# %% [markdown] # # notebook for create init and true test model # %% import numpy as np import math # grid R_earth = 6378.1370 rr1=6361 rr2=6381 tt1=(38.0-0.3)/180*math.pi tt2=(42.0+0.3)/180*math.pi pp1=(23.0-0.3)/180*math.pi pp2=(27.0+0.3)/180*math.pi n_rtp = [20,50,50] n_rtp.reverse() dr = (rr2-rr1)/n_rtp[2] dt = (tt2-tt1)/n_rtp[1] dp = (pp2-pp1)/n_rtp[0] rr = np.array([rr1 + x*dr for x in range(n_rtp[2])]) tt = np.array([tt1 + x*dt for x in range(n_rtp[1])]) pp = np.array([pp1 + x*dp for x in range(n_rtp[0])]) # initial model gamma = 0.0 s0 = 1.0/6.0 slow_p=0.06 ani_p=0.04 eta_init = np.zeros(n_rtp) xi_init = np.zeros(n_rtp) zeta_init = np.zeros(n_rtp) fun_init = np.zeros(n_rtp) vel_init = np.zeros(n_rtp) a_init = np.zeros(n_rtp) b_init = np.zeros(n_rtp) c_init = np.zeros(n_rtp) f_init = np.zeros(n_rtp) # true model eta_true = np.zeros(n_rtp) xi_true = np.zeros(n_rtp) zeta_true = np.zeros(n_rtp) fun_true = np.zeros(n_rtp) vel_true = np.zeros(n_rtp) a_true = np.zeros(n_rtp) b_true = np.zeros(n_rtp) c_true = np.zeros(n_rtp) f_true = np.zeros(n_rtp) c=0 for ir in range(n_rtp[2]): for it in range(n_rtp[1]): for ip in range(n_rtp[0]): #eta_init[ip,it,ir] = 0.0 #xi_init[ip,it,ir] = 0.0 zeta_init[ip,it,ir] = gamma*math.sqrt(eta_init[ip,it,ir]**2 + xi_init[ip,it,ir]**2) fun_init[ip,it,ir] = s0 vel_init[ip,it,ir] = 1.0/s0 a_init[ip,it,ir] = 1.0 + 2.0*zeta_init[ip,it,ir] b_init[ip,it,ir] = 1.0 - 2.0*xi_init[ip,it,ir] c_init[ip,it,ir] = 1.0 + 2.0*xi_init[ip,it,ir] f_init[ip,it,ir] = -2.0 * eta_init[ip,it,ir] # true model if (tt[it] >= 38.0/180.0*math.pi and tt[it] <= 42.0/180.0*math.pi \ and pp[ip] >= 23.0/180.0*math.pi and pp[ip] <= 27.0/180.0*math.pi): c+=1 sigma = math.sin(2.0*math.pi*(tt[it]-38.0/180.0*math.pi)/(4.0/180.0*math.pi))*math.sin(2.0*math.pi*(pp[ip]-23.0/180.0*math.pi)/(4.0/180.0*math.pi)) else: sigma = 0.0 if sigma < 0: psi = 60.0/180.0*math.pi elif sigma > 0: psi = 120.0/180.0*math.pi else: psi = 0.0 eta_true[ip,it,ir] = ani_p*abs(sigma)*math.sin(2.0*psi) xi_true[ip,it,ir] = ani_p*abs(sigma)*math.cos(2.0*psi) zeta_true[ip,it,ir] = gamma*math.sqrt(eta_true[ip,it,ir]**2 + xi_true[ip,it,ir]**2) fun_true[ip,it,ir] = s0/(1.0+sigma*slow_p) vel_true[ip,it,ir] = 1.0/fun_true[ip,it,ir] a_true[ip,it,ir] = 1.0 + 2.0*zeta_true[ip,it,ir] b_true[ip,it,ir] = 1.0 - 2.0*xi_true[ip,it,ir] c_true[ip,it,ir] = 1.0 + 2.0*xi_true[ip,it,ir] f_true[ip,it,ir] = -2.0 * eta_true[ip,it,ir] r_earth = 6378.1370 print("depminmax {} {}".format(r_earth-rr1,r_earth-rr2)) print(c) # %% # %% # write out import h5py fout_init = h5py.File('test_model_init.h5', 'w') fout_true = h5py.File('test_model_true.h5', 'w') # write out the arrays eta_init, xi_init, zeta_init, fun_init, a_init, b_init, c_init, f_init fout_init.create_dataset('eta', data=eta_init.T) fout_init.create_dataset('xi', data=xi_init.T) fout_init.create_dataset('zeta', data=zeta_init.T) fout_init.create_dataset('fun', data=fun_init.T) fout_init.create_dataset('fac_a', data=a_init.T) fout_init.create_dataset('fac_b', data=b_init.T) fout_init.create_dataset('fac_c', data=c_init.T) fout_init.create_dataset('fac_f', data=f_init.T) fout_init.create_dataset('vel', data=vel_init.T) # writeout