def get_model_syntest1_prem():
types = [ParameterType.VSH]
radii = np.array([3480. + 100 * i for i in range(5)])
model_params = ModelParameters(types, radii, mesh_type='boxcar')
model = SeismicModel.prem().boxcar_mesh(model_params)
values = np.array(
[.2 * (-1)**i if i < 2 else 0.
for i in range(model_params.get_n_grd_params())])
values_dict = {param_type: values for param_type in types}
values_mat = model_params.get_values_matrix(values_dict)
model_updated = model.multiply(values_mat)
return model_updated
def get_model_syntest1():
model_ref = SeismicModel.ak135()
types = [ParameterType.VSH]
radii = np.array([3479.5, 3680., 3880.], dtype='float')
model_params = ModelParameters(types, radii, mesh_type='boxcar')
model, mesh = model_ref.boxcar_mesh(model_params)
values = np.array(
[.2 * (-1)**i for i in range(model_params.get_n_grd_params())])
values_dict = {param_type: values for param_type in types}
values_mat = model_params.get_values_matrix(values_dict)
model_updated = mesh.multiply(values_mat)
return model_updated
def get_model_syntest1():
model_ref = SeismicModel.ak135()
types = [ParameterType.VSH]
radii = 6371. - np.array([493.33, 410.])
model_params = ModelParameters(types, radii, mesh_type='boxcar')
model, mesh = model_ref.boxcar_mesh(model_params)
values = np.array(
[.2 * (-1)**i for i in range(model_params._n_grd_params)])
values_dict = {param_type: values for param_type in types}
values_mat = model_params.get_values_matrix(values_dict)
mesh_mul = mesh.multiply(model_params.get_nodes(), values_mat)
model_mul = model + mesh_mul
return model_mul
def get_model_syntest1_prem_vshvsv():
types = [ParameterType.VSH, ParameterType.VSV]
radii = np.array([3480. + 20 * i for i in range(21)])
model_params = ModelParameters(types, radii, mesh_type='boxcar')
model = SeismicModel.prem().boxcar_mesh(model_params)
values_vsh = np.array(
[0.2 * (-1)**(i//5) if i <= 9 else 0.
for i in range(model_params.get_n_grd_params())])
values_vsv = np.array(
[-0.2 * (-1) ** (i // 5) if i <= 4 else 0.
for i in range(model_params.get_n_grd_params())]
)
values_dict = {
ParameterType.VSH: values_vsh,
ParameterType.VSV: values_vsv
}
values_mat = model_params.get_values_matrix(values_dict)
model_updated = model.multiply(values_mat)
return model_updated
reg = linear_model.Ridge(alpha=0.)
reg.fit(X_scaled_pca, y)
y_pred = reg.predict(X_scaled_pca)
mse = mean_squared_error(y, y_pred)
rmse = np.sqrt(mse)
print(rmse)
coefs_scaled_pca = reg.coef_.reshape(1, -1)
coefs_scaled = pca.inverse_transform(coefs_scaled_pca)
coefs = scaler.transform(coefs_scaled)
best_params = np.array(coefs).reshape(len(types), -1)
value_dict = {p_type: best_params[i] for i, p_type in enumerate(types)}
values = model_params.get_values_matrix(value_dict)
best_model = model.multiply(values)
fig0, ax0 = plt.subplots(1)
model.plot(types=types, ax=ax0, label='prem')
get_model_syntest1_prem().plot(types=types, ax=ax0, label='target')
best_model.plot(types=types, ax=ax0, label='inverted')
ax0.set_ylim([3480, 4000])
ax0.set_xlim([6.5, 8])
ax0.legend()
fig, ax = plt.subplots(1)
ax.imshow(X, aspect='auto')
fig2, ax2 = plt.subplots(1)
ax2.imshow(np.dot(X.T, X))
values_m = np.array(
[0. if i % 2 == 1 else 0. for i in range(model_params._n_grd_params)])
values = np.array(
[0. if i % 2 == 1 else 0. for i in range(model_params._n_grd_params)])
values[2] = -0.1
values[3] = 0
values_r = np.zeros(model_params._n_grd_params, dtype=np.float64)
values_r[2] = -20
values_r[3] = -0
values_dict = {ParameterType.VSH: values, ParameterType.RADIUS: values_r}
values_dict_m = {
ParameterType.VSH: values_m,
ParameterType.RADIUS: values_r
}
values_mat = model_params.get_values_matrix(values_dict)
values_mat_m = model_params.get_values_matrix(values_dict_m)
model_ = model_.multiply(model_params.get_nodes(), values_mat,
values_mat_m)
# # mesh
# model, mesh = ak135.boxcar_mesh(model_params)
# # multiply mesh with values
# values = np.array(
# [0.1 * (-1)**i for i in range(model_params._n_grd_params)])
# values_dict = {
# ParameterType.VSH: values}
# values_mat = model_params.get_values_matrix(values_dict)
# mesh_ = mesh.multiply(model_params.get_nodes(), values_mat)
# model_ = model + mesh_