiments identified a range of magnetite, maghemite, iron sulphides, siderite, goethite and titanohematite, some of which are part of the background signal, and some due to the presence of hydrocarbons. Typical concentrations of the magnetic minerals were ∼10–200 ppm. Importantly, we have identified an increasing presence of authigenic iron sulphides (likely pyrite and greigite) along the identified lateral hydrocarbon migration pathway (east to west). This is likely caused by biodegradation resulting in the precipitation of iron sulphides, however, though less likely, it could alternatively be caused by mature oil generation, which subsequently travelled with the migrating oil to the traps in the west. These observations suggest mineral magnetic techniques could be a rapid alternative method for identifying the severity of biodegradation or oil maturity in core sample, which can then be used to calibrate petroleum systems models.

SUMMARY When seismic waves propagate through viscoelastic media, the viscoelastic response can be presented as a fractional-order derivative of the strain. This fractional order $\beta $ controlling the degree of viscoelasticity of subsurface media is referred to as the viscoelastic parameter. However, the viscoelasticity is conventionally quantified by the quality factor Q, and there is a gap between the viscoelastic parameter $\beta $ and the Q factor. Here this paper bridges the gap by establishing a relationship between these two parameters. An exact Q model is derived analytically based on the viscoelastic parameter $\beta $. Since the exact Q model is frequency dependent, a constant-Q model which is frequency independent is proposed under a small-dissipation assumption. This constant-Q model is applicable to seismic data with a narrow frequency band and is consistent with Kolsky's attenuation model. Furthermore, an inverse function of the constant-Q model is presented for evaluating the viscoelastic parameter $\beta $ from any given Q factor. Thus, the viscoelastic parameter $\beta $ has an intuitive physical meaning that is directly linked to the Q factor.

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.

We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering the period 2001–2020. We focus on retrievals from the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms, showing how these compare to one another in time and space. We further employ speciated AOD estimates from Copernicus Atmospheric Monitoring Service (CAMS) reanalyses to help diagnose aerosol types and hence sources. Considering Australia as a whole, monthly mean AODs show similar temporal behaviour, with a well-defined seasonal peak in the Austral summer. However, excepting periods of intense biomass burning activity, MAIAC values are systematically higher than their DB counterparts by, on average, 50%. Decomposing into seasonal maps, the patterns of behaviour show distinct differences, with DB showing a larger dynamic range in AOD, with markedly higher AODs (ΔAOD∼0.1) in northern and southeastern regions during Austral winter and summer. This is counter-balanced by typically smaller DB values across the Australian interior. Site level comparisons with all available level 2 AOD data from Australian Aerosol Robotic Network (AERONET) sites operational during the study period show that MAIAC tends to marginally outperform DB in terms of correlation (RMAIAC = 0.71, RDB = 0.65) and root-mean-square error (RMSEMAIAC = 0.065, RMSEDB = 0.072). To probe this behaviour further, we classify the sites according to the predominant surface type within a 25 km radius. This analysis shows that MAIAC’s advantage is retained across all surface types for R and all but one for RMSE. For this surface type (Bare, comprising just 1.2% of Australia) the performance of both algorithms is relatively poor, (RMAIAC = 0.403, RDB = 0.332).

The Lee Tunnel was constructed as the first part of the Thames Tideway Improvement scheme, between 2010 and 2016. With tunnelling for the East section of the main Thames Tideway Tunnel, which joins the Lee Tunnel at Abbey Mills Pumping Station, beginning in early 2020, this paper investigates patterns of deformation in East London during construction of the Lee Tunnel. An unexpected geological feature, later identified as a drift filled hollow, was discovered during tunnelling. This study demonstrates that had eight years of ERS Persistent Scatterer Interferometry (PSI) data been analysed prior to tunnelling, the unusual pattern of displacement may have been recognised and further targeted borehole investigations taken place before the launch of the tunnel boring machine. Results also show how areas of different land use, including cemeteries and historic landfill, exhibit differences in settlement behaviour, compared with surrounding terraced housing. This research highlights the challenges in interpreting PSI results in an urban area with ongoing construction and the value of a long archive of data, which now spans almost three decades in London, that can be used to establish a baseline prior to construction.

Alluvial fans are important depositional landforms that offer valuable records of terrestrial sedimentation history if their surfaces can be mapped and dated accurately. Unfortunately, as this often depends on detailed field mapping and intensive absolute dating techniques, it can be a challenging, expensive and time-consuming exercise. In this study, we demonstrate that quantitative information about the ages of alluvial fan surfaces in Owens Valley, California, is recorded by Landsat-8 multispectral satellite imagery. We show that systematic changes in the wavelength-dependent brightness of fan surfaces occur gradually over a timescale of ~100 kyr in this semi-arid setting, and are highly correlated with known deposit ages. Using spectro-radiometry and X-ray diffraction analysis of sediment samples collected in the field, we interpret that surface reflectance evolves primarily in response to the in-situ production of secondary illite and iron oxide by weathering in this landscape. Furthermore, we demonstrate that first-order predictions of absolute fan surface age can be derived from multispectral imagery when an initial age calibration is available. These findings suggest that multispectral imagery, such as Landsat data, can be used (i) for preliminary mapping of alluvial fans prior to detailed field work and before choosing sampling sites for conventional dating techniques, and (ii) to extend age models to un-dated neighbouring surfaces with equivalent physical properties, once an age-brightness calibration has been established.

The U.K. government has a policy to dispose of higher activity radioactive waste in a geological disposal facility (GDF), which will have multiple protective barriers to keep the waste isolated and to ensure no harmful quantities of radioactivity are able to reach the surface. Currently no specific GDF site in the United Kingdom has been chosen but, once it has, the site is likely to be investigated using seismic methods. In this study, we explore whether 3-D full-waveform inversion (FWI) of seismic data can be used to map changes in physical properties caused by the construction of the site, specifically tunnel-induced fracturing. We have built a synthetic model for a GDF located in granite at 1000 m depth below the surface, since granite is one of the candidate host rocks due to its high strength and low permeability and the GDF could be located at such a depth. We use an effective medium model to predict changes in P-wave velocity associated with tunnel-induced fracturing, within the spatial limits of an excavated disturbed zone (EdZ), modelled here as an increase in fracture density around the tunnel. We then generate synthetic seismic data using a number of different experimental geometries to investigate how they affect the performance of FWI in recovering subsurface P-wave velocity structure. We find that the location and velocity of the EdZ are recovered well, especially when data recorded on tunnel receivers are included in the inversion. Our findings show that 3-D FWI could be a useful tool for characterizing the subsurface and changes in fracture properties caused during construction, and make a suite of suggestions on how to proceed once a potential GDF site has been identified and the geological setting is known.

