Fast, scalable and economical simulation of three-dimensional time-domain electromagnetic data using survey decomposition and massive parallelization

We have developed a scheme for decoupling the induced polarization (IP) effect from time-domain electromagnetic (TDEM) data. This scheme is achieved by simultaneously sampling the resistivity and pseudochargeability in a Bayesian framework. The TDEM and IP responses are simulated separately with the sampled model parameters and then are stacked to fit the IP-affected TDEM data. Thus, the influence of the IP phenomenon is eliminated in the process of recovering the resistivity. To reduce the computational cost brought by the Bayesian sampling, we use a 2D parametrization instead of sampling the full 3D space and we use a linear perturbation approximation for calculating the IP response. The linearized inversion results are used as the initial model, and a multiple proposed points algorithm is used to accelerate the sampling. We validate the proposed method with synthetic and field examples showing that it restores accurate estimates of electrical structures from the TDEM data that are significantly affected by the IP phenomenon. Our method could advance the application of the TDEM method to the scenario in which the IP may affect the TDEM data and mask the underlying geologic targets.

Errors of 1-D interpretation of 3-D TDEM data in the application of mapping saltwater/freshwater contact

Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks

Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques.

A denoising method based on wavelet analysis is presented for the removal of noise (background noise and random spike) from time domain electromagnetic (TEM) data. This method includes two signal processing technologies: wavelet threshold method and stationary wavelet transform. First, wavelet threshold method is used for the removal of background noise from TEM data. Then, the data are divided into a series of details and approximations by using stationary wavelet transform. The random spike in details is identified by zero reference data and adaptive energy detector. Next, the corresponding details are processed to suppress the random spike. The denoised TEM data are reconstructed via inverse stationary wavelet transform using the processed details at each level and the approximations at the highest level. The proposed method has been verified using a synthetic TEM data, the signal‐to‐noise ratio of synthetic TEM data is increased from 10.97 dB to 24.37 dB at last. This method is also applied to the noise suppression of the field data which were collected at Hengsha island, China. The section image results shown that the noise is suppressed effectively and the resolution of the deep anomaly is obviously improved.

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2015Extracting subtle IP responses from airborne time domain electromagnetic dataAuthors: Tianyou Chen*Greg HodgesAdam SmiarowskiTianyou Chen*CGG, Greg HodgesCGG, and Adam SmiarowskiCGGhttps://doi.org/10.1190/segam2015-5828596.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Induced polarization (IP) effects observed in airborne time domain electromagnetic (TEM) survey data offer information on the chargeability of the subsurface in addition to conductivity derived from TEM data. However the IP effect is generally weak and obscured in the total TEM response. As a result, the typical inverse transient associated with IP effect does not always manifest itself in the EM response. This causes difficulty for algorithms that rely on the inverse transient to estimate the chargeability of the subsurface. We have developed a robust method that decomposes the total electromagnetic response into a fundamental (inductive) and a polarization component and we estimate apparent chargeability from the polarization component. In this paper, we discuss the method and illustrate its effectiveness with examples. Keywords: airborne survey, induced polarization, time-domain, electromagnetic, decompositionPermalink: https://doi.org/10.1190/segam2015-5828596.1FiguresReferencesRelatedDetailsCited byCase Studies28 September 2021Robust scanning of AEM data for IP effects20 January 2021 | Exploration Geophysics, Vol. 52, No. 5Inversion of IP-Affected TEM Responses and Its Application in High Polarization Area4 March 2021 | Journal of Earth Science, Vol. 32, No. 1Inversion of time-domain airborne EM data with IP effect based on Pearson correlation constraints4 May 2021 | Applied Geophysics, Vol. 17, No. 4Modeling induced polarization effects in helicopter time-domain electromagnetic data: Field case studiesVladislav Kaminski and Andrea Viezzoli6 February 2017 | GEOPHYSICS, Vol. 82, No. 2 SEG Technical Program Expanded Abstracts 2015ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2015 Pages: 5634 publication data© 2015 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 19 Aug 2015 CITATION INFORMATION Tianyou Chen*, Greg Hodges, and Adam Smiarowski, (2015), "Extracting subtle IP responses from airborne time domain electromagnetic data," SEG Technical Program Expanded Abstracts : 2061-2066. https://doi.org/10.1190/segam2015-5828596.1 Plain-Language Summary Keywordsairborne surveyinduced polarizationtime-domainelectromagneticdecompositionPDF DownloadLoading ...

