To eliminate the dependency on a good initial model of the traditional full waveform inversion (FWI) method, we propose an optimisation method combining a derivative free optimisation method of modified particle swarm with gradient descent search. We worked with the acoustic wave approximation, in two dimensions, with the synthetic Marmousi velocity model as the test case. We were able to obtain a high-precision inversion of this model, comparable to traditional FWI methods, with the distinct advantage of not using an initial model close to the global optimal, as would usually be required. For this result, we used a progressive inversion scheme by consecutive layers, and a modified particle swarm optimisation algorithm where we introduced the gradient of the misfit function as a local search guide, and other regularization terms.

The seismic data inversion from observations contaminated by spurious measures (outliers) remains a significant challenge for the industrial and scientific communities. This difficulty is due to slow processing work to mitigate the influence of the outliers. In this work, we introduce a robust formulation to mitigate the influence of spurious measurements in the seismic inversion process. In this regard, we put forth an outlier-resistant seismic inversion methodology for model estimation based on the deformed Jackson Gaussian distribution. To demonstrate the effectiveness of our proposal, we investigated a classic geophysical data-inverse problem in three different scenarios: (i) in the first one, we analyzed the sensitivity of the seismic inversion to incorrect seismic sources; (ii) in the second one, we considered a dataset polluted by Gaussian errors with different noise intensities; and (iii) in the last one we considered a dataset contaminated by many outliers. The results reveal that the deformed Jackson Gaussian outperforms the classical approach, which is based on the standard Gaussian distribution.