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 time‐consuming 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 frequency‐domain 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.