nan Sungkono

Fast, simultaneous and robust VLF-EM data denoising and reconstruction via multivariate empirical mode decomposition

The measurement of Very Low Frequency Electromagnetic (VLF-EM) is important in many different applications, i.e, environmental, archeological, geotechnical studies, etc. In recent years, improving and enhancing VLF-EM data containing complex numbers (bivariate) was presented by several authors in order to produce reliable models, generally using univariate empirical mode decomposition (EMD). Applying univariate EMD separately on each data is problematic. This results in a different number of misaligned Intrinsic Mode Functions (IMFs) which can complicate the selection of some IMFs for denoising process. Thus, a filtering method based on the multivariate empirical mode decomposition (MEMD) approach to decompose simultaneously bivariate data is proposed. In this paper we address two issues by employing the recently introduced noise assisted MEMD (N-A MEMD) for improving bivariate VLF-EM data. Firstly, the N-A MEMD to decompose bivariate measurement of the VLF-EM data into IMFs and a residue is defined as VLF-EM signal or unwanted noise. Secondly, the proposed method is used to enhance VLF-EM data and to reject unwanted noise. Finally, the proposed method is applied to a synthetic data with two added sinusoids. To demonstrate the robustness of the N-A MEMD method, the method was tested on added-noise synthetic data sets and the results were compared to the Ensemble EMD (EEMD) and Bivariate EMD (BEMD). The N-A MEMD gave more robust and accurate results than the EEMD and BEMD methods and the method required less CPU time to obtain the IMFs compared to EEMD. The method was also tested on several field data sets. The results indicate that the filtered VLF-EM data based on the N-A MEMD make the data easier to interpret and to be analyzed further. In addition, the 2D resistivity profile estimated from the inversion of filtered VLF-EM data results was appropriate to the geological condition. This research applies N-A MEMD for denoising and reconstructing VLF-EM data.The method is able to decompose into IMFs and residue bivariate VLF-EM data simultaneously.NA-MEMD method gives more robust than the EEMD and MEMD and faster than EEMD in recovery bivariate VLF-EM data.The algorithm greatly enhanced the both VLF-EM data making the more interpretable and easier to invert data VLF-EM data.

Application of Multivariate EMD to Improve Quality VLF-EM Data: Synthetic and Fields Data

A method for enhancing VLF˗EM data based on Multivariate Empirical Mode Decomposition (EMD) was presented. The noise assisted multivariate empirical mode decomposition (NA-MEMD) approach to simultaneously decompose bivariate data.The NA-MEMD is applied to enhance bivariate VLF˗EM data. The method was also tested on a synthetic and two fields VLF-EM data sets. The results indicate that the filtered VLF˗EM data based on the NA-MEMD results better data and easier to interpret or further analyzed. In addition, the 2D resistivity profile result estimated from the inversion of filtered VLF˗EM data is appropriate to geological condition.

Differential evolution adaptive metropolis sampling method to provide model uncertainty and model selection criteria to determine optimal model for Rayleigh wave dispersion

The near-surface S-wave velocity is important tool for environmental studies. This parameter can be derived by inverting of Rayleigh wave dispersion. Inversion of Rayleigh wave dispersion has a nonunique solution. Thus, solving inverse problems is not only done to find the fittest model but also to characterize the uncertainty of the model result. In this paper, we applied and tested a Bayesian inversion method using a developed differential evolution adaptive metropolis (DREAM(ZS)) approach to provide posterior distribution of model parameters (PDMPs). This method consists of Markov chain Monte Carlo (MCMC) simulation method which rapidly estimates the PDMP. After obtaining the resulted posterior, we could estimate representative model (such as mean, mode, median, covariance, and percentile model, the maximum posterior model, and uncertainty model), the probability distributions for individual parameters, and the dispersion curve uncertainty of these models. For inversion of real data, the number of model parameters or the layer number (degrees of freedom (DoF)) which can be propagated by Rayleigh wave is unknown. Therefore, this layer number is needed to accurately estimate subsurface model parameters. For this problem, membership function of fuzzy (MFF) criteria is proposed and applied to the model selection criteria and the various model selection criteria such as the Bayesian information criteria (BIC), the Akaike’s information criterion (AIC), the generalized cross-validation (GCV), the Kullback information criterion (KIC), the finite prediction error (FPE), and information complexity (ICOMP) are compared to the proposed method for selection of the optimal model. The DREAM(ZS) method as well as the seven model selection criteria methods to select the optimal model are used to investigate the influence of noise on Rayleigh wave dispersion on the uncertainty of model parameter values for three synthetics, such as model with a linear velocity increase, model with a low-velocity layer (LVL), and model with a high-velocity layer (HVL). Our results demonstrated that the DREAM(ZS) method is effective to estimate the S-wave velocity and thickness of each layer and to quantify the uncertainty on the estimates, while all the model selection criteria approaches are able to determine the optimal model from different number of layers for noise-free and slightly noisy data, and only the MFF criteria is able to obtain the optimal model for noise-free and noisy data. The optimal model is typically close to the true model. Therefore, inverting Rayleigh wave dispersion using both approaches can produce the best model. We also applied these methods to Rayleigh wave dispersion data collected from the Ljubljana site in Slovenia. We compare our results with those estimated from the number of blow count in the standard penetrating test (N-SPT) data; it shows a good correlation toward each other.

