Time-domain Induced Polarization Data Research Articles

Extraction of High Chargeability Distribution using Joint Inversion of Direct Current Resistivity and Time Domain Induced Polarization Data: A Development and Test Results on Uranium Bearing Target Rock

Extraction of High Chargeability Distribution using Joint Inversion of Direct Current Resistivity and Time Domain Induced Polarization Data: A Development and Test Results on Uranium Bearing Target Rock

Processing Time-Domain Induced Polarization Data Using Variational Mode Decomposition

Processing Time-Domain Induced Polarization Data Using Variational Mode Decomposition

Conversion of Induced Polarization Data and Their Uncertainty from Time Domain to Frequency Domain Using Debye Decomposition

The time-domain (TD) induced polarization (IP) method is used as an extension of direct current (DC) resistivity measurements to capture information on the ability of the subsurface to develop electrical polarization. In the TD, the transient voltage decay is measured after the termination of the current injection. To invert tomographic TD IP data sets into frequency-domain (FD) models of complex electrical resistivity, a suitable approach for converting TD IP transients and their corresponding uncertainties into the FD is essential. To apply existing FD inversion algorithms to TD IP measurements, a conversion scheme must transform the measured decay curves into FD impedances and also propagate the corresponding measurement uncertainty from the TD to the FD. Here, we present such an approach based on a Debye decomposition (DD) of the decay curve into a relaxation-time distribution and the calculation of the equivalent spectrum. The corresponding FD data error can be obtained by applying error propagation through all of these steps. To accomplish the DD we implement a non-linear Gauss–Newton inversion scheme. We test the conversion scheme in a synthetic study and demonstrate its application to field data on a tomographic TD IP data set measured on the Maletoyvaemskoie ore field (Kamchatka, Russia). The proposed conversion scheme yields accurate impedance data for relaxation processes, which are resolved by the TD measurements. The error propagation scheme provides a reasonable FD uncertainty estimate, as confirmed by a Monte Carlo analysis of the underlying parameter distributions.

Identification of Porphyry Deposit Based On Time Domain Induced Polarization Data Analysis in The Papua Region

Papua is one of the islands in Indonesia which has an abundance of economic metal mineralization in Indonesia. The types of mineralized deposits on the island of Papua are porphyry, skarn and epithermal, where porphyry deposits are identified in the study area, which were identified early by geological conditions. The geophysical approach is carried out using Time Domain Induced Polarization data analysis, TDIP measurements are carried out on an area of 2.24km2, with a total of 3 line oriented southeast-northwest along 3 km. Based on TDIP data analysis, there’s low resistivity anomaly correlates with high chargeability which is interpreted as a Potassic body at an elevation of 500m, while above it there are Serisitic-Clay-Chlorite and Serisitic-Clay-Pyrite alterations with high metal sulfide content up to 1100m elevation.

Influence of the water content on the complex conductivity of bentonite

Compacted bentonite is considered as a potential buffer material for deep geological disposals of high-level nuclear wastes. Methodologies to non-intrusively monitor the water content of such sealing materials are important in the context of the safety of these storage facilities and for various engineering applications as well. Induced polarization is a non-intrusive geophysical method sensitive to the water content of porous media. We investigated the complex conductivity spectra of 69 samples made of 2 distinct MX80 bentonites, one in the form of powder from crushed pellets (Type I) and the other in the form of a granulated bentonite mixture (GBM, type II). The samples are prepared at different compaction states and saturations. The pore water conductivity of the porous samples is estimated to be ∼2.5 S m−1 (25 °C) by two different methods. The complex conductivity spectra were obtained at room temperature (25 ± 2 °C) in the frequency range 1 Hz-45 kHz. In-phase and quadrature conductivities reflect conduction (electromigration) and polarization processes, respectively. At a given frequency, both the in-phase and quadrature conductivities increase with the water content along a trend that is independent of the compaction state. An induced polarization model based on the dynamic Stern layer model is used to explain these results. The first Archie's exponent m is inferred from the formation factor F using the in-phase conductivity data versus the pore water conductivity data at different salinities (NaCl, 25 °C). The dynamic Stern layer model applied to polarization correctly predicts the dependence of the in-phase conductivity, quadrature conductivity, and normalized chargeability with the water content and the Cation Exchange Capacity (CEC). This petrophysical work can easily be applied to time-domain induced polarization data collected in field conditions to monitor hydraulic barriers.

