On the relationship between iron concentration and induced polarization in marsh soils

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. A1-A5 ◽  
Author(s):  
Nasser Mansoor ◽  
Lee Slater
Keyword(s):  
Surface Area ◽  
Iron Concentration ◽  
Induced Polarization ◽  
Model Parameters ◽  
Soil Geochemistry ◽  
Iron Cycling ◽  
Volume Formula ◽  
Cole Model ◽  
The Relationship ◽  
Fluid Conductivity

Induced polarization (IP) measurements [Formula: see text] were conducted on seventeen clay and peat marsh soils that were subsequently analyzed for heavy metal concentrations, moisture content, organic matter, porosity, specific surface area, and pore fluid conductivity. A Cole-Cole model was fit to each sample and model parameters analyzed in terms of physicochemical properties. We found a linear relation between the normalized chargeability [Formula: see text] and estimated surface area to pore volume [Formula: see text] when iron content (ranging from 0.25% to 1.63% by volume) is accounted for as a polarizable element of the soil. In fact, the dependence of [Formula: see text] on volumetric Fe concentration per unit volume of the bulk soil is described by a linear relationship with a correlation coefficient [Formula: see text] of 0.94. As Fe concentration is a critical biogeochemical parameter, our findings suggest that IP measurements may provide a hitherto unrecognized approach to probing soil geochemistry, iron cycling and anaerobic microbial activity. Furthermore, our results yield insights into physicochemical controls on IP in natural soils.

On the relationship between induced polarization and surface area in metal-sand and clay-sand mixtures

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. A1-A5 ◽  
Author(s):  
Lee Slater ◽  
Dimitrios Ntarlagiannis ◽  
DeBonne Wishart
Keyword(s):  
Surface Area ◽  
Gas Adsorption ◽  
Induced Polarization ◽  
Nitrogen Gas ◽  
Volume Formula ◽  
Percentage Weight ◽  
Mineral Constituent ◽  
Cole Model ◽  
The Relationship ◽  
Electrical Data

Induced polarization (IP) measurements were conducted on saturated kaolinite-, iron-, and magnetite-sand mixtures as a function of varying percentage weight of a mineral constituent: 0%–100% for iron and magnetite and 0%–32% for kaolinite. We determined the specific surface area for each mineral using nitrogen gas adsorption, where the porosity of each mixture was calculated from weight loss after drying. We fit a Cole-Cole model (Cole and Cole, 1941) to the electrical data obtained for the magnetite and iron mixtures. In contrast, the kaolinite mixtures showed a power-law dependence of phase-on frequency. The global polarization magnitude we obtained from the Cole-Cole modeling of the iron and magnetite mixtures displays a single, near-linear dependence on the ratio of surface area to pore volume ([Formula: see text]) calculated for the mixtures. A similar relationship is found using a local measure of polarization (imaginary conductivity at 1 Hz) for the clay-sand mixtures. The [Formula: see text] appears to be a critical parameter for determining IP in both metallic- and clay-containing soils. This result is not easily reconciled with traditional models of induced polarization.

Electrical properties of iron-sand columns: Implications for induced polarization investigation and performance monitoring of iron-wall barriers

Geophysics ◽  
2005 ◽  
Vol 70 (4) ◽  
pp. G87-G94 ◽  
Author(s):  
Lee D. Slater ◽  
Jaeyoung Choi ◽  
Yuxin Wu
Keyword(s):  
Relaxation Time ◽  
Surface Area ◽  
Performance Monitoring ◽  
Electrical Response ◽  
Induced Polarization ◽  
Iron Hydroxides ◽  
Interfacial Chemistry ◽  
Reactive Iron ◽  
Volume Formula ◽  
And Performance

