Controlled-source Electromagnetic Data Research Articles

A marine controlled-source electromagnetic application using towed and seafloor-based receivers capable of mapping seafloor and embedded massive sulfides

Deep-sea massive sulfide deposits formed by hydrothermal fluid circulation are potential metal resources. They can exist not only as mound manifestations on the seafloor (seafloor massive sulfides) but also as embedded anomalies buried beneath the seafloor (embedded massive sulfides). The distribution of embedded massive sulfides is largely unknown, despite their expected high economic value. Recent drilling surveys have revealed a complex model suggesting embedded massive sulfides coexist beneath seafloor massive sulfides. In the coexisting case, geophysical methods are required to distinguish and map seafloor and embedded massive sulfides for accurate resource estimation. Marine controlled-source electromagnetic (CSEM) methods are useful for mapping massive sulfides because they exhibit higher electrical conductivity compared with the surrounding host rock. However, CSEM applications capable of distinguishing and mapping the massive sulfides are lacking. We use a towed electric dipole transmitter with two types of receivers: stationary ocean-bottom electric (OBE) and short-offset towed receivers. This combination uses differences in sensitivity: the towed receiver data are sensitive to seafloor massive sulfides, and the stationary OBE receiver data are sensitive to embedded massive sulfides. Our synthetic data example demonstrates that the combined inversion of towed and OBE data can recover resistivities and positions of the massive sulfides more accurately than existing inversion methods using individual applications. We perform the combined inversion of measured CSEM data obtained from the middle Okinawa Trough. The inversion models demonstrate that a combined inversion can map the location and shape of embedded massive sulfides identified during drilling more accurately than the inversion of individual data sets.

Characterizing Offshore Freshened Groundwater Salinity Patterns Using Trans‐Dimensional Bayesian Inversion of Controlled Source Electromagnetic Data: A Case Study From the Canterbury Bight, New Zealand

AbstractThe study of offshore freshened groundwater (OFG) is gaining importance due to population growth and environmental pressure on coastal water resources. Marine controlled source electromagnetic (CSEM) methods can effectively map the spatial extent of OFG systems using electrical resistivity as a proxy. Integrating these resistivity models with sub‐surface properties, such as host‐rock porosity, allows for estimates of pore‐water salinity. However, evaluating the uncertainty in pore‐water salinity using resistivity models obtained from deterministic inversion approaches presents challenges, as they provide only one best‐fit model, with no associated estimate of uncertainty. To address this limitation, we employ trans‐dimensional Markov‐Chain Monte‐Carlo inversion on marine time‐domain CSEM data, acquired in the Canterbury Bight, New Zealand. We integrate resistivity posterior probability distributions with borehole and seismic reflection data to estimate pore‐water salinity with corresponding uncertainty estimates. The results highlight a low‐salinity groundwater body in the center of the survey area, hosted by consecutive silty‐ and fine‐sand layers approximately 20–60 km from the coast. The posterior probability distribution of resistivity models indicates freshening of the OFG body toward the shoreline within a permeable, coarse‐sand layer 40–150 m beneath the seafloor, suggesting an active connection between the OFG body and the terrestrial groundwater system. The approach demonstrates how Bayesian inversion constrains the uncertainties in resistivity models and subsequently in pore‐water salinity estimates. Our findings highlight the potential of Bayesian inversion to enhance our understanding of OFG systems and provide uncertainty constraints for hydrogeological modeling, thereby contributing to sustainable water resource development.