A study of time spectrum identification of induced polarization field based on Weibull distribution function

Identification of time spectrum is one of the core issues of the time-domain induced polarization (IP) method, which can be considered as one of the bases for distinguishing various polarized rocks. Fitting the IP data based on the spectrum forward model and obtaining optimal solution of the model parameters is a key step in spectrum identification. However, the suitable forward model should consider the observation conditions, such as the charging time and the time window. The time spectrum identification may be difficult to implement stably due to the lack of objective reference for the optimal solution. Therefore, our purpose is to improve the forward model and implement the spectrum identification for IP data. First, using the Weibull (WB) distribution function as the basis, a time spectrum forward model considering the charging time and observation time window is provided according to the typical measurement mode. Then, based on the WB spectrum model and Barzilai-Borwein gradient optimization, a method for solution of apparent spectral model parameters for spectrum identification is developed. Finally, this method is used to process the IP data from a mine in which the anomalies related to ore-bearing beds are identified based on the processing results. Results obtained demonstrate that the spectrum forward model based on the WB function is feasible in describing the IP data. The limited charging time and a wide observed time window should be considered to realize accurate simulations and description of the time-domain IP data. The essence of the time spectrum identification is to comprehensively reflect the time-varying state of the whole time channel through the spectral model parameters, whereas the decay field ratio of the adjacent time channel can be used as an objective reference. The parameters of the spectrum model characterizing the time-varying state are independent of polarization and resistivity and thus can be directly used for identification of IP anomalies.

Exploration of a Copper Ore Deposit in Elbistan/Turkey Using 2D Inversion of the Time-Domain Induced Polarization Data by Using Unstructured Mesh

We present the results of a direct current (DC) resistivity and time-domain induced polarization (TDIP) survey exploring a copper ore deposit in Elbistan/Turkey. The ore deposit is elongated below a valley and is of disseminated form with sulfide content. DC and IP data were acquired using the pole-dipole array on eight parallel profiles crossing the valley perpendicularly. The length of each profile was 300 m with an inter-profile distance of about 50 m. The data were interpreted by a newly developed 2D DC/TDIP inversion algorithm. The finite element algorithm uses a local smoothness constrained regularization on unstructured meshes. The finite element forward solution, as well as the inverse problem, is solved by an iterative preconditioned conjugate solver. The depth of investigation (DOI) was determined from cumulative sensitivities of the 2D inversion algorithm results. Because of the dissemination of the ore, the 2D inversion of the DC data was ambiguous: However, due to the sulfide content, a strong chargeability anomaly associated with the ore body was detected. We show that chargeability anomalies can generally be detected in the absence or presence of corresponding resistivity anomalies. This highly chargeable structure was confined in lateral direction. Although the lower boundary of the structure could not be resolved by the applied field set-up, a rough estimation of it could be derived at a depth of 90 m using synthetic modeling analyses. The 2D chargeability models are consistent with existing borehole information.

Data-driven modeling of time-domain induced polarization

Induced polarization (IP) measurements are affected by various types of noise, which should be removed prior to data interpretation. However, existing data processing methods often rely on empirical assumptions about the standard shape of IP decay curves. Our goal is to introduce a data-driven approach for modeling and processing time-domain IP measurements. To reach this goal, we train a variational autoencoder (VAE) on 1,600,319 IP decays collected in Canada, the United States, and Kazakhstan. The proposed deep learning approach is unsupervised and avoids the pitfalls of IP parameterization with empirical Cole-Cole and Debye decomposition models, simple power-law models, or mechanistic models. Four applications of VAEs are key to modeling and processing IP data: (1) synthetic data generation, (2) Bayesian denoising, (3) evaluation of signal-to-noise ratio, and (4) outlier detection. Furthermore, we interpret the IP data compilation’s latent representation and reveal a correlation between its first dimension and the average chargeability. Finally, we determine that a single real-valued scalar parameter contains sufficient information to encode IP data. This new finding suggests that modeling time-domain IP data using mathematical models governed by more than one free parameter is ambiguous, whereas modeling only the average chargeability is justified. A pretrained implementation of the VAE model is available as open-source Python code.

