Spectral Induced Polarization Data Research Articles

Spectral induced polarization tomography inversion: Hybridizing homotopic continuation with Bayesian inversion

Induced polarization tomography offers the potential to better characterize the subsurface structures by considering spectral content from data acquisition over a broad frequency range. Spectral induced polarization (SIP) tomography is generally defined as a nonlinear inverse problem commonly solved through deterministic gradient-based methods. To this end, the spectral parameters, i.e., direct current resistivity, chargeability, relaxation time, and frequency exponent, are resolved by individually or simultaneously inverting all frequency data, followed by fitting a generalized Cole-Cole model to the inverted complex resistivities. Due to the high correlation between the Cole-Cole model parameters and a lack of knowledge about the initial approximation of the spectral parameters, using the classical least-squares methods may lead to inaccurate solutions and impede reliable uncertainty analysis. To cope with these limitations, we introduce a new approach based on a hybrid application of a globally convergent homotopic continuation method and a Bayesian inference to reconstruct the distribution of the subsurface spectral parameters. The homotopic optimization, owing to its fast and global convergence, is first implemented to invert multifrequency SIP data sets aimed at retrieving the complex-valued resistivity. Then, Bayesian inversion based on a Markov chain Monte Carlo (MCMC) sampling method, along with a priori information including the lower and upper bounds of the prior distributions, is used to invert the complex resistivity for the Cole-Cole model parameters. By applying the MCMC inversion algorithm, a full nonlinear uncertainty appraisal can be provided. We numerically evaluate the performance of our method using synthetic and real data examples in the presence of topographical effects. Numerical results prove that the homotopic continuation method outperforms the classic, smooth inversion algorithm in the sense of approximation accuracy and computational efficiency. In addition, we determine that our hybrid inversion strategy provides reliable representations of the main features and structure of the earth’s subsurface in terms of the spectral parameters.

Electrical Responses of Modified Mineral Surfaces as Observed With Spectral Induced Polarization and Atomic Force Microscopy

AbstractAtomic force microscopy (AFM) and spectral induced polarization (SIP) are widely used to investigate the electrical properties of mineral surfaces at vastly different scales of measurement. We compare AFM and SIP measurements made on two different materials (glass beads and silica gel) subjected to etching, deposition of iron oxide particles, and inclusion of calcite grains. We found that the treatments produced qualitatively consistent behaviors in the AFM and SIP data. Direct AFM measurements of surface charge density for silica and calcite surfaces were quantitatively compared to values estimated from the SIP results using a grain polarization model. No statistically significant difference (at a 95% confidence level) was found between the surface charge density of silica estimated by AFM (2.3 ± 6.6 mC/m2 for glass beads and 1.6 ± 0.1 mC/m2 for silica gel) versus SIP (5.4 ± 4.4 mC/m2 for glass beads and 1.6 ± 0.5 mC/m2 for silica gel). The surface charge density for calcite determined by AFM (43.5 ± 12.9 mC/m2) was approximately 19 times higher than that found for silica. While the charge density of calcite surfaces determined by SIP was also generally higher than that found for silica, different treatments produced significantly different values between 4.7 and 258 mC/m2 (with a maximum 95% CI of ±8.7 mC/m2). Several possible explanations exist for the range of the observed SIP measurements, including aging of the calcite surfaces. Overall, this study suggests the potential for the complementary use of AFM and SIP measurements to constrain future investigations of polarization mechanisms in porous media.

Organic Matter Matters—The Imaginary Conductivity of Sediments Rich in Solid Organic Carbon

AbstractSolid organic matter (OM) is a biogeochemically relevant constituent of soils and sediments. It also affects sediments' geophysical properties, but is often overlooked in hydro‐ and biogeophysical approaches for the characterization of the shallow subsurface. Here, we explore the potential of spectral induced polarization (SIP) to delineate OM‐rich zones in the subsurface and provide insights into the mechanisms that drive OM‐polarization using measurements on both field cores and artificial OM‐sand mixtures. Both, field samples and artificial mixtures showed a linear relationship between the total organic carbon (TOC) content and charge storage (imaginary conductivity). The high cation exchange capacity of OM drives the increase in polarization and can help in delineating potentially microbially active OM‐rich zones in cores or field surveys. To avoid misinterpretation of SIP data in unconsolidated media, we strongly suggest quantifying TOC content in sediment samples to accompany the interpretation of field surveys.

