Grounded Electrical Source Airborne Transient Electromagnetic Research Articles

Research on De-Noising Method of Grounded Electrical Source Airborne Transient Electromagnetic Data Based on Singular Spectrum Analysis

The grounded electrical source airborne transient electromagnetic (GREATEM) system is widely used in groundwater resources detection, geothermal resource detection, geological structure detection, and other fields due to its wide detection range, high detection efficiency, and high resolution. The field data received by the GREATEM system is easily affected by various noises, such as instrument system noise, power frequency noise, sferics noise, and other noise, which reduce the data signal-to-noise ratio (SNR) and affects the data interpretation accuracy. This paper proposes a singular spectrum analysis (SSA) for the GREATEM data de-noising in response to this problem. First, we calculate the electromagnetic response of a uniform half-space using a GREATEM system with an electrical source to verify the effectiveness of the SSA algorithm for GREATEM data de-noising. To determine the appropriate parameters for SSA, we propose a particle swarm optimization algorithm to choose the window length. Later, SSA is used to decompose a synthetic quasi-two-dimensional earth model of GREATEM data. After SSA, the SNR of the reconstructed signal increased by 36 dB, and the RMSE does not exceed 4.9 × 10−6, which verifies the feasibility of the SSA for de-noising GREATEM data. Finally, through field measurement data processing, the effectiveness of the method is further confirmed.

1-D Inversion of GREATEM Data by Supervised Descent Learning

In this letter, the application of the supervised descent method (SDM) for solving controlled-source electromagnetic inversion is studied. The descent direction in each iteration step of the 1-D full-wave inversion (FWI) is learned from the training data set with certain prior information in the off-line training and then saved. In the online prediction, it is directly combined with the measured data and the forward model to implement the FWI. Compared with the traditional iterative method, the efficiency is significantly enhanced since the computation of the Jacobian matrix is circumvented. Both the synthesized and field-measured grounded electrical-source airborne transient electromagnetic (GREATEM) data are used to verify the feasibility and efficiency of SDM. In addition, the learning ability of the SDM is also studied.

The baseline wander correction based on the improved ensemble empirical mode decomposition (EEMD) algorithm for grounded electrical source airborne transient electromagnetic signals

Abstract. The grounded electrical source airborne transient electromagnetic (GREATEM) system is an important method for obtaining subsurface conductivity distribution as well as outstanding detection efficiency and easy flight control. However, there are the superposition of desired signals and various noises for the GREATEM signal. The baseline wander caused by the receiving coil motion always exists in the process of data acquisition and affects measurement results. The baseline wander is one of the main noise sources, which has its own characteristics such as being low frequency, large amplitude, non-periodic, and non-stationary and so on. Consequently, it is important to correct the GREATEM signal for an inversion explanation. In this paper, we propose improving the method of ensemble empirical mode decomposition (EEMD) by adaptive filtering (EEMD-AF) based on EEMD to suppress baseline wander. Firstly, the EEMD-AF method will decompose the electromagnetic signal into multi-stage intrinsic mode function (IMF) components. Subsequently, the adaptive filter will process higher-index IMF components containing the baseline wander. Lastly, the de-noised signal will be reconstructed. To examine the performance of our introduced method, we processed the simulated and field signal containing the baseline wander by different methods. Through the evaluation of the signal-to-noise ratio (SNR) and mean-square error (MSE), the result indicates that the signal using the EEMD-AF method can get a higher SNR and lower MSE. Comparing correctional data using the EEMD-AF and the wavelet-based method in the anomaly curve profile images of the response signal, it is proved that the EEMD-AF method is practical and effective for the suppression of the baseline wander in the GREATEM signal.

