Controlled-source Electromagnetic Research Articles

A Novel Method for Obtaining Optimal Apparent Resistivity Curve of Controlled-Source Electromagnetic

A Novel Method for Obtaining Optimal Apparent Resistivity Curve of Controlled-Source Electromagnetic

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.

3D Electromagnetic Forward Modeling in Anisotropic Medium Using the Secondary Field Formula

The electromagnetic (EM) method is an essential geophysical method to detect underground geological structures. With the increasing requirements for the reliability of data interpretation, we need an accurate anisotropic forward algorithm, which is applied several times in the inversion. At the same time, modeling the anisotropic response is an advantageous method for designing actual survey lines. In this paper, we utilize the quadratic field formulation in anisotropic media to derive the governing equations for electromagnetic fields. The governing equation is discretized using the finite difference method. Experimental results indicate that our algorithm is able to accurately model both the magnetotelluric (MT) and the controlled-source electromagnetic (CSEM) response.

An Efficient Parallel Iterative Solver for Controlled-source Electromagnetic 3-D Adaptive Forward Modeling in General Anisotropic Media

Controlled-source electromagnetic (CSEM) surveys are moving increasingly toward realistic and complicated scenarios where the survey region may contain undulated topography, complex geometries, and electrical anisotropy media. In this paper, the adaptive finite element numerical method is employed to discrete the total electric field equation, which can provide precise electromagnetic (EM) responses even with a coarse initial mesh. The unstructured tetrahedral grid is employed to effectively address arbitrary irregular geometry and mountain terrain. Then, the flexible generalized minimum residual solver (FGMRES), auxiliary-space Maxwell pre-conditioner, and grid division technique were used to solve the large-scale linear system of equations, which can stably solve ill-conditioned problems using fewer computing resources. Finally, we conducted a numerical experiment via our newly proposed forward modeling scheme on a synthetic model with multi-contrast electrical anisotropy. It validated that accurate EM fields could be obtained against the semi-analytic solutions, and this iterative solver has good robustness for various anisotropic media. As a result, we have developed a state-of-the-art 3D CSEM anisotropic forward modeling engine, which can quickly and accurately deal with large-scale and complex geo-models.

Monitoring casing corrosion of legacy wells using CSEM: Implications for large-scale energy and CO2 storage projects in shut-down hydrocarbon fields

Large-scale CO2 and energy storage is a mandatory part of the green shift to reduce CO2 emissions and limit consequences of climate change. Large-scale storage will require the use of shut-down depleted hydrocarbon fields to take advantage of well-characterized reservoirs and cap rocks. Thanks to extensive data from historical hydrocarbon production, the uncertainties related to storage capacity, injectivity, and containment are limited. However, legacy exploration and production infrastructure, and especially legacy wells, are the main threat for possible fluid leakage toward the surface. Such legacy wells are numerous and penetrate the full rock column. In this paper, we describe a workflow to screen and monitor legacy wells in the shut-down Frigg Field in the North Sea. By using numerical modeling of electromagnetic (EM) field propagation in one of the Frigg Field wells, we explore the complex interactions of fields, currents, and well structure in the presence of corrosion. The corrosion is implemented as a change in the electrical conductivity of the innermost steel casing at different depths along the structure. To enhance probing depth, we plug the dipole source (1 km long) into the casing at the seafloor and excite the casing as an antenna. We find that at moderate levels of corrosion, the current distribution is significantly modified with respect to the uncorroded case. This generates a signal that propagates and can be observed at the seafloor in the numerical results. Other elements of the well geometry (e.g., concentric overlapping cement casings) have their own effect on the signal. This leads the possibility of estimating the location of the corroded area within the well geometry. These results suggest that by relaxing some of the model's approximations and implementing realistic transmitters, it will be possible to evaluate and optimize controlled-source EM survey strategies for detecting and monitoring corrosion levels.

