We developed a new and simple method for denoising seismic data, which was inspired by data-driven empirical mode decomposition (EMD) algorithms. The method, which can be applied either as a trace-by-trace process or in the f-x domain, replaces the use of the cubic interpolation scheme, which is required to calculate the mean envelopes of the signal and the residues, by window averaging. The resulting strategy is not viewed as an EMD per se, but a userfriendly version of EMD-based algorithms that permits us to attain, in a fraction of the time, the same level of noise cancellation as standard EMD implementations. Furthermore, the proposed method requires less user intervention and easily processes millions of traces in minutes rather than in hours as required by conventional EMD-based techniques on a standard PC. We compared the performance of the new method against standard EMD methods in terms of computational cost and signal preservation and applied them to denoise synthetic and field (microseismic and poststack) data containing random, erratic, and coherent noise. The corresponding f-x EMDs implementations for lateral continuity enhancement were analyzed and compared against the classical f-x deconvolution to test the method.
Seismic monitoring of underground CO 2 accumulations is a subject of growing interest in applied geophysics. Due to their large impedance contrasts, attention is focused on accumulations of high CO 2 saturation in most cases. However, low-saturation zones with dispersed carbon dioxide, or saturation transition layers, may have an important role in the propagation of waves within the reservoir, giving rise to amplitude and phase changes of the seismic signals. With this motivation, we studied the reflectivity response of a simple reservoir model with a given CO 2 saturation-depth profile, on a theoretical basis. We investigated the influence of the overall saturation, vertical extent, and spatial fluid distribution of a carbon dioxide transition zone in the reflectivity of a reservoir. The parametric analysis entails the computation of the generalized P-wave reflection coefficient and its variations with ray angle (AVA) and frequency (AVF). The combined analysis of AVA and AVF can help to characterize and monitor CO 2 transition layers within geological storage sites.
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