There have been multiple occurrences in the literature in the past several years of what has been referred to as the induced polarisation (IP) effect in airborne time domain electromagnetic (TDEM) data. This phenomenon is known to be responsible for incorrect inversion modelling of electrical resistivity, lower interpreted depth of investigation (DOI) and lost information about chargeability of the subsurface and other valuable parameters. Historically, there have been many suggestions to account for the IP effect using the Cole-Cole model. It has been previously demonstrated that the Cole-Cole model can be effective in modelling synthetic TDEM transients. In the current paper we show the possibility of extracting IP information from airborne TDEM data using this same concept, including inverse modelling of chargeability from TDEM data collected by VTEM, with field examples from Canada (Mt Milligan deposit) and Russia (Amakinskaya kimberlite pipe).

ABSTRACTWithin the south‐western Mojave Desert, the Joshua Basin Water District is considering applying imported water into infiltration ponds in the Joshua Tree groundwater sub‐basin in an attempt to artificially recharge the underlying aquifer. Scarce subsurface hydrogeological data are available near the proposed recharge site; therefore, time‐domain electromagnetic (TDEM) data were collected and analysed to characterize the subsurface. TDEM soundings were acquired to estimate the depth to water on either side of the Pinto Mountain Fault, a major east‐west trending strike‐slip fault that transects the proposed recharge site. While TDEM is a standard technique for groundwater investigations, special care must be taken when acquiring and interpreting TDEM data in a two‐dimensional (2D) faulted environment. A subset of the TDEM data consistent with a layered‐earth interpretation was identified through a combination of three‐dimensional (3D) forward modelling and diffusion time‐distance estimates. Inverse modelling indicates an offset in water table elevation of nearly 40 m across the fault. These findings imply that the fault acts as a low‐permeability barrier to groundwater flow in the vicinity of the proposed recharge site. Existing production wells on the south side of the fault, together with a thick unsaturated zone and permeable near‐surface deposits, suggest the southern half of the study area is suitable for artificial recharge. These results illustrate the effectiveness of targeted TDEM in support of hydrological studies in a heavily faulted desert environment where data are scarce and the cost of obtaining these data by conventional drilling techniques is prohibitive.

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

The technique of joint inversion for resistivity and time domain electromagnetic data have been different applications such as groundwater, environments, mineral deposits and archeology. Joint inversion of resistivity/TEM data is an effective approach to characterize groundwater reservoirs. Thirty six stations were measured for DC resistivity (VES) of AB/2 ranging from 500 to 1000 m and 36 TEM stations were measured also beside VES stations of loop sides of 50 x50 m to apply the joint inversion technique. One station was measured beside borehole drilled in study area of depth 350 m for correlation and calibration between the results of joint inversion and data of borehole. The results of joint inversion indicate that the area contain two aquifers, the upper aquifer represents fresh water having moderate resistivity values (7-35 ohm.m) and thickness of 11-101 m at depth ranging from 15-70 m. The second aquifer represents the brackish water having low resistivity values (1-7 ohm.m) and thickness of 23-103 m at depth ranging from 25-130 m.

We have developed an innovative, simultaneous 1D optimization of electromagnetic (EM) data. Our scheme is suitable for the simultaneous analysis of magnetotelluric (MT) and time-domain EM (TDEM) data based on the probabilistic and evolutionary particle swarm optimization (PSO) algorithm. The simultaneous optimization also identifies and removes the static shift from the MT data. In our scheme, the static shift of the MT apparent resistivity curve is considered as an additional parameter [Formula: see text] to be optimized. We tested the suggested method on the synthetic data and then applied it to the data from an EM geophysical study carried out in the geothermal area of Larderello-Travale (Tuscany, Italy). Apart from the novelty of using the PSO algorithm to estimate the model parameters by joint analysis, the simultaneous optimization of the static shift parameter addresses a major problem in MT, i.e., how to define and remove the galvanic effects on MT curves according to independent information, such as that provided by TDEM data. The procedure is expected to strongly influence the application of MT, particularly in geothermal exploration, which commonly relies extensively on EM methods.

Wide applications of time-domain electromagnetic (TEM) data require 3D inversion. A possible strategy is to use the developed 3D inversion algorithms in frequency-domain (FD) electromagnetic (EM) methods. Thus, the key of the strategy is how to transform the time-domain ([Formula: see text]-[Formula: see text]) EM signal into the FD. An inversion algorithm has been developed to transform the [Formula: see text]-[Formula: see text] signal into a corresponding FD response. In this method, a step-off current is presumed. Under this assumption, the Fourier transform relating the EM FD response to the [Formula: see text]-[Formula: see text] signal becomes a sine or cosine transformation. Using the polynomial approximation method, the transformation turns into a linear equation. From a set of [Formula: see text]-[Formula: see text] signals, FD responses could be obtained by solving these linear equations in the least-squares sense. To reduce the nonuniqueness of the solution, and enhance the solution stability, an additional smoothness constraint on the FD response is imposed, thus converting the minimization problem into a regularization inversion problem. The algorithm is applied to synthetic and field vertical magnetic data in the in-loop TEM surveying mode. The numerical results show that in the entire audio-frequency range, the relative errors between the inversed and theoretical FD responses of the real and imaginary parts are almost all less than 1%, with the largest discrepancy of 5% occurring at high frequencies. There are two significances behind our work: First, the possibility of accurately transforming [Formula: see text]-[Formula: see text] response into FD response in audio-frequency range is coming into true, thereby (from the mathematical perspective) implementing the equivalence between the responses of the EM method in the time domain and the FD. Second, the algorithm provides a new approach to interpret TEM data in 3D mode by using developed 3D FEM inversion techniques.

