Irregular Sampling Research Articles

Off-the-grid vertical seismic profile data regularization by a compressive sensing method

Different from the surface survey, the vertical seismic profile (VSP) survey deploys sources on the surface and geophones in a well. VSP provides higher resolution information of subsurface structures. The faults that cannot be imaged with surface seismic data may be detected with VSP data, and detailed analysis of fracture zones can be achieved with multicomponent VSP. However, one of the main problems is that the sources seldom are acquired on a regular grid in realistic VSP surveys. The irregular samplings cause serious artifacts in migration or imaging, such that data regularization must be implemented first. We have developed a compressive sensing (CS)-based method to regularize nonstationary VSP data. Our method directly operates on irregularly gridded data sets, which is a key contribution compared to the existing CS-based reconstruction methods that work on regular grids. The CS framework consists of a sparsity constraint and a penalty term. We have used the curvelet transform for sparsity constraint of nonstationary events in the regularization term and the nonequispaced Fourier transform to regularize the VSP data in a penalty term. An alternative directional method of multipliers is used for solving the optimization problem. Our method is tested on synthetic, field 2D and 3D VSP data sets. Our method obtains improved reconstructions on continuities of the events and produces fewer artifacts compared to the well-known antileaking Fourier transform method.

Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC-Baltimore Metropolitan Area Using an Inverse Modeling Technique.

Since greenhouse gas mitigation efforts are mostly being implemented in cities, the ability to quantify emission trends for urban environments is of paramount importance. However, previous aircraft work has indicated large daily variability in the results. Here we use measurements of CO2, CH4, and CO from aircraft over 5 days within an inverse model to estimate emissions from the DC-Baltimore region. Results show good agreement with previous estimates in the area for all three gases. However, aliasing caused by irregular spatiotemporal sampling of emissions is shown to significantly impact both the emissions estimates and their variability. Extensive sensitivity tests allow us to quantify the contributions of different sources of variability and indicate that daily variability in posterior emissions estimates is larger than the uncertainty attributed to the method itself (i.e., 17% for CO2, 24% for CH4, and 13% for CO). Analysis of hourly reported emissions from power plants and traffic counts shows that 97% of the daily variability in posterior emissions estimates is explained by accounting for the sampling in time and space of sources that have large hourly variability and, thus, caution must be taken in properly interpreting variability that is caused by irregular spatiotemporal sampling conditions.

Chemometric handling of spectral-temporal dependencies for liquid chromatography data with online registering of excitation-emission fluorescence matrices

In this work, the generation and posterior chemometric resolution of third-order data, obtained from samples processed by liquid chromatography (LC) with online registering of excitation-emission fluorescence matrices (EEM) is reported. Samples were instrumentally processed in a relatively short time, and neither an intentional reduction of the linear flow rate nor an unconventional fluorescence instrument were required. Through the inclusion of external circuitry based on open-source hardware, the occurrence time of each individual fluorescence intensity reading was recorded. For the reported instrumental setup, irregular signal sampling was verified. In order to consider samples-specific time measurements, the PARAFAC (Parallel Factor Analysis) algorithm, and the derived APARAFAC (Augmented-PARAFAC) strategy, were adapted. The functional information was employed during the computational stages, through the development and implementation of smoothing strategies. To tackle differences between the rate of spectral acquisition and the rate of change in the concentration of the mobile fluorophores, Expectation Maximization was implemented. Data from samples with one calibrated analyte (Vitamin B6-Pyridoxine), in presence of uncalibrated interferents, were modeled. In order to preserve the original data structure, unfolding data operations were minimized. The resolved profiles of all species were in agreement with the corresponding chromatographic and spectral references. Results suggest that the effects derived from the loss of trilinearity previously reported in the literature for LC-EEM data, depend on interpretation and subsequent modeling of the data. The reported strategies can be useful with other flow techniques and kinetics.

Prediction of the droplet spreading dynamics on a solid substrate at irregular sampling intervals: Nonlinear Auto-Regressive eXogenous Artificial Neural Network approach (NARX-ANN)

This paper introduces a Nonlinear Auto-Regressive eXogenous Artificial Neural Network (NARX-ANN) model for prediction of spreading dynamics. 1220 experimental data of spreading dynamics of different droplets on various substrates have been collected from literature for model development. The model input parameters are Weber number, Ohnesorge number, and the tangent of equilibrium contact angle. An auxiliary input has been also added to take into account the irregular time sampling. D-optimal design of experiments has been utilized to determine the best parameters for the NARX model. It was found that the prediction capability of NARX model was better in comparison with KC and AGM models. The statistical parameters of the best developed NARX model including mean square error MSE=3.257×10-4, average absolute relative deviation (AARD) = 4.240%, and coefficient of determination (R2)=0.993 demonstrate the excellent prediction capability of the proposed model.

Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder

Seismic trace interpolation is an important technique because irregular or insufficient sampling data along the spatial direction may lead to inevitable errors in multiple suppression, imaging, and inversion. Many interpolation methods have been studied for irregularly sampled data. Inspired by the working idea of the autoencoder and convolutional neural network, we have performed seismic trace interpolation by using the convolutional autoencoder (CAE). The irregularly sampled data are taken as corrupted data. By using a training data set including pairs of the corrupted and complete data, CAE can automatically learn to extract features from the corrupted data and reconstruct the complete data from the extracted features. It can avoid some assumptions in the traditional trace interpolation method such as the linearity of events, low-rankness, or sparsity. In addition, once the CAE network training is completed, the corrupted seismic data can be interpolated immediately with very low computational cost. A CAE network composed of three convolutional layers and three deconvolutional layers is designed to explore the capabilities of CAE-based seismic trace interpolation for an irregularly sampled data set. To solve the problem of rare complete shot gathers in field data applications, the trained network on synthetic data is used as an initialization of the network training on field data, called the transfer learning strategy. Experiments on synthetic and field data sets indicate the validity and flexibility of the trained CAE. Compared with the curvelet-transform-based method, CAE can lead to comparable or better interpolation performances efficiently. The transfer learning strategy enhances the training efficiency on field data and improves the interpolation performance of CAE with limited training data.

SPATIAL VARIABILITY OF PHYSICAL ATTRIBUTES, LITTER AND SOIL CARBON STOCK IN A FAMILY FARMING SYSTEM IN TOCANTINS

The monitoring of soil attributes allows the evaluation of its ability to perform its functions within an agroecosystem. The objective of this work was to evaluate the spatial variability of soil physical attributes, litter and carbon stock in a family farming system in the Cerrado Tocantinense. The area is located in the southern region in the state in the municipality of Aliança do Tocantins. Four types of land use were diagnosed in the area: brachiaria pasture intercropped with stylosanthes, Andropogon pasture, orchard and native forest. The native forest was considered as a reference. The study area totaled 7.9 ha-1 in which it was distributed an irregular sample grid composed of 160 points. Deformed and undeformed samples were collected for each georeferenced point at depths of 0-10 and 10-20 cm, as well as samples to determine the litter and soil carbon stock. Data were submitted to exploratory analysis and geostatistical study. It was found that the conversion of native forest for different soil uses through orchard, brachiaria, andropogon and native forest caused spatial variability in physical attributes, litter and soil carbon stock at depths 0-10 and 10-20 cm. The orchard subarea stood out as a promising system in the accumulation of organic carbon due to cattle manure.

Regular and Irregular Sampling Linear Transforms in Series of Shift-Invariant Spaces

Regular and Irregular Sampling Linear Transforms in Series of Shift-Invariant Spaces

Health risk assessment of drinking water in correlation with water-related diseasess

Healthy and safe drinking water in sufficient quantities provided to consumers improves living conditions, raises health culture at a higher level and promotes the environment as a whole. The aim is to see if there is a correlation between irregular water samples with intestinal infectious diseases related to drinking water. Materials and method. The preparation of the Republic Computer Program (1996) for issuing laboratory findings (RCP) from the analyses of samples of drinking water (physico-chemical and bacteriological), in accordance with the methodology for application of ID numbers in the computer system in the Public Health Centers has created the possibility of complete health records of drinking water supply facilities under health surveillance for the territory of the entire Republic. Evaluation has been made of the results of basic physicо-chemical and bacteriological analyses of drinking water in the settlements of the Republic of North Macedonia and the water-related diseases – waterborne intestinal infectious diseases in the period 2014-2018. Results. Drinking water from city water supply in the examined period in terms of physico-chemical analysis ranged 2.9-4.3%, while in relation to bacteriological analysis 0.9-2.6%.. Waterborne intestinal infectious diseases show a decline in their incidence in 2018. Conclusion. The registered data from the physicо-chemical and bacteriological analysis showed that the drinking water from the city water supply systems in the period 2014-2018 was safe. According to the processed data, access to safe drinking water for the population was 97% by 2018, which ranks the country in the group of countries in the world with the highest access to safe drinking water, and there is no significant connection with drinking water related diseases.