Deep structural controls on the distribution of carbonate reservoirs and overburden heterogeneity in Central Luconia province, offshore Borneo revealed by 3D anisotropic inversion of regional controlled-source electromagnetic and magnetotelluric profile data

Understanding the deep structural controls on reservoir distribution and overburden heterogeneity is important for near-field exploration and green-field development of the Miocene to Holocene carbonate buildups in Central Luconia province in offshore northwest Borneo, Malaysia. Data from 59 stations along an approximately 180 km long controlled-source electromagnetic (CSEM) and magnetotelluric (MT) survey line with three segments recorded using different CSEM transmitter-towing directions were available. We applied three-dimensional (3D) anisotropic resistivity inversion with cross-gradient constraint and verified the resulting models using resistivity logs from nearby wells and the acoustic basement interpreted from seismic data. Our anisotropic resistivity models reveal a fragmented carbonate-rich zone atop a segmented basement comprising electrically resistive horsts coinciding with the northerly Mega-platform, Central and Southern Field Highs, separated by steep conductive zones coinciding with the West, East, and Southeast Troughs previously interpreted from seismic data. The structural highs correlate with the spatial distribution of the known carbonate buildups implying a genetic link. The carbonate bodies are overlain and underlain by persistent layers (C2 and C3) of low resistivity and high anisotropy, which we interpret as indicating compressional deformation or detachment zones. Overburden layer C2 (whose base coincides with the top of a key sedimentary package, Cycle V in seismic data) is thinnest over the East Trough and is discontinuous at the eastern margin of the West Trough, which are locations where drilled wells did not find hydrocarbons implying that the seal rocks are inefficient or breached at those localities. We used these observations to refine the existing seismic-based interpretation of carbonate play-types along our transect indicating how 3D joint CSEM-MT imaging can potentially contribute to derisking or optimizing future exploration and/or development work in this province.

Automated Seafloor Massive Sulfide Detection Through Integrated Image Segmentation and Geophysical Data Analysis: Revisiting the TAG Hydrothermal Field

AbstractAccessible seafloor minerals located near mid‐ocean ridges are noticed to mitigate the projected metal demands of the net‐zero energy transition, promoting growing interest in quantifying the global distributions of seafloor massive sulfides (SMS). Mineral potentials are commonly estimated using geophysical and geological data that lastly rely on additional confirmation studies using sparsely available, locally limited, seafloor imagery, grab samples, and coring data. This raises the challenge of linking in situ confirmation data to geophysical data acquired at disparate spatial scales to obtain quantitative mineral predictions. Although multivariate data sets for marine mineral research are incessantly acquired, robust, integrative data analysis requires cumbersome workflows and experienced interpreters. We introduce an automated two‐step machine learning approach that integrates the mound detection through image segmentation with geophysical data. SMS predictors are subsequently clustered into distinct classes to infer marine mineral potentials that help guide future exploration. The automated workflow employs a U‐Net convolutional neural network to identify mound structures in bathymetry data and distinguishes different mound classes through the classification of mound architectures and magnetic signatures. Finally, controlled source electromagnetic data are utilized together with in situ sampling data to reassess predictions of potential SMS volumes. Our study focuses on the Trans‐Atlantic Geotraverse area, which is among the most explored SMS areas worldwide and includes 15 known SMS sites. The automated workflow classifies 14 of the 15 known mounds as exploration targets of either high or medium priority. This reduces the exploration area to less than 7% of the original survey area from 49 to 3.1 km2.

Denoising land-based controlled-source electromagnetic data based on a same-site noise reference channel

SUMMARY The applications of land-based controlled-source electromagnetic (CSEM) exploration are severely limited by strong noise interferences, particularly in mining areas. In this study, we introduce a novel denoising method for CSEM data using a same-site noise reference channel (NRC). While recording data through the normal survey channel (NSC), an additional set of the NRC was added at the same site. The NRC had a different surveying azimuth compared to the NSC and contained minimal or no useful signals. However, the noise characteristics in both the NRC and NSC were considerably similar due to their simultaneous acquisition at the same site. By establishing a set of overdetermined equations for the NSC based on quantified spectrogram characteristics of the NRC, the noise can be effectively eliminated from the NSC using least-squares inversion, resulting in enhanced signal-to-noise ratio data. The effectiveness of the proposed CSEM data-denoising method was validated through its application on real data, and the proposed method is applicable to other types of artificial source data.

