11.21. CSUMT Bostick Depth And Survey Design

Controlled-Source Ultra-Audio Magnetotellurics ( CSUMT ) is a controlled-source electromagnetic method that works in the ultra-audio frequency band, typically higher than conventional AMT/MT. Instead of waiting for natural source fields, the survey uses a transmitter to inject a known signal and receivers measure the horizontal electric and magnetic fields along a line. The measured field ratios are converted to impedance tensor, then to apparent resistivity and phase, just as in other magnetotelluric workflows.

The important practical consequence is depth. High frequencies give good near-surface resolution but they do not penetrate as deeply as lower-frequency AMT/MT signals. A CSUMT design therefore starts with a simple question: for the expected resistivity, can the transmitter band reach the target depth? This page answers that question with Bostick depth estimates and quick vertical-resolution checks.

pycsamt.emtools.csumt provides two related workflows:

• plan a controlled-source survey from target depths and an estimated background resistivity;

• transform measured impedance data into quick Bostick depth and vertical-resolution diagnostics.

The module is intentionally lightweight. It does not run an inversion and it does not estimate a layered earth model. It gives you fast, transparent quantities that are useful before field acquisition and during early quality control of CSAMT/AMT lines.

Use these outputs as planning and quality-control diagnostics, not as a final geological model. A Bostick depth is a one-dimensional transform of apparent resistivity at one frequency; it helps you see the depth scale implied by the data, but it cannot replace a constrained 1-D, 2-D, or 3-D inversion when absolute layer geometry matters.

Full callable signatures live in the API reference. This page focuses on practical use, interpretation, and reproducible examples.

11.21.1. The Bostick Transform

The central approximation is:

\[D(f) = 356 \sqrt{\rho_a(f) / f}\]

where:

• D is Bostick depth in metres,

• rho_a is apparent resistivity in ohm-m,

• f is frequency in hertz.

The constant 356 comes from the skin depth relation 503 * sqrt(rho / f) divided by sqrt(2). Lower frequencies probe deeper. Higher apparent resistivity also pushes the estimated depth deeper.

For measured EDI data, pyCSAMT first computes a determinant-style apparent resistivity from the two off-diagonal impedance modes:

\[\rho_a = \sqrt{ \left(0.2 {|Z_{xy}|^2 \over f}\right) \left(0.2 {|Z_{yx}|^2 \over f}\right) }\]

That practical-unit formula matches the EDI impedance convention used elsewhere in emtools.

11.21.2. When To Use This Page

Use the planning side when you need to answer questions like:

• what transmitter frequencies are needed for 10 m, 25 m, and 50 m targets?

• does the CSUMT frequency band reach the required depth for the expected resistivity?

• how much vertical resolution is lost between adjacent frequencies?

Use the measured-data side when you need to answer questions like:

• which stations reach the deepest Bostick depths?

• where does the line have poor vertical resolution?

• do neighboring lines show similar depth-coverage patterns?

• are absolute depth numbers plausible enough to guide inversion setup?

11.21.3. Planning With No EDI Data

The planning helpers need only resistivity and frequency or target depth. The default CSUMT band used by the module is

\[f_{min} = 9.6\times10^3\ \mathrm{Hz}, \qquad f_{max} = 614.4\times10^3\ \mathrm{Hz}.\]

The first step is usually to plot the Bostick relation for plausible background resistivities.

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from pycsamt.emtools.csumt import (
...     F_MAX_CSUMT,
...     F_MIN_CSUMT,
...     bostick_depth_from_rho,
... )
>>> F_MIN_CSUMT, F_MAX_CSUMT
(9600.0, 614400.0)
>>> freq = np.logspace(2, 6, 200)
>>> resistivities = [30.0, 100.0, 300.0, 1000.0]
>>> fig, ax = plt.subplots(figsize=(7, 5))
>>> for rho in resistivities:
...     depth = bostick_depth_from_rho(rho, freq)
...     _ = ax.loglog(freq, depth, label=f"rho={rho:g} ohm.m")
...
>>> _ = ax.axvspan(F_MIN_CSUMT, F_MAX_CSUMT, color="0.9", zorder=0)
>>> _ = ax.set_xlabel("Frequency (Hz)")
>>> _ = ax.set_ylabel("Bostick depth (m)")
>>> _ = ax.legend()
>>> fig.tight_layout()

F_MIN_CSUMT and F_MAX_CSUMT are the pure planning constants imported alongside bostick_depth_from_rho, and the shaded axvspan marks that same band directly on the curves, so it is visually obvious which depths each resistivity assumption can actually reach with this instrument band – higher background resistivity always pushes the reachable depth deeper at a fixed frequency.