3D Numerical Modeling of Induced-Polarization Grounded Electrical-Source Airborne Transient Electromagnetic Response Based on the Fictitious Wave Field Methods

The grounded electrical-source airborne transient electromagnetic (GREATEM) system is widely used in mineral exploration. Meanwhile, the induced polarization (IP) effect, which indicates the polarizability of the earth, is often found. In this paper, the Maxwell equations in the frequency domain are transformed into fictitious wave domain, where Maxwell equations are solved by the time domain finite difference method. Then, an integral transformation method is used to convert the calculation results back to the time domain. A three-dimensional (3D) numerical simulation in a polarizable medium is presented. The accuracy of this method is proven by comparing it with the analytical solution and the existing method, and the calculation efficiency is increased five-fold. The simulation results show that the GREATEM system has a higher response amplitude in the conductive region, while IP effects cannot be identified in the conductive area. The GREATEM system has a higher response amplitude in the low-resistance region, but IP effects cannot be identified in the low-resistance area, and the detection of IP effects is more suitable for the high-resistance area. Therefore, it is necessary to improve the detection ability of the GREATEM system in the low-resistance area.

Application of Grounded Electrical Source Airborne Transient Electromagnetic (GREATEM) System in Goaf Water Detection

Since goaf water can cause water inrush, collapse or other severe geological hazards in coal mining, its detection has become a significant issue. As the grounded electrical source airborne transient electromagnetic (GREATEM) system can provide considerable prospecting depth, lateral resolution, and high detection efficiency, the method may be suitable for goaf water detection. To verify the feasibility, a field electromagnetic survey over Qinshui coal mine (Shanxi province, China) was conducted, and the system was used to detect the geoelectric characteristics of goaf water. After data acquisition, data processing, and apparent resistivity calculation, the resistivity profile of survey area is clearly presented, which is consistent with the information provided by Shanxi Coal Geology Geophysical Surveying Exploration Institute, China. The result shows that the application of GREATEM system is an effective technique for resistivity detection of goaf water, and can reduce the risks in coal mining.

Noise reduction of grounded electrical source airborne transient electromagnetic data using an exponential fitting-adaptive Kalman filter

The grounded electrical source airborne transient electromagnetic (GREATEM) system, which uses a grounded electrical transmitter and an aircraft for the receiver, offers deep exploration capability and detection efficiency. However, GREATEM field data usually includes mixed varied noises (white noise, sferics noise and human noise), which make identifying the exponential decaying signal too difficult. Traditional filtering methods mainly focus on suppressing specific noise types, which may cause the distortion of GREATEM signal, especially when the signal is affected by high residual sferics noise. This paper presents an exponential fitting-adaptive Kalman filter (EF-AKF) to remove mixed electromagnetic noises, while preserving the signal characteristics. The EF-AKF consists of an exponential fitting procedure and an adaptive scalar Kalman filter (SKF). The adaptive SKF uses the exponential fitting results in the weighting coefficients calculation. The EF-AKF is verified on an analytical three-layer model. It is compared with the SKF and wavelet threshold-exponential adaptive window width-fitting denoising algorithm (WEF) in synthetic data. The results showed that the EF-AKF outperformed the other methods in the noise reduction of GREATEM data. The EF-AKF is also tested on a synthetic quasi-2D earth model and applied to GREATEM field data in Huaide, Jilin province, China. Application of the EF-AKF allowed considerable improvement of the quality of the GREATEM field data.

A New Inversion Method Based on Distorted Born Iterative Method for Grounded Electrical Source Airborne Transient Electromagnetics

A new iterative inversion algorithm is proposed to reconstruct the electrical conductivity profile in a stratified underground medium for the grounded electrical source airborne transient electromagnetic (GREATEM) system. In forward modeling, we simplify the mathematical expressions of the magnetic fields generated by a finite line source in the layered ground to semianalytical forms in order to save the computation time. The Frechet derivative is derived for the electromagnetic response at the receivers due to a small perturbation of the conductivity in a certain layer underground. The initial expression of the Frechet derivative has an expensive triple integral and contains the Bessel function in the integrand. It is simplified by partially eliminating the integration along the source line and deriving the analytical expression for the integration in the vertical direction inside the perturbed layer. In the inverse solution, we use the distorted Born iterative method (DBIM). This is the first time that the DBIM is applied to data measured by the GREATEM system. Besides, the forward and inverse procedures are carried out in the frequency domain and based on the Frechet derivative of a line source. We demonstrate the validity of our forward model, Frechet derivative, inverse model, and the precision as well as robustness of the inversion algorithm through numerical computation and comparisons. Finally, we apply the inversion algorithm to the measured data and compare the retrieved conductivity to the actual drilling data.