3D inversion of multifrequency controlled-source electromagnetic data and its application to geothermal exploration in the Tianzhen region of the northern Datong Basin, China

Owing to its sensitivity to hydrothermal alteration and geothermal fluids, the distribution of subsurface electrical conductivity provides critical information for characterizing geothermal systems. For geothermal exploration, the controlled-source electromagnetic (CSEM) technique provides a crucial tool to image the subsurface resistivity structures, especially in areas with strong cultural noise. Usually, land-based CSEM surveys are carried out with dozens of operating frequencies to enhance spatial resolution. However, the 3D inversion of multifrequency CSEM data using the commonly used direct solver is challenging with limited computational resources. In this study, we implement a practical inversion strategy for interpreting 3D multifrequency CSEM data. By combining a hybrid direct-iterative solver with the inexact Gauss-Newton optimization, 3D inversion of CSEM data involving multiple frequencies can be performed effectively on typical workstations. First, we test the effectiveness of the developed approach using synthetic multifrequency 3D CSEM data generated for a simplified geothermal model. The inversion strategy is then applied to the multifrequency CSEM field data collected for the geothermal exploration at Tianzhen region in Shanxi Province, China. The comparison between the inversion results using the sparse data set (six frequencies) and the dense data set (12 frequencies) highlights the necessity of using data from more frequencies in the inversion to improve the resolution. The resulting 3D resistivity model clearly delineates the clay alteration layers and shallow thermal reservoirs of the potential high-temperature geothermal system within the study area, while its deep heat source is not revealed owing to the limited investigation depth of the survey.

Exploring geothermal resources using electromagnetic methods in coastal areas of volcanic islands: Challenges of nearshore and land 3D controlled-source electromagnetic data

Three-dimensional geophysical exploration is essential in identifying favorable areas and derisking high-temperature geothermal projects. In particular, electrical resistivity imaging plays a key role in this exploration due to its sensitivity to the presence of alteration products, geothermal fluid circulation, and temperature. As 3D seismic exploration is rarely effective in volcanic environments, resistivity methods such as magnetotellurics (MTs) are often used to extract deep structural information. However, deep electromagnetic (EM) imaging in coastal areas of volcanic islands with MT can be challenging due to anthropogenic noise induced by urbanized areas concentrated around the coast. The use of active EM sources, such as airborne EM (AEM) and controlled-source EM (CSEM), as a complement to MT is a solution for overcoming anthropogenic noise. However, applying these methods in this context is still challenging due to the proximity to the sea/land interface, large variations in topography and nearshore bathymetry, and the heterogeneity of the near surface. Our approach outlines the challenges of acquiring, processing, and inverting nearshore and land 3D CSEM data in such complex environments. The CSEM data are part of a multimethod geothermal exploration on the island of Martinique in the French West Indies, and its 3D inversion result is compared with the previous regional 3D MT and AEM inversion results. In addition to MT, CSEM may highlight the location of a potential high-temperature paleo-reservoir at shallow depths under the urbanized area of Petite-Anse covered by a paleo-clay cap ranging from a few meters to several hundred meters in thickness. We conclude by proposing possible improvements for CSEM and multimethod methodologies to obtain more reliable exploratory models.

Time-Lapse 3D CSEM for Reservoir Monitoring Based on Rock Physics Simulation of the Wisting Oil Field Offshore Norway.