We investigate the electrical response (0.1–1000 Hz) of reactive iron barriers by making measurements on zero valent iron ([Formula: see text])-sand columns under the following conditions: (1) variable [Formula: see text] surface area (0.1–100% by volume [Formula: see text] under constant electrolyte chemistry; (2) variable electrolyte activity (0.01–1 mol/liter), valence (mono trivalent), and pH under constant [Formula: see text]-sand composition; and (3) forced precipitation of iron hydroxides and iron carbonates on the [Formula: see text] surface. We model the measurements in terms of conduction magnitude, polarization magnitude, and polarization relaxation time. Our key findings are: (a) Polarization magnitude exhibits a linear relation to the surface area of [Formula: see text], whereas conduction magnitude is only weakly dependent on the [Formula: see text] concentration below 30% by volume [Formula: see text]. (b) Polarization magnitude shows a power law relation to electrolyte activity, with exponents decreasing from 0.9 for monovalent solutions to 0.7 for trivalent solutions. (c) The relaxation time parameter depends on activity and valence in a manner that is partly consistent with the variation in double layer thickness predicted from theory. (d) pH exerts minor control on the electrical parameters. (e) Polarization magnitude and relaxation time both increase as a result of precipitation induced on the surface of [Formula: see text]. Our results show that induced polarization parameters systematically change in response to changes in the [Formula: see text]-electrolyte interfacial chemistry.

On the estimation of specific surface per unit pore volume from induced polarization: A robust empirical relation fits multiple data sets

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. WA105-WA112 ◽  
Author(s):  
Andreas Weller ◽  
Lee Slater ◽  
Sven Nordsiek ◽  
Dimitrios Ntarlagiannis
Keyword(s):  
Specific Surface ◽  
Surface Area ◽  
Pore Volume ◽  
Empirical Relation ◽  
Induced Polarization ◽  
Single Frequency ◽  
Model Parameters ◽  
Data Sets ◽  
Multiple Data ◽  
Multiple Data Sets

We analyze the relationship between induced polarization (IP) parameters and the specific surface area normalized to the pore volume [Formula: see text] for an extensive sample database. We find that a single linear imaginary conductivity-[Formula: see text] relation holds across a range of single-frequency IP data sets composed of sandstones and unconsolidated sediments that lack an appreciable metallic mineral content. We also apply a recent approach defined as Debye decomposition (DD) to determine normalized chargeability [Formula: see text], a global estimate of polarization magnitude from available spectral IP (SIP) data sets. A strong linear relationship between [Formula: see text] and [Formula: see text] is also found across multiple data sets. However, SIP model parameters determined for samples containing metallic minerals are approximately two orders of magnitude greater than for the model parameters estimated for the nonmetallic sample database. We propose a concept of “polarizability of the mineral-fluid interface per unit [Formula: see text]” to explain this difference, which is supported by the observed dependence of IP parameters on fluid conductivity between sample types. We suggest that this linear IP-[Formula: see text] relation can be considered the IP equivalent of the classical Archie empirical relation. Whereas the Archie relation describes a power-law relation between electrical conductivity due to electrolytic conduction through the available interconnected pore volume, the IP-[Formula: see text] relation is an equivalent relation between mineral-fluid interfacial polarization and available pore surface area.

Time-domain induced polarization forward modeling and an apparent spectral parameter solution based on the Weibull growth model

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. D145-D155
Author(s):  
Qingxin Meng ◽  
Xiangyun Hu ◽  
Heping Pan ◽  
Huolin Ma ◽  
Miao Luo
Keyword(s):  
Growth Model ◽  
Time Domain ◽  
Forward Modeling ◽  
Induced Polarization ◽  
Forward Model ◽  
Model Parameters ◽  
Time Varying ◽  
Basic Algorithm ◽  
Cole Model ◽  
Physical Principles