Automatic processing of time domain induced polarization data using supervised artificial neural networks

SUMMARYProcessing of geophysical data is a time consuming task involving many different steps. One approach for accelerating and automating processing of geophysical data is to look towards machine learning (ML). ML encompasses a wide range of tools, which can be used to automate complicated and/or tedious tasks. We present strategies for automating the processing of time-domain induced polarization (IP) data using ML. An IP data set from Grindsted in Denmark is used to investigate the applicability of neural networks for processing such data. The Grindsted data set consists of eight profiles, with approximately 2000 data curves per profile, on average. Each curve needs to be processed, which, using the manual approach, can take 1–2 hr per profile. Around 20 per cent of the curves were manually processed and used to train and validate an artificial neural network. Once trained, the network could process all curves, in 6–15 s for each profile. The accuracy of the neural network, when considering the manual processing as a reference, is 90.8 per cent. At first, the network could not detect outlier curves, that is where entire chargeability curves were significantly different from their spatial neighbours. Therefore, an outlier curve detection algorithm was developed and implemented to work in tandem with the network. The automatic processing approach developed here, involving the neural network and the outlier curve detection, leads to similar inversion results as the manual processing, with the two significant advantages of reduced processing times and enhanced processing consistency.

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

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.

Identification of Potential Distribution of Gold Mineralization Zone of High Sulfidation Epithermal System using Time-Domain Induced Polarization (TDIP) and Magnetic Method at “N” Mountain Qrea

The high gold commodity as well as the Pongkor production field that has existed since decades ago made it necessary to find a new potential zone for gold mining production. To find out the zone of gold mineralization and its continuity, geophysical studies then conducted by using the methods of Time-Domain Induced Polarization (TDIP) and magnetic. TDIP data consisting of resistivity and chargeability data are interpreted to determine alteration zones and subsurface mineralization that are correlated with magnetic data that has been inversed in 3D to obtain susceptibility parameters. Based on the value of resistivity in the study area, the argillic alteration zones are thought to has a value of < 50 Ohm.m. Silicification alteration zones supposedly have the value > 50 Ohm.m. The value of the chargeability at the mineralized zone indicated has a value of 100-810 msec. Also, study in the area of the mineralized zone have a susceptibility value in the range of -0.073 to -0.021 cgs. From that parameter of the mineralized zone, it is assumed that the mineralization zone is in the eastern part of the study area and oriented North-South direction with the orientation of the main fault.

Spectral analysis of time domain induced polarization waveforms

Spectral induced polarization is a well-established geophysical method that can be measured in both time- and frequency domain. Time- and frequency domain methods are theoretically equivalent via the Fourier transform. However, data acquisition parameters such as the sampling frequency and pulse length affect the actual spectral content of real time domain waveforms. Comparative studies showing the practical equivalence of time- and frequency domain measurements are few and have been carried out using various approaches. Some of these studies concluded that it is not possible to resolve wide band spectral information with time domain waveforms. The few studies available presenting actual spectral analysis of time domain signals were carried out several decades ago on field data with limited data acquisition- and/or analysis methods. There is, therefore, a need to re-evaluate the possible spectral range of time domain induced polarization data considering modern instrument development during the last decades. In addition, there is a need to verify the physical importance of factors such as spectral energy distribution and measurement noise, which previously have been assumed to inherently limit the spectral content of time domain waveforms. In this study, the discrete Fourier transform is applied to analyze the spectral content of synthetic time domain waveforms with different data acquisition parameters and noise. The results are presented as the frequency domain parameters amplitude and phase and compared to the corresponding frequency domain Debye model. To study the effect of noise, a spectral analysis of synthetic full waveform data contaminated with Gaussian noise is also provided, where the noise level were guided by an example of measured field data. Finally, laboratory TD IP measurements were performed on a test circuit, transformed to the frequency domain and compared to the theoretical response of the circuit in terms of the FD IP parameters amplitude and phase. The results illustrate that spectral information contained in the time domain waveform is, as expected, limited by the fundamental frequency of the square wave at low frequencies and the sampling frequency at high frequencies. Variation of the relaxation time shows that the ability to correctly resolve high frequency relaxations is also dependent on the sampling frequency. Furthermore, the result show that high frequency noise might distort the frequency domain representation. However, this noise can be filtered away prior to the spectral analysis, which significantly improves the resolution in frequency domain. The results of this study is in contradiction to earlier studies stating that the spectral range of time domain waveforms is inherently narrow due to the domination of the fundamental frequency over the higher harmonics. The main reason is that the relative spectral energy of different harmonics is of no importance in time domain measurements; in contrast to frequency domain measurements, the total polarization of all length scales are excited simultaneously. The results and discussion in this study are expected to contribute to an improved comprehension of the spectral range that can be achieved with time domain induced polarization data collected with modern instruments.