The marine controlled-source electromagnetic (CSEM) method has been used in different applications, such as oil and gas reservoir exploration, groundwater investigation, seawater intrusion studies and deep-sea mineral exploration. Recently, the utilization of the marine CSEM method has shifted from petroleum exploration to active monitoring due to increased environmental concerns related to hydrocarbon production. In this study, we utilize the various dynamic reservoir properties available through reservoir simulation of the Wisting field in the Norwegian part of the Barents Sea. In detail, we first developed geologically consistent rock physics models corresponding to reservoirs at different production phases, and then transformed them into resistivity models. The constructed resistivity models pertaining to different production phases can be used as input models for a finite difference time domain (FDTD) forward modeling workflow to simulate EM responses. This synthetic CSEM data can be studied and analyzed in the light of production-induced changes in the reservoir at different production phases. Our results demonstrate the ability of CSEM data to detect and capture production-induced changes in the fluid content of a producing hydrocarbon reservoir. The anomalous CSEM responses correlating to the reservoir resistivity change increase with the advance of the production phase, and a similar result is shown in anomalous transverse resistance (ATR) maps derived from the constructed resistivity models. Moreover, the responses at 30 Hz with a 3000 m offset resulted in the most pronounced anomalies at the Wisting reservoir. Hence, the method can effectively be used for production-monitoring purposes.

Marine Controlled-Source Electromagnetic Data Denoising Method Using Symplectic Geometry Mode Decomposition

The marine controlled-source electromagnetic (CSEM) method is an efficient tool for hydrocarbon exploration. The amplitudes of signals decay rapidly with the increasing offset, so signals are easily contaminated by various kinds of noise. A denoising method is critical to improve the data quality, but the diversity of noise makes denoising difficult. Specific frequency signals are transmitted for exploration requirements, and thus traditional filtering methods are not suitable. Symplectic geometry mode decomposition (SGMD), a new method to decompose signals, has an outstanding decomposition performance and noise robustness. Furthermore, it can reduce multiple types of noise by reconstructing the single components. In this study, we introduced SGMD to reduce the noise of marine CSEM data and improved the data quality significantly. The experiments show that SGMD is better than variational mode decomposition and the sym4 wavelet method.

Marine controlled source electromagnetic data denoising while weak signal preserving based on jointly sparse model and dictionary learning

The amplitude of the marine controlled source electromagnetic (CSEM) signal decays dramatically with the augment of offset. The signal is easily contaminated by surrounding noise when the transmitter-receiver offset is large. Denoising is crucial for marine CSEM data processing and interpretation. Nowadays, most denoising methods focus on suppressing the noise influences for a single component. The inherent relations among different electromagnetic (EM) components are neglected. Besides, some weak signals are removed mistakenly to get a better denoising effect. In this paper, a new denoising method is proposed based on a jointly sparse model and dictionary learning, which utilizes the correlations among multi-components of marine CSEM data. Synthetic experiments prove that it can not only effectively remove noise, but also successfully protect weak signals. Field data application further validates the effectiveness of the proposed method.

IncepTCN: A new deep temporal convolutional network combined with dictionary learning for strong cultural noise elimination of controlled-source electromagnetic data

When the controlled-source electromagnetic (CSEM) data are contaminated by intense cultural noise and the signal-to-noise ratio (S/N) is lower than 0 dB, the existing denoising methods can hardly achieve good results. To overcome the problem, a new strong-noise elimination method called inception-temporal convolutional network-shift-invariant sparse coding (IncepTCN-SISC) is developed based on deep learning and dictionary learning. First, a novel deep neural network model called IncepTCN is created based on the inception block and temporal convolutional network (TCN). Then, IncepTCN is used to recognize strong-noise segments in the observed signal, which are then discarded. Finally, a dictionary-learning method based on shift-invariant convolutional coding is used to denoise the remaining weak-noise segments. A series of simulated and field data experiments indicate that the new proposed IncepTCN network has obvious advantages in accuracy and efficiency compared with alternative methods. The average recognition accuracy of IncepTCN is 96.5%, which is 25.5%, 3.2%, 1.1%, and 2.0% higher than that of the fuzzy C-means clustering, convolutional neural network (CNN), residual network (ResNet), and the nonimproved TCN, respectively. In addition, the test results of unfamiliar data indicate that the generalization ability of IncepTCN is significantly better than the CNN, ResNet, and nonimproved TCN. This IncepTCN-SISC method can improve the S/N of CSEM data from −5.0 dB to 3.1 dB or from 5.0 dB to 31.9 dB and solve the denoising problem of noisy data below 0 dB to a certain extent. After IncepTCN-SISC processing, the initially distorted apparent resistivity curves become smooth, and the result is better than dictionary learning. This method is intelligent without any manual intervention and is suitable for batch processing of CSEM data.

