地学数据三维可视化基于克里金插值的三维可视化

Question

地学数据三维可视化
题目：基于克里金插值的三维可视化
搭建一个基于PyQt+Mayavi的系统框架，在三维窗口中实现三维体数据的可视化。
实现内容：
基础部分：
1. 自行搭建基于PyQt+ Mayavi的可视化系统框架；
2.根据给定的离散点数据，实现三维数据体插值算法（克里金算法）；网格精度为50X50X50;
3. 实现原始散点数据可视化与切片等可视化。
进阶部分：
1. 实现三维三角网算法;
2.三维三角网可视化。
挑战部分：
1.从三维三角网抽取等值面;
2.等值面可视化。
点数据：
595299 3968125 2145 0.3023799924
595301.44523045 3968138.867591 2130.7975344203 0.2991230164
595303.93385719 3968152.7751897 2116.6418363608 0.2816928229
595306.49785182 3968166.6703583 2102.4873470484 0.2833124308
595309.10279147 3968180.5428608 2088.3180867689 0.2857778587
595311.731426 3968194.4146534 2074.1525233951 0.3068802547
595314.43261304 3968208.2598347 2059.9746093705 0.3136012514
595317.1979036 3968222.0924219 2045.7967413285 0.2895816787
595319.99895037 3968235.9342071 2031.6348735659 0.3177619445
595322.86619458 3968249.7434527 2017.4545087722 0.3049213416
595325.80246083 3968263.5103451 2003.247119916 0.2893747413
595328.80574989 3968277.2614791 1989.0385017815 0.3096521459
595331.896516 3968290.9932126 1974.8298513091 0.3109154583
595335.06431543 3968304.6819173 1960.5966930747 0.3035418351
595338.26361809 3968318.3544111 1946.3549480766 0.2986917884
595341.52277737 3968332.0190602 1932.1192871705 0.2854939233
595344.87395029 3968345.6422805 1917.8652782819 0.301837061
595348.28486108 3968359.2417038 1903.6027196266 0.3094026455
595351.73283427 3968372.8330779 1889.3413837456 0.3078619546
595355.22001909 3968386.3758104 1875.0433784486 0.3327506371
595358.75703633 3968399.8630036 1860.7051717552 0.2878018505
595362.32810191 3968413.3360663 1846.3620953113 0.314184628
595367.0016499 3968430.6544779 1838.7866242903 0.2909501766
595372.33294936 3968449.9307705 1838.7866242903 0.3264010599
595377.78694407 3968469.1727289 1838.7866242903 0.3012061185
595383.36507705 3968488.3790444 1838.7866242903 0.3196745328
595389.10390786 3968507.5379319 1838.7866242903 0.2946912805
595394.93798764 3968526.6681035 1838.7866242903 0.3053811609
595400.80043776 3968545.7895878 1838.7866242903 0.3127640875
595406.76124875 3968564.8805957 1838.7866242903 0.2738430847
595412.85187747 3968583.9306142 1838.7866242903 0.3298666737
595419.02888804 3968602.9527939 1838.7866242903 0.2329948506
595425.31369197 3968621.93961 1838.7866242903 0.3122042343
595431.72924866 3968640.8826683 1838.7866242903 0.2827767507
595438.22080173 3968659.7998377 1838.7866242903 0.291948126
595444.7832904 3968678.6925021 1838.7866242903 0
595203 3968210 2145 0.294616968
595193.46354069 3968219.5447684 2130.2368037821 0.2997126727
595184.03031272 3968229.0371868 2115.3739731227 0.2992319114
595174.73852609 3968238.4496534 2100.3718090456 0.3162243714
595165.53499724 3968247.8348459 2085.2982488229 0.3838498805
595156.40801479 3968257.243168 2070.1926005102 0.3152945162
595147.36692063 3968266.6628321 2055.0424568326 0.2814048276
595138.36660925 3968276.0794756 2039.8661229883 0.2987945903
595129.41573644 3968285.4754295 2024.6478189186 0.3069366903
595120.5158625 3968294.870354 2009.3989696274 0.2786683646
595111.64316703 3968304.2726461 1994.1388433468 0.3048323138
595102.83496236 3968313.6769693 1978.8426527164 0.2877355901
595094.06746208 3968323.0937801 1963.5307435279 0.290278886
595085.20862859 3968332.6036303 1948.3295975258 0.3332524239
595076.25545486 3968342.2064126 1933.2422802923 0.2958559028