11.21.4. Inverting Depth To Frequency

Survey design often starts from target depths. frequency_for_depth inverts the Bostick formula:

\[f = \rho \left(356 / D\right)^2\]

>>> from pycsamt.emtools.csumt import frequency_for_depth
>>> rho_estimate = 300.0
>>> targets_m = np.array([5.0, 10.0, 20.0, 35.0, 50.0, 75.0])
>>> freq_hz = frequency_for_depth(targets_m, rho_estimate)
>>> in_band = (freq_hz >= F_MIN_CSUMT) & (freq_hz <= F_MAX_CSUMT)
>>> for depth, freq, keep in zip(targets_m, freq_hz, in_band):
...     status = "inside CSUMT band" if keep else "outside CSUMT band"
...     print(f"{depth:5.1f} m -> {freq:9.1f} Hz  {status}")
...
  5.0 m -> 1520832.0 Hz  outside CSUMT band
 10.0 m ->  380208.0 Hz  inside CSUMT band
 20.0 m ->   95052.0 Hz  inside CSUMT band
 35.0 m ->   31037.4 Hz  inside CSUMT band
 50.0 m ->   15208.3 Hz  inside CSUMT band
 75.0 m ->    6759.3 Hz  outside CSUMT band

frequency_for_depth is the key conversion; the in_band mask alongside it is the practical check that matters – does the requested target depth actually map into the transmitter band? Here the shallowest (5 m) and deepest (75 m) targets both fall outside it, for opposite reasons: 5 m demands a frequency above F_MAX_CSUMT, and 75 m demands one below F_MIN_CSUMT.

11.21.5. Designing A Frequency Schedule

frequency_schedule converts target depths into transmitter frequencies, removes depths that map outside the allowed band, and can optionally add intermediate frequencies. It first maps each requested depth \(D_i\) to a candidate frequency,

\[f_i = \rho_c \left({356 \over D_i}\right)^2,\]

then keeps only candidates inside the allowed transmitter band,

\[\mathcal{F}_{keep} = \{f_i: f_{min} \le f_i \le f_{max}\}.\]

If min_resolution_m is given, extra log-spaced frequencies are inserted afterward wherever \(\Delta D(f_k, f_{k+1})\) between surviving neighbours would otherwise exceed that spacing.

>>> from pycsamt.emtools.csumt import frequency_schedule
>>> rho_estimate = 300.0
>>> targets_m = np.array([10.0, 20.0, 35.0, 50.0, 65.0])
>>> schedule_hz = frequency_schedule(targets_m, rho_estimate)
>>> padded_hz = frequency_schedule(
...     targets_m, rho_estimate, min_resolution_m=5.0,
... )
>>> schedule_khz = frequency_schedule(
...     targets_m, rho_estimate, min_resolution_m=5.0, as_khz=True,
... )
>>> len(targets_m), len(schedule_hz), len(padded_hz)
(5, 4, 11)
>>> schedule_khz
array([ 15.20832   ,  19.29075455,  24.46905452,  31.03738776,
        41.05860467,  54.31542857,  71.85255818,  95.052     ,
       150.88564479, 239.51603127, 380.208     ])
>>> ax = plot_frequency_schedule(targets_m, rho_estimate)

plot_frequency_schedule() makes that dropped target visible directly: every requested depth is drawn as an open circle at its raw frequency, the CSUMT band is shaded, and only the four targets whose raw frequency actually lands inside the band get a crimson cross – the 65 m target sits below F_MIN_CSUMT and is left unmarked, exactly matching schedule_hz’s dropped count above.

The important behaviour is the gap between the first two counts: frequency_schedule silently drops targets whose frequency falls outside f_min/f_max – it does not warn – so always compare the number of requested target depths with the number of returned frequencies. min_resolution_m=5.0 then pads those 4 surviving frequencies out to 11 by inserting log-spaced intermediate frequencies wherever the resolution gap between neighbours would otherwise exceed 5 m; as_khz=True only changes the output units.

11.21.6. Resolution Between Two Frequencies

vertical_resolution_pair estimates the depth gap between two adjacent frequencies for a fixed resistivity:

\[\Delta D = 356\sqrt{\rho} \left( {1 \over \sqrt{f_{lo}}} - {1 \over \sqrt{f_{hi}}} \right)\]

where f_lo is the lower frequency and therefore the deeper point.

