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, Marmousi velocity model. (Top) Velocity model used to generate the traveltime data. (Middle) Low-frequency target velocity model. (Bottom) Ray density map, p.69

, When using random vertically increasing gradient initialization, the optimizers converge faster toward low RMS velocity models. (Right) Example of 100 random vertically increasing gradient velocity models, the color scale indicating their RMS values. For CMA-ES, the model that yields the lowest RMS is chosen as the initial mean vector (red), 4.5 (Left) Average RMS over 20 runs as a function of iteration number

, 2D models (bottom) for different initializations. (Left) Fully random. (Middle) Homogeneous. (Right) Vertically increasing gradient. The mean velocity model (blue) fits the long wavelengths of the target velocity model (black) at all depths for gradient initialization. The results have been obtained using CPSO, 6 1D profiles (top) and, p.71

, Evolution of average RMS (left) and RMS deviation (right) with respect to iteration number for the 3 experiments with the 3 EA

, the mean (blue) and the best (green) velocity models at different locations for the 3 EA. The errors are indicated in gray shade

, the mean (blue) and the best (green) velocity models at different depths for the 3 EA. The errors are indicated in gray shade

, Vertical cross-sections of the difference between the target and mean velocity models

, Horizontal cross-sections of the difference between the target and mean velocity models

, Weighted mean velocity models and associated uncertainties for (top) DE, (middle) CPSO and (bottom) CMA-ES. The main structure and the ray coverage of the target velocity model are superimposed over the results

on a refraction tomography problem with a population size of 104. (Left) Speed up. (Right) Parallel efficiency ,

, 1 (Left) Attribute based automated picker seen as a neural network. (Right) Example of multi-attributes onset picker based on a neural network with four input features, one hidden layer and one output

, Bottom) SNR attribute with ?t = 50 samples. The vertical line corresponds to the phase onset given by the global maximum of the SNR attribute. Attribute values are normalized

, Top) Example trace. The shaded area indicates the time window with ?t = 200 samples. (Middle) AIC function. (Bottom) Windowed AIC function with ?t = 200 samples. The vertical line corresponds to the phase onset given by the global minimum of the AIC-W function, vol.87

, Middle) Kurtosis statistics F 1 and removal of negative slopes F 2 . (Bottom) Kurtosis attribute with ?t = 40 samples. The vertical line corresponds to the phase onset given by the global maximum of the Kurtosis attribute. Attribute values are normalized, p.89

, Neural network automated phase onset picking workflow

, The acquisition geometry consists of sixteen piezoelectric transducers, p.90

, Receivers 2, 4, 9, 10 and 11 were not working properly. The vertical lines indicate the manual picks

Top) Example trace. (Bottom) Predicted probability map. The manually picked and predicted phase onsets are indicated by the green and blue vertical lines, respectively. The prediction error is shown in blue shade, vol.92 ,

, The vertical lines indicate the predicted picks along with the picking errors in green shade. The seismic traces recorded by receivers 2, 4, 9, 10 and 11 have been rejected by the trained neural network, p.95

, Left) Evolution of the acoustic wave velocity during the experiment. (Right) Acoustic event locations. The color scale indicates the relative origin time, vol.12, p.96

Displacements for P-and S-waves (adapted from levee, p.106 ,

1 (Left) Modal dispersion curves for a three-layer model (500 m at 500 m/s, 300 m at 1000 m/s, half-space at 500 m/s). The vertical line (green) indicates a slice at ,

Right) Dispersion function at 5 Hz. The positions of the roots (i.e. zeros) correspond to the phase velocities for the different modes. The dispersion function is clipped between -1 and 1 ,

, The velocity models sampled by the different runs of CPSO are represented in the background with the color scale indicating their RMS values. The dashed lines (red) delimit the 68% confidence interval, Left) Picked (red) and inverted dispersion curves. (Right

1 (Left) 2D Rastrigin PDF sampled by MCMC. Comparison of the sampling capability of (middle) PSO and (right) CPSO on the 2D Rastrigin function, p.119 ,

, Left) 3D acquisition geometry. (Right) P-wave velocity model obtained from CPSO tomography. The 95 percent confidence intervals are represented by the grey lines, C, vol.2

, Elbow" method to determine the optimal number of clusters K. (Right) P-wave centroid models of the two most populated clusters