Application of Electromagnetic Methods for Reservoir Monitoring with Emphasis on Carbon Capture, Utilization, and Storage

The Controlled-Source ElectroMagnetic (CSEM) method provides crucial information about reservoir fluids and their spatial distribution. Carbon dioxide (CO2) storage, enhanced oil recovery (EOR), geothermal exploration, and lithium exploration are ideal applications for the CSEM method. The versatility of CSEM permits its customization to specific reservoir objectives by selecting the appropriate components of a multi-component system. To effectively tailor the CSEM approach, it is essential to determine whether the primary target reservoir is resistive or conductive. This task is relatively straightforward in CO2 monitoring, where the injected fluid is resistive. However, for scenarios involving brine-saturated (water-wet) or oil-wet (carbon capture, utilization, and storage—CCUS) reservoirs, consideration must also be given to conductive reservoir components. The optimization of data acquisition before the survey involves analyzing target parameters and the sensitivity of multi-component CSEM. This optimization process typically includes on-site noise measurements and 3D anisotropic modeling. Based on our experience, subsequent surveys tend to proceed smoothly, yielding robust measurements that align with scientific objectives. Other critical aspects to be considered are using magnetotelluric (MT) measurements to define the overall background resistivities and integrating real-time quality assurance during data acquisition with 3D modeling. This integration allows the fine tuning of acquisition parameters such as acquisition time and necessary repeats. As a result, data can be examined in real-time to assess subsurface information content while the acquisition is ongoing. Consequently, high-quality data sets are usually obtained for subsequent processing and initial interpretation with minimal user intervention. The implementation of sensitivity analysis during the inversion process plays a pivotal role in ensuring that the acquired data accurately respond to the target reservoirs’ expected depth range. To elucidate these concepts, we present an illustrative example from a CO2 storage site in North Dakota, USA, wherein the long-offset transient electromagnetic method (LOTEM), a variation of the CSEM method, and the MT method were utilized. This example showcases how surface measurements attain appropriately upscaled log-scale sensitivity. Furthermore, the sensitivity of the CSEM and MT methods was examined in other case histories, where the target reservoirs exhibited conductive properties, such as those encountered in enhanced oil recovery (EOR), geothermal, and lithium exploration applications. The same equipment specifications were utilized for CSEM and MT surveys across all case studies.

Marine CSEM synthetic study to assess the detection of CO2 escape and saturation changes within a submarine chimney connected to a CO2 storage site

SUMMARY Carbon capture and storage (CCS) within sealed geological formations is an essential strategy to reduce global greenhouse gas emissions, the primary goal of the 2015 United Nations Paris Agreement. Large-scale commercial development of geological CO2 storage requires high-resolution remote sensing methods to monitor CO2 migration during/after injection. A geological formation containing a CO2 phase in its pore space commonly exhibits higher electrical resistivity than brine-saturated (background) sediments. Here, we explore the added value of the marine controlled-source electromagnetic (CSEM) method as an additional and relevant geophysical tool to monitor moderate to significant changes in CO2 saturation within a fluid conduit breaking through the seal of a CCS injection reservoir, using a suite of synthetic studies. Our 2-D CSEM synthetic models simulate various geological scenarios incorporating the main structural features and stratigraphy of two North Sea sites, the Scanner Pockmark and the Sleipner CCS site. Our results show significant differentiation of leakage through the seal with CO2 saturation ($S_{{\rm CO}_2}$) ranging between 20 and 50 per cent, while our rock physics model predicts that detection below 20 per cent would be challenging for CSEM alone. However, we are able to detect with our 2-D inversion models the effects of saturation with 10 and 20 per cent CO2 within a chimney with 10 per cent porosity. We demonstrate that simultaneous inversion of Ey and Ez synthetic electric field data facilitates a sharper delineation of a CO2 saturated chimney structure within the seal, whereas Ez synthetic data present higher sensitivity than Ey to $S_{{\rm CO}_2}$ variation, demonstrating the importance of acquiring the full 3-D electric field. This study illustrates the value of incorporating CSEM into measurement, monitoring and verification strategies for optimal operation of marine CCS sites.