Characterizing the porosity structure and gas hydrate distribution at the southern Hikurangi Margin, New Zealand from offshore electromagnetic data.

The dynamics of accretionary prisms and the processes that take place along subduction interfaces are controlled, in part, by the porosity and fluid overpressure of both the forearc wedge and the sediments transported to the system by the subducting plate. The Hikurangi Margin, located offshore the North Island of New Zealand, is a particularly relevant area to investigate the interplay between the consolidation state of incoming plate sediments, dewatering and fluid flow in the accretionary wedge and observed geodetic coupling and megathrust slip behaviour along the plate interface. In its short geographic extent, the margin hosts a diversity of properties that impact subduction processes and that transition from north to south. Its southernmost limit is characterized by frontal accretion, thick sediment subduction, the absence of seafloor roughness, strong interseismic coupling and deep slow slip events. Here we use seafloor magnetotelluric (MT) and controlled-source electromagnetic (CSEM) data collected along a profile through the southern Hikurangi Margin to image the electrical resistivity of the forearc and incoming plate. Resistive anomalies in the shallow forearc likely indicate the presence of gas hydrates, and we relate deeper forerarc resistors to thrust faulting imaged in colocated seismic reflection data. Because MT and CSEM data are highly sensitive to fluid phases in the pore spaces of seafloor sediments and oceanic crust, we convert resistivity to porosity to obtain a representation of fluid distribution along the profile. We show that porosity predicted by the resistivity data can be well fit by an exponential sediment compaction model. By removing this compaction trend from the porosity model, we are able to evaluate the second-order, lateral changes in porosity, an approach that can be applied to EM data sets from other sedimentary basins. Using this porosity anomaly model, we examine the consolidation state of the incoming plate and accretionary wedge sediments. A decrease in porosity observed in the sediments approaching the trench suggests that a protothrust zone is developing ∼25km seaward of the frontal thrust. Our data also imply that sediments deeper in the accretionary wedge are slightly underconsolidated, which may indicate incomplete drainage and elevated fluid overpressures of the deep wedge.

Analyzing the effect of dipping anisotropy on 3D marine controlled-source electromagnetic response based on a goal-oriented adaptive finite element method

This paper presents a goal-oriented adaptive edge-based finite element (FE) algorithm for modelling marine controlled-source electromagnetic (CSEM) responses in three-dimensional (3-D) dipping anisotropic conductive media. We adopt the secondary field formulation for quasi-static Maxwell's equations to avoid the source singularity. The algorithm is implemented on unstructured tetrahedral grids, which allow for the simulation of complex model geometries, including tilted electrical interfaces and bathymetry. We validate the adaptive finite element code against a one-dimensional (1-D) layered model with vertical anisotropy. Further, we compare the 3-D FE numerical solutions with those obtained by a 2-D FE code for a two-dimensional dipping anisotropic model. The modelling results from the new 3-D FE algorithm are in good agreement with both the analytical solutions derived from the 1-D code and the numerical results from the anisotropic 2-D FE algorithm. We then use the algorithm to simulate the marine CSEM responses over different anisotropic 3-D models, including an anisotropic reservoir, anisotropic surrounding sediments, and an anisotropic overburden, and performed a detailed analysis of the effect of VTI, HTI, and TTI anisotropy on 3-D marine CSEM response. The modelling results illustrate that marine CSEM responses are considerably influenced by various anisotropic parameters, but to different extents. The research on different types of anisotropy within different media can help us avoid the misinterpretation of marine CSEM data when processing and interpreting of the measured geophysical electromagnetic data.