The application of the Cole-Cole model within time-domain induced polarization (TDIP) forward field modeling shows that the model parameters can characterize time-varying states of the TDIP field and support observed data analysis. The Cole-Cole model contains real and imaginary parts, and it requires a frequency-to-time conversion for TDIP forward modeling. However, the TDIP field is usually expressed by a real number, and its intuitive time-varying states field intensity increases with charging time. Therefore, the forward model should be constructed in a simpler form. We have aimed to develop a forward model using mathematical functions not based on physical principles. The Weibull (WB) growth model, which is primarily used to describe the time-varying curve features in regression analysis, is introduced into the basic algorithm of the TDIP forward model. Subsequently, a forward expression of the TDIP effect is established. Based on the time-varying shape and scale parameters, this expression describes the time-varying rate and relaxation states of the TDIP fields. Furthermore, based on the extensively used conjugate gradient optimization, an apparent WB parameter scheme is initiated to calculate the spectral parameters that represent the relaxation and time-varying rate obtained from the multi-time-channel TDIP data. Finally, this scheme is applied to interpret the different simulated and actual TDIP data. The results demonstrate that the WB growth model can be used for the TDIP forward model without involving physical principles, the model parameters without specific physical significance can be used to represent the time-varying states of TDIP fields, and apparent WB parameters can be used to discern different TDIP observed data. The setting of the TDIP forward model and model parameters can actually be more flexible and diverse, so as to obtain simpler forward expressions and ensure a highly efficient inverse solution.

Time Domain and Frequency Domain Induced Polarization Modeling for Three-dimensional Anisotropic Medium

2017 ◽  
Vol 22 (4) ◽  
pp. 435-439
Author(s):  
Weiqiang Liu ◽  
Pinrong Lin ◽  
Qingtian Lü ◽  
Rujun Chen ◽  
Hongzhu Cai ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Half Space ◽  
Anisotropic Medium ◽  
Time Domain ◽  
Three Dimensional ◽  
Induced Polarization ◽  
Model Parameters ◽  
Complex Resistivity ◽  
Cole Model ◽  
Synthetic Models

Time domain induced polarization (TDIP) and frequency domain induced polarization (FDIP) synthetic models, incorporating three-dimensional (3D) anisotropic medium, were tested. In TDIP modeling, both resistivity and chargeability of the medium were anisotropic, and the apparent chargeability values were calculated by carrying out two resistivity forward calculations using resistivity with and without an IP effect. We analyzed the TDIP response of a 3D isotropic cube model embedded in the anisotropic subsurface half-space. In FDIP modeling, the complex resistivity of the medium at various frequencies was anisotropic. The complex resistivity was determined by a Cole-Cole model with anisotropic model parameters. We then analyzed the FDIP response of a 3D anisotropic cube model embedded in an isotropic subsurface half-space. Both of the TDIP and FDIP simulation results suggest that IP responses acquired in two orthogonal directions on the surface are different when the same arrays are used and acquisition in orthogonal directions helps resolve the presence of anisotropy. The anisotropy should be taken into account in practice for TDIP and FDIP exploration.

A new approach to fitting induced-polarization spectra

Geophysics ◽  
2008 ◽  
Vol 73 (6) ◽  
pp. F235-F245 ◽  
Author(s):  
Sven Nordsiek ◽  
Andreas Weller
Keyword(s):  
Grain Size ◽  
Relaxation Time ◽  
Time Distribution ◽  
Induced Polarization ◽  
Model Parameters ◽  
Mass Percentage ◽  
Relaxation Time Distribution ◽  
Best Fitting ◽  
Cole Model ◽  
Polarization Spectra

Best fitting of induced-polarization (IP) spectra by different models of Cole-Cole type evidences discrepancies in the resulting model parameters. The time constant determined from the same data could vary in magnitude over several decades. This effect, which makes an evaluation of the results of different models nearly impossible, is demonstrated by induced polarization measurements in the frequency range between [Formula: see text] and [Formula: see text] on thirteen mixtures of quartz sand and slag grains. The samples differ in size and the amount of the slag grains. Parameters describing the IP spectra are derived by fitting models of the Cole-Cole type to the measured data. The fitting quality of the generalized Cole-Cole model, the standard Cole-Cole model, and the Cole-Davidson model is investigated. The parameters derived from these models are compared and correlated with mass percentage and grain size of the slag particles. An alternative fittingapproach is introduced, using the decomposition of observed IP spectra into a variety of Debye spectra. Four integrating parameters are derived and correlated with parameters of the slag-sand mixtures and Cole-Cole parameters, respectively. The alternative approach generally enables a better fitting of measured spectra compared with Cole-Cole type models. It proves to be more flexible and stable, even for complicated phase spectra that cannot be fitted by single Cole-Cole type models. The integrating parameters are well correlated with characterizing parameters of the slag-sand mixtures. The total chargeability well indicates the mass percentage of slag grains, and the mean relaxation time is related to the grain size. The relaxation time distribution can be displayed by cumulative normalized chargeability versus relaxation time, similar to granulation curves. Anologous to the latter, a nonuniformity parameter characterizes the width of the relaxation time distribution.