Cross-borehole tomography with full-decay spectral time-domain induced polarization for mapping of potential contaminant flow-paths

Soil contamination from industrial activities is a large problem in urban areas worldwide. Understanding the spreading of contamination to underlying aquifers is crucial to make adequate risk assessments and for designing remediation actions. A large part of the northern hemisphere has quaternary deposits consisting of glacial clayey till. The till often has a complex hydrogeological structure consisting of networks of fractures, sand stringers and sand lenses that each contribute to a transport network for water, free phase and dissolved contaminants. Thus, to determine the possible flow-paths of contaminants, the geology must be described in great detail. Normally, multiple boreholes would be drilled in order to describe the geology, but boreholes alone do not provide the needed resolution to map such sand lenses and their connectivity.Cross-borehole full-decay time-domain induced polarization (TDIP) is a new tool that allows for quantitatively mapping not only contrasts in bulk resistivity, but also contrasts in spectral IP parameters. We present a feasibility study with synthetic tests and a field application on a clayey moraine environment with embedded sand lenses, with hitherto unseen ground-truth verification. Indeed, the investigated area was above the water table, which allowed for digging out the entire area after the investigation for an unprecedented description of the lens interconnectivity.The TDIP data were acquired with a full-waveform acquisition at high sampling rate, signal-processed by harmonic denoising, background removal, and de-spiking, and subsequently the full-waveform data were stacked in log-increasing tapered gates (with 7 gates per decade). The resulting TDIP decays, with usable time-gates as early as two milliseconds, were inverted in terms of a re-parameterization of the Cole-Cole model. The inverted models of the field data show a remarkable delineation of the sand lenses/layers at the site, with structure in both the resistivity and the IP parameters matching the results from the ground-truthing. The synthetic examples show that in models both below and above the groundwater table, sand-lenses with thicknesses comparable to the vertical electrode spacing can be well resolved. This suggests that full-decay cross-borehole TDIP is an ideal tool for high-resolution sand-lens imaging.

Relaxation time distribution approach of mineral discrimination from time domain-induced polarisation data

ABSTRACTSource characterisation in time-domain induced polarisation (IP) data is more cumbersome than in frequency-domain IP data. The time-domain IP phenomenon is a linear superposition of individual relaxations caused by various polarisable sources. We have adopted the relaxation time distribution (RTD) method, a technique used in dielectric and chemical physics for analysing the relaxation phenomenon to characterise these sources. In this paper, the RTD method is first tested on synthetic data to evaluate its ability to estimate Cole–Cole spectral parameters, and then the effect of using a number of repetition rates is tested. Results indicate that chargeability and relaxation time are underestimated if the measurement duration is less than or equal to the relaxation time. Spectral parameters estimated using the RTD method on the published time-domain IP data are in good agreement with those estimated from empirical methods. The RTD method has also been tested on time-domain IP data measured on borehole core samples in the laboratory. These results inferred the possible polarisable sources and agree with petrological microscope observations. The RTD method was employed successfully to characterise the IP sources from ground time-domain IP data over four known mineralised bodies.