Sensor Fusion with Irregular Sampling and Varying Measurement Delays

In multisensor fusion, several sources of information are combined in order to increase the estimation quality for the quantity of interest. This activity finds many applications from tactical miss...

An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts

Near-infrared hyperspectral imaging (NIR-HSI) can be used for nondestructive, rapid, real-time detection in food safety; however, irregular sample topographies introduce variations in the spectral intensity that impair subsequent classification and inversion processes. In this study, the spectral variations in HSI images of peanut samples with irregular topographies were assessed via the classification gradient and singular spectrum analysis (SSA). An SSA based correction model (CMSSA) is proposed that assumes the spectral intensity of all pixels of peanuts should be equal. The method was validated via classification and the coefficient of variation (CV) and was found to eliminate the spectral variation caused by the irregular kernel topography while retaining chemical differences of interest. We anticipate this method will prove useful in food safety detection applications involving the quantitative inversion of parameters.

A lattice and random intermediate point sampling design for animal movement

AbstractAnimal movement studies have become ubiquitous in animal ecology for the estimation of space use and the analysis of movement behavior. In these studies, animal movement data are primarily collected at regular time intervals. We propose an irregular sampling design that could lead to greater efficiency and information gain in animal movement studies. Our novel sampling design, called lattice and random intermediate point (LARI), combines samples at regular and random time intervals. We compare the LARI sampling design to regular sampling designs in an example with common black carpenter ant location data, an example with guppy location data, and a simulation study of movement with a point of attraction. We modify a general stochastic differential equation model to allow for irregular time intervals and use this framework to compare sampling designs. When parameters are estimated reasonably well, regular sampling results in greater precision and accuracy in prediction of missing data. However, in each of the data and simulation examples explored in this paper, LARI sampling results in more accurate and precise parameter estimation and, thus, better prediction of missing data as well. This result suggests that researchers might gain greater insight into underlying animal movement processes by choosing LARI sampling over regular sampling.

An Efficient Sampling Algorithm With a K-NN Expanding Operator for Depth Data Acquisition in a LiDAR System

The spatial resolution of a depth-acquisition device, such as a Light Detection and Ranging (LiDAR) sensor, is limited because of the slow acquisition. To accurately reconstruct a depth image from limited spatial resolution, a two-stage sampling process has been widely used. However, two-stage sampling uses an irregular sampling pattern for the sampling operation, which requires complex computation for reconstruction and additional memory space for storage. A mathematical formulation of a LiDAR system demonstrates that two-stage sampling does not satisfy its timing constraint for practical use. To overcome the drawbacks of two-stage sampling, this paper proposes a new sampling method that reduces the computational complexity and memory requirements by generating the optimal representatives of a sampling pattern in down-sample data. A sampling pattern can be derived from a $k$ -NN expanding operation from the down-sampled representatives. The proposed algorithm is designed to preserve the object boundary by restricting the expansion-operation only to the object boundary or complex texture. In addition, the proposed algorithm runs in linear-time complexity and reduces the memory requirements using a down-sampling ratio. The experimental results demonstrate that the proposed sampling outperforms grid sampling by at most 7.92 dB. Consequently, the proposed sampling achieves reconstructed quality similar to that of optimal sampling, while substantially reducing the computation time and memory requirements.

Statistical Regularization for Enhanced TomoSAR Imaging

One of the main topics in synthetic aperture radar (SAR) tomography (TomoSAR) is the estimation of the vertical structures’ location, which scatter the field back toward the sensor, constrained to a reduced number of passes. Moreover, the introduction of artifacts and the increase in the ambiguity levels due to irregular sampling, consequence of nonuniform acquisition constellations, complicate the accurate estimation of the source parameters. Pursuing the alleviation of such drawbacks, the use of statistical regularization approaches, based on the maximum-likelihood estimation theory, has been successfully demonstrated in the previous related studies. However, these techniques are constrained to the assumption that the probability density function of the observed data is Gaussian. In this article, in order to solve the ill-posed nonlinear TomoSAR inverse problem, we relax this assumption and apply the weighted covariance fitting (WCF) criterion instead. The latter alleviates the previously mentioned drawbacks and retrieves a power spectrum pattern with an outline more similar to the expected one, i.e., recovered using matched spatial filtering with a higher number of tracks. First, we present the mathematical background of the related regularization methods, adapted to solve the TomoSAR inverse problem, from which we derive our novel technique, named WCF-based iterative spectral estimator (WISE). Then, the differences and similarities between the addressed regularization approaches are discussed, besides their main advantages and disadvantages. Finally, the implementation details of WISE are treated, along with simulated examples and experimental results obtained from a forested test site.

