scholarly journals Decay curve analysis for data error quantification in time-domain induced polarization imaging

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. E75-E86 ◽  
Author(s):  
Adrian Flores Orozco ◽  
Jakob Gallistl ◽  
Matthias Bücker ◽  
Kenneth H. Williams
Keyword(s):  
Time Domain ◽  
Decay Curve ◽  
Induced Polarization ◽  
Curve Analysis ◽  
Real Field ◽  
Acquisition Time ◽  
Transfer Resistance ◽  
Imaging Results ◽  
Error Characterization ◽  
Data Error

In recent years, the time-domain induced polarization (TDIP) imaging technique has emerged as a suitable method for the characterization and the monitoring of hydrogeologic and biogeochemical processes. However, one of the major challenges refers to the resolution of the electrical images. Hence, various studies have stressed the importance of data processing, error characterization, and the deployment of adequate inversion schemes. A widely accepted method to assess data error in electrical imaging relies on the analysis of the discrepancy between normal and reciprocal measurements. Nevertheless, the collection of reciprocals doubles the acquisition time and is only viable for a limited subset of commonly used electrode configurations (e.g., dipole-dipole [DD]). To overcome these limitations, we have developed a new methodology to quantify the data error in TDIP imaging, which is entirely based on the analysis of the recorded IP decay curve and does not require recollection of data (e.g., reciprocals). The first two steps of the methodology assess the general characteristics of the decay curves and the spatial consistency of the measurements for the detection and removal of outliers. In the third and fourth steps, we quantify the deviation of the measured decay curves from a smooth model for the estimation of random error of the total chargeability and transfer resistance measurement. The error models and imaging results obtained from this methodology — in the following referred to as “decay curve analysis” — are compared with those obtained following a conventional normal-reciprocal analysis revealing consistent results. We determine the applicability of our methodology with real field data collected at the floodplain scale (approximately 12 ha) using multiple gradient and DD configurations.

Data error quantification in spectral induced polarization imaging

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. E227-E237 ◽  
Author(s):  
Adrián Flores Orozco ◽  
Andreas Kemna ◽  
Egon Zimmermann
Keyword(s):  
Phase Error ◽  
Induced Polarization ◽  
Data Sets ◽  
Data Set ◽  
Polarization Imaging ◽  
Near Surface ◽  
Error Quantification ◽  
Inverse Power Law ◽  
Imaging Results ◽  
Data Error

Induced polarization (IP) imaging is being increasingly used in near-surface geophysical studies, particularly for hydrogeologic and environmental applications. However, the analysis of IP data error has received little attention, even though the importance of an adequate error parameterization has been demonstrated for electrical resistivity imaging. Based on the analysis of data sets measured in the frequency range from 1 Hz to 1 kHz, we proposed a model for the quantification of phase data errors in IP measurements. The analyzed data sets were collected on an experimental tank containing targets of different polarizability. Our study is based on the common practice that the discrepancy of measurements taken in normal and reciprocal configuration can be considered as a measure of data error. Statistical analysis of the discrepancies between normal and reciprocal measurements revealed that the phase error decreases with increasing resistance (i.e., signal strength). We proposed an inverse power-law model to quantify the phase error as a function of the measured resistances. We found that the adequate implementation of the proposed error model in an inversion scheme leads to improved IP imaging results in laboratory experiments. Application to a data set collected at the field-scale also demonstrated the superiority of the new model over previous assumptions.

Reliability of Time Domain Induced Polarization Data

2011 ◽  
Author(s):  
Aurélie Gazoty ◽  
Esben Auken ◽  
Jesper Pedersen ◽  
Gianluca Fiandaca ◽  
Anders Vest Christiansen
Keyword(s):  
Time Domain ◽  
Induced Polarization ◽  
Polarization Data

A special circumstance of airborne induced‐polarization measurements

Geophysics ◽  
1996 ◽  
Vol 61 (1) ◽  
pp. 66-73 ◽  
Author(s):  
Richard S. Smith ◽  
Jan Klein
Keyword(s):  
Time Domain ◽  
Eddy Currents ◽  
High Arctic ◽  
Induced Polarization ◽  
Laboratory Measurements ◽  
Dielectric Polarization ◽  
Special Circumstance ◽  
Polarization Measurements ◽  
Airborne Electromagnetic

Airborne induced‐polarization (IP) measurements can be obtained with standard time‐domain airborne electromagnetic (EM) equipment, but only in the limited circumstances when the ground is sufficiently resistive that the normal EM response is small and when the polarizability of the ground is sufficiently large that the IP response can dominate the EM response. Further, the dispersion in conductivity must be within the bandwidth of the EM system. One example of what is hypothesized to be IP effects are the negative transients observed on a GEOTEM® survey in the high arctic of Canada. The dispersion in conductivity required to explain the data is very large, but is not inconsistent with some laboratory measurements. Whether the dispersion is caused by an electrolytic or dielectric polarization is not clear from the limited ground follow‐up, but in either case the polarization can be considered to be induced by eddy currents associated with the EM response of the ground. If IP effects are the cause of the negative transients in the GEOTEM data, then the data can be used to estimate the polarizabilities in the area.