3D electrode configurations for spectral induced polarization surveys of landfills

There is growing interest in the use of spectral induced polarization (SIP) surveys to characterize the near-surface environment. Few attempts have been made to perform field SIP surveys in a 3D configuration; when done, they are typically conducted using a series of parallel 2D electrode lines with collinear measurements. However, such measurements are limited in the resolution between the lines which is critical in the case of heterogeneous subsurface conditions, such as in landfills. To overcome this, we investigate here the enhanced resolution in SIP measurements through true 3D measurements, i.e., the resolving capabilities of different electrode configurations distributed across measuring planes. First, we investigate, through a synthetic study, the difference between results from using 2D parallel collinear electrode arrays and true 3D configurations. Second, we collected SIP data (in the frequency range between 1 and 240 Hz) using 2D and 3D configurations in two landfills to evaluate the application of our results in real field conditions. Both the synthetic and the field experiments demonstrate that measurements of parallel 2D collinear arrays result in the creation of artifacts and the loss of resolution in the 3D structure, especially of polarizable features. In contrast, the 3D configurations are able to resolve polarizable anomalies in synthetic and field measurements, resulting in a better delineation of the geometry of waste units. Our results also demonstrate that 3D configurations are better suited to recover the frequency-dependence of the electrical properties; thus, permitting an improved interpretation of waste composition and the quantification of waste volume.

Bayesian inference of petrophysical properties with generative spectral induced polarization models

Mechanistic induced polarization (IP) models describe the relationships between the physical properties of geomaterials and their frequency-dependent complex conductivity. However, practitioners rarely use mechanistic models to interpret the IP data because the uncertainties associated with estimating petrophysical properties from complex conductivity spectra are still poorly understood. We propose a framework for critically assessing any IP model’s sensitivity and parameter estimation limitations. The framework consists of a conditional variational autoencoder (CVAE), an unsupervised Bayesian neural network specializing in data dimension reduction and generative modeling. We apply the framework in a case study of the “perfectly polarized interfacial polarization” model by training the CVAE on the IP signatures of synthetic mixtures of metallic mineral inclusions hosted in electrolyte-filled geomaterials. First, the CVAE’s Jacobian reveals the relative importance of each petrophysical property for generating the spectral IP data. The most critical parameters are the conductivity of the host, the volume fraction of the inclusions, the characteristic length of the inclusions, and the permittivity of the host. Contrastingly, the inclusions’ diffusion coefficient, permittivity, and conductivity, as well as the host’s diffusion coefficient, have marginal importance. A parameter estimation experiment using various model constraints yields the standardized accuracy of petrophysical properties and corroborates the sensitivity analysis results. Finally, we visualize the effects of data transformations and model constraints on the petrophysical parameter space. We conclude that a common logarithm data transformation yields optimal parameter estimation results and that constraining the electrochemical properties of a geomaterial improves the estimates of the size of its metallic inclusions and vice versa.

Research on linear inversion of SIP using the Debye decomposition method

Spectral-induced polarization data can provide information regarding the polarization properties of underground media. The characteristic relaxation time of polarized media is related in part to the composition of rocks and minerals and porosity. Therefore, the spectral-induced polarization method is widely used to obtain underground lithology/structure, hydrogeology, and geochemical data. Because the Debye decomposition method can flexibly fit the spectrum shape, it is being increasingly used to analyze the relaxation time distribution of spectral-induced polarization (SIP) signals. In this study, the Debye decomposition method was used to directly invert the relaxation time distribution curve from SIP data to extract the characteristic relaxation time of the target. First, we discuss the Debye decomposition method in terms of its physical principle and show the relationship between the multi-Cole–Cole model and Debye decomposition through a numerical simulation. We then used the linear inversion method to invert the synthetic and measured data based on the Debye decomposition parameterization and verify the effectiveness of the inversion. Compared with the numerical simulations of several models, we proved that the parameters of the Cole–Cole model are strongly correlated. Additionally, the inversion results show that the linear inversion method is suitable for inverting the Debye decomposition and that expanding the range of the relaxation time in the Debye decomposition can effectively alleviate the influence of the truncation error on inversion.

Ambiguity in induced polarization time constants and the advantage of the Pelton model

The perceived differences between two popular relaxation models (referred to here as the “Cole-Cole model” and the “Pelton model”), along with their corresponding time constants, have been a source of confusion in recent spectral induced polarization (SIP) literature. These differences complicate comparisons among experimental results recorded by different researchers. A circuit model approach is adopted to better understand the differences among these relaxation models. Either relaxation model can fit SIP data sets equally well, the sole difference corresponds to the relaxation time that varies for the impedance and admittance formulations of the circuit models. The reporting of time constants from a common relaxation model will advance petrophysical investigations of the controls on SIP measurements when exploring databases compiled from previously published data sets.

