Gravity Gradient Data Filtering Using Translation Invariant Wavelet (original) (raw)
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2006
Continuous gravity recordings in volcanic area could play a fundamental role in the monitoring of active volcanoes and in the prediction of eruptive events too. This geophysical methodology is utilized, on active volcanoes, in order to detect mass changes linked to magma transfer processes and, thus, to recognize forerunners to paroxysmal volcanic events. Spring gravimeters are still the most utilized instruments for microgravity studies because of their relatively low cost and small size, which make them easy to transport and install. Continuous gravity measurements are now increasingly performed at sites very close to active craters, where there is the greatest opportunity to detect significant gravity changes due to a volcanic activity. Unfortunately, spring gravity meters show a strong influence of meteorological parameters (i.e. pressure, temperature and humidity), especially in the adverse environmental conditions usually encountered at such places. As the gravity changes due ...
Geophysical Journal International, 2004
The discrete wavelet transform (dwt), using the good property of localization of wavelet bases has been used as a powerful tool in filtering and denoising problems. The continuous wavelet transform (cwt) exploits the upward continuation properties of the field horizontal derivative and allows the location of potential field singularities in a simple geometrical manner. Within the cwt space-scale framework, the lines formed by joining, at different scales, the modulus maxima of the wavelet coefficients (multiscale edge detection method) intersect each other at the position of the point source or along the edges of the causative body. As long as the multiscale edge detection method is applied to experimental data the procedure may, however, fail, since the observed anomalies are the superposition of effects of sources having different density contrast, geometrical size and depths. We show that wavelet transform modulus maxima lines attributed to deep sources do not converge toward the true depths, but yield completely erroneous solutions. On the other hand, use of nth-order derivatives of the potential field allows the enhancement of the shallowest source effects, preventing us from obtaining information on the deeper ones.In this paper we therefore try to overcome this problem by a joint application of cwt and dwt. A localized dwt filter coupled to compactness criterion allows the separation of the effects due to the deeper sources from those of the shallower ones. Hence, the multiscale edge detection method, applied separately to the original and the filtered signals enabled the estimation of the depth of shallower and deeper sources, respectively.This analysis, performed on the gravity anomalies of Sardinia (Italy), has given estimations of the depths to both the Campidano graben and the Moho discontinuity, in good agreement with previous interpretations of gravity and seismic data.
Application of wavelet theory to the analysis of gravity data
Various Green's functions occurring in Poisson potential field theory can be used to construct non-orthogonal, non-compact, continuous wavelets. Such a construction leads to relations between the horizontal derivatives of geophysical field measurements at all heights, and the wavelet transform of the zero height field. The resulting theory lends itself to a number of different applications in the processing of potential field data. Some simple, synthetic examples in 2-D illustrate one inversion approach based upon the maxima of the wavelet transform (multiscale edges). These examples are presented to illustrate, by way of explicit demonstration, the information content of the multiscale edges. We do not suggest the methods used in these examples be taken literally as a practical algorithm or inversion technique. Rather, we feel that the real thrust of the method is toward physically based, spatially local filtering of geophysical data images using Green's function wavelets, or compact approximations thereto. To illustrate our first steps in this direction, we present some preliminary results of a 3-D analysis of an aeromagnetic survey.
Iidentification of Suitable Discrete Wavelet for Gravity Data Decomposition
— So far, various edge detection methods have been proposed for potential field interpretation. Recognition of the anomaly source boundary can accelerate and facilitate the gravity field analysis. Wavelet transform (WT) is one of these suggested approaches. Several discrete and continuous mother wavelets have been defined. In this study, has been used of 2D discrete wavelet transform (DWT) as a method for determination of gravity anomaly source boundary. The DWT leads to a decomposition of the approximation coefficients in four distinct components: the approximation, horizontal, vertical and diagonal. For comparing the efficiency of wavelets, the synthetic gravity anomalies, with and without added random noise, have been decomposed at 1 level with six discrete, two-dimensional wavelets: Haar, Biorthogonal, Coiflets, Symlets, Discrete Meyer and Daubechies. In this study, for anomaly edge enhancement has been proposed a new formula namely HVC that is computed from the square root of the sum of the squares of the horizontal and vertical components. The results indicate the acceptable performance of the Haar and Biorthogonal wavelets in delineating the edges of the gravity anomaly sources.