Combination of Active and Passive Seismic Analyses for Embankment Characterization

Rayleigh wave dispersion is intensively used to determine near surface of shear wave velocity (Vs). The method has been known as non-invasive techniques which is costly effective and efficient to characterize subsurface. Acquisition of the Rayleigh wave can be approached in two ways, i.e. passive and active. Passive seismic is accurate to estimate dispersion curve in low frequency, although it is not accurate for high frequency. While active seismic is vice versa of passive seismic. The high frequency of Rayleigh wave dispersion reflects to near surface and vice versa. Therefore, we used the combination of both passive and active seismic method to overcome the limitations of each method. The Vs which is resulted by inversion of the combining data gives accurate model if compared to log and standard penetration test (N-SPT) data. Further, the approach has been used to characterize LUSI (Lumpur Sidoarjo) embankments. The result shows that embankment material (0-12 m) has higher Vs than that lower embankment material.

A complete quantitative analysis of self-potential anomaly using singular value decomposition algorithm

A new quantitative interpretation method of self potential anomaly related to geometric-shaped models such as horizontal cylinder, vertical cylinder, and sphere object has been proposed in this paper. This method is based on the concept of solving least-squares algorithm with singular value decomposition approach which is designed and implemented to calculate the depth, the electric dipole moment, the polarization angle, and the geometric shape factor of self potential anomaly. This approach uses singular value decomposition algorithm to solve non-linear inversion of self potential anomaly. The singular value decomposition algorithm was randomly tested on theoretical synthetic data which was generated by a chosen statistical distribution from a known model with different random noise level. The result shows there is a close agreement between the assumed and calculated parameters. Finally the method validity is tested on the real self potential data anomaly which is obtained from a cylindrical object that was buried at certain depth.

Assessment of Sidoarjo mud flow embankment stability using very low frequency electromagnetic method

The earth on LUSI embankment has a high failure potential due to several factors such as: seepage, leakage, vertical and horizontal deformation, cracking and fault (discontinuity), overtopping, mud slide, and swelling. Very low frequency electromagnetic method was carried out at LUSI embankment in order to delineate fracture and potential pathway of seepage occurring through the subsurface structure the embankment body. To reach these objectives, several methods were carried out in the selected area, such as direct current resistivity, total station and Rayleigh wave dispersion to provide information on the mechanical properties LUSI embankment subsurface. This study indicates that seepage and fracture through the LUSI embankment is presented by a set of lines which are possibly caused by deformation in the LUSI area. Furthermore, based on the fracture and seepage positions and mud or fluid flow direction, the unstable LUSI embankment is determined as located around the northwest and northern parts of the area.

Application of Noise-Assisted Multivariate Empirical Mode Decomposition in VLF-EM Data to Identify Underground River

Very low-frequency electromagnetic (VLF-EM) method can be used for imaging the subsurface resistivity, where this image can be used directly to determine subsurface condition. VLF-EM data are generally contaminated with unwanted noise which often leads to a mistake in the resistivity imaging result. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) was applied to reject the unwanted noise contained within the VLF-EM data which produced NA-MEMD-filtered VLF-EM data. The resistivity imaging resulted by filtered VLF-EM data has been used for determining the position of underground rivers over the karst area of Gunung Kidul district, Central Java province, Indonesia. The results show that the NA-MEMD-filtered VLF-EM data were more accurate in determining underground river tracks of the Suci cave areas. The overall result was supported by qualitative analyses (Fraser and K–Hjelt filters) of observed VLF-EM data as well as the NA-MEMD-filtered VLF-EM data.