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examples/field_data/TUC/field_data/Egbert_TUC_MT_data_original_error.txt
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Station name : Egbert_TUC_MT_data | ||
GG longitude : 9999.000 | ||
GG latitude : 9999.000 | ||
GM longitude : 9999.000 | ||
GM latitude : 9999.000 | ||
Number of data : 32 | ||
# TF_type period_id period n m real imag std_err | ||
Rho 1 16416.000000 9999 9999 1.405005 9999.000000 0.004615 | ||
Phase 1 16416.000000 9999 9999 54.000000 9999.000000 0.300000 | ||
Rho 2 19008.000000 9999 9999 1.395501 9999.000000 0.004891 | ||
Phase 2 19008.000000 9999 9999 55.800000 9999.000000 0.300000 | ||
Rho 3 25056.000000 9999 9999 1.384712 9999.000000 0.004835 | ||
Phase 3 25056.000000 9999 9999 58.000000 9999.000000 0.300000 | ||
Rho 4 31968.000000 9999 9999 1.364551 9999.000000 0.004502 | ||
Phase 4 31968.000000 9999 9999 59.500000 9999.000000 0.300000 | ||
Rho 5 36288.000000 9999 9999 1.349278 9999.000000 0.005829 | ||
Phase 5 36288.000000 9999 9999 59.500000 9999.000000 0.400000 | ||
Rho 6 56160.000000 9999 9999 1.291369 9999.000000 0.008215 | ||
Phase 6 56160.000000 9999 9999 62.700000 9999.000000 0.500000 | ||
Rho 7 61344.000000 9999 9999 1.298853 9999.000000 0.008730 | ||
Phase 7 61344.000000 9999 9999 63.900000 9999.000000 0.600000 | ||
Rho 8 69120.000000 9999 9999 1.266467 9999.000000 0.008230 | ||
Phase 8 69120.000000 9999 9999 64.100000 9999.000000 0.500000 | ||
Rho 9 76896.000000 9999 9999 1.274850 9999.000000 0.009456 | ||
Phase 9 76896.000000 9999 9999 64.600000 9999.000000 0.600000 | ||
Rho 10 101952.000000 9999 9999 1.225051 9999.000000 0.009829 | ||
Phase 10 101952.000000 9999 9999 66.100000 9999.000000 0.700000 | ||
Rho 11 123552.000000 9999 9999 1.215109 9999.000000 0.010851 | ||
Phase 11 123552.000000 9999 9999 66.900000 9999.000000 0.700000 | ||
Rho 12 157248.000000 9999 9999 1.198107 9999.000000 0.010183 | ||
Phase 12 157248.000000 9999 9999 68.100000 9999.000000 0.700000 | ||
Rho 13 203040.000000 9999 9999 1.141763 9999.000000 0.013787 | ||
Phase 13 203040.000000 9999 9999 68.500000 9999.000000 0.900000 | ||
Rho 14 266112.000000 9999 9999 1.079904 9999.000000 0.014452 | ||
Phase 14 266112.000000 9999 9999 68.100000 9999.000000 1.000000 | ||
Rho 15 345600.000000 9999 9999 1.051924 9999.000000 0.021194 | ||
Phase 15 345600.000000 9999 9999 65.800000 9999.000000 1.400000 | ||
Rho 16 432000.000000 9999 9999 1.044148 9999.000000 0.022362 | ||
Phase 16 432000.000000 9999 9999 67.000000 9999.000000 1.500000 |
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# Python script for plotting observed Cn-response data | ||
|
||
# Author: Hongbo Yao | ||
# Institute: School of Geosciences and Info-Physics, | ||
# Central South University (CSU) | ||
# Email: [email protected] | ||
# Date: 2022/01/26 | ||
|
||
# GitHub Page: https://github.com/hongbo-yao | ||
# Researchgate Page: https://www.researchgate.net/profile/Hongbo_Yao2 | ||
|
||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
import matplotlib | ||
matplotlib.rcParams['xtick.direction'] = 'in' | ||
matplotlib.rcParams['ytick.direction'] = 'in' | ||
|
||
# observed data | ||
observed = np.loadtxt('synthetic_Dst_Cn_data.txt') | ||
[n_observed,tmp] = observed.shape | ||
|
||
# start plotting | ||
figure = plt.figure(figsize=(6,5)) | ||