>>> from pycsamt.emtools.csumt import vertical_resolution_pair
>>> adjacent_pairs = [
...     (9600.0, 19200.0),
...     (19200.0, 38400.0),
...     (38400.0, 76800.0),
... ]
>>> for f_lo, f_hi in adjacent_pairs:
...     delta_m = vertical_resolution_pair(rho_estimate, f_lo, f_hi)
...     print(f"{f_lo:8.0f}-{f_hi:8.0f} Hz: {delta_m:6.2f} m")
...
    9600-   19200 Hz:  18.43 m
   19200-   38400 Hz:  13.03 m
   38400-   76800 Hz:   9.22 m

This is a planning calculation. It assumes the same background resistivity for both frequencies. Use it to design spacing before acquisition, or to compare measured resolution against an idealized background.

11.21.7. Measured Data Workflow

The sites-based functions accept the usual emtools input:

• a directory containing EDI files,

• one EDI-like object,

• a Sites container,

• an iterable of site-like objects.

Internally they call ensure_sites, so recursive, on_dup, strict, and verbose behave consistently with the rest of the user guide.

The measured workflow is:

1. compute one Bostick-depth row per station and frequency;

2. compute adjacent-frequency vertical resolution;

3. collapse each station to a coverage table;

4. plot a station x period depth pseudo-section.

>>> from pathlib import Path
>>> from pycsamt.emtools.csumt import (
...     bostick_depth,
...     depth_coverage_table,
...     plot_depth_section,
...     vertical_resolution,
... )
>>> edi_dir = Path("data/AMT/WILLY_DATA/L18PLT")
>>> depth = bostick_depth(edi_dir)
>>> resolution = vertical_resolution(edi_dir)
>>> coverage = depth_coverage_table(edi_dir)
>>> len(depth), len(resolution), len(coverage)
(1484, 1456, 28)
>>> ax = plot_depth_section(edi_dir)

The same input path is accepted by all four functions – one directory becomes a 1484-row per-frequency depth table, a 1456-row adjacent-pair resolution table (one row fewer per station than depth, since each station contributes n_freq - 1 pairs), a 28-row per-station coverage summary, and one pseudo-section.

11.21.8. Bostick Depth Table

Use bostick_depth when you want the raw station-frequency transform.

>>> depth = bostick_depth(
...     "data/AMT/WILLY_DATA/L18PLT",
...     recursive=True,
...     on_dup="replace",
...     strict=False,
...     verbose=0,
... )
>>> depth.head()
   station  freq_hz  period_s  rho_a_ohmm    depth_m
0  18-001A  10400.0  0.000096   76.998314  30.631900
1  18-001A   8707.0  0.000115   84.263304  35.021518
2  18-001A   7289.0  0.000137   88.743760  39.281217
3  18-001A   6102.0  0.000164  115.297433  48.935465
4  18-001A   5108.0  0.000196  152.193125  61.450026
>>> depth.to_csv("l18plt_bostick_depth.csv", index=False)

The output columns are:

• station: station name.

• freq_hz: measured frequency.

• period_s: inverse frequency.

• rho_a_ohmm: geometric-mean apparent resistivity from Zxy and Zyx.

• depth_m: Bostick depth in metres.

11.21.9. One Station Curve

A single-station curve is the easiest way to understand the transform before reading the full section.

>>> station = "18-001A"
>>> one = depth.loc[depth["station"] == station].sort_values("period_s")
>>> fig, ax = plt.subplots(figsize=(7, 4.5))
>>> _ = ax.loglog(one["period_s"], one["depth_m"], "o-")
>>> _ = ax.set_xlabel("Period (s)")
>>> _ = ax.set_ylabel("Bostick depth (m)")
>>> _ = ax.set_title(f"{station} depth vs. period")
>>> _ = ax.grid(True, which="both", alpha=0.3)
>>> fig.tight_layout()

Depth should generally increase with period, but it does not need to be perfectly smooth. Each point uses the apparent resistivity measured at that frequency. A noisy resistivity value can move one depth estimate above or below its neighbors.

11.21.10. Measured Vertical Resolution

Use vertical_resolution to compute the depth gap between adjacent frequencies at each station.