595067.28350414 3968351.8276518 1918.1779097721 0.3020210234
595058.24427082 3968361.4667686 1903.1653064208 0.293630506
595046.10178048 3968374.3403507 1895.1954328837 0.2463518892
595032.36257423 3968388.8742343 1895.1954328837 0.2662880978
595018.60181542 3968403.387726 1895.1954328837 0.3086286467
595004.83725636 3968417.8976158 1895.1954328837 0.263639599
594991.06636983 3968432.4014958 1895.1954328837 0.3171778421
594977.25758471 3968446.8692476 1895.1954328837 0.3003199871
594963.41724108 3968461.3068677 1895.1954328837 0.303439184
594949.56682314 3968475.7348202 1895.1954328837 0.2781147601
594935.70507637 3968490.1518917 1895.1954328837 0.3094487586
594921.84207125 3968504.5677537 1895.1954328837 0.2859496074
594907.97780843 3968518.9824056 1895.1954328837 0.3473722568
594894.10097385 3968533.3849509 1895.1954328837 0.2620092471
594880.18650653 3968547.7510912 1895.1954328837 0.2754467944
594866.24570738 3968562.0917365 1895.1954328837 0.3879670233
594852.42695569 3968576.549708 1895.1954328837 0.2914212244
594838.7258388 3968591.1195186 1895.1954328837 0.2878969198
594825.01709567 3968605.6821528 1895.1954328837 0.3111762243
594811.29945893 3968620.2364104 1895.1954328837 0.2976581586
594797.57674222 3968634.7858792 1895.1954328837 0
595176 3968113 2145 0.3215615198
595162.15236231 3968116.0306701 2130.8912252773 0.283831537
595148.31792272 3968119.1040168 2116.7787407284 0.2836631079
595134.51070716 3968122.2421819 2102.6538695752 0.2883198161
595120.72689064 3968125.4383636 2088.5191409099 0.2980962386
595106.93632075 3968128.6653099 2074.3980102085 0.2963776992
595093.14950334 3968131.9561327 2060.2879684921 0.2986503973
595079.39562868 3968135.3153056 2046.1618900662 0.2980078654
595065.64763934 3968138.6934051 2032.0345752466 0.3180164911
595051.89572158 3968142.0966607 2017.917143833 0.3101195
595038.1755827 3968145.5708888 2003.7860932054 0.299149658
595024.48533857 3968149.1025046 1989.6402557178 0.307053342
595007.07256387 3968153.6807036 1982.1788000898 0.3445234037
594987.7604213 3968158.8806731 1982.1788000898 0.3010648417
594968.47760308 3968164.1884255 1982.1788000898 0.3147667259
594949.20220549 3968169.5230991 1982.1788000898 0.3008599184
594929.93992846 3968174.9048477 1982.1788000898 0.3187672339
594910.71570003 3968180.4209378 1982.1788000898 0.2942983878
594891.5161747 3968186.0225429 1982.1788000898 0.3702736278
594872.31909061 3968191.6325263 1982.1788000898 0.2953533505
594853.13827386 3968197.2977605 1982.1788000898 0.2838097718
594833.99089716 3968203.0750481 1982.1788000898 0.2988008224
594814.862256 3968208.9141352 1982.1788000898 0.3001160702
594795.75568827 3968214.8249519 1982.1788000898 0.2835114667
594776.65481734 3968220.7541024 1982.1788000898 0.3252543613
594757.54203914 3968226.6449171 1982.1788000898 0.2760355636
594738.43286453 3968232.5474055 1982.1788000898 0.2966044645
594719.34339291 3968238.5132221 1982.1788000898 0.2397219252
594700.28172069 3968244.5672648 1982.1788000898 0.3182900198
594681.23540749 3968250.6695354 1982.1788000898 0.2430051564
594662.20354289 3968256.8166602 1982.1788000898 0.2926747972
594643.20523909 3968263.0666591 1982.1788000898 0.2682612155
594624.24710948 3968269.4375671 1982.1788000898 0.3057582068
