Seismic inversion methods are highly sensitive to the noise present in the data set. The need to enhance the signal-to-noise ratio (SNR) motivates the researchers do develop increasingly sophisticated denoising methods and combine them into other techniques. While some methodologies operate on a single scale, the curvelet transform established itself as multi-scale transform useful to decompose the seismic signals into multi-resolution elements. In this study, we evaluate the benefits of curvelet denoising as a preconditioning method to poststack seismic data in an 2D acoustic inversion processing using a Bayesian framework. Our tests on a synthetic data set modelled from the Marmousi model and the real data set from the Brazilian offshore Campos Basin have shown that the curvelet thresholding method can be successfully applied for random noise elimination. Even the use of a hard global threshold might allow improvements in the deepest parts. Future work will have to show whether alternatives that ensure a more robust way of selecting the coefficients can take into account the wavelength change with depth variation.


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