When seismic waves propagate through the Earth, their high-frequency energy is absorbed by subsurface viscoelastic media. Seismic wavelet appears to be stretched out, as it is dominated by low-frequency components. In order to enhance seismic resolution, we propose here a wavelet compression method that utilizes the scale characteristic in the Fourier transform. The novelty of the scheme is a frequency-dependent scaling that extends the amplitude spectrum to both high- and low-frequency axes simultaneously. This is for the first time to make this frequency-dependent proposal, instead of a constant scaling scheme in the classic Fourier theory. It compresses seismic wavelet in the time domain, and also simplifies the wavelet form effectively. This frequency-dependent scaling scheme leads to a transferring filter that is applicable to seismic field data. It results in an improvement in data resolution and in the ability of thin-layer identification, which will facilitate further seismic inversion and reservoir characterization.

An extremely sharp magnetic reversal observed in lacustrine sediments in central Italy has been interpreted as a record of the Matuyama-Brunhes geomagnetic polarity reversal that may represent less than a decade (Sagnotti et al., 2014. 2016). Here, we report new results from the same Sulmona Basin outcrop that question this interpretation. In particular, we find evidence of reversed (Matuyama) directions well above the proposed Matuyama-Brunhes Boundary (MBB). Coercivity spectra of anhysteretic remanent magnetization (ARM) imply a 3-component magnetic mineralogy: low-, intermediate-, and high-coercivity. The low-coercivity component is found in all but one of the samples and carries a strong modern overprint seen throughout the section. The high-coercivity component is dominated by volcanic material which is prone to remagnetization. Since it is much more magnetic than the surrounding lacustrine sediments, it may influence the remanence signal even when present at very low concentrations. The intermediate-coercivity component is the main carrier of any true primary remanence, but whether or not this can be isolated depends on the blocking-temperature and coercivity spectra of individual samples, and on the demagnetization method used. The complexity of the magnetization, the reversed zones above the proposed MBB, and the normal zones that Sagnotti and 2 colleagues found below it, lead to the conclusion that this section does not carry a reliable high-resolution record of the geomagnetic field. Thus, we feel that inferences about the stratigraphic position and duration of the MBB are premature.

With the ever-increasing demand in the analysis and understanding of aerial images in order to remotely recognise targets, this paper introduces a robust system for the detection and localisation of cars in images captured by air vehicles and satellites. The system adopts a sliding-window approach. It compromises a window-evaluation and a window-classification sub-systems. The performance of the proposed framework was evaluated on the Vaihingen dataset. Results demonstrate its superiority to the state of the art.

The earthquake of 12 May 2008 in Wenchuan County, Sichuan Province, China, devastated the entire Beichuan region. Sitting at the intersection of the Yingxiu-Beichuan and Pengguan faults, the region experienced seismic intensities of VIII–XI on the Liedu scale. High seismic intensity combined with inherent geomorphological and climatic susceptibility to slope failure resulted in widespread co-seismic geohazards (slope failures of various types), which decimated the region. The seismic characteristics of the Wenchuan earthquake and the co-seismic geohazard distribution in relation to various conditioning factors have previously been examined in depth. However, there has been a lack of regional assessment of temporal and spatial recovery from co-seismic geohazards. Triggered by the authors’ field observation of rapid recovery, this study presents a temporal series of geohazard maps, produced by manual interpretation of satellite imagery, to present an initial assessment of changes in geohazard occurrence in the Beichuan region since the Wenchuan earthquake. In particular, landscape recovery at the co-seismic geohazard sites, as indicated by re-vegetation, is analysed based on temporal/spatial characteristics of geohazard distribution, in relation to co-seismic deformation, distance from the rupture zone and slope angle. Eight years after the Wenchuan earthquake, the overall recovery stands at 65.48%, with approximately uniform annual rates of recovery at 13.45% a year between 2009 and 2011 and 10.56% a year between 2012 and 2016. Whilst co-seismic geohazards are concentrated on the hanging wall of the seismic fault, landscape recovery is more significant in the very highly deformed zone than in other areas. Recovery has been the greatest on slopes of

SUMMARY Estimating the location of geologic and tectonic features on a subducting plate is important for interpreting their spatial relationships with other observables including seismicity, seismic velocity and attenuation anomalies, and the location of ore deposits and arc volcanism in the over-riding plate. Here we present two methods for estimating the location of predictable features such as seamounts, ridges and fracture zones on the slab. One uses kinematic reconstructions of plate motions, and the other uses multidimensional scaling to flatten the slab onto the surface of the Earth. We demonstrate the methods using synthetic examples and also using the test case of fracture zones entering the Lesser Antilles subduction zone. The two methods produce results that are in good agreement with each other in both the synthetic and real examples. In the Lesser Antilles, the subducted fracture zones trend northwards of the surface projections. The two methods begin to diverge in regions where the multidimensional scaling method has its greatest likely error. Wider application of these methods may help to establish spatial correlations globally.

This article is concerned with the problem of multitarget parameter estimation in arrayed multiple-input multiple-output (MIMO) radar. In particular, the radar operates in the presence of moving targets, where the parameters of interests to be estimated for each target are the relative delay, Doppler frequency, direction-of-arrival (DOA) and complex path coefficients (or target's radar cross section). Using the novel concept of “array manifold extender,” the dimensionality of the observation space increases from $N$ to $N\mathcal {N}_{\mathrm{ext}}$ , making the radar more powerful than conventional MIMO radar systems by handling more complex targets and estimating their parameters with increased accuracy. Two “manifold extenders” are proposed in this article in conjunction with a novel spatiotemporal subspace-type framework for estimating the target parameters. The performance of proposed framework is examined using computer simulation studies.