594605.31301378 3968275.8795717 1982.1788000898 0.2996219681
594586.40836635 3968282.4074227 1982.1788000898 0.2854352893
594567.53641478 3968289.0292309 1982.1788000898 0
595189 3967984 2145 0.3083351467
595179.92606077 3967982.913092 2127.2101043279 0.2958819746
595170.87591204 3967981.8627552 2109.4058485069 0.2953311062
595161.80389248 3967980.812273 2091.6127465175 0.3227417546
595152.73564404 3967979.7838119 2073.8164653096 0.3010061822
595143.70604091 3967978.801259 2055.9979295931 0.3383760225
595134.6811581 3967977.8327932 2038.176218998 0.3098950023
595125.64964871 3967976.8827294 2020.3568826413 0.305890578
595116.64667498 3967975.9745379 2002.5209494352 0.4150762296
595107.6905569 3967975.098726 1984.6598171097 0.3075129271
595098.75967049 3967974.2372056 1966.7853370364 0.2990046168
595082.66247596 3967972.7370235 1956.8101498434 0.2258518225
595062.74339785 3967970.9400182 1956.8101498434 0.2884311225
595042.82027008 3967969.1882104 1956.8101498434 0.2448264729
595022.89698853 3967967.4381412 1956.8101498434 0.2813516943
595002.97190708 3967965.7089384 1956.8101498434 0.3156061781
594983.04032538 3967964.0562543 1956.8101498434 0.297462834
594963.10268723 3967962.4783746 1956.8101498434 0.2972764761
594943.16379888 3967960.9161529 1956.8101498434 0.3208114349
594923.22331102 3967959.3748141 1956.8101498434 0.322198919
594903.27553133 3967957.930941 1956.8101498434 0.2433560959
594883.32292097 3967956.5549641 1956.8101498434 0.31514842
594863.36588776 3967955.2451619 1956.8101498434 0.2873032354
594843.40132782 3967954.0555517 1956.8101498434 0.3038540974
594823.43129237 3967952.9617781 1956.8101498434 0.318873635
594803.45888588 3967951.9115731 1956.8101498434 0.2993164404
594783.48330339 3967950.9241197 1956.8101498434 0.348783489
594763.50229397 3967950.053477 1956.8101498434 0.3061241071
594743.5173353 3967949.2787458 1956.8101498434 0.3183235979
594723.53089594 3967948.5423834 1956.8101498434 0.3131080728
594703.54370373 3967947.8269513 1956.8101498434 0.3114639203
594683.55523559 3967947.148149 1956.8101498434 0.3322783915
594663.56577595 3967946.4990006 1956.8101498434 0.3097096982
594643.57548712 3967945.876019 1956.8101498434 0.2947687694
594623.58414448 3967945.2879281 1956.8101498434 0.2788977609
594603.59180284 3967944.7347295 1956.8101498434 0
595261 3967892 2145 0.2815328449
595259.3395029 3967885.6051512 2126.122845944 0.3111244655
595257.6321688 3967879.2174788 2107.247415987 0.3014176477
595255.74197248 3967872.2327836 2088.6065258663 0.3060991613
595253.83975012 3967865.2564425 2069.9637965486 0.2955175179
595252.07640997 3967858.9404932 2051.0694058355 0.2939310744
595250.28786206 3967852.6761659 2032.1601814269 0.3180049986
595248.48083977 3967846.4360472 2013.2447018073 0.2885438105
595246.64302866 3967840.1997672 1994.330923834 0.3211017329
595244.77776324 3967833.9940652 1975.4097982825 0.3255308424
595242.89744224 3967827.8360462 1956.4745716047 0.3208976091
595241.01417055 3967821.7048508 1937.5309275004 0.3074024297
595239.11395539 3967815.5945014 1918.582251115 0.2961502761
595237.18723532 3967809.5132542 1899.6268875342 0.2556592615
595235.25793458 3967803.4588532 1880.6631887791 0.3070740386
595233.33259056 3967797.4223005 1861.6933966241 0.3015598326
595231.3897225 3967791.4105344 1842.7175333166 0.2889064274