지상 송신원 항공 전자탐사 자료의 횡적 제한 역산

최근 도입된 지상 송신원 항공 전자탐사 시스템(grounded electrical-source airborne transient electromagnetic, GREATEM)은 신호가 강력하여 가탐심도 향상은 물론 잡음이 심한 지역에서도 적용 가능하다. 비록 GREATEM은 지상에 설치된 긴 전선을 송신원으로 사용하는 시간영역 전자탐사법이지만, 방대한 항공탐사 자료의 2차원 혹은 3차원 해석은 계산시간이 너무 많이 소요되어 실질적인 적용이 어렵기 때문에 GREATEM 탐사 자료는 주로 1차원 해석에 의존하고 있다. 일반적으로 방대한 항공 전자탐사 자료의 해석은 각 측점에서 얻어진 자료에 대한 1차원 역산 결과를 병합하여 전기비저항 2차원 단면을 작성하는 방법이 널리 사용되고 있다. 그러나 이러한 병합 단면은 전기비저항이 너무 급격하게 변하는 문제점을 보인다. 횡적 제한 역산법(laterally constrained inversion, LCI)은 이러한 문제점을 해결하기 위하여 개발되었으며, 연속성이 뛰어난 역산 단면을 제공하게 된다. 이 연구에서는 우선 수치 모델링을 통하여 곡선 전류원에 대한 GREATEM 탐사 자료의 특성을 분석하였다. 또한 GREATEM 탐사 자료에 대한 횡적 제한 역산법을 개발하였다. 이 방법은 각 측점에서 획득된 모든 1차원 자료와 층서 모델을 하나의 역산 시스템에 병합하여 처리하므로 수평적 연속성이 뛰어난 역산 단면을 제공하게 된다. 개발된 역산 알고리듬을 GREATEM 탐사 자료에 적용한 결과, 해당 지역의 층서를 효과적으로 반영하는 역산 영상을 얻을 수 있었다. Recently, the grounded electrical-source airborne transient electromagnetic (GREATEM) system with high power source was introduced to achieve deeper investigation depth and to overcome high noise level. Although the GREATEM is a transient electromagnetic system using a long grounded wire as the transmitter, GREATEM data have been interpreted with 1D earth models because 2D or 3D modeling and inversion of vast airborne data are complicated and expensive to calculate. Generally, 1D inversion is subsequently applied to every survey point and combining 1D images together forms the stitched conductivity-depth image. However, the stitched models often result in abrupt variations in neighboring models. To overcome this problem, laterally constrained inversion (LCI) has been developed in inversion of ATEM data, which can yield layered sections with lateral smooth transitions. In this study, we analysed the GREATEM data through 1D numerical modeling for a curved grounded wire source. Furthermore, we developed a laterally constrained inversion scheme for continuous GREATEM data based on a layered earth model. All 1D data sets and models are inverted as one system, producing layered sections with lateral smooth transitions. Applying the developed LCI technique to the GREATEM data, it was confirmed that the laterally constrained inversion can provide laterally smooth model sections that reflect the layering of the survey area effectively.

