![]() In regions where salt is present in the BP 2004 model, we obtained a significant improvement by adding prior information through the relative entropy for synthetic data. Thus, the addition of entropy relative to full waveform inversion can provide a result with better resolution. When we include the logarithmic weighting that constitutes entropy to the inverse problem, we will suppress the low-intensity ripples and sharpen the point events. In all cases, the prior information can be incorporated very quickly into the full waveform inversion and lead the inversion to the desired solution. The idea is that the prior information can help to find the path of the global minimum at the beginning of the inversion process. We use a dynamic weighting scheme to add prior information through entropy. We will discuss some aspects of relative entropy and show three different ways of using them to add prior information through entropy in the inverse problem. To avoid this step, we propose a deterministic application to the full waveform inversion. ![]() The application of entropy in inverse problems usually involves formulating the problem, so that it is possible to use statistical concepts. In this context, entropy will be just a nomenclature for regularisation and will have the role of helping the converge to the global minimum. For this, we propose adding a relative entropy term to the formalism of the full waveform inversion. In the petroleum industry, mainly in reservoir characterisation, it is common to use information from wells as previous information to decrease the ambiguity of the obtained results. Full waveform inversion is an advantageous technique for obtaining high-resolution subsurface information.
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