The study of animal behavior in the wild requires the ability to locate and observe animals with the minimum disturbance to their natural behavior. This can be challenging for animals that avoid humans, are difficult to detect, or range widely between sightings. Global Positioning System (GPS) collars provide one solution but limited battery life, and the disturbance to the animal caused by capture and collaring can make this impractical in many applications. Wild wolves Canis lupus are an example of a species that is difficult to study in the wild, yet are of considerable conservation and management importance. This manuscript presents a system for accurately locating wolves using differences in the time of arrival of howl vocalizations at multiple recorders (multilateration), synchronized via GPS. This system has been deployed in Yellowstone National Park for two years and has recorded over 1200 instances of howling behavior. As most instances of howling occur at night, or when human observers are not physically present, the system provides location information that would otherwise be unavailable to researchers. The location of a vocalizing animal can, under some circumstances, be determined to within an error of approximately 20 m and at ranges up to 7 km.

Basal stem rot (BSR) is a type of disease that induces oil palm death within a short span of the appearance of symptoms. BSR early detection would facilitate to curb this by adopting appropriate strategies. In this paper, a systematic review was undertaken to demonstrate the need for authentic health condition monitoring of oil palm plantations. The currently used remotely sensed (RS) techniques for BSR detection and classification were reviewed. Several kinds of RS techniques were exerted for BSR detection and its severity classification up to four levels. It was identified that applied geospatial technologies, including multispectral and hyperspectral remote sensing, terrestrial laser scanning, spatial maps, tomography images, intelligent e-nose and Microfocus X-ray fluorescence, were capable of distinguishing infected oil palms from the non-infected ones. Furthermore, some of them are able to categorize BSR severity level up to four levels as well as of its early detection.

© 2016 The Royal Institute of Navigation. The acquisition and tracking strategies of the BeiDou navigation satellite signals are affected by the modulation of Neumann-Hoffman code (NH code), which increases the complexity of receiver baseband signal processing. Based on the analysis of probability statistics of the NH code, a special sequence of incoming signals is proposed to evade the bit transitions caused by the NH code, and an NH Code Evasion and Stripping method (NCES) based on the NH-pre-modulated code is proposed. The NCES can be applied in both 20-bit NH code and 10-bit NH code. The fine acquisition eliminates the impact of NH code on the traditional tracking loop. These methods were verified with a BeiDou PC-based software-defined receiver using the actual sampled signals. Compared with other acquisition schemes which try to determine or ignore the NH code phase, the NCES needs fewer incoming signals and the actual runtime is greatly reduced without sacrificing much time to search in the secondary code dimension, and the success rate of acquisition is effectively improved. An extension of Fast Fourier Transform (FFT)-based parallel code-phase search acquisition gives the NCES an advantage in engineering applications.

Investigation of rainfall–run-off modelling is an important subject to develop any available means to water supply, which maintains human life such as run-off harvesting method. This study aims to analyse and understand the rainfall–run-off relationship in a part of Babil city, Iraq. Curve number which is a function of land use, soil texture, soil moisture and land slope is used in this study. Remote sensing and GIS are used to analyse the data and to produce the run-off depth map for the study area. Then, the run-off depth is used with rainfall to investigate the relationship between them using linear correlation. This study showed a strong linear relationship between rainfall and run-off (R2 = 0.992). It indicates that in the absence of rainfall data, run-off data can be used to estimate rainfall amount. Also, the study revealed through water balance analysis that there is an average monthly change in storage.

This study attempts to identify and forecast future land cover (LC) by using the Land Transformation Model (LTM), which considers pixel changes in the past and makes predictions using influential spatial features. LTM applies the Artificial Neural Networks algorithm) in conducting the analysis. In line with these objectives, two satellite images (Spot 5 acquired in 2004 and 2010) were classified using the Maximum Likelihood method for the change detection analysis. Consequently, LC maps from 2004 to 2010 with six classes (forest, agriculture, oil palm cultivations, open area, urban, and water bodies) were generated from the test area. A prediction was made on the actual soil erosion and the soil erosion rate using the Universal Soil Loss Equation (USLE) combined with remote sensing and GIS in the Semenyih watershed for 2004 and 2010 and projected to 2016. Actual and potential soil erosion maps from 2004 to 2010 and projected to 2016 were eventually generated. The results of the LC change detections indicated that three major changes were predicted from 2004 to 2016 (a period of 12 years): (1) forest cover and open area significantly decreased at rates of almost 30 and 8 km2, respectively; (2) cultivated land and oil palm have shown an increment in sizes at rates of 25.02 and 5.77 km2, respectively; and, (3) settlement and Urbanization has intensified also by almost 5 km2. Soil erosion risk analysis results also showed that the Semenyih basin exhibited an average annual soil erosion between 143.35 ton ha−1 year−1 in 2004 and 151 in 2010, followed by the expected 162.24 ton ha−1 year−1. These results indicated that Semenyih is prone to water erosion by 2016. The wide range of erosion classes were estimated at a very low level (0–1 t/ha/year) and mainly located on steep lands and forest areas. This study has shown that using both LTM and USLE in combination with remote sensing and GIS is a suitable method for forecasting LC and accurately measuring the amount of soil losses in the future.

The authors study the effect of clouds on the atmospheric circulation response to CO2 quadrupling in an aquaplanet model with a slab ocean lower boundary. The cloud effect is isolated by locking the clouds to either the control or 4xCO2 state in the shortwave (SW) or longwave (LW) radiation schemes. In the model, cloud radiative changes explain more than half of the total poleward expansion of the Hadley cells, midlatitude jets, and storm tracks under CO2 quadrupling, even though they cause only one-fourth of the total global-mean surface warming. The effect of clouds on circulation results mainly from the SW cloud radiative changes, which strongly enhance the equator-to-pole temperature gradient at all levels in the troposphere, favoring stronger and poleward-shifted midlatitude eddies. By contrast, quadrupling CO2 while holding the clouds fixed causes strong polar amplification and weakened midlatitude baroclinicity at lower levels, yielding only a small poleward expansion of the circulation. The results show that 1) the atmospheric circulation responds sensitively to cloud-driven changes in meridional and vertical temperature distribution and 2) the spatial structure of cloud feedbacks likely plays a dominant role in the circulation response to greenhouse gas forcing. While the magnitude and spatial structure of the cloud feedback are expected to be highly model dependent, an analysis of 4xCO2 simulations of CMIP5 models shows that the SW cloud feedback likely forces a poleward expansion of the tropospheric circulation in most climate models.