>>> from pycsamt.emtools.csumt import vertical_resolution
>>> measured = vertical_resolution("data/AMT/WILLY_DATA/L18PLT")
>>> fixed_rho = vertical_resolution(
...     "data/AMT/WILLY_DATA/L18PLT",
...     rho_override=300.0,
... )
>>> measured.head()
   station  freq_lo_hz  freq_hi_hz  ...    depth_hi_m  delta_depth_m   rho_a_ohmm
0  18-001A       1.008       1.204  ...  15344.900917   -1741.179129  1814.534975
1  18-001A       1.204       1.438  ...  11898.474477    3446.426441  1895.605946
2  18-001A       1.438       1.718  ...  11376.860958     521.613519  1678.822421
3  18-001A       1.718       2.052  ...  20671.989002   -9295.128044  3484.215253
4  18-001A       2.052       2.451  ...  13906.815678    6765.173324  5087.100314
[5 rows x 7 columns]
>>> fixed_rho.head()
   station  freq_lo_hz  freq_hi_hz  ...   depth_hi_m  delta_depth_m  rho_a_ohmm
0  18-001A       1.008       1.204  ...  5619.496200     522.087278       300.0
1  18-001A       1.204       1.438  ...  5141.989463     477.506737       300.0
2  18-001A       1.438       1.718  ...  4704.343719     437.645744       300.0
3  18-001A       1.718       2.052  ...  4304.492417     399.851302       300.0
4  18-001A       2.052       2.451  ...  3938.573638     365.918779       300.0
[5 rows x 7 columns]

The measured output columns are:

• station: station name.

• freq_lo_hz and freq_hi_hz: adjacent frequency pair, sorted ascending.

• depth_lo_m and depth_hi_m: Bostick depths at the pair.

• delta_depth_m: depth_lo_m - depth_hi_m.

• rho_a_ohmm: geometric-mean apparent resistivity used for the pair.

When rho_override is omitted, each pair uses measured apparent resistivity. When rho_override is set, the calculation uses a fixed background resistivity everywhere. That is useful for comparing field data against an idealized survey-design assumption.

11.21.11. Depth Coverage Table

Use depth_coverage_table when you want one row per station.

>>> from pycsamt.emtools.csumt import depth_coverage_table
>>> coverage = depth_coverage_table("data/AMT/WILLY_DATA/L18PLT")
>>> ranked = coverage.sort_values("depth_max_m", ascending=False)
>>> ranked[[
...     "station", "n_freq", "freq_min_hz", "freq_max_hz",
...     "depth_min_m", "depth_max_m", "median_resolution_m",
... ]].head(10)
    station  n_freq  ...   depth_max_m  median_resolution_m
12  18-013U      53  ...  36009.355793            64.592879
19  18-020A      53  ...  32408.420051            75.994660
20  18-021U      53  ...  30301.925460            79.090618
0   18-001A      53  ...  25738.828561            48.506018
21  18-021B      53  ...  22190.349906            52.988073
8   18-009A      53  ...  21357.805389            67.062557
26  18-024U      53  ...  20271.948048            12.972740
6   18-007U      53  ...  19380.271567            50.198288
7   18-008U      53  ...  19024.796081            50.476517
16  18-017U      53  ...  17389.552044            48.953237
[10 rows x 7 columns]

The output columns are:

• n_freq: number of measured frequencies.

• freq_min_hz and freq_max_hz: frequency range for the station.

• depth_min_m and depth_max_m: shallowest and deepest Bostick depths estimated at the station.

• mean_resolution_m and median_resolution_m: adjacent-frequency depth gaps summarized for the station.

The deepest station is not automatically the best station. Very high apparent resistivity at low frequency can create a large Bostick depth. Use the table as a ranking and a quality-control clue, then inspect the curves and pseudo-section.

For each station \(s\), the table is the reduction

\[D_{min,s} = \min_j D_{s,j}, \qquad D_{max,s} = \max_j D_{s,j},\]

with resolution summaries taken from the adjacent-frequency gaps \(\Delta D_{s,j}\). This is why two stations with the same frequency schedule can still have different depth coverage: the resistivity term in \(D_{s,j}=356\sqrt{\rho_{a,s,j}/f_{s,j}}\) changes from station to station.

Ranking every station this way, rather than reading the top-10 table alone, makes the full spread across the line visible at once:

>>> from pycsamt.emtools.csumt import plot_depth_coverage_ranking
>>> ax = plot_depth_coverage_ranking(
...     "data/AMT/WILLY_DATA/L18PLT",
...     title="L18PLT per-station Bostick depth coverage",
... )

Bars are coloured by whether a station’s median_resolution_m is coarser (red) or at-or-finer (blue) than the survey-wide median, so a station that only reaches deep with coarse resolution – like 18-021U, third-deepest but with the widest resolution gap in the top ten – reads differently from a comparably deep station with fine resolution, like 18-001A right below it.

11.21.12. Depth Pseudo-Section

plot_depth_section colors station x period cells by Bostick depth.

>>> fig, ax = plt.subplots(figsize=(10, 5))
>>> _ = plot_depth_section(
...     edi_dir,
...     log_color=True,
...     sort_by="name",
...     period_axis=True,
...     ax=ax,
... )
>>> fig.tight_layout()
>>> fig.savefig("l18plt_bostick_depth_section.png", dpi=200)

Useful plotting options:

• log_color=True colors by log10(depth_m). This is the default and is usually easier to read when depths span orders of magnitude.