Three-dimensional resistivity modeling of GREATEM survey data from Ontake Volcano, northwest Japan

Ontake Volcano is located in central Japan, 200 km northwest of Tokyo and erupted on September 27, 2014. To study the structure of Ontake Volcano and discuss the process of its phreatic eruption, which can help in future eruptions mitigation, airborne electromagnetic (AEM) surveys using the grounded electrical-source airborne transient electromagnetic (GREATEM) system were conducted over Ontake Volcano. Field measurements and data analysis were done by OYO Company under the Sabo project managed by the Ministry of Land, Infrastructure, Transport and Tourism. Processed data and 1D resistivity models were provided by this project. We performed numerical forward modeling to generate a three-dimensional (3D) resistivity structure model that fits the GREATEM data where a composite of 1D resistivity models was used as the starting model. A 3D electromagnetic forward-modeling scheme based on a staggered-grid finite-difference method was modified and used to calculate the response of the 3D resistivity model along each survey line. We verified the model by examining the fit of magnetic-transient responses between the field data and 3D forward-model computed data. The preferred 3D resistivity models show that a moderately resistive structure (30–200 Ω m) is characteristic of most of the volcano, and were able to delineate a hydrothermal zone within the volcanic edifice. This hydrothermal zone may be caused by a previous large sector collapse.

지상 송신원 항공 전자 탐사 1차원 모델링

Airborne transient electromagnetic (ATEM) surveying was introduced several decades ago in the mining industry to detect shallow conductive targets. However, conventional ATEM systems have limited depth of investigation because of weak signal strength. Recently, the grounded electrical source airborne transient electromagnetic (GREATEM) system was proposed to increase the depth of investigation. The GREATEM is a semi-airborne transient electromagnetic system because a long grounded wire is used as the transmitter. Traditionally, ATEM sounding data have been interpreted with 1D earth models to save the computing time because modern ATEM systems generally collect large data sets. However, the GREATEM 1D modeling requires numerical integration along the wire, so it takes much more time than the 1D modeling of conventional ATEM. In this study, the adaptive Born forward mapping (ABFM) was applied to the ATEM 1D modeling because the ABFM is incommensurably faster than the ordinary GREATEM 1D modeling. Comparing the results from ordinary and ABFM 1D modeling, it was confirmed that the ABFM can be applied to the 1D modeling of GEATEM.

Three-dimensional resistivity modelling of grounded electrical-source airborne transient electromagnetic (GREATEM) survey data from the Nojima Fault, Awaji Island, south-east Japan

An airborne electromagnetic (AEM) survey using the grounded electrical-source airborne transient electromagnetic (GREATEM) system was conducted over the Nojima Fault on Awaji Island, south-east Japan, to assess GREATEM survey applicability for studying coastal areas with complex topographic features. To obtain high-quality data with an optimised signal-to-noise ratio, a series of data processing techniques was used to acquire the final transient response curves from the field survey data.The 1D inversion results were feasible in that the horizontal resistivity contrast was not much higher than the true contrast, but they were not reasonable in that the horizontal resistivity values were greatly changed. To circumvent this problem, we performed numerical forward modelling using a finite-difference staggered-grid method (Fomenko and Mogi, 2002) adding a finite-length electrical dipole source routine to generate a three-dimensional (3D) resistivity structure model from GREATEM survey data of the Nojima Fault area. The 3D model was based on an initial model consisting of two adjacent onshore and offshore layers of different conductivity such that, a highly conductive sea of depth (10–40 m) is placed on top of a uniform half-space, assuming the presence of topographic features on the inland side. We examined the fit of the magnetic transient responses between field data and 3D forward-model computed data, the latter were convolved with the measured system response of the corresponding dataset. The inverted 3D resistivity structures showed that the GREATEM system has the capability to map underground resistivity structures as deep as 500 m onshore and offshore. The GREATEM survey delineated how seawater intrudes on the landside of the fault and indicated that the fault is a barrier to seawater invasion.

