scholarly journals 2D Model Study of CO2Plumes in Saline Reservoirs by Borehole Resistivity Tomography

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Said A. al Hagrey
Keyword(s):  
Measurement Errors ◽  
Optimization Approach ◽  
Data Set ◽  
Current Electrode ◽  
Resistivity Tomography ◽  
Model Study ◽  
Cap Rock ◽  
The Common ◽  
Current Flows ◽  
Host Reservoir

The performance of electrical resistivity tomography (ERT) in boreholes is studied numerically regarding changes induced by CO2sequestration in deep saline reservoirs. The new optimization approach is applied to generate an optimized data set of only 4% of the comprehensive set but of almost similar best possible resolution. Diverse electrode configurations (mainly tripotentialαandβ) are investigated with current flows and potential measurements in different directions. An extensive 2.5D modeling (>100,000 models) is conducted systematically as a function of multiparameters related to hydrogeology, CO2plume, data acquisition and methodology. ERT techniques generally are capable to resolve storage targets (CO2plume, saline host reservoir, and impermeable cap rock), however with the common smearing effects and artefacts. Reconstructed tomograms show that the optimized and multiply oriented configurations have a better-spatial resolution than the lateral arrays with splitting of potential and current electrode pairs between boreholes. The later arrays are also more susceptible to telluric noise but have a lower level of measurement errors. The resolution advance of optimized and multiply oriented configurations is confirmed by lower values for ROI (region of index) and residual (relative model difference). The technique acceptably resolves targets with an aspect ratio down to 0.5.

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.

Numerical and experimental mapping of small root zones using optimized surface and borehole resistivity tomography

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. G25-G35 ◽  
Author(s):  
Said Attia al Hagrey ◽  
Torsten Petersen
Keyword(s):  
Small Data ◽  
Optimization Approach ◽  
Electrode Arrays ◽  
Spatiotemporal Resolution ◽  
Data Set ◽  
Resistivity Tomography ◽  
Root Zones ◽  
Small Root ◽  
Input Model ◽  
Surface Borehole

An exact mapping of root zones is essential to understand plant growth, root biomass, and soil functions important for environmental and climatic management and protection. Numerical and experimental techniques of the electrical resistivity tomography were applied in 2D and 3D to resolve small root zones in the centimeter range. Numerically, we studied two scenarios of conductive and resistive root zones as a function of (1) eight different quadripole electrode configurations (standard, nonstandard, and optimized), (2) four different survey designs with electrode arrays at the soil surface and in boreholes, and (3) eight different inversion constraints. The best resolved output tomogram was evaluated semiquantitatively using the criteria of visual similarity to the input model, least data set, rms error, and iteration number and quantitatively by the model difference relative to the input model. The results showed that the surface-borehole configurations have the best resolution for the whole root zone. The single-surface and borehole surveys resolve only the respective upper and middle-lower root parts. The results reflect the potential of the optimization approach to generate small data sets of far higher resolution than the standard sets. Based on these results, we used the surface-borehole survey around a young hibiscus planted in a sandy soil in a laboratory experiment. The surface-borehole surveys using small, optimized configurations result in an optimum spatiotemporal resolution for simultaneous applications for 3D mapping of targets (root zones and water and soil heterogeneities) and 4D monitoring of their processes.

A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit

2019 ◽  
Vol 70 (3) ◽  
pp. 184-192
Author(s):  
Toan Dao Thanh ◽  
Vo Thien Linh
Keyword(s):  
Processing System ◽  
Warning System ◽  
Raspberry Pi ◽  
Data Set ◽  
Lower Lip ◽  
Driver Drowsiness ◽  
Image Sensing ◽  
The Face ◽  
The Common ◽  
University Laboratory

In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and “frontal face detectors” in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of “Normal”, “Close eyes”, “Yawn” or “Distraction”

scholarly journals Osmotically inactive sodium and potassium storage: lessons learned from the Edelman and Boling data