595229.41951672 3967785.4355644 1823.7328693032 0.2882365062
595227.43415765 3967779.4725698 1804.7460062691 0.2496295828
595225.42387014 3967773.5162374 1785.7596911107 0.3153414088
595223.37989852 3967767.5801021 1766.7706336822 0.2503479803
595221.31777781 3967761.6765846 1747.7733884962 0.2805222409
595219.24514344 3967755.7943044 1728.7706804643 0.3462282273
595217.15439871 3967749.8902962 1709.7767083549 0.3087727309
595215.03951979 3967743.9755287 1690.7887469669 0.2543056266
595209.95508864 3967729.9603204 1679.2574531295 0.3022029405
595203.12103365 3967711.2093697 1677.9546153717 0.3192685682
595196.27915187 3967692.4623735 1676.6361039147 0.3582882059
595189.4141635 3967673.7282309 1675.2566446551 0.3061940867
595182.53215277 3967655.0033059 1673.837141161 0.305576768
595175.61257951 3967636.2922489 1672.4176346374 0.2989609246
595168.59376599 3967617.6189942 1670.9876820817 0.3104759071
595161.47614554 3967598.9749593 1669.6709171909 0.311405602
595154.3023655 3967580.3546313 1668.3245544295 0.2883106122
595147.09672934 3967561.7581403 1666.8249799158 0.3014080248
595139.84863223 3967543.1773184 1665.3358484455 0
595292 3968036 2145 0.2849259819
595295.23588416 3968032.1518109 2125.6423064723 0.3005888253
595298.46273268 3968028.3136873 2106.2811216994 0.2986793106
595301.58975459 3968024.5949633 2086.8804268434 0.3007733159
595304.66915216 3968020.9335313 2067.46110177 0.2880724681
595307.71799205 3968017.3084391 2048.0302081524 0.2810902637
595310.62342322 3968013.8556169 2028.5460806754 0.2987378823
595313.43422355 3968010.518859 2009.0277644626 0.2877649497
595316.19233613 3968007.2446697 1989.491350768 0.3002587551
595318.84460928 3968004.0955371 1969.9198344283 0.3227376509
595321.41763452 3968001.0405144 1950.3227381563 0.3010370891
595323.89336051 3967998.1004953 1930.6957014491 0.3138938711
595326.50562429 3967994.9983323 1911.1122796842 0.2602957437
595329.55653646 3967991.3752576 1891.6822447075 0.3161771472
595333.01208961 3967987.2716564 1872.4160007288 0.2977880322
595336.90281814 3967982.6512677 1853.3513355416 0.3115308521
595341.25789793 3967977.4794442 1834.5300286373 0.2952947854
595345.92066701 3967971.9422279 1815.8862453349 0.3241137638
595350.63791867 3967966.3403115 1797.2753606363 0.3095582248
595355.2881451 3967960.81799 1778.6238641663 0.3229254371
595359.93417903 3967955.3006474 1759.9698430825 0.2101683285
595364.60956311 3967949.7484503 1741.3334836941 0.3073194956
595369.25140114 3967944.2360904 1722.6769516341 0.2932611752
595373.81763896 3967938.8135087 1703.9755224128 0.3093654018
595378.39648243 3967933.3759573 1685.2815532965 0.3062994968
595387.62643425 3967922.4150371 1672.3973412221 0.3170227426
595400.06994723 3967907.6378904 1667.22096032 0.2647068684
595412.51346022 3967892.8607437 1662.044579418 0.2621264244
595424.9569732 3967878.0835969 1656.8681985159 0.2891133427
595437.40048619 3967863.3064502 1651.6918176139 0.2915360619
595449.84399918 3967848.5293035 1646.5154367118 0.2990878354
595462.28751216 3967833.7521568 1641.3390558098 0.3664999928
595474.73102515 3967818.97501 1636.1626749077 0.3055459226
595487.17453814 3967804.1978633 1630.9862940057 0.2820642808
595499.61805112 3967789.4207166 1625.8099131036 0.2889293178
595512.06156411 3967774.6435699 1620.6335322016 0