The use of Body Surface Index as a Better Clinical Health indicators Compare to Body Mass Index and Body Surface Area for Clinical Application

2018 ◽  
pp. 131-136
Author(s):  
Shirazu I. ◽  
Theophilus. A. Sackey ◽  
Elvis K. Tiburu ◽  
Mensah Y. B. ◽  
Forson A.
Keyword(s):  
Surface Area ◽  
Body Surface Area ◽  
Clinical Application ◽  
Body Surface ◽  
Body Height ◽  
Health Indicators ◽  
The Body ◽  
Body Parameters ◽  
The Relationship ◽  
Individual Body

The relationship between body height and body weight has been described by using various terms. Notable among them is the body mass index, body surface area, body shape index and body surface index. In clinical setting the first descriptive parameter is the BMI scale, which provides information about whether an individual body weight is proportionate to the body height. Since the development of BMI, two other body parameters have been developed in an attempt to determine the relationship between body height and weight. These are the body surface area (BSA) and body surface index (BSI). Generally, these body parameters are described as clinical health indicators that described how healthy an individual body response to the other internal organs. The aim of the study is to discuss the use of BSI as a better clinical health indicator for preclinical assessment of body-organ/tissue relationship. Hence organ health condition as against other body composition. In addition the study is `also to determine the best body parameter the best predict other parameters for clinical application. The model parameters are presented as; modeled height and weight; modelled BSI and BSA, BSI and BMI and modeled BSA and BMI. The models are presented as clinical application software for comfortable working process and designed as GUI and CAD for use in clinical application.

The relationship between brain structure and general psychopathology in preadolescents

2020 ◽  
Author(s):  
Louise Mewton ◽  
Briana Lees ◽  
Lindsay Squeglia ◽  
Miriam K. Forbes ◽  
Matthew Sunderland ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Surface Area ◽  
Brain Structure ◽  
Parental Education ◽  
Mental Illnesses ◽  
General Psychopathology ◽  
Control Approach ◽  
Cortical Volume ◽  
Subcortical Regions ◽  
The Relationship

Categorical mental disorders are being recognized as suboptimal targets in clinical neuroscience due to poor reliability as well as high rates of heterogeneity within, and comorbidity between, mental disorders. As an alternative to the case-control approach, recent studies have focused on the relationship between neurobiology and latent dimensions of psychopathology. The current study aimed to investigate the relationship between brain structure and psychopathology in the critical preadolescent period when psychopathology is emerging. This study included baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study® (n = 11,721; age range = 9-10 years; male = 52.2%). General psychopathology, externalizing, internalizing, and thought disorder dimensions were based on a higher-order model of psychopathology and estimated using Bayesian plausible values. Outcome variables included global and regional cortical volume, thickness, and surface area. Higher levels of psychopathology across all dimensions were associated with lower volume and surface area globally, as well as widespread and pervasive alterations across the majority of cortical and subcortical regions studied, after adjusting for sex, race/ethnicity, and parental education. The relationships between general psychopathology and brain structure were attenuated when adjusting for cognitive functioning. There was evidence of a relationship between externalizing psychopathology and frontal regions of the cortex that was independent of general psychopathology. The current study identified lower cortical volume and surface area as transdiagnostic biomarkers for general psychopathology in preadolescence. The widespread and pervasive relationships between general psychopathology and brain structure may reflect cognitive dysfunction that is a feature across a range of mental illnesses.

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