the arrays eta_true, xi_true, zeta_true, fun_true, a_true, b_true, c_true, f_true fout_true.create_dataset('eta', data=eta_true.T) fout_true.create_dataset('xi', data=xi_true.T) fout_true.create_dataset('zeta', data=zeta_true.T) fout_true.create_dataset('fun', data=fun_true.T) fout_true.create_dataset('fac_a', data=a_true.T) fout_true.create_dataset('fac_b', data=b_true.T) fout_true.create_dataset('fac_c', data=c_true.T) fout_true.create_dataset('fac_f', data=f_true.T) fout_true.create_dataset('vel', data=vel_true.T) fout_init.close() fout_true.close() # %% [markdown] # # prepare src station file # # ``` # 26 1992 1 1 2 43 56.900 1.8000 98.9000 137.00 2.80 8 305644 <- src 　: id_src year month day hour min sec lat lon dep_km mag num_recs id_event # 26 1 PCBI 1.8900 98.9253 1000.0000 P 10.40 18.000 <- arrival : id_src id_rec name_rec lat lon elevation_m phase epicentral_distance_km arrival_time_sec # 26 2 MRPI 1.6125 99.3172 1100.0000 P 50.84 19.400 # 26 3 HUTI 2.3153 98.9711 1600.0000 P 57.84 19.200 # # ``` # %% import random random.seed(1145141919810) # dummys year_dummy = 1998 month_dummy = 1 day_dummy = 1 hour_dummy = 0 minute_dummy = 0 second_dummy = 0 mag_dummy = 3.0 id_dummy = 1000 st_name_dummy = 'AAAA' phase_dummy = 'P' dist_dummy = 100.0 arriv_t_dummy = 0.0 tt1deg = tt1 * 180.0/math.pi tt2deg = tt2 * 180.0/math.pi pp1deg = pp1 * 180.0/math.pi pp2deg = pp2 * 180.0/math.pi n_src = 1 n_rec = [60 for x in range(n_src)] lines = [] nij_src = math.sqrt(n_src) nij_rec = math.sqrt(n_rec[0]) pos_src=[] pos_rec=[] # create dummy src for i_src in range(n_src): # define one point in the domain (rr1 bottom, rr2 top) # random #dep = random.uniform((R_earth-rr1)*0.95,(R_earth-rr1)*0.98) #lon = random.uniform(pp1deg,pp2deg) #lat = random.uniform(tt1deg,tt2deg) # regularl dep = (R_earth-rr1)*0.9 tmp_ilon = i_src%nij_src tmp_ilat = int(i_src/nij_src) lon = pp1deg + tmp_ilon*(pp2deg-pp1deg)/nij_src lat = tt1deg + tmp_ilat*(tt2deg-tt1deg)/nij_src src = [i_src, year_dummy, month_dummy, day_dummy, hour_dummy, minute_dummy, second_dummy, lat, lon, dep, mag_dummy, n_rec[i_src], id_dummy] lines.append(src) pos_src.append([lon,lat,dep]) # create dummy station for i_rec in range(n_rec[i_src]): #elev_rec = random.uniform(0.0,-10.0) # elevation in m #lon_rec = random.uniform(pp1deg,pp2deg) #lat_rec = random.uniform(tt1deg,tt2deg) # regularly elev_rec = -10.0 tmp_ilon = i_rec%nij_rec tmp_ilat = int(i_rec/nij_rec) lon_rec = pp1deg + tmp_ilon*(pp2deg-pp1deg)/nij_rec lat_rec = tt1deg + tmp_ilat*(tt2deg-tt1deg)/nij_rec rec = [i_src, i_rec, st_name_dummy, lat_rec, lon_rec, elev_rec, phase_dummy, dist_dummy, arriv_t_dummy] lines.append(rec) pos_rec.append([lon_rec,lat_rec,elev_rec]) # write out ev_arrivals file fname = 'src_rec_test.dat' with open(fname, 'w') as f: for line in lines: for elem in line: f.write('{} '.format(elem)) f.write('\n') # %% # draw src and rec positions import matplotlib.pyplot as plt for i_src in range(n_src): plt.scatter(pos_src[i_src][1],pos_src[i_src][0],c='r',marker='o') # %% # plot receivers for i_rec in range(n_rec[0]): plt.scatter(pos_rec[i_rec][1],pos_rec[i_rec][0],c='b',marker='o') # %%