A multiple parameter extraction and electromagnetic coupling correction technique for time domain induced polarisation full waveform data

ABSTRACTThe time domain induced polarisation (TDIP) method is characterised by a simple explanation and operational approach, and is used widely to prospect for various metal sulfide deposits. However, the existing TDIP method is parameterised with fewer interpretive output attributes than the frequency domain induced polarisation (FDIP) method, and there has been far less emphasis on electromagnetic (EM) coupling in the TDIP method. A new and simple method has been devised for the extraction of multiple parameters and the suppression of EM-coupling responses in full waveform TDIP data. The essence of this approach is the transformation of the TDIP rectangular wave into the fundamental wave and a series of odd harmonics using a discrete Fourier transform. By using real and imaginary components of the fundamental and odd harmonics, we can calculate various FDIP parameters, including the per cent frequency effect (PFE) and the phase. The decoupled PFE coefficients and decoupled phases can then be used to remove the EM-coupling response. This new method has been applied to a data set collected near a poly-metallic mine in the Tibetan Plateau. A comparison between the TDIP and FDIP methods with central gradient arrays was performed. The results indicate that the TDIP apparent chargeability results are affected greatly by the EM-coupling response. The PFEs and phases extracted from the TDIP full waveform data are in good agreement with those obtained from the FDIP data. Moreover, the locations of the high decoupled PFE coefficients and the large negative decoupled phases are consistent with the locations of known ore veins. This method can provide additional parameters for the subsurface media, improve the practical application effect and enrich the functionality of TDIP methods.

3D geostatistical inversion of induced polarization data and its application to coal seam fires

We applied the principal component geostatistical approach (PCGA) to the inversion of time-domain induced polarization data in terms of resistivity and chargeability distributions. The PCGA presents two major advantages over standard methods: (1) It avoids the storage of the usually large covariance matrix, which contains the geostatistical information, by factorizing it in a product of low-rank matrices. (2) It does not assemble the Jacobian matrix per se. We determine the robustness of this approach with three examples. We first reconstruct the electrical conductivity and chargeability fields of two synthetic models generated using the geostatistical software Stanford Geostatistical Modeling Software. The PCGA approach performs better than the Tikhonov-based regularization approach when the true fields are very heterogeneous and the amount of data is limited. The third example is devoted to a field study over the former Lewis coal mine in Colorado (USA). We perform a 3D localization of the burning front of this coal seam fire by applying our geostatistical inverse methodology to a time-domain induced-polarization data set. In this case, the horizontal components of the semivariogram are determined from a self-potential map and the correlation length scale for the vertical component is determined from the known thickness of the coal bed. The tomogram presents a high normalized chargeability associated with the burning front. We evaluate the high normalized chargeability of the burning front in terms of the physical mechanism associated with the cation exchange capacity of the coal and the effect of temperature. This demonstrates the potential of the geostatistical inversion and its suitability for inverting geophysical data, especially when the data density is sparse. In the case of coal seam fires, we determine the suitability of the induced polarization method to localize the burning front and the effect of temperature on the normalized chargeability.

Inversion of time-domain induced polarization data based on time-lapse concept

Induced polarization (IP) surveys, measuring overvoltage phenomena of the medium, are widely and increasingly performed not only for exploration of mineral resources but also for engineering applications. Among several IP survey methods such as time-domain, frequency-domain and spectral IP surveys, this study introduces a noble inversion method for time-domain IP data to recover the chargeability structure of target medium. The inversion method employs the concept of 4D inversion of time-lapse resistivity data sets, considering the fact that measured voltage in time-domain IP survey is distorted by IP effects to increase from the instantaneous voltage measured at the moment the source current injection starts. Even though the increase is saturated very fast, we can consider the saturated and instantaneous voltages as a time-lapse data set. The 4D inversion method is one of the most powerful method for inverting time-lapse resistivity data sets. Using the developed IP inversion algorithm, we invert not only synthetic but also field IP data to show the effectiveness of the proposed method by comparing the recovered chargeability models with those from linear inversion that was used for the inversion of the field data in a previous study. Numerical results confirm that the proposed inversion method generates reliable chargeability models even though the anomalous bodies have large IP effects.

