Experimental design: Electrical resistivity data sets that provide optimum subsurface information

Geophysics ◽  
2004 ◽  
Vol 69 (1) ◽  
pp. 120-139 ◽  
Author(s):  
Peter Stummer ◽  
Hansruedi Maurer ◽  
Alan G. Green
Keyword(s):  
Electrical Resistivity ◽  
Experimental Design ◽  
Design Procedure ◽  
Data Sets ◽  
Electrode Arrays ◽  
Design Algorithm ◽  
Comprehensive Data ◽  
Data Points ◽  
Depth Ranges ◽  
2D Resistivity

Although multielectrode electrical‐resistivity systems have been commercially available for more than a decade, resistivity imaging of the subsurface continues to be based on data sets recorded using one or more of the standard electrode arrays (e.g., the Wenner or conventional dipole‐dipole array). To exploit better the full capabilities of multielectrode acquisition systems, we have developed an experimental design procedure to identify suites of electrode configurations that provide subsurface information according to predefined optimization criteria. The experimental design algorithm includes a goodness function that ranks the sensitivity of every possible electrode configuration to changes in the subsurface parameters. To examine the potential and limitations of the new algorithm, comprehensive data sets that included data from all standard and nonstandard electrode configurations were (a) generated for a complex 2D resistivity model and (b) recorded across a well‐studied test site in Switzerland. Images determined from the resultant comprehensive data sets were used as benchmarks against which the images derived from the optimized data sets were assessed. Images from relatively small optimized data sets, containing 265–282 data points, provided more information than did those from standard data sets of equal size. By far the best images, comparable to those determined from the much larger comprehensive data sets, were obtained from optimized data sets with 1000–6000 data points. These images supplied reliable information over depth ranges that were three times greater than the depth ranges resolved by the standard images. The first ∼600 electrode configurations selected by the experimental design procedure were nonstandard dipole‐dipole‐type arrays, whereas the following ∼4800 electrode configurations were an approximately equal mix of nonstandard dipole‐dipole‐type arrays and nested configurations (i.e., mostly gradient and other nonstandard arrays).

scholarly journals Computation of Optimized Electrode Arrays for 3-D Electrical Resistivity Tomography Surveys

2021 ◽  
Vol 11 (14) ◽  
pp. 6394
Author(s):  
Kleanthis Simyrdanis ◽  
Nikos Papadopoulos ◽  
Dimitrios Oikonomou
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Jacobian Matrix ◽  
Optimization Technique ◽  
Optimization Approach ◽  
Electrode Arrays ◽  
Data Set ◽  
Resistivity Tomography ◽  
Parallel Lines ◽  
Comprehensive Data

The present study explores the applicability and effectiveness of an optimization technique applied to electrical resistivity tomography data. The procedure is based on the Jacobian matrix, where the most sensitive measurements are selected from a comprehensive data set to enhance the least resolvable parameters of the reconstructed model. Two existing inversion programs in two and three dimensions are modified to incorporate this new approach. Both of them are selecting the optimum data from an initial comprehensive data set which is comprised of merged conventional arrays. With the two-dimensional (2-D) optimization approach, the most sensitive measurements are selected from a 2-D survey profile and then a clone of the resulting optimum profile reproduces a three-dimensional (3-D) optimum data set composed of equally spaced parallel lines. In a different approach, with the 3-D optimization technique, the optimum data are selected from a 3-D data set of equally spaced individual parallel lines. Both approaches are compared with Stummer’s optimization technique which is based on the resolution matrix. The Jacobian optimization approach has the advantage of selecting the optimum data set without the need for the solution of the inversion problem since the Jacobian matrix is calculated as part of the forward resistivity problem, thus being faster from previous published approached based on the calculation of the sensitivity matrix. Synthetic 3-D data based on the extension of previous published works for the 2-D optimization case and field data from two case studies in Greece are tested, thus verifying the validity of the present study, where fewer measurements from the initial data set (about 15–50%) are able to reconstruct a model similar with the one produced from the original comprehensive data set.