S U M M A R Y This paper presents a generalized wave equation which unifies viscoelastic and pure elastic cases into a single wave equation. In the generalized wave equation, the degree of viscoelasticity varies between zero and unity, and is defined by a controlling parameter. When this viscoelastic controlling parameter equals to 0, the viscous property vanishes and the generalized wave equation becomes a pure elastic wave equation. When this viscoelastic controlling parameter equals to 1, it is the Stokes equation made up of a stack of pure elastic and Newtonian viscous models. Given this generalized wave equation, an analytical solution is derived explicitly in terms of the attenuation and the velocity dispersion. It is proved that, for any given value of the viscoelastic controlling parameter, the attenuation component of this generalized wave equation perfectly satisfies the power laws of frequency. Since the power laws are the fundamental characteristics in physical observations, this generalized wave equation can well represent seismic wave propagation through subsurface media.

Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and the Airborne Research Interferometer Evaluation System (ARIES). Co-located measurements of the two sensors allow observations of the upwelling radiance for clear and cloudy conditions across the far- and mid-infrared part of the spectrum. Theoretical sensitivity studies show that the performance of the CIC algorithm improves with cloud altitude. These tests also suggest that, for conditions encompassing those sampled by the flight campaigns, the additional information contained within the far-infrared improves the algorithm’s performance compared to using mid-infrared data only. When the CIC is applied to the airborne radiance measurements, the classification performance of the algorithm is very high. However, in this case, the limited temporal and spatial variability in the measured spectra results in a less obvious advantage being apparent when using both mid- and far-infrared radiances compared to using mid-infrared information only. These results suggest that the CIC algorithm will be a useful addition to existing cloud classification tools but that further analyses of nadir radiance observations spanning the infrared and sampling a wider range of atmospheric and cloud conditions are required to fully probe its capabilities. This will be realised with the launch of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, ESA’s 9th Earth Explorer.

Background COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records. Methods We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters using a decision tree classifier. Results We identified and characterized five COPD patient clusters with distinct patient characteristics with respect to demographics, comorbidities, risk of death and exacerbations. The four subgroups were associated with 1) anxiety/depression; 2) severe airflow obstruction and frailty; 3) cardiovascular disease and diabetes and 4) obesity/atopy. A fifth cluster was associated with low prevalence of most comorbid conditions. Conclusions COPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients. Electronic supplementary material The online version of this article (10.1186/s12911-019-0805-0) contains supplementary material, which is available to authorized users.

Background Technology can potentially enable the implementation of a value-based healthcare system, where the impact of quality of care is offered at optimised cost for maximised patient benefit. Technology can deliver value by aiding in data collection to evaluate outcomes and measure costs on a patient and population level. Healthcare organisations, however, face several challenges and risks that result almost exclusively from the use of these technologies. Discussion Some challenges associated with healthcare technology include their unsustainability, due to lack of scale-up plans and timely evaluations. Other risks include noncompliance with data protection policies, inadequate data governance, and overestimated expectations resulting from the rapid introduction of new technologies. Conclusion Organisations need to consider the risks and challenges associated with the use of technology and develop comprehensive strategies that mitigate factors leading to non-adoption and to realise benefits for achieving a value-based healthcare system.

For shot-encoded seismic waveform tomography, the restarted limited-memory BFGS (L-BFGS) algorithm is an effective technique to suppress the crosstalk effect among encoded seismic shots. It restarts the L-BFGS calculation at each iteration segment, consisted of a group of iterations, and recodes the individual shots randomly not only at the beginning but also at the inside of the iteration segment. Here, we simplified this scheme using an invariant shot-encoding within each iteration segment and recoding individual shots only at the beginning of the segment. This simplification did compromise the image quality at the early stage of inversion, as the crosstalk effect appeared on the inversion result of the low-frequency data. However, it eventually achieved both the computation efficiency and the good quality for a multiscale inversion procedure, which inverts the seismic data from low-frequency components to high-frequency components in sequence.

Public confidence in the police is crucial to effective policing. Improving understanding of public confidence at the local l evel will better enable the police to conduct proactive confidence interventions to meet the concerns of local communities. Conventional approaches do not consider that public confidence varies across geographic space as well as in time. Neighbourhood leve l approaches to modelling public confidence in the police are hampered by the small number problem and the resulting instability in the estimates and uncertainty in the results. This research illustrates a spatiotemporal Bayesian approach for estimating an d forecasting public confidence at the neighbourhood level and we use it to examine trends in public confidence in the police in London, UK, for Q2 2006 to Q3 2013. Our approach overcomes the limitations of the small number problem and specifically , we inv estigate the effect of the spatiotemporal representation structure chosen on the estimates of public confidence produced. We then investigate the use of the model for forecasting by producing one - step ahead forecasts of the final third of the time - series . The results are compared with the forecasts from traditional time - series forecasting methods like naïve, exponential smoothing, ARIMA, STARIMA and others. A model with spatially structured and unstructured random effects as well as a normally distributed s patiotemporal interaction term was the most parsimonious and produced the most realistic estimates. It also provided the best forecasts at the London - wide, Borough and neighbourhood level.

