Efficient automatic denoising of gravity gradiometry data

Gravity gradiometry data are prized for the high frequency information they provide. However, as any other geophysical data, gravity gradient measurements are contaminated by high-frequency noise. Separation of the high-frequency signal from noise is a crucial component of data processing. The separation can be performed in the frequency domain, which usually requires tuning filter parameters at each survey line to obtain optimal results. Because a modern gradiometry survey generates more data than a traditional gravity survey, such timeconsuming manual operations are not very practical. In addition, they may also introduce subjectivity into the process. To address this difficulty, we propose an automatic, data-adaptive 1D wavelet filtering technique specially designed to process gravity gradiometry data. The method is based on the thresholding of the wavelet coefficients to filter out high-frequency noise while preserving localized sharp signal features. We use an energy analysis across scales (specific for gravity gradiometry data) to select denoising thresholds and to identify sharp features of interest. We compare the proposed method with traditional Fourier-domain filters by applying them to synthetic data sets contaminated with either correlated or uncorrelated noise. The results demonstrate that the proposed filter is efficient and, when applied in the fully automated mode, produces results that are comparable to the best results achievable through frequencydomain filters. We further illustrate the method by applying it to a set of gravity gradiometry data acquired in the Gulf of Mexico and by characterizing the removed noise. Both synthetic and field examples show that the proposed method is an efficient and better alternative to other traditional frequency domain methods. Manuscript received by the Editor September 17, 2002; revised manuscript received July 21, 2003. ∗Formerly Gravity and Magnetics Research Consortium, Colorado School of Mines, Department of Geophysics, Golden, Colorado 80401; presently Petrobras, Av. Chile 65 s.1301, Rio de Janeiro 20031-912, Brazil. E-mail: [email protected]. ‡Colorado School of Mines, Department of Mathematics and Computer Sciences, Golden, Colorado 80401. E-mail: [email protected]. ∗∗Colorado School of Mines, Department of Geophysics, Gravity and Magnetics Research Consortium, Golden, Colorado 80401. E-mail: [email protected]. c © 2004 Society of Exploration Geophysicists. All rights reserved. INTRODUCTION Gravity gradiometry was successfully applied to petroleum exploration in the early 1900s, but the technique faded away because of the development of modern gravimeters and the introduction of seismic reflection technology. However, the recently declassified gravity gradiometry technology developed originally by the U.S. Navy for stealth submarine navigation has breathed new life into gravity gradiometry methods in oil and gas exploration. Because of its dense data coverage and high signal-to-noise ratio, gravity gradiometry has potential to significantly improve seismic models in areas of complex geology and to reduce the exploration risk for complex subsalt targets. Modern gravity gradiometry has the capability to capture localized short-wavelength features because it directly measures the rate of change of the gravity field in different directions. In addition, the instrument itself is more effective than gravimeters at rejecting common-mode noise during acquisition, thus providing data with much more information in the high-frequency band. Unfortunately, the noise that contaminates gravity gradiometry data typically varies in intensity and frequency along the survey lines, and the noises’ power spectrum can overlap with the power spectra of the localized features of interest. Characterizing the noise and identifying its possible sources are also objectives of this investigation. Frequency-domain filters can be applied to attenuate the effect of noise in the data, but these filters also have the adverse effect of smoothing out high-frequency signals. To fully use the advantage of modern gravity gradiometry data, we need to denoise the data instead of simply smoothing them. Although smoothing and denoising are commonly used synonymously, they are different both conceptually and fundamentally. Denoising seeks to remove noise and preserve signals, whereas smoothing simply removes all high-frequency content in the data regardless of whether it belongs to signal or noise. For the kind of noise that contaminates gravity gradient data, smoothing techniques may not always be appropriate because they