Ensemble scenario‐based inversion: A new approach for estimating the uncertainty of resistivity models derived from 3D controlled source electromagnetic data

AbstractNon‐linear inversion of controlled source electromagnetic data is non‐unique. Inversion ambiguity and uncertainty grow with model complexity and limitations in sensitivity, for example when imaging deep targets. Uncertainties should be estimated. The best way to do that is by statistical inversion techniques. Because of the large number of parameters of 3D models, these methods are too costly for 3D applications. But neglecting or underestimating uncertainties often leads to erroneous interpretation and poor exploration decisions. The number of inversion parameters can be dramatically reduced while still being able to describe a complex 3D subsurface. To obtain that, I represent prior information by an input ensemble of geologically reasonable prior models. The model space is defined by the principal directions of this ensemble, where the inversion parameters are the coefficients of the different components. In this model space, inversion becomes much more efficient and can produce an updated ensemble of posterior models at the same computational cost as a controlled‐source electromagnetic inversion project using conventional non‐linear inversion techniques. I present examples where unique Gauss–Newton inversions lead to difficult interpretations and poor conclusions about hydrocarbon presence and saturation. The new approach provides a quantitative estimation of resistivity ambiguities and uncertainties leading to better interpretation and safer decisions. The examples use 2D synthetic controlled‐source electromagnetic data and 3D controlled‐source electromagnetic field data.

Controlled-source electromagnetic modeling using a high-order finite-difference time-domain method on a nonuniform grid

Simulation of 3D low-frequency electromagnetic (EM) fields propagating in the earth is computationally expensive. We have developed a fictitious wave-domain high-order finite-difference time-domain (FDTD) modeling method on nonuniform grids to compute frequency-domain 3D controlled-source EM data. The method overcomes the inconsistency issue widely present in the conventional second-order staggered-grid finite-difference scheme over a nonuniform grid, achieving high accuracy with an arbitrarily high-order scheme. The finite-difference coefficients adaptive to the node spacings can be accurately computed by inverting a Vandermonde matrix system using an efficient algorithm. A generic stability condition applicable to nonuniform grids is established, revealing the dependence of the time step and these finite-difference coefficients. A recursion scheme using fixed-point iterations is designed to determine the stretching factor to generate the optimal nonuniform grid. The grid stretching in our method reduces the number of grid points required in the discretization, making it more efficient than the standard high-order FDTD with a densely sampled uniform grid. Instead of stretching in vertical and horizontal directions, better accuracy of our method is observed when the grid is stretched along the depth without horizontal stretching. The efficiency and accuracy of our method are demonstrated by numerical examples.

Enhanced detectability using differential fields for marine controlled-source EM data in shallow waters

ABSTRACT In shallow waters, the interpretation of the frequency-domain marine controlled-source electromagnetic (CSEM) data is challenging due to the airwave. The airwave dominates and will lead to misinterpretation of the data set. A differential-field approach is presented to attenuate the airwave for the shallow-water marine CSEM data. The difference of the fields between two consecutive receivers or source points is calculated and weighted by the geometric spreading related factor. By using differential fields, the detectability given by the field ratio between the models with and without the target is enhanced, which indicates that the impact of airwaves is suppressed.