Informed experimental design for electrical resistivity imaging

2011 ◽  
Vol 9 (5) ◽  
pp. 469-482 ◽  
Author(s):  
Vanessa Nenna ◽  
Adam Pidlisecky ◽  
Rosemary Knight
Keyword(s):  
Electrical Resistivity ◽  
Experimental Design ◽  
Electrical Resistivity Imaging ◽  
Resistivity Imaging

scholarly journals ON GENERATING DIGITAL ELEVATION MODELS FROM LIDAR DATA – RESOLUTION VERSUS ACCURACY AND TOPOGRAPHIC WETNESS INDEX INDICES IN NORTHERN PEATLANDS

2012 ◽  
Vol 38 (2) ◽  
pp. 57-69 ◽  
Author(s):  
Abdulghani Hasan ◽  
Petter Pilesjö ◽  
Andreas Persson
Keyword(s):  
Large Scale ◽  
Drainage Area ◽  
Data Sets ◽  
Topographic Wetness Index ◽  
Absolute Deviation ◽  
Digital Elevation ◽  
Elevation Data ◽  
Scale Modelling ◽  
Data Points ◽  
Emission Modelling

Global change and GHG emission modelling are dependent on accurate wetness estimations for predictions of e.g. methane emissions. This study aims to quantify how the slope, drainage area and the TWI vary with the resolution of DEMs for a flat peatland area. Six DEMs with spatial resolutions from 0.5 to 90 m were interpolated with four different search radiuses. The relationship between accuracy of the DEM and the slope was tested. The LiDAR elevation data was divided into two data sets. The number of data points facilitated an evaluation dataset with data points not more than 10 mm away from the cell centre points in the interpolation dataset. The DEM was evaluated using a quantile-quantile test and the normalized median absolute deviation. It showed independence of the resolution when using the same search radius. The accuracy of the estimated elevation for different slopes was tested using the 0.5 meter DEM and it showed a higher deviation from evaluation data for steep areas. The slope estimations between resolutions showed differences with values that exceeded 50%. Drainage areas were tested for three resolutions, with coinciding evaluation points. The model ability to generate drainage area at each resolution was tested by pair wise comparison of three data subsets and showed differences of more than 50% in 25% of the evaluated points. The results show that consideration of DEM resolution is a necessity for the use of slope, drainage area and TWI data in large scale modelling.

scholarly journals Inter- and Intralaboratory Comparison of JC Polyomavirus Antibody Testing Using Two Different Virus-Like Particle-Based Assays

2014 ◽  
Vol 21 (11) ◽  
pp. 1581-1588 ◽  
Author(s):  
Piotr Kardas ◽  
Mohammadreza Sadeghi ◽  
Fabian H. Weissbach ◽  
Tingting Chen ◽  
Lea Hedman ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Data Sets ◽  
Antibody Testing ◽  
Jc Polyomavirus ◽  
Virus Like Particle ◽  
Interlaboratory Variability ◽  
Increased Risk ◽  
Basic Protocol ◽  
Data Points ◽  
Reference Serum

ABSTRACTJC polyomavirus (JCPyV) can cause progressive multifocal leukoencephalopathy (PML), a debilitating, often fatal brain disease in immunocompromised patients. JCPyV-seropositive multiple sclerosis (MS) patients treated with natalizumab have a 2- to 10-fold increased risk of developing PML. Therefore, JCPyV serology has been recommended for PML risk stratification. However, different antibody tests may not be equivalent. To study intra- and interlaboratory variability, sera from 398 healthy blood donors were compared in 4 independent enzyme-linked immunoassay (ELISA) measurements generating >1,592 data points. Three data sets (Basel1, Basel2, and Basel3) used the same basic protocol but different JCPyV virus-like particle (VLP) preparations and introduced normalization to a reference serum. The data sets were also compared with an independent method using biotinylated VLPs (Helsinki1). VLP preadsorption reducing ≥35% activity was used to identify seropositive sera. The results indicated that Basel1, Basel2, Basel3, and Helsinki1 were similar regarding overall data distribution (P= 0.79) and seroprevalence (58.0, 54.5, 54.8, and 53.5%, respectively;P= 0.95). However, intra-assay intralaboratory comparison yielded 3.7% to 12% discordant results, most of which were close to the cutoff (0.080 < optical density [OD] < 0.250) according to Bland-Altman analysis. Introduction of normalization improved overall performance and reduced discordance. The interlaboratory interassay comparison between Basel3 and Helsinki1 revealed only 15 discordant results, 14 (93%) of which were close to the cutoff. Preadsorption identified specificities of 99.44% and 97.78% and sensitivities of 99.54% and 95.87% for Basel3 and Helsinki1, respectively. Thus, normalization to a preferably WHO-approved reference serum, duplicate testing, and preadsorption for samples around the cutoff may be necessary for reliable JCPyV serology and PML risk stratification.