fig = plt.subplot(1,1,1) | ||
# plt.errorbar(observed[:,1],observed[:,2],yerr=observed[:,4],fmt='ro',ecolor='r',elinewidth=1,ms=4,mfc='none',mec='r',capthick=1,capsize=3,label='Observed, Re') | ||
# plt.errorbar(observed[:,1],observed[:,3],yerr=observed[:,4],fmt='cd',ecolor='c',elinewidth=1,ms=5,mfc='none',mec='c',capthick=1,capsize=3,label='Observed, Im') | ||
plt.errorbar(observed[:,1],observed[:,2],yerr=observed[:,4],fmt='o',elinewidth=1,ms=4,mfc='none',capthick=1,capsize=3,label='Observed, Re') | ||
plt.errorbar(observed[:,1],observed[:,3],yerr=observed[:,4],fmt='d',elinewidth=1,ms=5,mfc='none',capthick=1,capsize=3,label='Observed, Im') | ||
plt.xlabel('Period (seconds)',fontsize=14) | ||
plt.ylabel('C-response (km)',fontsize=14) | ||
plt.xlim([0.8*observed[0,1],1.2*observed[n_observed-1,1]]) | ||
fig.set_xscale('log') | ||
plt.legend(loc='upper left', facecolor='none', frameon=False, fontsize=12) | ||
plt.tick_params(labelsize=13) | ||
plt.tight_layout() | ||
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plt.savefig('observed_responses.eps', dpi=300, format='eps') | ||
# plt.savefig('observed_responses.png', dpi=600, format='png') | ||
|
||
plt.show() |
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# SH degree n | ||
1 | ||
# Minimum period in seconds | ||
178200 | ||
# Maximum period in seconds | ||
9936000 | ||
# Number of periods | ||
20 | ||
# Gaussian random error | ||
0 | ||
# Data uncertainty | ||
0.05 | ||
# Number of layers | ||
48 | ||
# Depth of top surface (m) Conductivity (S/m) | ||
0 0.00025858 | ||
6000 0.00022585 | ||
9000 0.00023000 | ||
21000 0.00024673 | ||
36000 0.00029715 | ||
51000 0.00041647 | ||
64000 0.00065260 | ||
79000 0.0011423 | ||
94000 0.0021429 | ||
111000 0.0041624 | ||
131000 0.0083051 | ||
154000 0.016047 | ||
179000 0.028327 | ||
209000 0.043435 | ||
244000 0.057136 | ||
279000 0.067812 | ||
319000 0.076692 | ||
359000 0.086451 | ||
399000 0.098966 | ||
439000 0.11987 | ||
489000 0.16353 | ||
549000 0.27167 | ||
599000 0.52970 | ||
659000 1.0265 | ||
729000 1.3529 | ||
799000 1.4295 | ||
879000 1.4412 | ||
979000 1.4412 | ||
1079000 1.4466 | ||
1179000 1.4618 | ||
1279000 1.4816 | ||
1379000 1.5405 | ||
1479000 1.6010 | ||
1579000 2.1073 | ||
1679000 2.5613 | ||
1779000 2.9786 | ||
1879000 3.3019 | ||
1979000 3.5067 | ||
2079000 3.6441 | ||
2179000 3.6828 | ||
2279000 3.7038 | ||
2379000 3.7079 | ||
2479000 3.6978 | ||
2579000 3.6937 | ||
2679000 3.6881 | ||
2779000 3.6830 | ||
2879000 3.6741 | ||
2979000 100000 |
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1 178200.00 595.280623 -189.206804 31.231324 | ||
1 220200.57 619.309065 -190.658211 32.399625 | ||
1 272100.39 642.663616 -193.020762 33.551212 | ||
1 336232.66 665.595905 -196.676602 34.702291 | ||
1 415480.48 688.403094 -201.937894 35.870522 | ||
1 513406.49 711.398912 -209.067182 37.074165 | ||
1 634413.01 734.906294 -218.309336 38.332304 | ||
1 783939.98 759.277012 -229.917897 39.666227 | ||
1 968709.47 784.929527 -244.156892 41.101306 | ||
1 1197027.91 812.383794 -261.269350 42.668170 | ||
1 1479159.50 842.270568 -281.422595 44.402094 | ||
1 1827787.64 875.312479 -304.652321 46.340721 | ||
1 2258585.15 912.289564 -330.802714 48.520683 | ||
1 2790918.78 953.952489 -359.441644 50.971160 | ||
1 3448719.95 1000.801358 -389.829192 53.702192 | ||