A wavelet-based baseline drift correction method for grounded electrical source airborne transient electromagnetic signals

A grounded electrical source airborne transient electromagnetic (GREATEM) system on an airship enjoys high depth of prospecting and spatial resolution, as well as outstanding detection efficiency and easy flight control. However, the movement and swing of the front-fixed receiving coil can cause severe baseline drift, leading to inferior resistivity image formation. Consequently, the reduction of baseline drift of GREATEM is of vital importance to inversion explanation. To correct the baseline drift, a traditional interpolation method estimates the baseline ‘envelope’ using the linear interpolation between the calculated start and end points of all cycles, and obtains the corrected signal by subtracting the envelope from the original signal. However, the effectiveness and efficiency of the removal is found to be low. Considering the characteristics of the baseline drift in GREATEM data, this study proposes a wavelet-based method based on multi-resolution analysis. The optimal wavelet basis and decomposition levels are determined through the iterative comparison of trial and error. This application uses the sym8 wavelet with 10 decomposition levels, and obtains the approximation at level-10 as the baseline drift, then gets the corrected signal by removing the estimated baseline drift from the original signal. To examine the performance of our proposed method, we establish a dipping sheet model and calculate the theoretical response. Through simulations, we compare the signal-to-noise ratio, signal distortion, and processing speed of the wavelet-based method and those of the interpolation method. Simulation results show that the wavelet-based method outperforms the interpolation method. We also use field data to evaluate the methods, compare the depth section images of apparent resistivity using the original signal, the interpolation-corrected signal and the wavelet-corrected signal, respectively. The results confirm that our proposed wavelet-based method is an effective, practical method to remove the baseline drift of GREATEM signals and its performance is significantly superior to the interpolation method.

Three-dimensional resistivity characterization of a coastal area: Application of Grounded Electrical-Source Airborne Transient Electromagnetic (GREATEM) survey data from Kujukuri Beach, Japan

Abstract An airborne electromagnetic (AEM) survey using the Grounded Electrical-Source Airborne Transient Electromagnetic (GREATEM) system was conducted over the Kujukuri coastal plain in southeast Japan to assess the system's ability to accurately describe the geological structure beneath shallow seawater. To obtain high-quality data with an optimized signal-to-noise ratio, a series of data processing techniques were used to obtain the final transient response curves from the field survey data. These steps included movement correction, coordinate transformation, the removal of local noise, data stacking, and signal portion extraction. We performed numerical forward modeling to generate a three-dimensional (3D) resistivity structure model from the GREATEM data. This model was developed from an initial one-dimensional (1D) resistivity structure that was also inverted from the GREATEM field survey data. We modified a 3D electromagnetic forward-modeling scheme based on a finite-difference staggered-grid method and used it to calculate the response of the 3D resistivity model along each survey line. We verified the model by examining the fit of the magnetic-transient responses between field data and the 3D forward-model computed data, the latter of which were convolved with the measured system responses of the corresponding data set. The inverted 3D resistivity structures showed that the GREATEM system has the capability to map resistivity structures as far as 800 m offshore and as deep as 300–350 m underground in coastal areas of relatively shallow seawater depth (5–10 m).

Grounded electrical-source airborne transient electromagnetic (GREATEM) survey of Mount Bandai, north-eastern Japan

Airborne electromagnetics (AEM) is a useful tool for investigating volcanic structures because it can survey large and inaccessible areas. Disadvantages include lower accuracy and limited depth of investigation. The Grounded Electrical Source Airborne Transient Electromagnetic (GREATEM) survey system was developed to increase the depth of investigation possible using AEM. The method was tested in a survey at Mount Bandai in north-eastern Japan. Mount Bandai is an andesitic stratovolcano that rises 1819 m above sea level. An eruption in July 1888 left a hoof-shaped collapsed wall in its northern crater and avalanche debris at its base. Previous surveys of Mount Bandai allow for comparisons of data on its structure and collapse mechanism as obtained by GREATEM and other geophysical methods. The results show resistive structures in recent volcanic cones and conductive structures in the collapsed-crater area. Conductive areas around the collapsed wall correspond to an alteration zone resulting from hydrothermal activity, supporting the contention that a major cause of the collapse associated with the 1888 eruption was hydrothermal alteration that structurally weakened the interior of the volcanic edifice.