Spectral induced polarization imaging to investigate an ice-rich mountain permafrost site in Switzerland

Abstract. Spectral induced polarization (SIP) measurements were collected at the Lapires talus slope, a long-term permafrost monitoring site located in the western Swiss Alps, to assess the potential of the frequency dependence (within the frequency range of 0.1–225 Hz) of the electrical polarization response of frozen rocks for an improved permafrost characterization. The aim of our investigation was to (a) find a field protocol that provides SIP imaging data sets less affected by electromagnetic coupling and easy to deploy in rough terrains, (b) cover the spatial extent of the local permafrost distribution, and (c) evaluate the potential of the spectral data to discriminate between different substrates and spatial variations in the volumetric ice content within the talus slope. To qualitatively assess data uncertainty, we analyse the misfit between normal and reciprocal (N&R) measurements collected for all profiles and frequencies. A comparison between different cable setups reveals the lowest N&R misfits for coaxial cables and the possibility of collecting high-quality SIP data in the range between 0.1–75 Hz. We observe an overall smaller spatial extent of the ice-rich permafrost body compared to its assumed distribution from previous studies. Our results further suggest that SIP data help to improve the discrimination between ice-rich permafrost and unfrozen bedrock in ambiguous cases based on their characteristic spectral behaviour, with ice-rich areas showing a stronger polarization towards higher frequencies in agreement with the well-known spectral response of ice.

Simultaneous inversion of spectral IP data with frequency constraints

Spectral-induced polarization (SIP) measures the apparent complex resistivities (CRs) of electrical current sources at frequencies of less than 10 kHz. The measured apparent CRs are usually inverted to recover the intrinsic subsurface CRs, which depend on the source frequencies. The Cole-Cole (CC) model is commonly used to describe the dispersion characteristics of the intrinsic CRs; electrical resistivity, chargeability, and the time and relaxation constants can be derived using this model. For quantitative estimation of these parameters, a reliable inversion method for multifrequency SIP datasets is required. Conventionally, SIP data at each frequency are independently and sequentially inverted. This does not consider the relationships between SIP datasets of different frequencies. To consider such relationships, here, we simultaneously invert multi-frequency SIP datasets by introducing frequency-smoothness constraints between adjacent frequencies. We numerically verified the utility of the approach in terms of both amplitude and phase recovery; the method yielded more reliable relaxation models (e.g., CC models) than conventional inversion. For field verification, the method was applied to SIP data of several frequencies obtained in South Korea.

Global optimization algorithms for particle swarm optimization to the derivation of horizontal multilayered soil models considering the frequency dependence of soil parameters

AbstractThe frequency dependence of soil parameters has been confirmed by many experiments. Most of them are done by the measurement of soil sample in laboratories. Although some studies are based on field measurement, only homogeneous ground or two‐layer model are considered. The study of frequency dependence of soil parameters with considering multilayered model is lacking. The frequency dependence of the horizontally multilayered soil model is studied by the inversion of soil parameters in the frequency domain. The inversion of frequency domain soil parameters is translated into an optimization problem. The model parameters are determined by spectral induced polarization data, and particle swarm optimization is applied to optimizing model parameters. The performances of three different methods to trap particles inside boundaries are compared. In order to improve the computational efficiency of the inversion program, parallel computing is combined with particle swarm optimization to solve the optimization problem. The execution time of the developed algorithm before and after the application of parallel computing is compared. The performances of other two optimization algorithms, simulated annealing and genetic algorithm, are compared with that of particle swarm optimization. So it would be more clear which optimization algorithm should be selected among these three commonly used algorithms when dealing with the inversion problem of soil parameters. The accuracy of the proposed method is verified by a numerical simulation experiment. Then, this method is applied to interpreting field data, and the frequency dependence of soil parameters can be observed from the inversion results.