Earth observation (EO) datasets are commonly provided as collection of scenes, where individual scenes represent a temporal snapshot and cover a particular region on the Earth’s surface. Using these data in complex spatiotemporal modeling becomes difficult as soon as data volumes exceed a certain capacity or analyses include many scenes, which may spatially overlap and may have been recorded at different dates. In order to facilitate analytics on large EO datasets, we combine and extend the geospatial data abstraction library (GDAL) and the array-based data management and analytics system SciDB. We present an approach to automatically convert collections of scenes to multidimensional arrays and use SciDB to scale computationally intensive analytics. We evaluate the approach in three study cases on national scale land use change monitoring with Landsat imagery, global empirical orthogonal function analysis of daily precipitation, and combining historical climate model projections with satellite-based observations. Results indicate that the approach can be used to represent various EO datasets and that analyses in SciDB scale well with available computational resources. To simplify analyses of higher-dimensional datasets as from climate model output, however, a generalization of the GDAL data model might be needed. All parts of this work have been implemented as open-source software and we discuss how this may facilitate open and reproducible EO analyses.

Deviation of seismic surface waves from the great-circle between source and receiver is illustrated by the anomalies in the arrival angle, that is the difference between the observed backazimuth of the incident waves and the great-circle. Such arrival angle anomalies have been known for decades, but observations remain scattered. We present a systematic study of arrival angle anomalies of fundamental mode Rayleigh waves (20–100 s period interval) from 289 earthquakes and recorded by a broadband network LAPNET, located in northern Finland. These observations are compared with those of full waveform synthetic seismograms for the same events, calculated in a 3-D Earth and also compared with those of seismograms obtained by ambient noise correlation. The arrival angle anomalies for individual events are complex, and have significant variations with period. On average, the mean absolute deviation decreases from ∼9° at 20 s period to ∼3° at 100 s period. The synthetic seismograms show the same evolution, albeit with somewhat smaller deviations. While the arrival angle anomalies are fairly well simulated at long periods, the deviations at short periods are very poorly modelled, demonstrating the importance of the continuous improvement of global crustal models. At 20–30 s period, both event data and numerical simulations have strong multipathing, and relative amplitude changes between different waves will induced differences in deviations between very closely located events. The source mechanism has only limited influence on the deviations, demonstrating that they are directly linked to propagation effects, including near-field effects in the source area. This observation is confirmed by the comparison with seismic noise correlation records, that is where the surface waves correspond to those emitted by a point source at the surface, as the two types of observations are remarkably similar in the cases where earthquakes are located close to seismic stations. This agreement additionally confirms that the noise correlations capture the complex surface wave propagation.

Detection of small targets, more specifically cars, in aerial images of urban scenes, has various applications in several domains, such as surveillance, military, remote sensing, and others. This is a tremendously challenging problem, mainly because of the significant interclass similarity among objects in urban environments, e.g., cars and certain types of nontarget objects, such as buildings’ roofs and windows. These nontarget objects often possess very similar visual appearance to that of cars making it hard to separate the car and the noncar classes. Accordingly, most past works experienced low precision rates at high recall rates. In this paper, a novel framework is introduced that achieves a higher precision rate at a given recall than the state of the art. The proposed framework adopts a sliding-window approach and it consists of four stages, namely, window evaluation, extraction and encoding of features, classification, and postprocessing. This paper introduces a new way to derive descriptors that encode the local distributions of gradients, colors, and texture. Image descriptors characterize the aforementioned cues using adaptive cell distributions, wherein the distribution of cells within a detection window is a function of its dominant orientation, and hence, neither the rotation of the patch under examination nor the computation of descriptors at different orientations is required. The performance of the proposed framework has been evaluated on the challenging Vaihingen and Overhead Imagery Research data sets. Results demonstrate the superiority of the proposed framework to the state of the art.

Background Mobile devices may facilitate depression screening in the waiting area of antenatal clinics. This can present implementation challenges, of which we focused on survey layout and technology deployment. Methods We assessed the feasibility of using tablet computers to administer a socio-demographic survey, the Whooley questions and the Edinburgh Postnatal Depression Scale (EPDS) to 530 pregnant women attending National Health Service (NHS) antenatal clinics across England. We randomised participants to one of two layout versions of these surveys: (i) a scrolling layout where each survey was presented on a single screen; or (ii) a paging layout where only one question appeared on the screen at any given time. Results Overall, 85.10% of eligible pregnant women agreed to take part. Of these, 90.95% completed the study procedures. Approximately 23% of participants answered Yes to at least one Whooley question, and approximately 13% of them scored 10 points of more on the EPDS. We observed no association between survey layout and the responses given to the Whooley questions, the median EPDS scores, the number of participants at increased risk of self-harm, and the number of participants asking for technical assistance. However, we observed a difference in the number of participants at each EPDS scoring interval (p = 0.008), which provide an indication of a woman’s risk of depression. A scrolling layout resulted in faster completion times (median = 4 min 46 s) than a paging layout (median = 5 min 33 s) (p = 0.024). However, the clinical significance of this difference (47.5 s) is yet to be determined. Conclusions Tablet computers can be used for depression screening in the waiting area of antenatal clinics. This requires the careful consideration of clinical workflows, and technology-related issues such as connectivity and security. An association between survey layout and EPDS scoring intervals needs to be explored further to determine if it corresponds to a survey layout effect. Future research needs to evaluate the effect of this type of antenatal depression screening on clinical outcomes and clinic workflows. Trial registration This study was registered in ClinicalTrials.gov under the identifier NCT02516982 on 20 July 2015.

The cooling rates at which rocks acquire thermoremanent magnetizations (TRMs), affect their unblocking temperatures in thermal demagnetization experiments; similarly the heating rates at which the thermal demagnetization experiments are done also affect the unblocking temperature. We have tested the effects of variable cooling and heating rates on the unblocking temperatures of two natural non-interacting, magnetically uniform (single-domain, SD) (titano)magnetite samples and a synthetic SD magnetoferritin sample. While previous studies have only considered unblocking temperatures for stepwise thermal demagnetization data (i.e. the room-temperature magnetization after incremental heating), in this work we derive an expression for continuous thermal demagnetization of both TRMs and viscous remanent magnetizations (VRMs) and relate the heating rate to an effective equivalent hold time of a stepwise thermal demagnetization experiment. Through our analysis we reach four main conclusions: First, the theoretical expressions for the heating/cooling rate effect do not accurately predict experimentally observed blocking temperatures. Empirically, the relation can be modified incorporating a factor that amplifies both the temperature and the heating rate dependence of the heating/cooling rate effect. Using these correction factors, Pullaiah nomograms can accurately predict blocking temperatures of both TRMs and VRMs for continuous heating/cooling. Second, demagnetization temperatures are approximately predicted by published ‘Pullaiah nomograms’, but blocking occurs gradually over temperature intervals of 5–40 K. Third, the theoretically predicted temperatures correspond to ∼54–82 per cent blocking, depending on the sample. Fourth, the blocking temperatures can be used to obtain estimates of the atomic attempt time τ0, which were found to be 3 × 10−10 s for large grained (titano)magnetite, 1 × 10−13 s for small grained (titano)magnetite below the Verwey transition and 9 × 10−10 s for magnetoferritin (∼8 nm).