Answer 1

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问题：如何基于克里金插值实现三维地学数据的可视化？ 回答：

1. 搭建基于PyQt+Mayavi的可视化系统框架： 首先，我们需要创建一个PyQt的GUI界面，提供用户操作界面，然后在界面中嵌入Mayavi的3D可视化窗口。用户可以在PyQt界面中选择数据文件、调整参数等操作，然后通过Mayavi在3D窗口中展示可视化结果。

# 安装 PyQT 和 Mayavi 库
pip install PyQt5
pip install mayavi

import sys
from PyQt5.QtWidgets import QApplication, QMainWindow
from mayavi import mlab
class DataVisualizationApp(QMainWindow):
    def __init__(self):
        super().__init__()
        self.initUI()
    def initUI(self):
        self.setWindowTitle('三维地学数据可视化')
        self.setGeometry(100, 100, 800, 600)
        self.show()
        self.initializeMayavi()
    def initializeMayavi(self):
        self.mayavi_widget = mlab.figure()
        # 将Mayavi的3D窗口嵌入到PyQt界面中
if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = DataVisualizationApp()
    sys.exit(app.exec_())

1. 实现克里金插值算法： 克里金插值是一种用于空间内插的方法，通过对离散点数据进行插值，生成连续的三维数据体。在这里，我们可以使用Scipy库中的interpolate模块来实现克里金插值算法。

import numpy as np
from scipy.interpolate import griddata
# 在点数据中提取xyz坐标和值
points = np.array([[595299, 3968125, 2145],
                   [595301.44523045, 3968138.867591, 2130.7975344203],
                   ...])
values = points[:, -1]
xyz = points[:, :-1]
# 在网格上进行插值
grid_x, grid_y, grid_z = np.mgrid[min(xyz[:, 0]):max(xyz[:, 0]):50j,
                                  min(xyz[:, 1]):max(xyz[:, 1]):50j,
                                  min(xyz[:, 2]):max(xyz[:, 2]):50j]
grid_points = np.vstack([grid_x.ravel(), grid_y.ravel(), grid_z.ravel()]).T
grid_values = griddata(xyz, values, grid_points, method='cubic').reshape(grid_x.shape)

1. 实现原始散点数据可视化和切片： 可以利用Mayavi库中的相关函数，将原始的散点数据在3D窗口中可视化，并实现对数据进行切片的功能。

mlab.points3d(xyz[:, 0], xyz[:, 1], xyz[:, 2], values, scale_mode='none', scale_factor=100)
mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(grid_values), plane_orientation='z_axes', slice_index=25)

通过以上步骤，你可以搭建一个基于克里金插值的三维地学数据的可视化系统，并实现可视化、插值、三角网格等功能。希望这个回答对你有所帮助！