Finding buried metallic pipes using a non-destructive approach based on 3D time-domain induced polarization data

The location of buried utility pipes is often unknown. We use the time-domain induced polarization method to non-intrusively localize metallic pipes. A new approach, based on injecting a primary electrical current between a pair of electrodes and measuring the time-lapse voltage response on a set of potential electrodes after shutting down this primary current is used. The secondary voltage is measured on all the electrodes with respect to a single electrode used as a reference for the electrical potential, in a way similar to a self-potential time lapse survey. This secondary voltage is due to the formation of a secondary current density in the ground associated with the polarization of the metallic pipes. An algorithm is designed to localize the metallic object using the secondary voltage distribution by performing a tomography of the secondary source current density associated with the polarization of the pipes. This algorithm is first benchmarked on a synthetic case. Then, two laboratory sandbox experiments are performed with buried metallic pipes located in a sandbox filled with some clean sand. In Experiment #1, we use a horizontal copper pipe while in Experiment #2 we use an inclined stainless steel pipe. The result shows that the method is effective in localizing these two pipes. At the opposite, electrical resistivity tomography is not effective in localizing the pipes because they may appear resistive at low frequencies. This is due to the polarization of the metallic pipes which blocks the charge carriers at its external boundaries.

Resolution of well-known resistivity equivalences by inclusion of time-domain induced polarization data

The principle of equivalence is known to cause nonuniqueness in interpretations of direct current (DC) resistivity data. Low- or high-resistivity equivalences arise when a thin geologic layer with a low/high resistivity is embedded in a relative high-/low-resistivity background formation causing strong resistivity-thickness correlations. The equivalences often make it impossible to resolve embedded layers. We found that the equivalence problem could be significantly reduced by combining the DC data with full-decay time-domain induced polarization (IP) measurements. We applied a 1D Markov chain Monte Carlo algorithm to invert synthetic DC data of models with low- and high-resistivity equivalences. By applying this inversion method, it is possible to study the space of equivalent models that have an acceptable fit to the observed data, and to make a full sensitivity analysis of the model parameters. Then, we include a contrast in chargeability into the model, modeled in terms of spectral Cole-Cole IP parameters, and invert the DC and IP data in combination. The results show that the addition of IP data largely resolves the DC equivalences. Furthermore, we present a field example in which DC and IP data were measured on a sand formation with an embedded clay layer known from a borehole drilling. Inversion results show that the DC data alone do not resolve the clay layer due to equivalence problems, but by adding the IP data to the inversion, the layer is resolved.

Induced polarization response of porous media with metallic particles — Part 3: A new approach to time-domain induced polarization tomography

The secondary voltage associated with time-domain induced polarization data of disseminated metallic particles (such as pyrite and magnetite) in a porous material can be treated as a transient self-potential problem. This self-potential field is associated with the generation of a secondary-source current density. This source current density is proportional to the gradient of the chemical potentials of the [Formula: see text]- and [Formula: see text]-charge carriers in the metallic particles or ionic charge carriers in the pore water including in the electrical double layer coating the surface of the metallic grains. This new way to treat the secondary voltages offers two advantages with respect to the classical approach. The first is a gain in terms of acquisition time. Indeed, the target can be illuminated with a few primary current sources, all the other electrodes being used simultaneously to record the secondary voltage distribution. The second advantage is with respect to the inversion of the obtained data. Indeed, the secondary (source) current is linearly related to the secondary voltage. Therefore, the inverse problem of inverting the secondary voltages is linear with respect to the source current density, and the inversion can be done in a single iteration. Several iterations are, however, required to compact the source current density distribution, still obtaining a tomogram much faster than inverting the apparent chargeability data using the classical Gauss-Newton approach. We have performed a sandbox experiment with pyrite grains locally mixed to sand at a specific location in the sandbox to demonstrate these new concepts. A method initially developed for self-potential tomography is applied to the inversion of the secondary voltages in terms of source current distribution. The final result compares favorably with the classical inversion of the time-domain induced polarization data in terms of chargeability, but it is much faster to perform.