def setUp(self): aSpacing = 2.5 nElecs = 5 surveySize = nElecs * aSpacing - aSpacing cs = surveySize / nElecs / 4 self.mesh = discretize.TensorMesh( [ [(cs, 10, -1.3), (cs, surveySize / cs), (cs, 10, 1.3)], [(cs, 3, -1.3), (cs, 3, 1.3)], # [(cs, 5, -1.3), (cs, 10)] ], "CN", ) survey_end_points = np.array([-surveySize / 2, surveySize / 2, 0, 0]) source_list = generate_dcip_sources_line("dipole-dipole", "volt", "2D", survey_end_points, 0., 5, 2.5) self.d_d_survey = dc.survey.Survey(source_list) source_list = generate_dcip_sources_line("dipole-pole", "volt", "2D", survey_end_points, 0., 5, 2.5) self.d_p_survey = dc.survey.Survey(source_list) source_list = generate_dcip_sources_line("pole-dipole", "volt", "2D", survey_end_points, 0., 5, 2.5) self.p_d_survey = dc.survey.Survey(source_list) source_list = generate_dcip_sources_line("pole-pole", "volt", "2D", survey_end_points, 0., 5, 2.5) self.p_p_survey = dc.survey.Survey(source_list)
# # Define survey line parameters survey_type = "dipole-dipole" dimension_type = "2D" data_type = "volt" end_locations = np.r_[-400.0, 400.0] station_separation = 40.0 num_rx_per_src = 10 # Generate source list for DC survey line source_list = generate_dcip_sources_line( survey_type, data_type, dimension_type, end_locations, topo_2d, num_rx_per_src, station_separation, ) # Define survey survey = dc.survey.Survey(source_list, survey_type=survey_type) ############################################################### # Create Tree Mesh # ------------------ # # Here, we create the Tree mesh that will be used to predict DC data. #
data_type = "volt" dimension_type = "3D" end_locations_list = [ np.r_[-1000.0, 1000.0, 0., 0.], np.r_[-300., -300., -1000.0, 1000.0], np.r_[300., 300., -1000.0, 1000.0] ] station_separation = 100.0 num_rx_per_src = 10 # The source lists for each line can be appended to create the source # list for the whole survey. source_list = [] for ii in range(0, len(end_locations_list)): source_list += generate_dcip_sources_line(survey_type, data_type, dimension_type, end_locations_list[ii], xyz_topo, num_rx_per_src, station_separation) # Define the survey survey = dc.survey.Survey(source_list) ############################################################### # Create OcTree Mesh # ------------------ # # Here, we create the OcTree mesh that will be used to predict DC data. # dh = 40.0 # base cell width dom_width_x = 10000.0 # domain width x
def setUp(self): aSpacing = 2.5 nElecs = 5 surveySize = nElecs * aSpacing - aSpacing cs = surveySize / nElecs / 4 mesh = discretize.TensorMesh( [ [(cs, 10, -1.3), (cs, surveySize / cs), (cs, 10, 1.3)], [(cs, 3, -1.3), (cs, 3, 1.3)], [(cs, 5, -1.3), (cs, 10)], ], "CNN", ) survey_end_points = np.array([-surveySize / 2, surveySize / 2, 0, 0]) source_list = generate_dcip_sources_line( "dipole-dipole", "volt", "3D", survey_end_points, 0.0, 5, 2.5 ) survey = dc.survey.Survey(source_list) A = survey.locations_a B = survey.locations_b M = survey.locations_m N = survey.locations_n # add some other receivers and sources to the mix # electrode_locations = np.unique(np.r_[A, B, M, N], axis=0) electrode_locations = survey.electrode_locations rx_p = dc.receivers.Pole(electrode_locations[[2]]) rx_d = dc.receivers.Dipole(electrode_locations[[2]], electrode_locations[[3]]) tx_pd = dc.sources.Pole([rx_d], electrode_locations[0]) tx_pp = dc.sources.Pole([rx_p], electrode_locations[0]) tx_dp = dc.sources.Dipole( [rx_p], electrode_locations[0], electrode_locations[1] ) source_list = survey.source_list source_list.append(tx_pd) source_list.append(tx_pp) source_list.append(tx_dp) survey = dc.Survey(source_list) self.survey = survey # This survey is a mix of d-d, d-p, p-d, and p-p txs and rxs. self.sim1 = dc.Simulation3DNodal( survey=survey, mesh=mesh, solver=Pardiso, storeJ=False, sigmaMap=maps.IdentityMap(mesh), miniaturize=False, ) self.sim2 = dc.Simulation3DNodal( survey=survey, mesh=mesh, solver=Pardiso, storeJ=False, sigmaMap=maps.IdentityMap(mesh), miniaturize=True, ) self.model = np.ones(mesh.nC) self.f1 = self.sim1.fields(self.model) self.f2 = self.sim2.fields(self.model)
np.r_[-1000.0, 1000.0, 0.0, 0.0], np.r_[-300.0, -300.0, -1000.0, 1000.0], np.r_[300.0, 300.0, -1000.0, 1000.0], ] station_separation = 100.0 num_rx_per_src = 10 # The source lists for each line can be appended to create the source # list for the whole survey. source_list = [] for ii in range(0, len(end_locations_list)): source_list += generate_dcip_sources_line( survey_type, data_type, dimension_type, end_locations_list[ii], xyz_topo, num_rx_per_src, station_separation, ) # Define the survey dc_survey = dc.survey.Survey(source_list) ############################################################### # Create OcTree Mesh # ------------------ # # Here, we create the OcTree mesh that will be used to predict DC data. #