scholarly journals Large-scale 3-D modeling by integration of resistivity models and borehole data through inversion

2014 ◽  
Vol 18 (11) ◽  
pp. 4349-4362 ◽  
Author(s):  
N. Foged ◽  
P. A. Marker ◽  
A. V. Christansen ◽  
P. Bauer-Gottwein ◽  
F. Jørgensen ◽  
...  
Keyword(s):  
Large Scale ◽  
Clay Fraction ◽  
Relative Length ◽  
High Resistivity ◽  
Depth Interval ◽  
Borehole Data ◽  
Data Set ◽  
Resistivity Data ◽  
Resistivity Model ◽  
D Model

Abstract. We present an automatic method for parameterization of a 3-D model of the subsurface, integrating lithological information from boreholes with resistivity models through an inverse optimization, with the objective of further detailing of geological models, or as direct input into groundwater models. The parameter of interest is the clay fraction, expressed as the relative length of clay units in a depth interval. The clay fraction is obtained from lithological logs and the clay fraction from the resistivity is obtained by establishing a simple petrophysical relationship, a translator function, between resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3-D clay fraction model, which holds information from the resistivity data set and the borehole data set in one variable. Finally, we use k-means clustering to generate a 3-D model of the subsurface structures. We apply the procedure to the Norsminde survey in Denmark, integrating approximately 700 boreholes and more than 100 000 resistivity models from an airborne survey in the parameterization of the 3-D model covering 156 km2. The final five-cluster 3-D model differentiates between clay materials and different high-resistivity materials from information held in the resistivity model and borehole observations, respectively.

scholarly journals Large scale 3-D modeling by integration of resistivity models and borehole data through inversion

2014 ◽  
Vol 11 (2) ◽  
pp. 1461-1492 ◽  
Author(s):  
N. Foged ◽  
P. A. Marker ◽  
A. V. Christansen ◽  
P. Bauer-Gottwein ◽  
F. Jørgensen ◽  
...  
Keyword(s):  
Large Scale ◽  
Clay Fraction ◽  
Relative Length ◽  
Depth Interval ◽  
Borehole Data ◽  
Subsurface Structures ◽  
Spatially Distributed ◽  
Resistivity Model ◽  
Airborne Survey ◽  
D Model

Abstract. We present an automatic method for parameterization of a 3-D model of the subsurface, integrating lithological information from boreholes with resistivity models through an inverse optimization, with the objective of further detailing for geological models or as direct input to groundwater models. The parameter of interest is the clay fraction, expressed as the relative length of clay-units in a depth interval. The clay fraction is obtained from lithological logs and the clay fraction from the resistivity is obtained by establishing a simple petrophysical relationship, a translator function, between resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3-D clay fraction model, which holds information from the resistivity dataset and the borehole dataset in one variable. Finally, we use k means clustering to generate a 3-D model of the subsurface structures. We apply the concept to the Norsminde survey in Denmark integrating approximately 700 boreholes and more than 100 000 resistivity models from an airborne survey in the parameterization of the 3-D model covering 156 km2. The final five-cluster 3-D model differentiates between clay materials and different high resistive materials from information held in resistivity model and borehole observations respectively.

Structure of the Séchilienne unstable slope from large-scale three-dimensional electrical tomography using a Resistivity Distributed Automated System (R-DAS)

2019 ◽  
Vol 219 (1) ◽  
pp. 129-147 ◽  
Author(s):  
M Lajaunie ◽  
J Gance ◽  
P Nevers ◽  
J-P Malet ◽  
C Bertrand ◽  
...  
Keyword(s):  
Large Scale ◽  
Electrical Potential ◽  
Three Dimensional ◽  
Automated System ◽  
Data Uncertainty ◽  
Complex Data ◽  
Electrical Tomography ◽  
Data Set ◽  
Resistivity Model ◽  
Unstable Slope

SUMMARY This work presents a 3-D resistivity model of the Séchilienne unstable slope acquired with a network of portable resistivimeters in summer 2017. The instrumentation consisted in distributed measuring systems (IRIS Instruments FullWaver) to measure the spatial variations of electrical potential. 23 V-FullWaver receivers with two 50 m dipoles have been deployed over an area of circa 2 km2; the current was injected between a fixed remote electrode and a mobile electrode grounded successively at 30 locations. The data uncertainty has been evaluated in relation to the accuracy of electrodes positioning. The software package BERT (Boundless Electrical Resistivity Tomography) is used to invert the apparent resistivity and model the complex data set providing the first 3-D resistivity model of the slope. Stability tests and synthetic tests are realized to assess the interpretability of the inverted models. The 3-D resistivity model is interpreted up to a depth of 500 m; it allows identifying resistive and conductive anomalies related to the main geological and hydrogeological structures shaping the slope. The high fracturation of the rock in the most active zone of the landslide appears as a resistive anomaly where the highest resistivity values are located close to the faults. A major drain formed by a fault in the unaltered micaschist is identified through the discharge of a perched aquifer along the conductive zone producing an important conductive anomaly contrasting with the unaltered micaschist.