Adaptive and Feature-Preserving Mesh Denoising Schemes Based on Developmental Guidance

Distinguishing among different kinds of features as well as noises on 3D mesh models is crucial for feature-preserving mesh denoising. This paper proposes to address this issue via in-depth analysis of the intermediate products of the denoising processes, and one framework is presented for raising adaptive and feature-preserving mesh denoising schemes. Firstly, by analyzing the changes of the facet normals during the denoising process, we propose the definition of developmental guidance, which helps to assess the current filtering status and predict the positions of feature and smooth regions. Then, by incorporating the guidance, we put forward one interpolation-based denoising scheme, which affords an efficient way to interpolate and recover different levels of features and is robust to severe noises. Besides, we also introduce the guidance to the optimization-based model, and the achieved global scheme is tested to be stable and robust to irregular samplings. Both the theoretical analysis and extensive experimental results on synthetic and real-world noises have demonstrated the attractive advantages of our whole framework, such as being adaptive, efficient, robust, feature-preserving, etc.

Cinema:Bandit: a visualization application for beamline science demonstrated on XFEL shock physics experiments.

A new visualization tool, Cinema:Bandit, and its demonstration with a continuous workflow for analyzing shock physics experiments and visually exploring the data in real time at X-ray light sources is presented. Cinema:Bandit is an open-source, web-based visualization application in which the experimenter may explore an aggregated dataset to inform real-time beamline decisions and enable post hoc data analysis. The tool integrates with experimental workflows that process raw detector data into a simple database format, and it allows visualization of disparate data types, including experimental parameters, line graphs, and images. Use of parallel coordinates accommodates the irregular sampling of experimental parameters and allows for display and filtering of both experimental inputs and measurements. The tool is demonstrated on a dataset of shock-compressed titanium collected at the Matter in Extreme Conditions hutch at the Linac Coherent Light Source.

Aircraft Skin Rivet Detection Based on 3D Point Cloud via Multiple Structures Fitting

Rivet detection is usually the first step for almost all surface and rivet inspection methods in aircraft skins. With 3D laser scanners, one can rapidly obtain the precise 3D information, i.e. point cloud, of the surface and rivets. Subsequently, rivet detection can be converted to a multiple-structure fitting problem from 3D point clouds. However, robust structure fitting from scanned 3D point cloud remains an open problem due to its challenging nature, such as noise and outliers, irregular sampling density and missing scanning. To reduce the fitting variability, this paper presents an automated density-aware multiple-structure fitting algorithm to perform rivet detection based on a 3D point cloud. The key observation is that the local density of points belonging to the rivet contour is relatively higher. We hereby formulate rivet detection as a multiple structure fitting problem with a density-based significance measure. By considering the local distribution characteristics, we first perform adaptive density enhancement on the basic local density. Subsequently, we detect the potential circle hypotheses and thereby extract rivet contours. By performing the mode-seeking algorithm on hypergraphs, all the circle structures can be obtained simultaneously. Overall, the proposed extraction algorithm is able to efficiently and effectively detect rivets from the raw scanned point clouds. We also demonstrate that the proposed algorithm achieves significant superiority over several state-of-the-art model fitting methods on the real scanned point cloud via experimental results. Moreover, we give the application of our algorithm on rivet flush inspection, showing that our method can assist in the rapid measurement of riveting quality.

Covariance-regularized Reconstruction of Data Cubes in Integral Field Spectroscopy and Application to MaNGA Data

Abstract Integral field spectroscopy can map astronomical objects spatially and spectroscopically. Due to instrumental and atmospheric effects, it is common for integral field instruments to yield a sampling of the sky image that is both irregular and wavelength dependent. Most subsequent analysis procedures require a regular, wavelength-independent sampling (for example a fixed rectangular grid), and thus an initial step of fundamental importance is to resample the data onto a new grid. The best possible resampling would produce a well-sampled image, with a resolution equal to that imposed by the intrinsic spatial resolution of the instrument, telescope, and atmosphere, and with no statistical correlations between neighboring pixels. A standard method in the field to produce a regular set of samples from an irregular set of samples is Shepard’s method, but Shepard’s method typically yields images with a degraded resolution and large statistical correlations between pixels. Here we introduce a new method, which improves on Shepard’s method in both these respects. We apply this method to data from the Mapping Nearby Galaxies at Apache Point Observatory survey, part of Sloan Digital Sky Survey IV, demonstrating a full width at half maximum close to that of the intrinsic spatial resolution (and ∼16% better than Shepard’s method) and low statistical correlations between pixels. These results nearly achieve the ideal resampling. This method can have broader applications to other integral field data sets and to other astronomical data sets (such as dithered images) with irregular sampling.