Interpretation of gravity data using 2-D continuous wavelet transformation and 3-D inverse modeling
Journal of Applied Geophysics, 2015
Recently the continuous wavelet transform has been proposed for interpretation of potential field anomalies. In this paper, we introduced a 2D wavelet based method that uses a new mother wavelet for determination of the location and the depth to the top and base of gravity anomaly. The new wavelet is the first horizontal derivatives of gravity anomaly of a buried cube with unit dimensions. The effectiveness of the proposed method is compared with Li and Oldenburg inversion algorithm and is demonstrated with synthetics and real gravity data. The real gravity data is taken over the Mobrun massive sulfide ore body in Noranda, Quebec, Canada. The obtained results of the 2D wavelet based algorithm and Li and Oldenburg inversion on the Mobrun ore body had desired similarities to the drill-holes depth information. In the all of inversion algorithms the model non-uniqueness is the challenging problem. Proposed method is based on a simple theory and there is no the model non-uniqueness on it.
Energy, Exploration & Exploitation, 2012
Well logging is an important tool for the characterization of subsurface rocks, being commonly used in the study of reservoir geology. It is well known that signals obtained as responses from geological media contain noise that can affect their interpretation, and that wavelet transform is more suitable than the Fourier transform to denoise non-stationary signals, as the ones obtained from well logs. On the other hand, there are several parameters that must be considered when working with wavelet transform, such as the choice of the wavelet basis function (mother wavelet), the decomposition level and also the function and rules that "control" which and how the coefficients will be used for signal reconstruction. This study analyzes the process of denoising well log data by discrete wavelet transform. Since the well log data are usually used in lithological classification, we propose a method associated with the k-nearest neighbor classification algorithm to investigate how different combinations of parameters affect the output signals and its performance in the classification, thus making it a data driven process. We propose a new thresholding function that shows better results when compared with traditional ones. The potential of wavelet transform as a tool to aid geological interpretation is evidenced by the identification of important geological features of the Namorado Field, Campos Basin, Brazil.
On the Potential of Wavelets for Filtering and Thresholding Airborne Gravity Data
2000
Wavelets can be used in the decomposition and analysis of airborne gravity data. In this paper, multiresolution analysis is applied to de-noise gravity disturbance and different de-noising techniques are studied. The first objective is testing the usefulness of wavelets for analyzing and filtering airborne gravity data. The second one is a comparison between the usage of the wavelet transform and
Wavelet denoising of gravity gradiometry data
SEG Technical Program Expanded Abstracts 2001, 2001
In this paper I propose an automatic 1D wavelet filtering technique, specially designed to process gravity gradiometry data. The method uses compactly supported orthonormal wavelets that selectively filter out localized high-frequency noise with little effect on other sharp features present in the signal. The method is applied to synthetic data sets contaminated with both correlated and uncorrelated noise and compared with traditional Fourier domain filters. The overall results show that the performance of the proposed wavelet-based filter is comparable with the best results achieved with the Fourier filters. The possibility of getting reliable results with automatic choice of filter parameters makes the proposed filter a faster and valuable tool for processing large amounts of data, as in gravity gradiometry surveys.
Gravity Anomaly Separation Using 2 - D Wavelet Approach and Average Depth Calculation
Doğuş Üniversitesi Dergisi
In this paper, 2-D Multi-Resolution Analysis (MRA) is used to per form Discrete-Parameter Wavelet Transform (DPWT) and applied to gravity anom aly separation problem. The advantages of this method are that it introduces little dis tortion to the shape of the original image and that it is not effected significantly by fac tors such as the overlap power spectra of regional and residual fields. The pro p o s ed method is tested using a synthetic example and satisfactory results have been found. Then average depth of the buried objects have been estimated by power spectrum analysis.
Optimization of signal denoising in discrete wavelet transform
Chemometrics and Intelligent Laboratory Systems, 1999
A method to optimize the parameters used in signal denoising in the wavelet domain is presented. The method, which is Ž . based on cross-validation CV procedure, permits to select the best decomposition level and the best wavelet filter function to denoise a signal in the discrete wavelet domain. The procedure was validated by using computer generated signals to which white noise was added. Signals having different features and a range of signal to noise ratios were explored. The method was shown to give reliable results for all cases studied. The proposed method was applied to experimental gravitation field flow fractionation records, and the results were compared with classical low pass filtering in the Fourier domain. q 1999 Elsevier Science B.V. All rights reserved. 0169-7439r99r$ -see front matter q 1999 Elsevier Science B.V. All rights reserved.