Merging tTEM data and borehole lithological information to generate hydrogeologic structural models and redox conditions through Direct Sampling

2021 ◽  
Author(s):  
Niels Claes ◽  
Rasmus Rumph Frederiksen ◽  
Troels Norvin Vilhelmsen ◽  
Nikolaj Foged ◽  
Hyojin Kim ◽  
...  
Keyword(s):  
Structural Information ◽  
Clay Fraction ◽  
Redox Condition ◽  
Redox Conditions ◽  
Training Dataset ◽  
Data Set ◽  
Nitrate Retention ◽  
Resistivity Data ◽  
Direct Sampling ◽  
Geophysical Mapping

<p>Detailed 3D structural information of the subsurface is fundamental for the development of both hydrological and geochemical models. This structural information is often derived from geophysical mapping results. Some parts of a catchments areas are however inaccessible for the geophysical mapping or might suffer from low data quality, which results in information gaps. Multipoint statistics can be used to remediate these data gaps and incorporate uncertainty in the construction of the hydrogeological models. This results in an ensemble of plausible 3D hydrogeological models.</p><p>This project focusses on nitrate retention mapping. The approach taken is to start from the resistivity models that are obtained from the tTEM measurement campaign. These resistivity datasets are combined with borehole lithological data from the Danish national well-database in an automated procedure that estimates resistivity-to sand/clay translator functions. This results in a clay fraction – resistivity data pair for every point in the subsurface where resistivity data is collected. These clay fraction – resistivity data pairs are converted to discrete hydrogeological units through clustering. This procedure is performed because the groundwater model that uses the end-product of this workflow, uses hydrogeological units rather than resistivity values or clay fractions to define zones of similar hydrogeological behavior.</p><p>Direct sampling is used to go from the cluster information obtained at the resistivity model location to fill out the full model volume and generate multiple plausible model realizations. This method allows, at the same time, for incorporating uncertainty through separation of data into a hard  data set for the cluster information with higher probability, and a soft data set for the cluster information with lower probability. Since the redox conditions in the subsurface are related to the hydrogeological conditions, we are using this method to co-simulate hydrogeological units and redox conditions by merging the cluster training dataset with a redox condition training dataset that is constructed based on the cluster dataset and hydrogeochemical samples that are collected across the catchment. We combine the three training images: resistivity, cluster and redox condition, to simultaneous simulate the three variables in each grid point as a vector, instead of simulating them as separate variables.  The resulting set of  3D hydrogeologic structural models and redox condition models retains the complex geostatistical spatial relationships that can exists between the different type of datasets within the training image, making them suitable for nitrate retention modeling at catchment scale.</p>

Two‐dimensional joint inversion of magnetotelluric and dipole‐dipole resistivity data

Geophysics ◽  
1989 ◽  
Vol 54 (2) ◽  
pp. 254-262 ◽  
Author(s):  
Yutaka Sasaki
Keyword(s):  
Field Data ◽  
Least Squares Method ◽  
Joint Inversion ◽  
Computation Time ◽  
Schmidt Method ◽  
The Finite Element Method ◽  
Data Set ◽  
Resistivity Data ◽  
D Model ◽  
Derivatives Of

This paper describes 2-D joint inversion of MT and dipole‐dipole resistivity data with the emphasis on the computer algorithm. The algorithm produces a 2-D model composed of a large number of rectangular blocks, each of which has constant resistivity. The solutions to two forward problems are based on the finite‐element method. The computation time for the partial derivatives of MT responses is reduced by using the reciprocity relation and the concept of a fictitious source. The smoothness‐constrained least‐squares method, together with the modified Gram‐Schmidt method, is also to stabilize the solution and avoid spurious resistivity features. Synthetic and field data examples show that the 2-D joint inversion can be effective for improving the resolution attained by the 2-D interpretation of a single kind of data set.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

scholarly journals Relationship between large-scale ionospheric field-aligned currents and electron/ion precipitations: DMSP observations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Chao Xiong ◽  
Claudia Stolle ◽  
Patrick Alken ◽  
Jan Rauberg
Keyword(s):  
Time Distribution ◽  
Large Scale ◽  
Particle Flux ◽  
Particle Energy ◽  
Lower Latitude ◽  
Particle Precipitation ◽  
Data Set ◽  
The Mean ◽  
Two Parameters ◽  
Meteorological Satellite

Abstract In this study, we have derived field-aligned currents (FACs) from magnetometers onboard the Defense Meteorological Satellite Project (DMSP) satellites. The magnetic latitude versus local time distribution of FACs from DMSP shows comparable dependences with previous findings on the intensity and orientation of interplanetary magnetic field (IMF) By and Bz components, which confirms the reliability of DMSP FAC data set. With simultaneous measurements of precipitating particles from DMSP, we further investigate the relation between large-scale FACs and precipitating particles. Our result shows that precipitation electron and ion fluxes both increase in magnitude and extend to lower latitude for enhanced southward IMF Bz, which is similar to the behavior of FACs. Under weak northward and southward Bz conditions, the locations of the R2 current maxima, at both dusk and dawn sides and in both hemispheres, are found to be close to the maxima of the particle energy fluxes; while for the same IMF conditions, R1 currents are displaced further to the respective particle flux peaks. Largest displacement (about 3.5°) is found between the downward R1 current and ion flux peak at the dawn side. Our results suggest that there exists systematic differences in locations of electron/ion precipitation and large-scale upward/downward FACs. As outlined by the statistical mean of these two parameters, the FAC peaks enclose the particle energy flux peaks in an auroral band at both dusk and dawn sides. Our comparisons also found that particle precipitation at dawn and dusk and in both hemispheres maximizes near the mean R2 current peaks. The particle precipitation flux maxima closer to the R1 current peaks are lower in magnitude. This is opposite to the known feature that R1 currents are on average stronger than R2 currents.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

2021 ◽  
pp. 1-13
Author(s):  
Usman Naseem ◽  
Imran Razzak ◽  
Matloob Khushi ◽  
Peter W. Eklund ◽  
Jinman Kim
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Data Set ◽  
Twitter Data
Sign in / Sign up

Export Citation Format

Share Document