2016 ◽  
Vol 311 (3) ◽  
pp. F539-F547 ◽  
Author(s):  
Minhtri K. Nguyen ◽  
Dai-Scott Nguyen ◽  
Minh-Kevin Nguyen
Keyword(s):  
Linear Regression ◽  
Measurement Errors ◽  
Linear Regression Analysis ◽  
Sodium Concentration ◽  
Lessons Learned ◽  
Dynamic Changes ◽  
Data Set ◽  
Storage Pool ◽  
Total Body ◽  
Sodium And Potassium

Because changes in the plasma water sodium concentration ([Na+]pw) are clinically due to changes in the mass balance of Na+, K+, and H2O, the analysis and treatment of the dysnatremias are dependent on the validity of the Edelman equation in defining the quantitative interrelationship between the [Na+]pw and the total exchangeable sodium (Nae), total exchangeable potassium (Ke), and total body water (TBW) (Edelman IS, Leibman J, O'Meara MP, Birkenfeld LW. J Clin Invest 37: 1236–1256, 1958): [Na+]pw = 1.11(Nae + Ke)/TBW − 25.6. The interrelationship between [Na+]pw and Nae, Ke, and TBW in the Edelman equation is empirically determined by accounting for measurement errors in all of these variables. In contrast, linear regression analysis of the same data set using [Na+]pw as the dependent variable yields the following equation: [Na+]pw = 0.93(Nae + Ke)/TBW + 1.37. Moreover, based on the study by Boling et al. (Boling EA, Lipkind JB. 18: 943–949, 1963), the [Na+]pw is related to the Nae, Ke, and TBW by the following linear regression equation: [Na+]pw = 0.487(Nae + Ke)/TBW + 71.54. The disparities between the slope and y-intercept of these three equations are unknown. In this mathematical analysis, we demonstrate that the disparities between the slope and y-intercept in these three equations can be explained by how the osmotically inactive Na+ and K+ storage pool is quantitatively accounted for. Our analysis also indicates that the osmotically inactive Na+ and K+ storage pool is dynamically regulated and that changes in the [Na+]pw can be predicted based on changes in the Nae, Ke, and TBW despite dynamic changes in the osmotically inactive Na+ and K+ storage pool.

Decennial multi-approach monitoring of thermo-hydro-mechanical processes, Kammstollen outdoor laboratory, Zugspitze (Germany)

2021 ◽  
Author(s):  
Riccardo Scandroglio ◽  
Till Rehm ◽  
Jonas K. Limbrock ◽  
Andreas Kemna ◽  
Markus Heinze ◽  
...  
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Environmental Research ◽  
Permafrost Degradation ◽  
Earth Surface ◽  
Data Set ◽  
Resistivity Tomography ◽  
Near Surface ◽  
Permafrost Rock ◽  
Austrian Alps

<p>The warming of alpine bedrock permafrost in the last three decades and consequent reduction of frozen areas has been well documented. Its consequences like slope stability reduction put humans and infrastructures at high risk. 2020 in particular was the warmest year on record at 3000m a.s.l. embedded in the warmest decade.</p><p>Recently, the development of electrical resistivity tomography (ERT) as standard technique for quantitative permafrost investigation allows extended monitoring of this hazard even allowing including quantitative 4D monitoring strategies (Scandroglio et al., in review). Nevertheless thermo-hydro-mechanical dynamics of steep bedrock slopes cannot be totally explained by a single measurement technique and therefore multi-approach setups are necessary in the field to record external forcing and improve the deciphering of internal responses.</p><p>The Zugspitze Kammstollen is a 850m long tunnel located between 2660 and 2780m a.s.l., a few decameters under the mountain ridge. First ERT monitoring was conducted in 2007 (Krautblatter et al., 2010) and has been followed by more than one decade of intensive field work. This has led to the collection of a unique multi-approach data set of still unpublished data. Continuous logging of environmental parameters such as rock/air temperatures and water infiltration through joints as well as a dedicated thermal model (Schröder and Krautblatter, in review) provide important additional knowledge on bedrock internal dynamics. Summer ERT and seismic refraction tomography surveys with manual and automated joints’ displacement measurements on the ridge offer information on external controls, complemented by three weather stations and a 44m long borehole within 1km from the tunnel.</p><p>Year-round access to the area enables uninterrupted monitoring and maintenance of instruments for reliable data collection. “Precisely controlled natural conditions”, restricted access for researchers only and logistical support by Environmental Research Station Schneefernerhaus, make this tunnel particularly attractive for developing benchmark experiments. Some examples are the design of induced polarization monitoring, the analysis of tunnel spring water for isotopes investigation, and the multi-annual mass monitoring by means of relative gravimetry.</p><p>Here, we present the recently modernized layout of the outdoor laboratory with the latest monitoring results, opening a discussion on further possible approaches of this extensive multi-approach data set, aiming at understanding not only permafrost thermal evolution but also the connected thermo-hydro-mechanical processes.</p><p> </p><p> </p><p>Krautblatter, M. et al. (2010) ‘Temperature-calibrated imaging of seasonal changes in permafrost rock walls by quantitative electrical resistivity tomography (Zugspitze, German/Austrian Alps)’, Journal of Geophysical Research: Earth Surface, 115(2), pp. 1–15. doi: 10.1029/2008JF001209.</p><p>Scandroglio, R. et al. (in review) ‘4D-Quantification of alpine permafrost degradation in steep rock walls using a laboratory-calibrated ERT approach (in review)’, Near Surface Geophysics.</p><p>Schröder, T. and Krautblatter, M. (in review) ‘A high-resolution multi-phase thermo-geophysical model to verify long-term electrical resistivity tomography monitoring in alpine permafrost rock walls (Zugspitze, German/Austrian Alps) (submitted)’, Earth Surface Processes and Landforms.</p>