Spectral Induced Polarization Characterization of Non‐Consolidated Clays for Varying Salinities—An Experimental Study

AbstractClay material characterization is of importance for many geo‐engineering and environmental applications, and geo‐electrical methods are often used to detect them in the subsurface. Spectral induced polarization (SIP) is a geo‐electric method that nonintrusively measures the frequency‐dependent complex electrical conductivity of a material, in the mHz to the kHz range. We present a new SIP data set of four different types of clay (a red montmorillonite sample, a green montmorillonite sample, a kaolinite sample, and an illite sample) at five different salinities (initially de‐ionized water, 10−3, 10−2, 10−1, and 1 mol/L of NaCl). We propose a new laboratory protocol that allows the repeatable characterization of clay samples. The complex conductivity spectra are interpreted with the widely used phenomenological double‐Pelton model. We observe an increase of the real part of the conductivity with salinity for all types of clay, while the imaginary part presents a nonmonotonous behavior. The decrease of polarization over conduction with salinity is interpreted as evidence that conduction increases with salinity faster than polarization. We test the empirical petrophysical relationship between and and validate this approach based on our experimental data and two other datasets from the literature. With this data set we can better understand the frequency‐dependent electrical response of different types of clay. This unique data set of complex conductivity spectra for different types of clay samples is a step forward toward better characterization of clay formations in situ.

Cole-Cole Model Parameter Estimation from Multi-frequency Complex Resistivity Spectrum Based on the Artificial Neural Network

In near surface electrical exploration, it is often necessary to estimate the Cole-Cole model parameters according to the measured multi-frequency complex resistivity spectrum of ore and rock samples in advance. Parameter estimation is a nonlinear optimization problem, and the common method is least square fitting. The disadvantage of this method is that it relies on initial value and the result is unstable when data is confronted with noise interference. To further improve the accuracy of parameter estimation, this paper applied artificial neural network (ANN) method to the Cole-Cole model estimation. Firstly, a large number of forward models are generated as samples to train the neural network and when the data fitting error is lower than the error threshold, the training ends. The trained neural network is directly used to efficiently estimate the parameters of vast amounts of new data. The efficiency of the artificial neural network is analyzed by using simulated and measured spectral induced polarization data. The results show that artificial neural network method has a faster computing speed and higher accuracy in Cole-Cole model parameter estimation.

PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data

Spectral induced polarization (SIP) is a non-intrusive geophysical method that collects chargeability information (the ability of a material to retain charge) in the time domain or its phase shift in the frequency domain. Although SIP is a temporal method, it cannot measure the dynamics of flow and solute/species transport in the subsurface over long times (i.e., 10–100 s of years). Data collected with the SIP technique need to be coupled with fluid flow and reactive-transport models in order to capture long-term dynamics. To address this challenge, PFLOTRAN-SIP was built to couple SIP data to fluid flow and solute transport processes. Specifically, this framework couples the subsurface flow and transport simulator PFLOTRAN and geoelectrical simulator E4D without sacrificing computational performance. PFLOTRAN solves the coupled flow and solute-transport process models in order to estimate solute concentrations, which were used in Archie’s model to compute bulk electrical conductivities at near-zero frequency. These bulk electrical conductivities were modified while using the Cole–Cole model to account for frequency dependence. Using the estimated frequency-dependent bulk conductivities, E4D simulated the real and complex electrical potential signals for selected frequencies for SIP. These frequency-dependent bulk conductivities contain information that is relevant to geochemical changes in the system. This study demonstrated that the PFLOTRAN-SIP framework is able to detect the presence of a tracer in the subsurface. SIP offers a significant benefit over ERT in the form of greater information content. It provided multiple datasets at different frequencies that better constrained the tracer distribution in the subsurface. Consequently, this framework allows for practitioners of environmental hydrogeophysics and biogeophysics to monitor the subsurface with improved resolution.

Spectral Induced Polarization Survey with Distributed Array System for Mineral Exploration: Case Study in Saudi Arabia

The Saudi Arabian Glass Earth Pilot Project is a geophysical exploration program to explore the upper crust of the Kingdom for minerals, groundwater, and geothermal resources as well as strictly academic investigations. The project began with over 8000 km2 of green-field area. Airborne geophysics including electromagnetic (EM), magnetics, and gravity were used to develop several high priority targets for ground follow-up. Based on the results of airborne survey, a spectral induced polarization (SIP) survey was completed over one of the prospective targets. The field data were collected with a distributed array system, which has the potential for strong inductive coupling. This was examined in a synthetic study, and it was determined that with the geometries and conductivities in the field survey, the inductive coupling effect may be visible in the data. In this study, we also confirmed that time domain is vastly superior to frequency domain for avoiding inductive coupling, that measuring decays from 50 ms to 2 s allow discrimination of time constants from 1 ms to 5 s, and the relaxation parameter C is strongly coupled to intrinsic chargeability. We developed a method to fully include all 3D EM effects in the inversion of induced polarization (IP) data. The field SIP data were inverted using the generalized effective-medium theory of induced polarization (GEMTIP) in conjunction with an integral equation-based modeling and inversion methods. These methods can replicate all inductive coupling and EM effects, which removes one significant barrier to inversion of large bandwidth spectral IP data. The results of this inversion were interpreted and compared with results of drill hole set up in the survey area. The drill hole intersected significant mineralization which is currently being further investigated. The project can be considered a technical success, validating the methods and effective-medium inversion technique used for the project.