Three-dimensional inversion of semi-airborne electromagnetic data with a second-order finite-element forward solver

SUMMARYThe analysis of controlled-source electromagnetic (EM) data recorded with semi-airborne exploration systems requires advanced simulation and inversion tools that are capable of handling realistic survey geometries. Semi-airborne EM setups with elongated transmitters deployed in mountainous terrain prohibit the exploitation of secondary-field formulations in numerical approximations without producing hardly quantifiable errors. Building upon the open-source software custEM for forward modeling and pyGIMLi for geophysical inversion, we present an inverse modeling procedure based on highly accurate second-order finite-element forward solutions on irregular grids and fast-converging Gauss–Newton minimization. Using the total-field formulation of the electric field approach in combination with a direct solver enables calculating explicit sensitivities with comparatively cheap back-substitutions for thousands of ground and airborne receiver stations in multiple flight areas. Second-order basis functions show general superiority over first-order basis-functions regarding the accuracy and performance of the forward problem. Beyond that, synthetic and real data inversion studies related to semi-airborne geometries indicate that second-order basis functions help particularly to avoid high modeling errors for the weakest field components and artifacts in the vicinity of transmitters or at the surface. This leads generally to a better convergence and final inversion results of higher robustness and quality. The presented tools are freely available such as the underlying software.

Parallelized 3-D Inversion of Controlled-Source Electromagnetic Data Based on Spectral Element Method With Infinite Element Boundary

Parallelized 3-D Inversion of Controlled-Source Electromagnetic Data Based on Spectral Element Method With Infinite Element Boundary

Three-dimensional inversion of controlled-source electromagnetic data for surveying the Jiutianshan high-speed railway Tunnel, China

Controlled-source electromagnetic surveying was conducted to identify underground faults and potential water hazards that could affect the construction of the Jiutianshan High-speed Railway Tunnel in the Qinling Mountains, western China. The transmitter length was 978 m, and the receivers were positioned along five profiles that were located about 9.5 km from the transmitter. The observation parameter, the electric-field amplitude |Ex|, was inverted using a finite-memory quasi-Newton method in three-dimensional mode. The resultant model is divided into horizontal blocks and vertical layers. The vertical cross-section along the tunnel shows a pattern of alternating high- and low-resistivity layers. The electrical resistivity pattern is divided into two vertical layers, with a clear boundary between the high- and low-resistivity zones. Combined with the results from previous exploration, geological survey, and drilling data, the inversion results identify the strata and faults along the planned route, and four risk areas are delineated. Our results highlight the power of employing geophysical investigations for risk assessments along tunnels.

3-D complex resistivity imaging using controlled source electromagnetic data: a multistage procedure using a second order polynomial parametrization

SUMMARY In some Earth materials, significant induced polarization (IP) phenomena are occurring when an electric perturbation is applied. These mechanisms are described by a frequency-dependent complex resistivity (CR). The study of the CR spectral signature allows to access indirectly to several properties of interest of the subsurface linked to the interaction between the pore space and fluids. CR is usually studied using the electrical method with a direct current approximation, neglecting by the way electromagnetic (EM) induction that can occur in the data. However, EM induction increases with frequency and offset, resulting in limitations at high frequencies or for the investigation of deep target. We implemented a frequency-dependent CR in a 3-D finite-differences (FD) modelling and inversion code for frequency domain controlled-source electromagnetic (CSEM) data to take into consideration IP information contained in EM data or reciprocally. The CSEM methods are resistivity imaging techniques using multifrequency EM fields that fully take into account EM induction with large investigation depth. Following a preliminary sensitivity study, a multistage inversion framework was designed to constrain the multiparameter inverse problem. Furthermore, to manage the increasing number of parameters, a second-order polynomial parametrization is used to describe independently frequency variation of CR norm and phase. We demonstrate the method through 1-D and 3-D synthetic data inversions for a deep-target model. We show that we were able to recover the CR and its frequency variation from CSEM data in the IP/EM coupling domain for 1-D targets. The problem of deep polarizable 3-D targets is more challenging and the resolution of the recovered CR spectrum was impacted. Nevertheless, we retrieved from a model containing several polarizable anomalies some crucial information allowing the discrimination of the targets from the non-polarizable background and from different spectral CR signatures. Our inversion strategy allows thus accessing to IP parameters of the medium in an extended frequency domain by fully taking EM induction information into account.