scholarly journals A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 126 ◽  
Author(s):  
Lina Aboulmouna ◽  
Shakti Gupta ◽  
Mano Maurya ◽  
Frank DeVilbiss ◽  
Shankar Subramaniam ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Metabolic Regulation ◽  
Metabolic Flux ◽  
Optimal Parameter ◽  
Gene Knockout ◽  
Model Parameters ◽  
Mouse Bone Marrow ◽  
Optimization Approach ◽  
Substrate Uptake ◽  
Data Set

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation.

Imaging shallow objects and heterogeneities with scattered guided waves

Geophysics ◽  
2000 ◽  
Vol 65 (1) ◽  
pp. 247-252 ◽  
Author(s):  
Gérard C. Herman ◽  
Paul A. Milligan ◽  
Robert J. Huggins ◽  
J. W. Rector
Keyword(s):  
Rayleigh Waves ◽  
Guided Waves ◽  
Guided Wave ◽  
Imaging Method ◽  
Data Set ◽  
Survey Line ◽  
Weathered Layer ◽  
First Arrival ◽  
The Common ◽  
Object Imaging

Current surface seismic reflection techniques based on the common‐midpoint (CMP) reflection stacking method cannot be readily used to image small objects in the first few meters of a weathered layer. We discuss a seismic imaging method to detect such objects; it uses the first‐arrival (guided) wave, scattered by shallow heterogeneities and converted into scattered Rayleigh waves. These guided waves and Rayleigh waves are dominant in the shallow weathered layer and therefore might be suitable for shallow object imaging. We applied this method to a field data set and found that we could certainly image meter‐size objects up to about 3 m off to the side of a survey line consisting of vertical geophones. There are indications that cross‐line horizontal geophone data could be used to identify shallow objects up to 10 m offline in the same region.

scholarly journals Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach

2010 ◽  
Vol 14 (3) ◽  
pp. 545-556 ◽  
Author(s):  
J. Rings ◽  
J. A. Huisman ◽  
H. Vereecken
Keyword(s):  
Water Content ◽  
Particle Filtering ◽  
Field Data ◽  
Synthetic Data ◽  
Time Domain Reflectometry ◽  
Second Step ◽  
Parameter Estimates ◽  
Posterior Distributions ◽  
Data Set ◽  
Resistivity Tomography

Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

scholarly journals Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space

2021 ◽  
Author(s):  
Yunhong Gong ◽  
Yanan Sun ◽  
Dezhong Peng ◽  
Peng Chen ◽  
Zhongtai Yan ◽  
...  
Keyword(s):  
Common Sense ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Data Set ◽  
The Past ◽  
Computed Tomography Images ◽  
Intensive Use ◽  
Hospital Resources ◽  
The Common ◽  
Ct Data

AbstractThe COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations

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