1 4261560.52 1052.798316 -421.146366 56.695427 | ||
1 5265982.25 1109.357943 -453.044769 59.915036 | ||
1 6507139.56 1169.934141 -486.139593 63.345829 | ||
1 8040829.48 1235.010487 -521.848142 67.036863 | ||
1 9936000.00 1306.803816 -561.368079 71.113823 |
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@@ -0,0 +1,48 @@ | ||
0 0.00025858 | ||
6000 0.00022585 | ||
9000 0.00023 | ||
21000 0.00024673 | ||
36000 0.00029715 | ||
51000 0.00041647 | ||
64000 0.0006526 | ||
79000 0.0011423 | ||
94000 0.0021429 | ||
111000 0.0041624 | ||
131000 0.0083051 | ||
154000 0.016047 | ||
179000 0.028327 | ||
209000 0.043435 | ||
244000 0.057136 | ||
279000 0.067812 | ||
319000 0.076692 | ||
359000 0.086451 | ||
399000 0.098966 | ||
439000 0.11987 | ||
489000 0.16353 | ||
549000 0.27167 | ||
599000 0.5297 | ||
659000 1.0265 | ||
729000 1.3529 | ||
799000 1.4295 | ||
879000 1.4412 | ||
979000 1.4412 | ||
1.079e+06 1.4466 | ||
1.179e+06 1.4618 | ||
1.279e+06 1.4816 | ||
1.379e+06 1.5405 | ||
1.479e+06 1.601 | ||
1.579e+06 2.1073 | ||
1.679e+06 2.5613 | ||
1.779e+06 2.9786 | ||
1.879e+06 3.3019 | ||
1.979e+06 3.5067 | ||
2.079e+06 3.6441 | ||
2.179e+06 3.6828 | ||
2.279e+06 3.7038 | ||
2.379e+06 3.7079 | ||
2.479e+06 3.6978 | ||
2.579e+06 3.6937 | ||
2.679e+06 3.6881 | ||
2.779e+06 3.683 | ||
2.879e+06 3.6741 | ||
2.979e+06 100000 |
52 changes: 52 additions & 0 deletions
52
generate_synthetic_data/Dst_G2LTF/plot_observed_responses.py
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# Python script for plotting observed long-period G2L TFs | ||
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||
# Author: Hongbo Yao | ||
# Institute: School of Geosciences and Info-Physics, | ||
# Central South University (CSU) | ||
# Email: [email protected] | ||
# Date: 2022/01/27 | ||
|
||
# GitHub Page: https://github.com/hongbo-yao | ||
# Researchgate Page: https://www.researchgate.net/profile/Hongbo_Yao2 | ||
|
||
import os | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
import matplotlib | ||
matplotlib.rcParams['xtick.direction'] = 'in' | ||
matplotlib.rcParams['ytick.direction'] = 'in' | ||
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# observed data | ||
filename = 'synthetic_Dst_G2LTF_data.txt' | ||
in_stream = open(filename,'r') | ||
line = in_stream.readline() | ||
coords = line.split() | ||
n_observed = int(in_stream.readline()) | ||
observed = np.zeros([n_observed,4], dtype=float) | ||
i = 0 | ||
while i<n_observed: | ||
line = in_stream.readline() | ||
data = line.split() | ||
observed[i,:] = data[2:6] | ||
i = i+1 | ||
in_stream.close() | ||
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# start plotting | ||
figure = plt.figure(figsize=(6,5)) | ||
fig = plt.subplot(1,1,1) | ||
# plt.errorbar(observed[:,0],observed[:,1],yerr=observed[:,3],fmt='ro',ecolor='r',elinewidth=1,ms=4,mfc='none',mec='r',capthick=1,capsize=3,label='Observed, Re') | ||
# plt.errorbar(observed[:,0],observed[:,2],yerr=observed[:,3],fmt='cd',ecolor='c',elinewidth=1,ms=5,mfc='none',mec='c',capthick=1,capsize=3,label='Observed, Im') | ||
plt.errorbar(observed[:,0],observed[:,1],yerr=observed[:,3],fmt='o',elinewidth=1,ms=4,mfc='none',capthick=1,capsize=3,label='Observed, Re') | ||
plt.errorbar(observed[:,0],observed[:,2],yerr=observed[:,3],fmt='d',elinewidth=1,ms=5,mfc='none',capthick=1,capsize=3,label='Observed, Im') | ||
