Real case: Mannsworth - Tomo2D

This notebook implements the Tomography-like approach on data from Barone et al. (2021)

[1]:

from pyswapp import *

/home/alberto/anaconda3/envs/pyswapp/lib/python3.9/site-packages/obspy/core/util/base.py:26: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources

[2]:

# directories
prj_dir = '../../data/real_data' # project directory
path2raw = os.path.join(prj_dir,'raw') # path to raw data
path2geom = f'{prj_dir}/Geometry_mannsworth.csv' # path to the geometry file

[3]:

# processing and plotting settings
settings = create_settings(fmin=5, fmax=30,                  # frequency range
                         vmin=10, vmax=500, velstep=1)     # testing phase velocity range and step

[4]:

swam = Tomo2DManager(f'{prj_dir}/proc/tomo2D',             # project directory path
                   path2raw=path2raw,                       # optional, copy & per default rename data from path (outside project directory)
                   path2geom=path2geom,                     # optional, copy geom from path (outside project directory)
                   settings=settings,                       # optional, define settings for visualisations and processing
                   rename = False,                          # optional, rename copied raw data to preferred filename format (Shotfile_<index>), default value is False!
                   overwrite = False,                       # optional, overwrite database .db file if it already exists --> create new project data base
                   )

Warning: Ignoring XDG_SESSION_TYPE=wayland on Gnome. Use QT_QPA_PLATFORM=wayland to run on Wayland anyway.

Loading project:
Reading seismic shot files ..... 10.33 s
Read 82 files and applied geometry and settings.
Active procset label: tomo2D

Existing procsets: raw proc1 tomo2D
Loading amplitude data from "tomo2D" ..... 0.25 s

[5]:

# set a new procset label
procset = 'tomo2D'
swam.set_procset_label(procset)

Active procset label: tomo2D

[6]:

# Retrieve subsets from data corresponding to forward and reverse shots
swam.prepare_streams(min_offset=5, #max_offset=1e6,
                     min_rec =12, #max_rec=None
                    )

Trimming data based on offset (SIN,REP) = (43, 2) ..... 9.67 s

A. Pick interactively the FK filter

[7]:

# Isolate the fundamental mode in FK domain
swam.gui_interact('filter','FK')

Loading the GUI ..... 2.32 s
Applying FK filter to (SIN,REP,WID) = (1, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (1, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (2, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (2, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (3, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (3, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (4, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (4, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (5, 1, 0) and writing to database..... 0.09 s
Applying FK filter to (SIN,REP,WID) = (5, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (6, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (6, 2, 0) and writing to database..... 0.09 s
Applying FK filter to (SIN,REP,WID) = (6, 2, 1) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (7, 2, 0) and writing to database..... 0.09 s
Applying FK filter to (SIN,REP,WID) = (8, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (9, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (9, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (10, 1, 0) and writing to database..... 0.09 s
Applying FK filter to (SIN,REP,WID) = (10, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (11, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (11, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (12, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (12, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (13, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (14, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (15, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (16, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (17, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (17, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (18, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (18, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (19, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (19, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (20, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (20, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (21, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (21, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (22, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (22, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (23, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (23, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (24, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (24, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (25, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (25, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (26, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (26, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (27, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (27, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (28, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (28, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (29, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (29, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (30, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (30, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (31, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (31, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (32, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (32, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (33, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (33, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (34, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (34, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (35, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (35, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (36, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (36, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (37, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (37, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (38, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (38, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (39, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (39, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (40, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (40, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (41, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (41, 2, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (42, 1, 0) and writing to database..... 0.08 s
Applying FK filter to (SIN,REP,WID) = (42, 2, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (43, 1, 0) and writing to database..... 0.07 s
Applying FK filter to (SIN,REP,WID) = (43, 2, 0) and writing to database..... 0.07 s

[8]:

# save picked filter from database to disk for whole procset
swam.save_filter(fname = f'{prj_dir}/proc/FKfilter.txt', ftype='FK')

B. Apply an existing FK filter

[7]:

# Filter in FK based on a file containing the filter (points along line to mute data abov or below)
path2fk = f'{prj_dir}/proc/FKfilter.txt' # path to the geometry file

swam.import_filter(fname = path2fk)
swam.apply_filter(ftype = 'FK')

Applying FK filter to (SIN,REP) = (43, 2) ..... 76.61 s

Phase diff and run tomo

[8]:

# compute phase differences
swam.compute_phasediff()

Calculating phase differences for (SIN,REP) = (43,2) ..... 6.15 s
Calculating mean and std of phase differences for (SIN) = (43) ..... 2.01 s