Author(s): Leroy, P; Li, S; Jougnot, D; Revil, A; Wu, Y | Abstract: © The Authors 2017. When pH and alkalinity increase, calcite frequently precipitates and hence modifies the petrophysical properties of porous media. The complex conductivity method can be used to directly monitor calcite precipitation in porous media because it is sensitive to the evolution of the mineralogy, pore structure and its connectivity. We have developed a mechanistic grain polarization model considering the electrochemical polarization of the Stern and diffuse layers surrounding calcite particles. Our complex conductivity model depends on the surface charge density of the Stern layer and on the electrical potential at the onset of the diffuse layer, which are computed using a basic Stern model of the calcite/water interface. The complex conductivity measurements of Wu et al. on a column packed with glass beads where calcite precipitation occurs are reproduced by our surface complexation and complex conductivity models. The evolution of the size and shape of calcite particles during the calcite precipitation experiment is estimated by our complex conductivity model. At the early stage of the calcite precipitation experiment, modelled particles sizes increase and calcite particles flatten with time because calcite crystals nucleate at the surface of glass beads and grow into larger calcite grains. At the later stage of the calcite precipitation experiment, modelled sizes and cementation exponents of calcite particles decrease with time because large calcite grains aggregate over multiple glass beads and only small calcite crystals polarize.

Background Chronic obstructive pulmonary disease (COPD) and asthma are considered as the two most widespread obstructive lung diseases, whereas they affect more than 500 million people worldwide. Unfortunately, the requirement for detailed geometric models of the lungs in combination with the increased computational resources needed for the simulation of the breathing did not allow great progress to be made in the past for the better understanding of inflammatory diseases of the airways through detailed modelling approaches. In this context, computational fluid dynamics (CFD) simulations accompanied by fluid particle tracing (FPT) analysis of the inhaled ambient particles are deemed critical for lung function assessment. Also they enable the understanding of particle depositions on the airways of patients, since these accumulations may affect or lead to inflammations. In this direction, the current study conducts an initial investigation for the better comprehension of particle deposition within the lungs. More specifically, accurate models of the airways obstructions that relate to pulmonary disease are developed and a thorough assessment of the airflow behavior together with identification of the effects of inhaled particle properties, such as size and density, is conducted. Our approach presents a first step towards an effective personalization of pulmonary treatment in regards to the geometric characteristics of the lungs and the in depth understanding of airflows within the airways. Methods A geometry processing technique involving contraction algorithms is established and used to employ the different respiratory arrangements associated with lung related diseases that exhibit airways obstructions. Apart from the normal lung case, two categories of obstructed cases are examined, i.e. models with obstructions in both lungs and models with narrowings in the right lung only. Precise assumptions regarding airflow and deposition fraction (DF) over various sections of the lungs are drawn by simulating these distinct incidents through the finite volume method (FVM) and particularly the CFD and FPT algorithms. Moreover, a detailed parametric analysis clarifies the effects of the particles size and density in terms of regional deposition upon several parts of the pulmonary system. In this manner, the deposition pattern of various substances can be assessed. Results For the specific case of the unobstructed lung model most particles are detected on the right lung (48.56% of total, when the air flowrate is 12.6 L/min), a fact that is also true when obstructions arise symmetrically in both lungs (51.45% of total, when the air flowrate is 6.06 L/min and obstructions occur after the second generation). In contrast, when narrowings are developed on the right lung only, most particles are pushed on the left section (68.22% of total, when the air flowrate is 11.2 L/min) indicating that inhaled medication is generally deposited away from the areas of inflammation. This observation is useful when designing medical treatment of lung diseases. Furthermore, particles with diameters from 1 μm to 10 μm are shown to be mainly deposited on the lower airways, whereas particles with diameters of 20 μm and 30 μm are mostly accumulated in the upper airways. As a result, the current analysis indicates increased DF levels in the upper airways when the particle diameter is enlarged. Additionally, when the particles density increases from 1000 Kg/m3 to 2000 Kg/m3, the DF is enhanced on every generation and for all cases investigated herein. The results obtained by our simulations provide an accurate and quantitative estimation of all important parameters involved in lung modeling. Conclusions The treatment of respiratory diseases with inhaled medical substances can be advanced by the clinical use of accurate CFD and FPT simulations and specifically by evaluating the deposition of inhaled particles in a regional oriented perspective in regards to different particle sizes and particle densities. Since a drug with specific characteristics (i.e. particle size and density) exhibits maximum deposition on particular lung areas, the current study provides initial indications to a qualified physician for proper selection of medication.

There is increasing demand for navigation capability for space vehicles. The idea to extend the application of Global Navigation Satellite Systems (GNSS) from terrestrial to space applications by the use of main beam and side lobe signals has been shown to be feasible. In order to understand the performance and the potential space applications GNSS can support, this paper characterises the Space Service Volume (SSV) in terms of the four parameters of minimum received power, satellite visibility, pseudorange accuracy and Geometric Dilution of Precision (GDOP). This new definition enables the position errors to be estimated. An analytical methodology is proposed to characterise minimum received power for the worst location. Satellite visibility and GDOP are assessed based on grid points at different height layers (to capture the relationship between height and visibility) for single and multiple GNSS constellations, the former represented by BeiDou III (BDS III) and the latter, BDS III in various combinations with GPS, GLONASS and GALILEO. Additional simulation shows that GNSS can potentially support lunar exploration spacecraft at the Earth phasing orbit. This initial assessment of SSV shows the potential of GNSS for space vehicle navigation.

