multi-interface 0.0.2

reflection and deflection between Crust1.0 and Vertical Gravity Gradient

This packages is designed for the multi-interface reflection and deflection model.

The gravity anomaly is caused by the fluctuation of several density interfaces. Given the altitudes of these interfaces and the density assumption between those interfaces, the vertical gravity anomalies shall be obtained, which is implied as the forward process in the Software. On the other hand, given the vertical gravity anomalies, some fluctuation of density interfaces shall be determined, which is implied as the downward process in the Software.

mult_interface.py contains the forward and downward calculation

data_provider.py and generate_vgg.py are used for extracting data and generating simulated gravity anomaly data.

from multi_interface import Provider, PltConter, Metrics from multi_interface import Parker, Chao

"""-------------------------Intialize the plot tools--------------------------------------------------------""" pcr = PltConter() mcr = Metrics() """------------------------Initialize the factory-----------------------------------------------------------""" target_size = (251, 251) cut_off = 20 lon = [r'$150^{\circ}$W', r'$155^{\circ}$W', r'$160^{\circ}$W'] lat = [r'$20^{\circ}$N', r'$25^{\circ}$N', r'$30^{\circ}$N'] kwargs = {'lat_up': 30, 'lat_down': 20, 'lon_left': 150, 'lon_right': 160, 'delta-vgg': 'delta-g-3.dg'} clr = Provider(kwargs) """--------------------calculate the parameters for Multi-Inversor------------------------------------------""" mix0 = clr.format_layer(target_size=target_size, layer_number=1).min() mix = clr.format_layer(target_size=target_size, layer_number=8).min()

bnds0 = { 0: clr.format_layer(target_size=target_size, layer_number=1) - mix0, 1: clr.format_layer(target_size=target_size, layer_number=0) - mix0 } bnds = { 0: clr.format_layer(target_size=target_size, layer_number=8) - mix, 1: clr.format_layer(target_size=target_size, layer_number=5) - mix, 2: clr.format_layer(target_size=target_size, layer_number=1) - mix, 3: clr.format_layer(target_size=target_size, layer_number=0) - mix } rhos0 = { 0: 1.82, 1: 1.01999999 } rhos = { 0: 3.3561983471, 1: 2.8500000000, 2: 1.82, 3: 1.01999999 } observation_plane = -mix longrkm = 1100 longckm = 1100 """-------------------Initialize the Multi-Inversor---------------------------------------------------------""" parker0 = Parker(bnds0, rhos0, observation_plane, longrkm, longckm) parker0_vgg = parker0.forward(t=18) parker = Parker(bnds, rhos, observation_plane, longrkm, longckm) parker_vgg = parker.forward(t=18) chao0 = Chao(bnds0, rhos0, observation_plane, longrkm, longckm) chao0_vgg = chao0.forward(t=18) chao = Chao(bnds, rhos, observation_plane, longrkm, longckm) chao_vgg = chao.forward(t=18) pcr.plt_3d(parker0_vgg, lat, lon) pcr.plt_3d(parker_vgg, lat, lon) pcr.plt_3d(chao0_vgg, lat, lon) pcr.plt_3d(chao_vgg, lat, lon)

"""------------------------Inversion procedure-----------------------------------------------------------"""

from multi_interface import Provider, PltConter, Metrics from multi_interface import ParkerI, ChaoI

"""-------------------------Intialize the plot tools--------------------------------------------------------""" pcr = PltConter() mcr = Metrics() """------------------------Initialize the factory-----------------------------------------------------------""" target_size = (251, 251) cut_off = 20 lon = [r'$150^{\circ}$W', r'$155^{\circ}$W', r'$160^{\circ}$W'] lat = [r'$20^{\circ}$N', r'$25^{\circ}$N', r'$30^{\circ}$N'] kwargs = {'lat_up': 30, 'lat_down': 20, 'lon_left': 150, 'lon_right': 160, 'delta-vgg': 'delta-g-3.dg'} clr = Provider(kwargs) """--------------------calculate the parameters for Multi-Inversor------------------------------------------""" mix0 = clr.format_layer(target_size=target_size, layer_number=1).min() mix = clr.format_layer(target_size=target_size, layer_number=8).min() bnds0 = { # 0: clr.format_layer(target_size=target_size, layer_number=1) - mix0, 1: clr.format_layer(target_size=target_size, layer_number=0) - mix0 } bnds = { 0: clr.format_layer(target_size=target_size, layer_number=8) - mix, 1: clr.format_layer(target_size=target_size, layer_number=5) - mix, # 2: clr.format_layer(target_size=target_size, layer_number=1) - mix, 3: clr.format_layer(target_size=target_size, layer_number=0) - mix } rhos0 = { 0: 1.82, 1: 1.01999999 } rhos = { 0: 3.3561983471, 1: 2.8500000000, 2: 1.82, 3: 1.01999999 } vgg = clr.vgg() observation_plane = -mix observation_plane0 = -mix0 longrkm = 1100 longckm = 1100 wh = 0.3 alpha = 8 """-------------------Initialize the Multi-Inversor---------------------------------------------------------""" target_layer = 2 target_depth = clr.format_layer(target_size=target_size, layer_number=target_layer).mean() - mix parkeri = ParkerI(vgg, bnds, rhos, observation_plane, longrkm, longckm, wh, alpha, target_layer, target_depth) bnd = parkeri.downward(t=18, truncate=0.1) pcr.plt_3d(bnd[cut_off:-cut_off, cut_off:-cut_off], lat, lon) """-------------------Initialize the Multi-Inversor---------------------------------------------------------""" target_layer0 = 0 target_depth0 = clr.format_layer(target_size=target_size, layer_number=1).mean() - mix0 parkeri0 = ParkerI(vgg, bnds0, rhos0, observation_plane0, longrkm, longckm, wh, alpha, target_layer0, target_depth0) bnd0 = parkeri0.downward(t=18, truncate=0.1) pcr.plt_3d(bnd0[cut_off:-cut_off, cut_off:-cut_off], lat, lon) """-------------------Initialize the Multi-Inversor---------------------------------------------------------""" target_layer = 2 target_depth = clr.format_layer(target_size=target_size, layer_number=target_layer).mean() - mix parkeri = ChaoI(vgg, bnds, rhos, observation_plane, longrkm, longckm, wh, alpha, target_layer, target_depth) bnd = parkeri.downward(t=18, truncate=0.1) pcr.plt_3d(bnd[cut_off:-cut_off, cut_off:-cut_off], lat, lon) """-------------------Initialize the Multi-Inversor---------------------------------------------------------""" target_layer0 = 0 target_depth0 = clr.format_layer(target_size=target_size, layer_number=1).mean() - mix0 chaoi0 = ChaoI(vgg, bnds0, rhos0, observation_plane0, longrkm, longckm, wh, alpha, target_layer0, target_depth0) bnd0 = chaoi0.downward(t=18, truncate=0.1) pcr.plt_3d(bnd0[cut_off:-cut_off, cut_off:-cut_off], lat, lon) Chao