[9]:

# offset in meters
min_offset = 10
max_offset = 12
err_r = 2/100
lam = 20

typ = 'tomo2D'

# Run the tomo2D
swam.run(lam = lam, min_offset = min_offset, max_offset = max_offset, rel_err = err_r)
#swam.run(min_offset = min_offset, max_offset = max_offset, rel_err = err_r, opt_lam=True)

Running tomographic-like approach
  5.00 Hz | Cost = 1.517
  5.50 Hz | Cost = 1.137
  6.00 Hz | Cost = 1.586
  6.50 Hz | Cost = 0.953
  7.00 Hz | Cost = 1.065
  7.50 Hz | Cost = 0.901
  8.00 Hz | Cost = 0.832
  8.50 Hz | Cost = 0.823
  9.00 Hz | Cost = 0.819
  9.50 Hz | Cost = 0.723
 10.00 Hz | Cost = 0.766
 10.50 Hz | Cost = 0.817
 11.00 Hz | Cost = 0.602
 11.50 Hz | Cost = 0.677
 12.00 Hz | Cost = 0.773
 12.50 Hz | Cost = 0.856
 13.00 Hz | Cost = 1.033
 13.50 Hz | Cost = 1.024
 14.00 Hz | Cost = 0.871
 14.50 Hz | Cost = 0.822
 15.00 Hz | Cost = 0.885
 15.50 Hz | Cost = 1.046
 16.00 Hz | Cost = 1.154
 16.50 Hz | Cost = 1.021
 17.00 Hz | Cost = 1.081
 17.50 Hz | Cost = 1.375
 18.00 Hz | Cost = 3.241
 18.50 Hz | Cost = 1.782
 19.00 Hz | Cost = 1.081
 19.50 Hz | Cost = 1.162
 20.00 Hz | Cost = 2.098
 20.50 Hz | Cost = 1.533
 21.00 Hz | Cost = 1.747
 21.50 Hz | Cost = 1.660
 22.00 Hz | Cost = 1.398
 22.50 Hz | Cost = 2.765
 23.00 Hz | Cost = 1.369
 23.50 Hz | Cost = 1.615
 24.00 Hz | Cost = 3.270
 24.50 Hz | Cost = 4.931
 25.00 Hz | Cost = 1.553
 25.50 Hz | Cost = 4.242
 26.00 Hz | Cost = 1.867
 26.50 Hz | Cost = 4.787
 27.00 Hz | Cost = 6.094
 27.50 Hz | Cost = 4.801
 28.00 Hz | Cost = 5.673
 28.50 Hz | Cost = 7.087
 29.00 Hz | Cost = 5.376
 29.50 Hz | Cost = 6.181
 30.00 Hz | Cost = 4.324
Finished tomographic-like approach ..... 125.91 s

[10]:

# plot pseudosection
c_map = 'jet'

fig,ax = plt.subplots(figsize=(8,3))
swam.plot('pseudosection', method = typ, cmap = c_map, axes = ax,
                         vmin = 120, vmax = 270,
                         fmin  = 10, fmax = 25
         )
fig.suptitle(f'lam = {lam}, min_offset = {min_offset}, max_offset = {max_offset}, rel_err = {err_r}', fontsize=10)
fig.tight_layout(rect=[0, 0, 1, 0.95])

fig.savefig(f'{prj_dir}/proc/tomo2D/04_figs/pseudo_tomo2d_raw.png', dpi=200)

../../_images/examples_real_nb3_tomo2D_15_0.png

[11]:

# Process the dispersion curves
swam.process_curves(type = 'smooth', method = typ)

Applying smooth to dispersion curves ..... 3.56 s

[12]:

c_map = 'jet'

fig,ax = plt.subplots(figsize=(8,3))
swam.plot('pseudosection', method = typ, cmap = c_map, axes = ax,
                         vmin = 120, vmax = 280,
                         fmin  = 10, fmax = 25
         )
fig.suptitle(f'lam = {lam}, min_offset = {min_offset}, max_offset = {max_offset}, rel_err = {err_r}', fontsize=10)
fig.tight_layout(rect=[0, 0, 1, 0.95])

fig.savefig(f'{prj_dir}/proc/tomo2D/04_figs/pseudo_tomo2d_smooth.png', dpi=200)

../../_images/examples_real_nb3_tomo2D_17_0.png

[13]:

# Save the dispersion curves
swam.save(procset=procset, method=typ, dc_mode=0, format = 'csv')

Saving disperion curves to "../../data/real_data/proc/tomo2D/03_proc/tomo2D_tomo2D/Mode0" ..... 0.47 s