Time-lapse monitoring of an electrokinetic soil remediation process through frequency-domain electrical measurements

The electrokinetic (EK) method is an emerging technique for soil remediation, even though a monitoring system of the contaminant removal through geophysical methods has not been developed yet. In this paper, frequency-domain time-lapse measurements are used on heavy-metal contaminated sediments for monitoring an EK remediation process in a small-scale measuring cell. Our goal is to monitor the development of the electrokinetic process within the sediment and to evaluate the total time needed for the treatment. In fact, frequency-domain electrical monitoring provides complex resistivity spectra at different time steps that can be correlated to changes in the physical properties of the sediments. We perform laboratory spectral induced polarization (SIP) measurements on different samples before, during and after the EK treatment, using different electrolyte solutions (acids and tap water), commonly employed in EK remediation. Direct-current measurements (resistivity and chargeability) were also acquired on one sample for testing the reliability of the system by a comparison with a widespread commercial instrumentation for field measurements.Results indicate that resistivity is a diagnostic parameter as long as it is linked to changes in water saturation, pH and ionic concentration and not to the percentage of metal extraction. The resistivity exhibited well-defined signatures as a function of time that changes depending on the conditioning agent and the grain size distribution. These peculiarities were used to understand the physical processes occurring within the cell and consequently to assess the effectiveness of the electrokinetic treatment.Conversely, the polarization effect was negligible using acids as conditioning agents at the electrolyte chambers. Therefore, the SIP method is not effective under these conditions, being the polarization effect significant only when tap water was used at both ends of the measuring cell. In this case, we were able to correlate changes in water saturation with the time-shift observed on relaxation time distributions (RTDs) after inversion of SIP data and to observe, using normalized chargeability, that polarization is stronger at high pH values.On these basis, resistivity is suitable to monitor the development of the remediation, to optimise the energy levels required for treatment and to assess the end time of the EK process (time when metal mobilization ends). In fact, the end time of treatment can be associated with the time at which resistivity becomes stable. This time is highly dependent on the particular working conditions and sediment grain size as demonstrated by our experiments.

Anisotropic complex electrical conductivity of black shale and mudstone from the Moffat Shale Group (Ireland)

ABSTRACTThe geological setting in the north of Ireland, especially concerning the origin of the Moffat Shale Group, has long been under discussion. Within the Tellus Programme of the Geological Survey Ireland, airborne electromagnetic measurements revealed high‐conductivity anomalies that have been interpreted as the response of a black shale. In order to petrophysically characterize the Moffat Shale, a laboratory study using material from two shallow boreholes was carried out. The study focuses on spectral induced polarization measurements on 23 oriented samples in the frequency range from 10−4 to 105 Hz.The sample material can be categorized into two groups. A mudstone‐like rock type shows weakly frequency‐dependent, porosity‐driven conductivities with a strong anisotropy. On the other hand, black shale samples are characterized by moderately anisotropic but strong polarization effects especially at low frequencies and a strong conductivity increase towards higher frequencies. The polarization in the black shale is controlled by the texture and volume fraction of the polarizable components. The spectral induced polarization data are processed by means of a Debye decomposition approach. The anisotropy of the complex electrical conductivity is determined by utilizing the foliation dip angle and assuming tilted transverse isotropic conditions. The relevance of the laboratory findings for airborne electromagnetic surveys is addressed with a synthetic one‐dimensional modelling study.