Abstract. Increasingly, ground-based and airborne geophysical data sets are used to inform groundwater models. Recent research focuses on establishing coupling relationships between geophysical and groundwater parameters. To fully exploit such information, this paper presents and compares different hydrogeophysical inversion approaches to inform a field-scale groundwater model with time domain electromagnetic (TDEM) and electrical resistivity tomography (ERT) data. In a sequential hydrogeophysical inversion (SHI) a groundwater model is calibrated with geophysical data by coupling groundwater model parameters with the inverted geophysical models. We subsequently compare the SHI with a joint hydrogeophysical inversion (JHI). In the JHI, a geophysical model is simultaneously inverted with a groundwater model by coupling the groundwater and geophysical parameters to explicitly account for an established petrophysical relationship and its accuracy. Simulations for a synthetic groundwater model and TDEM data showed improved estimates for groundwater model parameters that were coupled to relatively well-resolved geophysical parameters when employing a high-quality petrophysical relationship. Compared to a SHI these improvements were insignificant and geophysical parameter estimates became slightly worse. When employing a low-quality petrophysical relationship, groundwater model parameters improved less for both the SHI and JHI, where the SHI performed relatively better. When comparing a SHI and JHI for a real-world groundwater model and ERT data, differences in parameter estimates were small. For both cases investigated in this paper, the SHI seems favorable, taking into account parameter error, data fit and the complexity of implementing a JHI in combination with its larger computational burden.

We focus on the performances of three nature-inspired metaheuristic methods for the optimization of time-domain electromagnetic (TDEM) data: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) algorithms. While GA and PSO have been used in a plethora of geophysical applications, GWO has received little attention in the literature so far, despite promising outcomes. This study directly and quantitatively compares GA, PSO and GWO applied to TDEM data. To date, these three algorithms have only been compared in pairs. The methods were first applied to a synthetic example of noise-corrupted data and then to two field surveys carried out in Italy. Real data from the first survey refer to a TDEM sounding acquired for groundwater prospection over a known stratigraphy. The data set from the second survey deals with the characterization of a geothermal reservoir. The resulting resistivity models are quantitatively compared to provide a thorough overview of the performances of the algorithms. The comparative analysis reveals that PSO and GWO perform better than GA. GA yields the highest data misfit and an ineffective minimization of the objective function. PSO and GWO provide similar outcomes in terms of both resistivity distribution and data misfits, thus providing compelling evidence that both the emerging GWO and the established PSO are highly valid tools for stochastic inverse modeling in geophysics.

We have used the radio imaging method (RIM) to delineate attenuating zones in two borehole sections in the area of the Pyhäsalmi volcanogenic massive sulfide (VMS) copper-zinc deposit located in central Finland. The frequency band (312.5–2500 kHz) is higher and thus provides better resolution and sensitivity to conductive targets than traditional ground-level and borehole electromagnetic (EM) methods. When EM waves are assumed to be propagated along straight rays, the simultaneous iterative reconstruction technique can be used and the decayed amplitudes of the electric field are converted to the attenuation coefficient in dB/m. The straight-ray assumption was, however, not met in this study. The reconstruction results of two borehole sections were compared with time-domain EM (TEM) data and electric logging data. Electric logging reveals the nearby conducive mineralizations, and when compared with RIM data, the continuation of attenuating formations can be better predicted. The intersections interpreted from the TEM data were consistent with the RIM data. However, continuation of the attenuating domains could only be established from RIM data. Low ray densities at the upper and lower edges, violation of the straight-ray assumption, and out-of-plane targets may generate artifacts. In addition, the constructions suffer from smearing in the direction of the raypath. According to the results, we can recover the shape and orientation of attenuating targets in the borehole sections, but the physical properties are underestimated due to the straight-ray assumption. The comparison studies confirmed that RIM is well-suited to estimating subsurface conductivity properties and to predicting the continuation of attenuating domains between the boreholes at the Pyhäsalmi VMS deposit.