Merging multiple data files from the same survey (ESBJERG)

Data from Esbjerg was recorded in 8 individual surveys, using 4 different calibations (leading to 4 different system GEX files)

”TX07_20230906_2x4_RC20-33.gex”: 20230921, 20230922, 20230925, 20230926 “TX07_20231016_2x4_RC20-33.gex”: 20231026, 20231027 “TX07_20231127_2x4x1_RC20_33.gex”: 20240109 “TX07_20240125_2x4_RC20-33.gex”: 20240313

For the first 3 surveys the same number of gates were used, but it differedin the lasy (TX07_20240125_2x4_RC20-33.gex)

Below is an example of how to

A: invert each survey individually, and then combine the results

• a1) combine survey files (data) for each calibration file

• b1) invert each survey individually

• c1) merge all posterior results in to one posterior file

B: Combine all data (for the first three surveys) and one system file to invert all data

try:
    # Check if the code is running in an IPython kernel (which includes Jupyter notebooks)
    get_ipython()
    # If the above line doesn't raise an error, it means we are in a Jupyter environment
    # Execute the magic commands using IPython's run_line_magic function
    get_ipython().run_line_magic('load_ext', 'autoreload')
    get_ipython().run_line_magic('autoreload', '2')
except:
    # If get_ipython() raises an error, we are not in a Jupyter environment
    # # # #%load_ext autoreload
    # # # #%autoreload 2
    pass

import integrate as ig
import numpy as np
import matplotlib.pyplot as plt

GET The raw data, and merge surveys

8 DATA files are available, corresponding to 8 different surveys obtained with 4 different GEX files

case = 'ESBJERG'
files = ig.get_case_data(case=case, loadType='premerge')

Case A: Treat each area individually

Define the surveys with the same systemfile (gex)

f_gex1 ='TX07_20230906_2x4_RC20-33.gex'
f_data1 = []
f_data1.append('20230921_AVG_export.h5')
f_data1.append('20230922_AVG_export.h5')
f_data1.append('20230925_AVG_export.h5')
f_data1.append('20230926_AVG_export.h5')

f_gex2 ='TX07_20231016_2x4_RC20-33.gex'
f_data2 = []
f_data2.append('20231026_AVG_export.h5')
f_data2.append('20231027_AVG_export.h5')

f_gex3 = 'TX07_20231127_2x4x1_RC20_33.gex'
f_data3 = []
f_data3.append('20240109_AVG_export.h5')

f_gex4 = 'TX07_20240125_2x4_RC20-33.gex'
f_data4 = []
f_data4.append('20240313_AVG_export.h5')

Merge the data

f_data_merged1 = ig.merge_data(f_data1, f_gex1, f_data_merged_h5='ESBJERG_1.h5')
f_data_merged2 = ig.merge_data(f_data2, f_gex2, f_data_merged_h5='ESBJERG_2.h5')
f_data_merged3 = ig.merge_data(f_data3, f_gex3, f_data_merged_h5='ESBJERG_3.h5')
f_data_merged4 = ig.merge_data(f_data4, f_gex4, f_data_merged_h5='ESBJERG_4.h5')

Plot the geometry for each of the 4 areas

ig.plot_geometry(f_data_merged4, pl='LINE')
ig.plot_geometry(f_data_merged3, pl='LINE')
ig.plot_geometry(f_data_merged2, pl='LINE')
ig.plot_geometry(f_data_merged1, pl='LINE')

Run multiple inversions

# Set PRIOR model: The prior is the same for all sets of data
N=100000
f_prior_h5 = ig.prior_model_layered(N=N,lay_dist='chi2', NLAY_deg=4, RHO_min=1, RHO_max=3000)

# Go through each of the 4 areas and invert the data seperately, using the same prior model, but different GEX files and hence prior data
f_data_all = [f_data_merged1, f_data_merged2, f_data_merged3, f_data_merged4]
f_prior_data_all=[]
f_post_all=[]
for f_data_h5 in f_data_all:


    file_gex = ig.get_gex_file_from_data(f_data_h5)
    f_prior_data_h5 = ig.prior_data_em(f_prior_h5, file_gex, showInfo=0)
    #f_prior_data_h5 = ig.prior_data_em(f_prior_h5, f_data_h5, parallel=parallel, showInfo=0)
    f_prior_data_all.append(f_prior_data_h5)

    f_post_h5 = ig.integrate_rejection(f_prior_data_h5,
                                    f_data_h5,
                                    showInfo=0)
                                    #updatePostStat=False)
    f_post_all.append(f_post_h5)

    ig.plot_feature_2d(f_post_h5,im=1,iz=20, key='Median', uselog=1, cmap='jet', s=1)
    plt.show()

Merge the posterior data into one posterior

f_post_merged_h5, f_data_merged_h5  = ig.merge_posterior(f_post_all[0:-2], f_data_all[0:-2], f_post_merged_h5='POST_ESBJERG_MERGED.h5')
ig.integrate_posterior_stats(f_post_merged_h5)
ig.plot_feature_2d(f_post_merged_h5,im=1,iz=20, key='Median', uselog=1, cmap='jet', s=1)
plt.show()

Case B: Combine all data, and use a single system GEX file

# Combine f_data1, f_data2, and f_data3 into a single data set, and use a single system GEX file (this leads to an error)
## Cannot used the last dataset witjh rest as it has a different form!!!
# f_data = f_data1 + f_data2 + f_data3 + f_data4

f_data = f_data1 + f_data2 + f_data3
f_data_all_h5 = ig.merge_data(f_data1+f_data2+f_data3, f_gex1, f_data_merged_h5='ESBJERG_ALL.h5')
#ig.plot_geometry(f_data_merged_h5, pl='LINE')

file_gex = ig.get_gex_file_from_data(f_data_all_h5)
f_prior_data_all_h5 = ig.prior_data_em(f_prior_h5, file_gex, showInfo=0)

f_post_all_h5 = ig.integrate_rejection(f_prior_data_all_h5,
                                f_data_all_h5,
                                showInfo=0,
                                updatePostStat=True)

ig.plot_feature_2d(f_post_all_h5,im=1,iz=20, key='Median', uselog=1, cmap='jet', s=1)
plt.show()

ig.plot_T_EV(f_post_all_h5, pl='T')
plt.show()
ig.plot_T_EV(f_post_merged_h5, pl='T')
plt.show()