Interpolation with Fourier-radial adaptive thresholding

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB95-WB102 ◽  
Author(s):  
William Curry
Keyword(s):  
Convex Sets ◽  
Plane Waves ◽  
Synthetic Data ◽  
Sampling Interval ◽  
Polar Coordinates ◽  
Adaptive Thresholding ◽  
Sampled Data ◽  
Data Set ◽  
Before And After ◽  
Reflectivity Data

Many interpolation methods are effective with regularly sampled or randomly sampled data, whereas the spatial sampling of seismic reflectivity data is typically neither regular nor random. Fourier-radial adaptive thresholding (FRAT) is a sparsity-promoting method in which the interpolated result is sparse in the frequency-wavenumber domain and is coherent in a manner consistent with that of a collection of unaliased plane waves. The sparsity and the desired pattern in the [Formula: see text] domain are promoted by iterative soft thresholding and adaptive weighting; data in the [Formula: see text] domain are transformed to polar coordinates and then low-pass filtered along the radial axis to generate the nonlinear weight. FRAT interpolates data that are randomly sampled and aliased; i.e., where the minimum distance between adjacent traces is greater than the Nyquist sampling interval. A conventional approach to solving this problem is to apply a cascade of two procedures: first a sparsity-based method, such as projection onto convex sets (POCS) to interpolate the data onto a regularly sampled but aliased grid, followed by a “beyond aliasing” approach such as Gülünay [Formula: see text] interpolation to further interpolate the regularly sampled POCS result. In a simple synthetic example of two dipping plane waves with irregular, aliased sampling, FRAT outperformed this cascaded approach. In another experiment, the Sigsbee2A prestack synthetic data set was sampled using the source geometry from a 3D offshore survey where POCS will have difficulty with the semiregularity of this sampling pattern. FRAT produced results superior to those of POCS before and after the data were migrated.

AVO and AVA inversion for fractured reservoir characterization

Geophysics ◽  
2002 ◽  
Vol 67 (1) ◽  
pp. 300-306 ◽  
Author(s):  
Matteo Mario Beretta ◽  
Giancarlo Bernasconi ◽  
Giuseppe Drufuca
Keyword(s):  
Reservoir Characterization ◽  
Spectral Component ◽  
Plane Waves ◽  
Synthetic Data ◽  
Transversely Isotropic ◽  
Fractured Reservoir ◽  
Fracture Density ◽  
P Waves ◽  
Data Set ◽  
Inversion Procedure

Seismic wave reflection amplitudes are used to detect fluids and fracture properties in reservoirs. This paper studies the characterization of a vertically fractured fluid‐filled reservoir by analyzing the reflection amplitudes of P‐waves with varying incident and azimuthal angles. The reservoir is modeled as a horizontal transversely isotropic medium embedded in an isotropic background, and the linearized P‐waves reflection coefficient are considered. The conditioning of the inverse problem is analyzed, and fracture density is found to be the best conditioned parameter. Using diffraction tomography under the Born approximation, an inversion procedure is proposed in the transformed k–ω domain to detect fracture density variations within the reservoir. Seismic data are rearranged in pairs of incident and reflected plane waves, enlightening only one spectral component of the fracture density field at a time. Only the observable spectral components are inverted. Moreover, working in the transformed domain, picking reflection amplitudes is not required. An example of the inversion applied to a synthetic data set is presented. The limitation of source and receiver numbers and the finite bandwidth of the wavelet produce a loss of resolution, but the overall fracture density variations are recovered correctly.

scholarly journals A posteriori noise estimation in variable data sets

2018 ◽  
Vol 609 ◽  
pp. A39 ◽  
Author(s):  
S. Czesla ◽  
T. Molle ◽  
J. H. M. M. Schmitt
Keyword(s):  
Standard Deviation ◽  
Synthetic Data ◽  
Light Curves ◽  
Weighted Sums ◽  
Data Sets ◽  
A Posteriori ◽  
Sampled Data ◽  
Data Set ◽  
Specific Parameter ◽  
Data Points

Most physical data sets contain a stochastic contribution produced by measurement noise or other random sources along with the signal. Usually, neither the signal nor the noise are accurately known prior to the measurement so that both have to be estimated a posteriori. We have studied a procedure to estimate the standard deviation of the stochastic contribution assuming normality and independence, requiring a sufficiently well-sampled data set to yield reliable results. This procedure is based on estimating the standard deviation in a sample of weighted sums of arbitrarily sampled data points and is identical to the so-called DER_SNR algorithm for specific parameter settings. To demonstrate the applicability of our procedure, we present applications to synthetic data, high-resolution spectra, and a large sample of space-based light curves and, finally, give guidelines to apply the procedure in situation not explicitly considered here to promote its adoption in data analysis.