A Support Based Initialization Algorithm for Categorical Data Clustering

2018 ◽  
Vol 11 (2) ◽  
pp. 53-67
Author(s):  
Ajay Kumar ◽  
Shishir Kumar
Keyword(s):  
Categorical Data ◽  
Selection Process ◽  
Numerical Data ◽  
Real Data ◽  
Data Sets ◽  
Data Set ◽  
Data Object ◽  
Data Points ◽  
Wu Method ◽  
Selection Algorithms

Several initial center selection algorithms are proposed in the literature for numerical data, but the values of the categorical data are unordered so, these methods are not applicable to a categorical data set. This article investigates the initial center selection process for the categorical data and after that present a new support based initial center selection algorithm. The proposed algorithm measures the weight of unique data points of an attribute with the help of support and then integrates these weights along the rows, to get the support of every row. Further, a data object having the largest support is chosen as an initial center followed by finding other centers that are at the greatest distance from the initially selected center. The quality of the proposed algorithm is compared with the random initial center selection method, Cao's method, Wu method and the method introduced by Khan and Ahmad. Experimental analysis on real data sets shows the effectiveness of the proposed algorithm.

Production Levels of Bauxite and Aluminum: Global Historical Survey (1850–2015)

2019 ◽  
Author(s):  
Charles Christopher Sorrell ◽  
Pramod Koshy
Keyword(s):  
Primary Aluminum ◽  
Data Sets ◽  
List Type ◽  
Historical Survey ◽  
Secondary Aluminum ◽  
Comprehensive Data ◽  
Conversion Rates ◽  
Global Data

The present work provides a concise examination of the historical global production levels of bauxite, primary aluminum, and secondary aluminum from 1850 to 2015. It also provides a contextual background to these data by summarizing the main etymological, technical, and commercial developments throughout most of this period. It further includes comprehensive data for the pricing of primary aluminum in both historical and 2015 dollars on the basis of economy cost. The syntheses of the data include the following: Global production levels of bauxite based on three data setsGlobal production levels of primary aluminum based on three data setsGlobal production levels of secondary aluminum based on three data setsComparative global production levels of secondary aluminum based on five data setsPrices of primary aluminum in historical US dollars and in US dollars indexed to 2015 values based on four data setsConversion rates for historical US dollars against 2015 valuesIn addition to the preceding global data, extensive pre-1900 data for French, Swiss, and US production levels and French and US prices of primary aluminum are compiled.

Mahalanobis distance informed by clustering

2018 ◽  
Vol 8 (2) ◽  
pp. 377-406
Author(s):  
Almog Lahav ◽  
Ronen Talmon ◽  
Yuval Kluger
Keyword(s):  
Mahalanobis Distance ◽  
High Dimensional Data ◽  
Hidden Variables ◽  
Real Data ◽  
Risk Groups ◽  
High Dimensional ◽  
Data Sets ◽  
Kaplan Meier ◽  
Data Points ◽  
Survival Plot

Abstract A fundamental question in data analysis, machine learning and signal processing is how to compare between data points. The choice of the distance metric is specifically challenging for high-dimensional data sets, where the problem of meaningfulness is more prominent (e.g. the Euclidean distance between images). In this paper, we propose to exploit a property of high-dimensional data that is usually ignored, which is the structure stemming from the relationships between the coordinates. Specifically, we show that organizing similar coordinates in clusters can be exploited for the construction of the Mahalanobis distance between samples. When the observable samples are generated by a nonlinear transformation of hidden variables, the Mahalanobis distance allows the recovery of the Euclidean distances in the hidden space. We illustrate the advantage of our approach on a synthetic example where the discovery of clusters of correlated coordinates improves the estimation of the principal directions of the samples. Our method was applied to real data of gene expression for lung adenocarcinomas (lung cancer). By using the proposed metric we found a partition of subjects to risk groups with a good separation between their Kaplan–Meier survival plot.

scholarly journals Analysis of erroneous data entries in paper based and electronic data collection

2019 ◽  
Author(s):  
Benedikt Ley ◽  
Komal Raj Rijal ◽  
Jutta Marfurt ◽  
Nabaraj Adhikari ◽  
Megha Banjara ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Categorical Variables ◽  
Data Sets ◽  
Continuous Variables ◽  
Electronic Data ◽  
Suitable Alternative ◽  
Electronic Data Collection ◽  
Data Points ◽  
Time Variables

Abstract Objective: Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category. Results: Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3,580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1,074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1,370/12,530). Overall 64% (1,499/2,352) of all discrepancies were due to data omissions, 76.6% (1,148/1,499) of missing entries were among categorical data. Omissions in PBDC (n=1002) were twice as frequent as in EDC (n=497, p<0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.

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