plt.xlabel('Period (seconds)',fontsize=14) | ||
plt.ylabel('Transfer functions (nT)',fontsize=14) | ||
plt.xlim([0.8*observed[0,0],1.2*observed[n_observed-1,0]]) | ||
fig.set_xscale('log') | ||
plt.legend(loc='upper left', facecolor='none', frameon=False, fontsize=12) | ||
plt.tick_params(labelsize=13) | ||
plt.tight_layout() | ||
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plt.savefig('observed_responses.eps', dpi=300, format='eps') | ||
# plt.savefig('observed_responses.png', dpi=600, format='png') | ||
|
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plt.show() |
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# Goemagnetic longitude of station, 0-360 degrees | ||
187.04 | ||
# Goemagnetic colatitude of station, 0-180 degrees | ||
59.87 | ||
# SH degree n | ||
1 | ||
# SH degree m | ||
0 | ||
# Minimum period in seconds | ||
178200 | ||
# Maximum period in seconds | ||
9936000 | ||
# Number of periods | ||
20 | ||
# Gaussian random error | ||
0 | ||
# Data uncertainty | ||
0.05 | ||
# Number of layers | ||
48 | ||
# Depth of top surface (m) Conductivity (S/m) | ||
0 0.00025858 | ||
6000 0.00022585 | ||
9000 0.00023000 | ||
21000 0.00024673 | ||
36000 0.00029715 | ||
51000 0.00041647 | ||
64000 0.00065260 | ||
79000 0.0011423 | ||
94000 0.0021429 | ||
111000 0.0041624 | ||
131000 0.0083051 | ||
154000 0.016047 | ||
179000 0.028327 | ||
209000 0.043435 | ||
244000 0.057136 | ||
279000 0.067812 | ||
319000 0.076692 | ||
359000 0.086451 | ||
399000 0.098966 | ||
439000 0.11987 | ||
489000 0.16353 | ||
549000 0.27167 | ||
599000 0.52970 | ||
659000 1.0265 | ||
729000 1.3529 | ||
799000 1.4295 | ||
879000 1.4412 | ||
979000 1.4412 | ||
1079000 1.4466 | ||
1179000 1.4618 | ||
1279000 1.4816 | ||
1379000 1.5405 | ||
1479000 1.6010 | ||
1579000 2.1073 | ||
1679000 2.5613 | ||
1779000 2.9786 | ||
1879000 3.3019 | ||
1979000 3.5067 | ||
2079000 3.6441 | ||
2179000 3.6828 | ||
2279000 3.7038 | ||
2379000 3.7079 | ||
2479000 3.6978 | ||
2579000 3.6937 | ||
2679000 3.6881 | ||
2779000 3.6830 | ||
2879000 3.6741 | ||
2979000 100000 |
22 changes: 22 additions & 0 deletions
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generate_synthetic_data/Dst_G2LTF/synthetic_Dst_G2LTF_data.txt
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187.04 59.87 | ||
20 | ||
1 0 178200.00 0.129692 -0.037377 0.006749 | ||
1 0 220200.57 0.134431 -0.037405 0.006977 | ||
1 0 272100.39 0.139016 -0.037616 0.007201 | ||
1 0 336232.66 0.143503 -0.038078 0.007423 | ||
1 0 415480.48 0.147955 -0.038843 0.007648 | ||
1 0 513406.49 0.152436 -0.039952 0.007879 | ||
1 0 634413.01 0.157011 -0.041439 0.008119 | ||
1 0 783939.98 0.161750 -0.043341 0.008373 | ||
1 0 968709.47 0.166735 -0.045690 0.008644 | ||
1 0 1197027.91 0.172064 -0.048512 0.008939 | ||
1 0 1479159.50 0.177855 -0.051811 0.009262 | ||
1 0 1827787.64 0.184234 -0.055564 0.009622 | ||
1 0 2258585.15 0.191331 -0.059705 0.010022 | ||
1 0 2790918.78 0.199258 -0.064116 0.010466 | ||
1 0 3448719.95 0.208064 -0.068629 0.010955 | ||
1 0 4261560.52 0.217696 -0.073076 0.011482 | ||
1 0 5265982.25 0.228009 -0.077392 0.012039 | ||
1 0 6507139.56 0.238890 -0.081677 0.012623 | ||
1 0 8040829.48 0.250419 -0.086136 0.013241 | ||
1 0 9936000.00 0.262948 -0.090876 0.013910 |
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