Seismic-electromagnetic projection attribute: Application in integrating seismic quantitative interpretation and 3D controlled-source electromagnetic-magnetotelluric broadband data inversion for robust ranking and sweet spotting of hydrocarbon prospects in offshore northwest Borneo

We introduce a new seismic-electromagnetic (EM) projection (SEMP) attribute to improve the characterization of pay sands by integrating seismic P impedance and S impedance with electrical resistivity measurements leveraging on the high sensitivity of resistivity to fluid saturation and the capability of seismic data to delineate thin beds. Porosity is predicted using an extended elastic impedance (EEI) approach to reduce the risk of encountering SEMP anomalies related to low porosity. First, we determine the seismic-EM integration concept using well logs from a well that encountered hydrocarbons in a deepwater fold belt in offshore Borneo. The SEMP logs supported by the EEI log successfully delineate the pay zones in the well. Second, we extend the approach in three dimensions and evaluate its effectiveness using large-size seismic controlled-source EM (CSEM) and magnetotelluric (MT) data from the same area. The seismic data are inverted to produce P- and S-impedance volumes. EEI volume calibrated to total porosity also is produced. We then perform 3D anisotropic cross-gradient joint CSEM and MT inversion of a large-size survey comprising 647 receivers with receiver spacing of 1.5–3 km at prospect to regional scale. The SEMP volumes computed using the seismic and CSEM-MT inversion results are then interpreted with the EEI volume to identify possible geobodies of pay sand. Resistive geobodies with low porosity are assigned higher risks because they are likely due to compaction rather than hydrocarbon presence, whereas geobodies with high porosity are assigned lower risks and identified as potential sweet spots. The geobodies need to be corroborated with independent geologic studies to mature them into exploration prospects and assess their associated risk and potential. Although a vertical transverse isotropy approximation is used in our CSEM-MT study, the SEMP concept also is applicable to cases considering tilted transverse isotropy or arbitrary anisotropy where available and operational for such large-size exploration data.

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.

A feasibility study on time-lapse controlled-source electromagnetic method for hydraulic fracturing monitoring of Well Eyangye-2HF in Yichang, Hubei Province, China

Abstract Hydraulic fracturing plays a crucial role in enhancing reserves and production of unconventional oil and gas resources. Injecting fracturing fluids into the ground to improve reservoirs also introduces the risk of inducing earthquakes; thus, monitoring the migration of these fluids is crucial. The microseismic positioning method determines the fracturing fluid by locating microseismic events generated by the fractured rock strata; however, this method is susceptible to errors. Low-resistivity subsurface fluids can directly change electromagnetic field signals, making the electromagnetic method a technically advantageous approach for monitoring the migration of hydraulic fracturing fluids. The monitoring test data of Well Eyangye-2HF show that the time-lapse controlled-source electromagnetic (CSEM) method is suitable for hydraulic fracturing and has good monitoring effects. The results of CSEM method can also compensate for deficiencies in microseismic monitoring. The electric field (Ex) observed using the CSEM method can directly predict the distribution edge of the fracturing fluid, and the anomalous zone of the Exrate of change is consistent with the fracturing-fluid injection parameters and microseismic monitoring results. The analysis of field data and forward simulation, based on electrical logging results, led to the conclusion that hydraulic fracturing operations can cause changes in the resistivity of the target layer and surrounding strata. These changes are attributed to the synergistic effects of formation stress, temperature, and the fracturing fluid. The electric-field changes observed using the CSEM method may be caused by multiple factors; however, use of time-lapse CSEM for monitoring hydraulic fracturing is still feasible.