The Clouds and the Earth’s Radiant Energy System (CERES) observations of global top-of-atmosphere radiative energy fluxes for the period March 2000–February 2013 are examined for robust trends and variability. The trend in Arctic ice is clearly evident in the time series of reflected shortwave radiation, which closely follows the record of ice extent. The data indicate that, for every 106 km2 decrease in September sea ice extent, annual-mean absorbed solar radiation averaged over 75°–90°N increases by 2.5 W m−2, or about 6 W m−2 between 2000 and 2012. CMIP5 models generally show a much smaller change in sea ice extent over the 1970–2012 period, but the relationship of sea ice extent to reflected shortwave is in good agreement with recent observations. Another robust trend during this period is an increase in reflected shortwave radiation in the zonal belt from 45° to 65°S. This trend is mostly related to increases in sea ice concentrations in the Southern Ocean and less directly related to cloudiness trends associated with the annular variability of the Southern Hemisphere. Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) produce a scaling of cloud reflection to zonal wind increase that is similar to trend observations in regions separated from the direct effects of sea ice. Atmospheric Model Intercomparison Project (AMIP) model responses over the Southern Ocean are not consistent with each other or with the observed shortwave trends in regions removed from the direct effect of sea ice.

Modeling and mitigating carrier-phase multi- path errors continue to be a significant challenge for high- accuracy positioning using global navigation satellite sys- tems. The multipath error is dependent on the operational environment and therefore cannot be mitigated by differ- encing techniques. The effect of multipath is accentuated when observables based on linear combinations of mea- surements from two or more frequencies are formulated. We develop a new carrier-phase multipath error observable that isolates the inter-frequency carrier-phase multipath error for linear combinations of observables, such as wide- lane (WL). Real-time kinematic positioning results from varying baseline lengths show that a significant reduction in between 14 and 47 % in the time to initial ambiguity resolution are achieved by correcting the WL observable using the new carrier-phase multipath error observable.

In full waveform inversion (FWI) with the least-squares (L2) norm, the direct amplitude matching is never perfect and the accurate estimation of the seismic source strength is not always available. In contrast, the normalized zero-lag cross-correlation objective function relaxes on the amplitude constraints and emphasizes the phase information when measuring the closeness between the simulated and observed data. This FWI method becomes insensitive to differences in amplitude. Based on this property, we investigate the effectiveness and robustness of FWI with the normalized zero-lag cross-correlation function (CFWI) against the noise and unpredictable amplitude of the data that cannot be modeled by the wavefield extrapolation operator. The effectiveness is firstly tested by noise-free data and data contaminated by Gaussian white noise. In addition, CFWI can invert the data set with incorrect source strength when compared with the L2 norm. Moreover, the data set with incorrect source signature illustrates that CFWI is slightly more insensitive to the error in source signature than the L2 norm. However, a source inversion is still needed when the source signature is severely erroneous. With non-Gaussian noise data, such as contaminated by strong ground motion noise and even by spike-type noise, CFWI provides a comparable result with that of the robust Huber norm. Numerical experiments with non-Gaussian noise also indicate that CFWI can suppress noise in data to produce clearer images when compared with the Huber norm. Besides, CFWI is free of the threshold criterion that controls the transition between the L2 and L1 norms used with the Huber and Hybrid norms and therefore free from tedious trial-and-error tests. Several numerical examples support that CFWI is an alternative and reliable inversion method. However, a numerical test with a one-dimensional initial model confirms that CFWI is more sensitive to the cycle-skipping problem caused by less-accurate initial velocity model than the L2 norm, which is due to the wrong matched events contributing to spurious local minima of the objective function of CFWI, but to an increase in the objective function used with the L2 nrom.

A strong correlation between the speed of the eddy-driven jet and the width of the Hadley cell is found to exist in the Southern Hemisphere, both in reanalysis data and in twenty-first-century integrations from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report multimodel archive. Analysis of the space‐time spectra of eddy momentum flux reveals that variations in eddy-driven jet speed are related to changes in the mean phase speed of midlatitude eddies. An increase in eddy phase speeds induces a poleward shift of the critical latitudes and a poleward expansion of the region of subtropical wave breaking. The associated changes in eddy momentum flux convergence are balanced by anomalous meridional winds consistent with a wider Hadley cell. At the same time, faster eddies are also associated with a strengthened polewardeddymomentumflux,sustainingastronger westerlyjet in midlatitudes. The proposedmechanismis consistent with the seasonal dependence of the interannual variability of the Hadley cell width and appears to explain at least part of the projected twenty-first-century trends. One of the most salient features of Earth’s general circulation is the presence of Hadley cells (HCs) in the tropical belts of both hemispheres. Associated with their large-scale motions are some of the main characteristics of tropical and subtropical climates: the intertropical convergence zone in the rising branch and the subtropical dry zones in the descending branches (e.g., Hartmann 1994). Studying the factors determining the meridional extent of these overturning circulations is therefore essential to understand the spatial distribution of tropical and subtropical climates and its changes. The role of the HCs is particularly crucial in the context of climate change and the observed widening of the atmospheric circulation. Observations show a poleward shift of the eddy-driven jets and an expansion of the HCs in recent decades, particularly in the Southern Hemisphere (Hu and Fu 2007; Johanson and Fu 2009; Lu et al. 2009), and the role of radiative forcings induced by greenhouse gases and stratospheric ozone has been shown to be crucial in this context (Polvani et al. 2011b

In order to enhance the acquisition performance of global positioning system (GPS) receivers in weak signal conditions, a high-sensitivity acquisition scheme aided by strapdown inertial navigation system (SINS) information is proposed. The carrier Doppler shift and Doppler rate are pre-estimated with SINS aiding and GPS ephemeris, so that the frequency search space is reduced, and the dynamic effect on the acquisition sensitivity is mitigated effectively. Meanwhile, to eliminate the signal-to-noise ratio gain attenuation caused by data bit transitions, an optimal estimation of the unknown data bits is implemented with the Viterbi algorithm. A differential correction method is then utilized to improve the acquisition accuracy of Doppler shift and therefore to meet the requirement of carrier-tracking loop initialization. Finally, the reacquisition experiments of weak GPS signals are implemented in short signal blockage situations. The simulation results show that the proposed scheme can significantly improve the acquisition accuracy and sensitivity and shorten the reacquisition time. © 2013 Springer-Verlag Berlin Heidelberg.