The Cole‐Cole model in time domain induced polarization

Geophysics ◽  
1981 ◽  
Vol 46 (6) ◽  
pp. 932-933 ◽  
Author(s):  
T. Lee
Keyword(s):  
Equivalent Circuit ◽  
Time Domain ◽  
Induced Polarization ◽  
Relaxation Model ◽  
Trivial Case ◽  
Alternative Expression ◽  
Transient Voltage ◽  
Transient Voltages ◽  
Cole Model ◽  
The Given

Recently Pelton et al. (1978) used a Cole‐Cole relaxation model to simulate the transient voltages that are observed during an induced‐polarization survey. These authors took the impedance of the equivalent circuit Z(ω) to be [Formula: see text]They then gave the expression for the transient voltage [Formula: see text] as [Formula: see text]In equation (2), [Formula: see text] was misprinted as [Formula: see text]. In these equations, [Formula: see text] and [Formula: see text], [Formula: see text] and τ are constants to be determined for the given model. [Formula: see text] is the height of the step current that will flow in the transmitter. A disadvantage of equation (2) is that it is only slowly convergent for large t/τ. Pelton et al. (1978) used a τ which ranged from [Formula: see text] to [Formula: see text]. The purpose of this note is to provide an alternative expression for [Formula: see text] that is valid only at the later stages but which does not have this disadvantage. The trivial case of c = 1.0 is ignored.

Determining frequency dependence of carbon turnover in peat using spectral induced polarization

2021 ◽  
Author(s):  
Timea Katona ◽  
Benjamin Gilfedder ◽  
Sven Frei ◽  
Lukas Aigner ◽  
Matthias Bücker ◽  
...  
Keyword(s):  
Frequency Dependence ◽  
Iron Oxides ◽  
Induced Polarization ◽  
Electrode Spacing ◽  
Electrode Polarization ◽  
Biogeochemical Processes ◽  
Carbon Turnover ◽  
Spectral Induced Polarization ◽  
Polarization Response ◽  
Imaging Results

<p>Our study discusses imaging results from a spectral induced polarization (SIP) survey to identify concurring processes (such as aerobic respiration, denitrification, or sulfate- and iron reduction) in a biogeochemically active peat in a wetland located in the Lehstenbach catchment in Southeastern Germany. Terrestrial wetland ecosystems such as peatlands are a critical element in the global carbon cycle. Due to their role as natural carbon sinks and ecological importance for an array of flora and fauna, there is a growing demand to conserve and restore degraded peatlands. Biogeochemical processes occur with non-uniform reaction rates within the peat, making the environment sensitive to physical disturbances. To investigate biogeochemical processes in-situ, it is important to avoid disturbing the redox-sensitive conditions in the subsurface by bringing oxygen into anoxic areas.  Our previous study demonstrated that the induced polarization (IP) was able to identify biogeochemically active and inactive areas of the peat. The IP response was sensitive to the presence of carbon turnover and P release in the absence of iron sulfide. These highly polarizable areas have high iron concentrations, but most likely in an oxidized form. As most iron oxides are poor conductors, the strong polarization response is unlikely related to an electrode polarization process.</p><p>Here we also analyzed the frequency dependence of the SIP data to investigate whether iron oxides and carbon-iron complexes, two possible mechanisms for the high polarization response, can be distinguished. SIP imaging data sets covered the frequency range between 0.06 and 225 Hz and were collected with varying electrode spacing (20 and 50 cm) at different locations within the Waldstein catchment characterized by different properties, e.g., saturated and non-saturated soils. Our imaging results reveal variations of the IP effect within the peat layer, indicating substantial heterogeneities in the peat composition and biogeochemical activity. The frequency dependence allowed us to resolve a sharper contrast between the different features of the peat. Geochemical analyses on a freeze core and pore water samples are used to validate our results and find correlations between the Cole-Cole parameters of the SIP response and the geochemical parameters.</p>

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