Inversions of time-domain spectral induced polarization data using stretched exponential

SUMMARY We provide a two-stage approach to extract spectral induced polarization (SIP) information from time-domain IP data. In the first stage we invert dc data to recover the background conductivity. In the second, we solve a linear inverse problem and invert all time channels simultaneously to recover the IP parameters. The IP decay curves are represented by a stretched exponential (SE) rather than the traditional Cole–Cole model, and we find that defining the parameters in terms of their logarithmic values is advantageous. To demonstrate the capability of our simultaneous SIP inversion we use synthetic data simulating a porphyry mineral deposit. The challenge is to image a mineral body that is hosted within an alteration halo having the same chargeability but a different time constant. For a 2-D problem, we were able to distinguish the body using our simultaneous inversion but we were not successful in using a sequential (or conventional) SIP inversion approach. For the 3-D problem we recovered 3-D distributions of the SIP parameters and used those to construct a 3-D rock model having four rock units. Three chargeable units were distinguished. The compact mineralization zone, having a large time constant, was distinguished from the circular alteration halo that had a small time constant. Finally, to promote the use of the SIP technique, and to have further development of SIP inversion, all examples presented in this paper are available in our open source resources (https://github.com/simpeg-research/kang-2018-spectral-inducedpolarization).

The Inversion of One-Dimensional Soil Parameters in the Frequency Domain With Considering Multilayered Earth Based on Simulated Annealing Algorithm

A method to study the soil parameters’ frequency dependence of horizontally multilayered earth is developed. It is achieved by multifrequency inversion. Apparent resistivity can be obtained from the potential difference and the injecting current. Since the inversion is performed simultaneously at multiple frequencies and the inversion at each studied frequency is independent, parallel computing can be used to improve the efficiency of the inversion algorithm. The inverse problem in this study is an unconstrained optimization problem. So one of the central issues is the optimization of model parameters. Simulated annealing algorithm adopted in this study is an effective method to solve this kind of problem. The difference between the two calculation results based on the dynamic state field theory and quasi-static field theory is studied. The proposed method is validated by the interpretation of measurement data. The frequency dependence of soil parameters is studied by applying the method to interpret spectral-induced polarization data.

Combining spectral induced polarization with X-ray tomography to investigate the importance of DNAPL geometry in sand samples

Although many studies have been performed to investigate the spectral induced polarization (SIP) response of nonaqueous phase liquid (NAPL)-contaminated soil samples, there are still many uncertainties in the interpretation of the data. A key issue is that altered pore space geometries due to the presence of a NAPL phase will change the measured IP spectra. However, without any information on the NAPL distribution in the pore space, assumptions are necessary for the SIP data interpretation. Therefore, experimental data of SIP signals directly associated with different NAPL distributions are needed. We used high-resolution X-ray tomography and 3D image processing to quantitatively assess NAPL distributions in samples of fine-grained sand containing different concentrations of tetrachloroethylene and link this to SIP measurements on the same samples. The total concentration of the sample constituents as well as the volumes of the individual NAPL blobs were calculated and used for the interpretation of the associated SIP responses. The X-ray tomography and image analysis showed that the real sample properties (porosity and NAPL distributions) differed from the targeted ones. Both contaminated samples contained less NAPL than expected from the manual sample preparation. The SIP results showed higher real conductivity and lower imaginary conductivity in the contaminated samples compared to a clean sample. This is interpreted as an effect of increased surface conductivity along interconnected NAPL blobs and decreased surface areas in the samples due to NAPL blobs larger than and enclosing grains. We conclude that the combination of SIP, X-ray tomography, and image analysis is a very promising approach to achieve a better understanding of the measured SIP responses of NAPL-contaminated samples.

Using a combination of genetic algorithm and particle swarm optimization algorithm for GEMTIP modeling of spectral-induced polarization data

The generalized effective-medium theory of induced polarization (GEMTIP) is a newly developed relaxation model that incorporates the petro-physical and structural characteristics of polarizable rocks in the grain/porous scale to model their complex resistivity/conductivity spectra. The inversion of the GEMTIP relaxation model parameter from spectral-induced polarization data is a challenging issue because of the highly non-linear dependency of the observed data on the model parameter and non-uniqueness of the problem. To solve these problems as well as scape the local minima of the highly complicated cost function, the genetic algorithm (GA) can be applied but it has proven to be time-intensive computationally. However, this drawback can be resolved by incorporating a faster algorithm, e.g. particle swarm optimization (PSO). The aim of this work is to investigate whether recovering the model parameter of the ellipsoidal GEMTIP model from SIP data using the combined GA and PSO algorithms is possible. To achieve this aim, we set the best calculated individuals using GA as the search space of PSO, and then the best location achieved by PSO in each iteration is assigned as the updated model parameters. The results of our research work reveal that the model parameters can effectively be recovered using the approach proposed in this paper but the time constant of a noisy data that arises from the adverse dependency of this parameter on the ellipticity of a polarizable grain. Moreover, the execution time of the ellipsoidal GEMTIP modeling of complex resistivity data can be significantly improved using the proposed algorithm.