BÜYÜK ÖLÇEKLİ KÖMÜR ARININDA GERÇEKLEŞTİRİLEN SCHMİDT SERTLİK İNDEKSİ VE NOKTA YÜKLEME DAYANIMI DENEY SONUÇLARININ DEĞERLENDİRİLMESİ

Kömür ve kömür damarı içerisinde konumlanan ara kesmelerin (t=0,5m) yerinde malzeme özelliklerinin belirlenmesine yönelik bu çalışma, TKİ-GLİ-Ömerler yeraltı maden ocağında gerçekleştirilmiştir. Kömür arın yüksekliği 3,5 m ve uzunluğu ise 110 m olan A1 hazırlık panosunda, yaklaşık 385 m2’ lik yüzey alanı üzerinde her biri yaklaşık 15 m2 olan 4 deney alanı belirlenmiştir. Bu deney bölgeleri arının iki kenar uç tarafında ve arın ortasında konumlanmıştır. Deney bölgeleri kendi içlerinde toplam 238 birim hücreye bölünmüştür. Birim hücrelerin orta noktasında en az üç kez tekrarlanmak kaydı ile Schmidt sertlik belirleme deneyleri yapılmıştır. Nokta yükleme dayanımı indeks deneyi için A1 panosunun kömür arını yaklaşık 5’ er metre aralıklar ile toplam 21 birim bölgeye ayrılmıştır. Her birim bölge ise kendi içerisinde beş alt birim hücreye bölünerek her bir alt birim hücreden alınan düzensiz örnekler ile toplam 563 adet nokta yükleme dayanımı belirleme deneyleri yapılmıştır. Sonuç olarak yapılması pratik olan Schmidt sertlik ölçümleri ile nokta yükleme dayanım deney sonuçları arasındaki mekanik ilişkiler ve bu deney sonuçlarına dayalı kazı kolaylığına ait bir sınıflama bu çalışmada önerilmiştir.

Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

Digital soil mapping for soil texture is mostly an understanding of how soil texture fractions vary in space as influenced by environmental variables mainly derived from the digital elevation model (DEM). In this study, topsoil texture models were generated and evaluated by multiple linear regression (MLR), ordinary kriging (OK), simple kriging (SK) and universal kriging (UK) using free and open-source R, System for Automated Geoscientific Analyses, and QGIS software. Comparing these models is the main objective of the study. The study site covers an area of 124 km2 of the Municipality of Barili, Cebu. A total of 177 soil samples were gathered and analyzed from irregular sample points. DEM derivatives and remote sensing data (Landsat 8) were used as environmental variables. Exploratory analyses revealed no outlier in the data. Skewness and kurtosis values of the untransformed data vary greatly between –3.85 to 7.20 and 1.8 to 70.7, respectively; an indication that variables are highly skewed with heavy tails. Thus, Tukey’s ladder of powers transformation was applied that resulted to normal or nearly normal distribution having skewness values close to zero and kurtosis values have lighter tails. All data analysis from MLR modeling, variography, kriging, and cross-validations of models were implemented using the transformed data. Forward selection, backward elimination, and stepwise selection methods were adapted for predictors selection in MLR. The MLR, OK, SK, and UK were applied and cross validated for topsoil texture prediction. Likewise, exponential, Gaussian, and spherical models were fitted for the experimental variograms. Backward elimination method for clay, sand, and silt have the lowest MAE and highest R2 in MLR. The UK fitted with exponential variogram model has the highest R2 of 0.878, 0.821, and 0.893 for clay, sand, and silt, respectively. These models can be adapted as a decision support for agricultural land use planning and crop suitability development in the area.

Vision-Based Target-Following Guider for Mobile Robot

A vision-based target-following guider for mobile robot is presented in this paper. It consists of three parts: the visual tracking part; the target redetection part; and the visual servo part. In the visual tracking part, the target contour band is proposed for irregular sampling, which can improve the performance of the correlation filter-based methods. In the target redetection part, potential targets are searched by an offline trained detector. Then an online training module is used to determine the real target and achieve accurate positioning. The interaction matrix of point features is used in the visual servo part for motion control to maintain the relative pose between the target and the robot. The visual tracking part and the target redetection part are tested respectively on many videos, which proved to work well. To show the effectiveness of our method, some state-of-the-art methods are also tested. The target-following guider is evaluated on mobile robots. In our experiments, the robot can robustly follow the human target over a long distance, which strongly proved the validity of our target-following guider.