In vector tracking loop (VTL), the relativity among received signals is exploited to deeply integrate the entire information within signal processing channels. However, the tracking error in one channel may corrupt other channels and lead to an increasing degradation in the tracking performance. An adaptive GPS vector tracking loop with the detection and isolation of contaminated channels is proposed to suppress the propagation of tracking error and to make the vector-based receiver less vulnerable in poor signal quality environments resulting from signal attenuation, interference and jamming. The vectorial transfer function models and the noise bandwidths of both the vector frequency lock loop and the vector delay lock loop are established to accurately calculate bandwidths and optimally design parameters for VTL. The autonomous fault detection algorithm based on uniformly most powerful test for VTL is designed to detect and isolate contaminated channels (with large tracking error due to the poor signal quality) by monitoring the bandwidth statistics of all tracking loops. The results of trials in harsh situations show that the proposed adaptive VTL is superior to baseline VTL, allowing the accuracy, the availability and the reliability of the vector-based receiver to be improved. In conclusion, the proposed adaptive VTL with the detection and isolation of contaminated channels is a powerful method for applications in GPS-challenged environments.

Viability for the development of an engineered geothermal system (EGS) in the oilsands region near Fort McMurray, Alberta, is investigated by studying the structure of the Precambrian basement rocks with magnetotellurics (MT). MT data were collected at 94 broad-band stations on two east–west profiles. Apparent resistivity and phase data showed little variation along each profile. The short period MT data detected a 1-D resistivity structure that could be identified as the shallow sedimentary basin underlain by crystalline basement rocks to a depth of 4–5 km. At lower frequencies a strong directional dependence, large phase splits, and regions of out-of-quadrant (OOQ) phase were detected. 2-D isotropic inversions of these data failed to produce a realistic resistivity model. A detailed dimensionality analysis found links between large phase tensor skews (∼15°), azimuths, OOQ phases and tensor decomposition strike angles at periods greater than 1 s. Low magnitude induction vectors, as well as uniformity of phase splits and phase tensor character between the northern and southern profiles imply that a 3-D analysis is not necessary or appropriate. Therefore, 2-D anisotropic forward modelling was used to generate a resistivity model to interpret the MT data. The preferred model was based on geological observations of outcropping anisotropic mylonitic basement rocks of the Charles Lake shear zone, 150 km to the north, linked to the study area by aeromagnetic and core sample data. This model fits all four impedance tensor elements with an rms misfit of 2.82 on the southern profile, and 3.3 on the northern. The conductive phase causing the anisotropy is interpreted to be interconnected graphite films within the metamorphic basement rocks. Characterizing the anisotropy is important for understanding how artificial fractures, necessary for EGS development, would form. Features of MT data commonly interpreted to be 3-D (e.g. out of OOQ phase and large phase tensor skew) are shown to be interpretable with this 2-D anisotropic model.

Background An increasing number of clinical trials are conducted in primary care settings. Making better use of existing data in the electronic health records to identify eligible subjects can improve efficiency of such studies. Our study aims to quantify the proportion of eligibility criteria that can be addressed with data in electronic health records and to compare the content of eligibility criteria in primary care with previous work. Methods Eligibility criteria were extracted from primary care studies downloaded from the UK Clinical Research Network Study Portfolio. Criteria were broken into elemental statements. Two expert independent raters classified each statement based on whether or not structured data items in the electronic health record can be used to determine if the statement was true for a specific patient. Disagreements in classification were discussed until 100 % agreement was reached. Statements were also classified based on content and the percentages of each category were compared to two similar studies reported in the literature. Results Eligibility criteria were retrieved from 228 studies and decomposed into 2619 criteria elemental statements. 74 % of the criteria elemental statements were considered likely associated with structured data in an electronic health record. 79 % of the studies had at least 60 % of their criteria statements addressable with structured data likely to be present in an electronic health record. Based on clinical content, most frequent categories were: “disease, symptom, and sign”, “therapy or surgery”, and “medication” (36 %, 13 %, and 10 % of total criteria statements respectively). We also identified new criteria categories related to provider and caregiver attributes (2.6 % and 1 % of total criteria statements respectively). Conclusions Electronic health records readily contain much of the data needed to assess patients’ eligibility for clinical trials enrollment. Eligibility criteria content categories identified by our study can be incorporated as data elements in electronic health records to facilitate their integration with clinical trial management systems.

Typical mobile agent networks, such as multi-unmanned aerial vehicle (UAV) systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e., the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this paper, we aim at attaining an optimal tradeoff between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme.

3-D full-waveform inversion (FWI) is an advanced seismic imaging technique that has been widely adopted by the oil and gas industry to obtain high-fidelity models of P-wave velocity that lead to improvements in migrated images of the reservoir. Most industrial applications of 3-D FWI model the acoustic wavefield, often account for the kinematic effect of anisotropy, and focus on matching the low-frequency component of the early arriving refractions that are most sensitive to P-wave velocity structure. Here, we have adopted the same approach in an application of 3-D acoustic, anisotropic FWI to an ocean-bottom-seismometer (OBS) field data set acquired across the Endeavour oceanic spreading centre in the northeastern Pacific. Starting models for P-wave velocity and anisotropy were obtained from traveltime tomography; during FWI, velocity is updated whereas anisotropy is kept fixed. We demonstrate that, for the Endeavour field data set, 3-D FWI is able to recover fine-scale velocity structure with a resolution that is 2–4 times better than conventional traveltime tomography. Quality assurance procedures have been employed to monitor each step of the workflow; these are time consuming but critical to the development of a successful inversion strategy. Finally, a suite of checkerboard tests has been performed which shows that the full potential resolution of FWI can be obtained if we acquire a 3-D survey with a slightly denser shot and receiver spacing than is usual for an academic experiment. We anticipate that this exciting development will encourage future seismic investigations of earth science targets that would benefit from the superior resolution offered by 3-D FWI.