Input Parameters

The input parameter file is in the yaml format. The input parameter file should contain the following sections with parameters for the inversion:

`data`

src_rec_file_ph: path to the travel-time data file of phase velocity
src_rec_file_gr: path to the travel-time data file of group velocity
iwave: type of surface wave (1 Love wave (not included in the current version), 2 Rayleigh wave )
vel_type: Bool list with 2 elements, indicating the type of velocity model e.g., [True, False] for phase velocity only.
weights: Float list with 2 elements, indicating the weight of phase and group velocity data e.g., [1.0, 0.0] for phase velocity only.

`domain`

depth_min_max: List with 2 elements, indicating the depth range of the model.
grid_method: Int, indicating the method to generate the inversion grid (0: grid by interval, 1: grid by min max and number of grid).
grid_method_0: Parameters for grid generation by interval method.
- interval: List with 3 elements, indicating the interval of the model along longitude, latitude, and depth.
- num_grid_margin: Int, indicating the grid number of margin area for the domain.
grid_method_1: Parameters for grid generation by min max and number of grid method.
- lon_min_max: List with 2 elements, indicating the longitude range of the model.
- lat_min_max: List with 2 elements, indicating the latitude range of the model.
- n_grid: List with 3 elements, indicating the number of grid along longitude, latitude, and depth.

`model`

init_model_type: # 0: increase from v0 to v1; 1: 1D inversion for average tt; 2: use a 3D vs model as the initial model.
vel_range: List with 2 elements, indicating the range of the initial model

The vel_range is valid when

init_model_type is 0, and the initial model will be generated by increasing from vel_range[0] to vel_range[1] linearly.
init_model_type is 1, and the initial model will be generated by 1D inversion with the average surface wave velocity data as the reference velocity. The initial model of the 1D inversion will be generated by increasing from vel_range[0] to vel_range[1] linearly.

init_model_path: Path to initial model. Only valid in init_model_type: 2.

`topo`

is_consider_topo: Bool, indicating whether to consider the model with topography.
topo_file: path to the surface topography file in netcdf format.
wavelen_factor: Float, indicating the smoothing factor of the topography.

We assume the wavelen_factor as $\alpha$ and the wavelength of the surface wave is $\lambda$ . A gaussian smoothing filter with a standard deviation of $\sigma = \alpha \lambda$ is applied to the topography.

`postproc`

kdensity_coe: Coefficient to rescale the misfit kernel.

We assume kdensity_coe = $\alpha$ , and the kernel is rescaled as:

K_{\mathrm{rescaled}} = \frac{K}{K_{\mathrm{den}}^\alpha}

where $K_{\mathrm{den}}$ is the total kernel density. The $\alpha$ is usually set between 0.0 and 1.0.

independent_smooth_ani: Bool, indicating whether to smooth the anisotropic kernel independently.
smooth_method: Int, indicating the method to smooth the kernel (0: PDE-based Gaussian smoothing, 1: multi-grid smoothing).
smooth_method_0: Parameters for PDE-based Gaussian smoothing
- sigma: List with 2 elements, indicating the standard deviation of the Gaussian smoothing filter in horizontal and vertical direction when independent_smooth_ani is false.
- sigma_ani: List with 2 elements, indicating the standard deviation of the Gaussian smoothing filter in horizontal and vertical direction for anisotropic kernel when independent_smooth_ani is true.
smooth_method_1: Parameters for multi-grid smoothing
- n_inv_components: Int, indicating the number of components of the inversion grids.
- n_inv_grid: List with 3 elements, indicating the number of inversion grids along longitude, latitude, and depth for isotropic kernel.
- n_inv_grid_ani: List with 3 elements, indicating the number of inversion grids along longitude, latitude, and depth for anisotropic kernel.

`output`

output_path: path to the output files.
output_initial_model: Bool, whether to output the initial model.
output_in_process_data: Bool, whether to output the synthetic travel-time data in each iteration.
output_in_process_model: Bool, whether to output the gradient and updated model in each iteration.
log_level: Int, indicating the log level of the output information (1: debug mode, 2: info mode, 3: warning mode, 4: error mode).

`inversion`

niter: maximum iteration number of the inversion.
min_derr: minimum error change of the inversion.
optim_method: Optimization method of the inversion (0: Grad_descent, 1: L-BFGS).

The L-BFGS method is recommended, due to its fast convergence.

step_length: Step length of the inversion.
maxshrink: Maximum step length descent.
c1 and c2: Wolfe condition parameters for line search.
max_sub_niter: Maximum sub-iterations for line search.