Paulo E. M. de Melo

SVD filtering applied to ground-roll attenuation

We present a singular value decomposition (SVD) filtering method for attenuation of the ground roll. Before the SVD computation, normal move-out (NMO) correction is applied to the seismograms, with the purpose of flattening the reflections. SVD is performed on a small number of traces in a sliding window. The output trace is the central trace of the first few eigenimages. These contain mostly horizontally aligned signals, and other noise in the data will be suppressed. By performing this action with the sliding window moving in steps of one trace, the number of output traces is equal to the number of input traces. The new method preserves the character and frequency content of the horizontal reflections and attenuates all other type of events. We illustrate the method using land seismic data of the Tacutu basin, located in the northeast part of Brazil. The results show that the proposed method is effective and is able to reveal reflections masked by the ground roll. The new SVD filtering approach provides the results of better quality, when compared with the results obtained from the conventional f-k filtering method.

Ground-roll Attenuation Based On SVD Filtering

We present a singular value decomposition (SVD) filtering method for attenuation of the ground roll. Before the SVD computation, the normal move-out (NMO) correction is applied to the seismograms, with the purpose of flattening the reflections. SVD is performed on a small number of traces in a sliding window. The output trace is the central trace of the first few eigenimages. These contains mostly horizontally aligned signals, and other noise in the data will be suppressed. The new method preserves the character and frequency content of the horizontal reflections and attenuates all other type of events. We illustrate the method using land seismic data of the Tacutu basin, located in the north-east part of Brazil. The results show that the proposed method is effective and is able to reveal reflections masked by the ground-roll.

An Adaptive Local-slope SVD Filtering Approach to Enhance Events On Seismic Sections

We present an adaptive singular value decomposition (SVD) filtering method for enhancement of the spacial coherence of the reflections and for the attenuation of the uncorrelated noise. The SVD filtering is performed on a small number of traces and a small number of samples collected around each data component. The method uses the local slope of the reflections to re-sample the data set surrounding each data component and the SVD filtering is locally applied to compute the filtered data. The filtered data component is obtained by stacking the components of the first K eigenimages along the slope. The method is applied in two steps: (i) before the SVD computation, the normal move-out (NMO) correction is applied to the seismograms, with the purpose of flattening the reflections. We use the local slopes equal to 90◦ to preserve the horizontal coherence of the primary reflections and (ii) for the second step the SVD filtering uses as input the filtered data of step-1 and the method is applied in the common-offset domain. Now the local slopes of the reflections are used in order to drive the SVD filtering. We illustrate the method using land seismic data of the Tacutu basin, located in the Northeast of Brazil. The results show that the proposed method is effective and is able to reveal reflections masked by the ground-roll.

High resolution imaging of seismic data using a sparsity norm

In this paper we present an iterative least squares approach for wavelet deconvolution based on the lp norm. The prediction errors associated with the deconvolved seismic trace are raised to an exponent related to the applied norm, thus defining a nonlinear relationship between the filter coefficients and its output. By using a first order Taylor approximation, a weighted system of linear equations may be formed and solved in the least squares sense. The new iterative wavelet deconvolution approach is initialized with the minimum-phase WienerLevinson (WL) filter. The predictive filter is adapted and the iteration continues. This causal inverse filter works to compress the minimum-delay component of the wavelet. Following a few iterations, the process is restarted by using the reverse of the WL filter to compress the non minimum-delay component of the wavelet. This forward and backward deconvolution is able to spike the wavelet independent of its phase properties. The new method was tested on synthetic and real marine seismic data. The results obtained are superior to those obtained using the conventional Wiener-Levinson approach which is based on the l2 norm. The synthetic examples illustrate that the proposed method recovers a full band sparse impulse response.