SWAN: A surface-towed modular controlled-source electromagnetic system for mapping submarine groundwater discharge and offshore groundwater resources

Offshore freshened groundwater (OFG) and submarine groundwater discharge (SGD) are important components of coastal hydrologic systems. A lack of understanding of offshore groundwater systems and their interactions with onshore systems along the majority of global coastlines still exists due to a general paucity of field data. Recently, controlled-source electromagnetic (CSEM) techniques have emerged as a promising noninvasive method for identifying and characterizing OFG and SGD. Unfortunately, only a few systems are available in academic and research institutions worldwide, and applications are limited to specific regions. These systems are often limited by relatively high deployment costs, slow data acquisition rates, logistical complexity, and lack of modification options. A relatively inexpensive and user-friendly CSEM system is needed to overcome these limitations. We present the initial theoretical and practical developments of SWAN — a low-cost, modular, surface-towed hybrid time-frequency domain CSEM system capable of detecting OFG and SGD to water depths of 100 m. A field test of the system was carried out in the central Adriatic Sea at water depths between several tens to approximately 160 m to illustrate its capabilities. Through its ability to facilitate continuous measurements in both the time and frequency domain, the system has demonstrated its effectiveness in acquiring high-quality data while operating at towing speeds ranging from 2.5 to 3 kn. The resulting data coverage enables the system to detect variations in subsurface resistivity to depths of approximately 150–200 m below seafloor. With its modular, user-friendly design, SWAN provides an accessible, cost-efficient means to investigate the hydrogeology of shallow offshore environments.

3D fictitious wave domain CSEM inversion by adjoint source estimation

Marine controlled-source electromagnetic (CSEM) method has proved its potential in detecting highly resistive hydrocarbon bearing formations. To image the subsurface resistivity, this paper presents the first application of a frequency domain CSEM inversion approach using fictitious wave domain timestepping modelling. Each inversion iteration requires the gradient of the misfit with respect to resistivity computed by the product between the forward and adjoint fields. Simulation of the adjoint field using fictitious time modelling is challenging as it requires the time domain adjoint source functions while only a few discrete frequencies of the data residual are available for the inversion. To overcome this issue, we formulate a regularized linear inverse problem to estimate a long time series from very few frequency samples. It can then be solved using linear optimization technique in matrix-free manner. Instead of computing adjoint source time function one by one at each receiver location, a basis function implementation has been designed, such that the linear inversion can be solved only once and then reused every time to construct all time-domain adjoint sources. The method allows computing all frequencies of the EM fields in one go without heavy memory and computational overhead. Numerical examples are employed to demonstrate the application of our method.

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.

Twenty Years of CSEM Exploration in the Brazilian Continental Margin

The controlled source electromagnetic (CSEM) method is frequently used as a risk reduction tool in hydrocarbon exploration. This paper aims to provide a comprehensive historical review of the CSEM method’s twenty-year history in the Brazilian continental margin. Since 2003, we have significantly improved our understanding of CSEM resistivity data across various geological scenarios. This review presents a roadmap of the technical advancements in acquisition design and interpretation techniques. As a result, our understanding of the methodology has broadened from traditional to more general use, such as salt imaging, gas hydrates, geohazard mapping, and reservoir characterization. Finally, we indicate the potential upcoming CSEM applications in new energy resources and carbon capture and storage.

EMFEM: A parallel 3D modeling code for frequency-domain electromagnetic method using goal-oriented adaptive finite element method

We have developed an open-source parallel 3D forward modeling code called EMFEM for the frequency-domain electromagnetic method (FDEM). This code is based on the adaptive finite element method and uses the total-field approach to solve the time-harmonic Maxwell equations. The use of tetrahedral meshes enables modeling of problems with complex geometry and topography. We utilize an iterative solver and an optimal block-diagonal preconditioner and auxiliary Maxwell solver to accelerate the convergence rate. We also use goal-oriented error estimation to adaptively refine the mesh and improve the accuracy of the solution. In addition, we develop a parallel algorithm based on the mesh partitioning approach. The effectiveness of the adaptive mesh refinement is demonstrated through a 1D layered model for marine controlled-source electromagnetic (CSEM) problems. We further present two 3D models for CSEM and magnetotelluric (MT) problems to study the scalability of the parallel algorithm, and the accuracy of the solutions is cross-validated with other open-source codes. The code is implemented in C++ and follows the modular design principle, making it easy to extend and maintain. The code is capable of modeling large-scale 3D problems and utilizing high-performance computing platforms. Furthermore, as the code is freely available, it can serve as a cross-validation tool for other 3D modeling codes and can also be utilized to implement 3D inversion routines.

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.