Seismic demigration/migration in the curvelet domain

Geophysics ◽  
2008 ◽  
Vol 73 (2) ◽  
pp. S35-S46 ◽  
Author(s):  
Hervé Chauris ◽  
Truong Nguyen
Keyword(s):  
Input Data ◽  
Plane Waves ◽  
Synthetic Data ◽  
Velocity Model ◽  
Local Velocity ◽  
Data Set ◽  
Migration Operator ◽  
Local Plane ◽  
Seismic Images

Curvelets can represent local plane waves. They efficiently decompose seismic images and possibly imaging operators. We study how curvelets are distorted after demigration followed by migration in a different velocity model. We show that for small local velocity perturbations, the demigration/migration is reduced to a simple morphing of the initial curvelet. The derivation of the expected curvature of the curvelets shows that it is easier to sparsify the demigration/migration operator than the migration operator. An application on a 2D synthetic data set, generated in a smooth heterogeneous velocity model and with a complex reflectivity, demonstrates the usefulness of curvelets to predict what a migrated image would become in a locally different velocity model without the need for remigrating the full input data set. Curvelets are thus well suited to study the sensitivity of a prestack depth-migrated image with respect to the heterogeneous velocity model used for migration.

Dynamic Relationship between Crude Oil and Stock Prices in India: Before And After the Subprime Financial Crisis 2008

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 96-104
Author(s):  
P. Sakthivel ◽  
S. Rajaswaminathan ◽  
R. Renuka ◽  
N. R.Vembu
Keyword(s):  
Financial Crisis ◽  
Crude Oil ◽  
Stock Prices ◽  
Oil Prices ◽  
Energy Index ◽  
Data Set ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
Long Run ◽  
Before And After

This paper empirically discovered the inter-linkages between stock and crude oil prices before and after the subprime financial crisis 2008 by using Johansan co-integration and Granger causality techniques to explore both long and short- run relationships.  The whole data set of Nifty index, Nifty energy index, BSE Sensex, BSE energy index and oil prices are divided into two periods; before crisis (from February 15, 2005 to December31, 2007) and after crisis (from January 1, 2008 to December 31, 2018) are collected and analyzed. The results discovered that there is one-way causal relationship from crude oil prices to Nifty index, Nifty energy index, BSE Sensex and BSE energy index but not other way around in both periods. However, a bidirectional causality relationship between BSE Energy index and crude oil prices during post subprime financial crisis 2008. The co-integration results suggested that the absence of long run relationship between crude oil prices and market indices of BSE Sensex, BSE energy index, Nifty index and Nifty energy index before and after subprime financial crisis 2008.

scholarly journals Approach to hand posture recognition based on hand shape features for human–robot interaction

2021 ◽  
Author(s):  
Jing Qi ◽  
Kun Xu ◽  
Xilun Ding
Keyword(s):  
Gaussian Mixture ◽  
Human Robot Interaction ◽  
Polar Coordinates ◽  
Support Vector ◽  
Hand Posture ◽  
Data Set ◽  
Hand Shape ◽  
Hand Posture Recognition ◽  
Hand Segmentation ◽  
Posture Recognition

AbstractHand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

scholarly journals The effect of COVID19 pandemic restrictions on an urban rodent population

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Miguel A. Bedoya-Pérez ◽  
Michael P. Ward ◽  
Max Loomes ◽  
Iain S. McGregor ◽  
Mathew S. Crowther
Keyword(s):  
Binary Data ◽  
Sampling Interval ◽  
Resource Scarcity ◽  
Health Authorities ◽  
Before And After ◽  
Directional Change ◽  
Public Health Authorities ◽  
Bait Stations ◽  
Rodent Populations ◽  
The City

AbstractShortly after the enactment of restrictions aimed at limiting the spread of COVID-19, various local government and public health authorities around the world reported an increased sighting of rats. Such reports have yet to be empirically validated. Here we combined data from multi-catch rodent stations (providing data on rodent captures), rodent bait stations (providing data on rodent activity) and residents’ complaints to explore the effects of a six week lockdown period on rodent populations within the City of Sydney, Australia. The sampling interval encompassed October 2019 to July 2020 with lockdown defined as the interval from April 1st to May 15th, 2020. Rodent captures and activity (visits to bait stations) were stable prior to lockdown. Captures showed a rapid increase and then decline during the lockdown, while rodent visits to bait stations declined throughout this period. There were no changes in the frequency of complaints during lockdown relative to before and after lockdown. There was a non-directional change in the geographical distribution of indices of rodent abundance suggesting that rodents redistributed in response to resource scarcity. We hypothesize that lockdown measures initially resulted in increased rodent captures due to sudden shortage of human-derived food resources. Rodent visits to bait stations might not show this pattern due to the nature of the binary data collected, namely the presence or absence of a visit. Relocation of bait stations driven by pest management goals may also have affected the detection of any directional spatial effect. We conclude that the onset of COVID-19 may have disrupted commensal rodent populations, with possible implications for the future management of these ubiquitous urban indicator species.

scholarly journals Localization of Scattering Objects Using Neural Networks

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 11
Author(s):  
Domonkos Haffner ◽  
Ferenc Izsák
Keyword(s):  
Neural Networks ◽  
Plane Waves ◽  
Measurement Data ◽  
Scattered Wave ◽  
Training Data ◽  
Data Set ◽  
Main Compound ◽  
Incident Plane ◽  
The Neural Networks ◽  
Simulation Package

The localization of multiple scattering objects is performed while using scattered waves. An up-to-date approach: neural networks are used to estimate the corresponding locations. In the scattering phenomenon under investigation, we assume known incident plane waves, fully reflecting balls with known diameters and measurement data of the scattered wave on one fixed segment. The training data are constructed while using the simulation package μ-diff in Matlab. The structure of the neural networks, which are widely used for similar purposes, is further developed. A complex locally connected layer is the main compound of the proposed setup. With this and an appropriate preprocessing of the training data set, the number of parameters can be kept at a relatively low level. As a result, using a relatively large training data set, the unknown locations of the objects can be estimated effectively.

scholarly journals Visual Analysis of Biomarkers Reveals Differences in Lipid Profiles and Liver Enzymes before and after Gastric Sleeve and Bypass

Obesity Facts ◽  
2021 ◽  
pp. 1-11
Author(s):  
Marijn Marthe Georgine van Berckel ◽  
Saskia L.M. van Loon ◽  
Arjen-Kars Boer ◽  
Volkher Scharnhorst ◽  
Simon W. Nienhuijs
Keyword(s):  
Bariatric Surgery ◽  
Visual Analytics ◽  
Biochemical Markers ◽  
Visual Analysis ◽  
Large Population ◽  
High Volume ◽  
Cholesterol Concentration ◽  
Health State ◽  
Data Set ◽  
Before And After

<b><i>Introduction:</i></b> Bariatric surgery results in both intentional and unintentional metabolic changes. In a high-volume bariatric center, extensive laboratory panels are used to monitor these changes pre- and postoperatively. Consecutive measurements of relevant biochemical markers allow exploration of the health state of bariatric patients and comparison of different patient groups. <b><i>Objective:</i></b> The objective of this study is to compare biomarker distributions over time between 2 common bariatric procedures, i.e., sleeve gastrectomy (SG) and gastric bypass (RYGB), using visual analytics. <b><i>Methods:</i></b> Both pre- and postsurgical (6, 12, and 24 months) data of all patients who underwent primary bariatric surgery were collected retrospectively. The distribution and evolution of different biochemical markers were compared before and after surgery using asymmetric beanplots in order to evaluate the effect of primary SG and RYGB. A beanplot is an alternative to the boxplot that allows an easy and thorough visual comparison of univariate data. <b><i>Results:</i></b> In total, 1,237 patients (659 SG and 578 RYGB) were included. The sleeve and bypass groups were comparable in terms of age and the prevalence of comorbidities. The mean presurgical BMI and the percentage of males were higher in the sleeve group. The effect of surgery on lowering of glycated hemoglobin was similar for both surgery types. After RYGB surgery, the decrease in the cholesterol concentration was larger than after SG. The enzymatic activity of aspartate aminotransferase, alanine aminotransferase, and alkaline phosphate in sleeve patients was higher presurgically but lower postsurgically compared to bypass values. <b><i>Conclusions:</i></b> Beanplots allow intuitive visualization of population distributions. Analysis of this large population-based data set using beanplots suggests comparable efficacies of both types of surgery in reducing diabetes. RYGB surgery reduced dyslipidemia more effectively than SG. The trend toward a larger decrease in liver enzyme activities following SG is a subject for further investigation.

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