• sort_by="name" sorts stations by station name. "lon" and "lat" are also supported when coordinates are available.

• period_axis=True uses period on the y-axis. Set it to False for frequency.

• ax=... lets you place several lines in one figure.

Mathematically, the pseudo-section is only a gridded display of the table produced by bostick_depth:

\[G_{k,s} = D(f_k, s) = 356\sqrt{\rho_a(f_k, s) \over f_k}.\]

When log_color=True, the displayed color value is \(\log_{10}(G_{k,s})\). That logarithmic color scale prevents a few very deep estimates from hiding the shallow CSUMT/AMT structure.

11.21.13. Resolution Coarsening With Depth

The fixed-resistivity formula says vertical resolution should become coarser as the sampled depth increases. The measured-data check below bins adjacent-frequency gaps by their midpoint depth and compares the median measured gap with the analytical curve for the line’s median apparent resistivity.

>>> from pycsamt.emtools.csumt import bostick_depth_from_rho
>>> survey = "data/AMT/WILLY_DATA/L18PLT"
>>> depth = bostick_depth(survey)
>>> resolution = vertical_resolution(survey)
>>> resolution["mid_depth"] = (
...     0.5 * (resolution["depth_lo_m"] + resolution["depth_hi_m"]).abs()
... )
>>> bins = np.logspace(0.5, 4, 12)
>>> centers = np.sqrt(bins[:-1] * bins[1:])
>>> bin_idx = np.digitize(resolution["mid_depth"], bins)
>>> binned = (
...     resolution.groupby(bin_idx)["delta_depth_m"]
...     .apply(lambda s: np.median(np.abs(s)))
...     .loc[lambda s: (s.index >= 1) & (s.index <= len(centers))]
... )
>>> rho_med = float(depth["rho_a_ohmm"].median())
>>> f_sweep = np.logspace(
...     np.log10(depth["freq_hz"].min()),
...     np.log10(depth["freq_hz"].max()),
...     40,
... )
>>> analytic_depth = []
>>> analytic_resolution = []
>>> for f_lo, f_hi in zip(f_sweep[:-1], f_sweep[1:]):
...     analytic_depth.append(
...         np.sqrt(
...             bostick_depth_from_rho(rho_med, f_lo)
...             * bostick_depth_from_rho(rho_med, f_hi)
...         )
...     )
...     analytic_resolution.append(
...         vertical_resolution_pair(rho_med, f_lo, f_hi)
...     )
...
>>> fig, ax = plt.subplots(figsize=(7, 5))
>>> _ = ax.loglog(centers[binned.index - 1], binned, "o-")
>>> _ = ax.loglog(analytic_depth, analytic_resolution, "--", color="0.3")
>>> _ = ax.set_xlabel("Depth (m)")
>>> _ = ax.set_ylabel("Vertical resolution (m)")
>>> fig.tight_layout()

For a measured adjacent pair, the midpoint depth used for binning is

\[D_{mid,j} = {1 \over 2}|D(f_j) + D(f_{j+1})|,\]

and each bin reports

\[\widetilde{\Delta D}_b = \operatorname{median}_{j\in B_b} \left(|D(f_j)-D(f_{j+1})|\right).\]

The comparison is useful because it separates a normal consequence of log-frequency sampling from true data irregularity. If the measured curve follows the analytical trend, the resolution loss is mostly geometric. If it departs strongly, the apparent-resistivity structure is controlling the depth estimates.

11.21.14. Comparing Neighboring Lines

Use a shared y-axis to compare two survey lines with the same depth color scale logic.

>>> l18 = "data/AMT/WILLY_DATA/L18PLT"
>>> l22 = "data/AMT/WILLY_DATA/L22PLT"
>>> fig, (ax18, ax22) = plt.subplots(1, 2, figsize=(12, 5), sharey=True)
>>> _ = plot_depth_section(l18, ax=ax18)
>>> _ = ax18.set_title("L18PLT")
>>> _ = plot_depth_section(l22, ax=ax22)
>>> _ = ax22.set_title("L22PLT")
>>> fig.tight_layout()
>>> c18 = depth_coverage_table(l18)
>>> c22 = depth_coverage_table(l22)
>>> c18["depth_max_m"].mean()
16171.310165020153
>>> c22["depth_max_m"].mean()
18017.082535636182

Neighboring lines should not be identical, but a severe mismatch is a reason to check station ordering, coordinate metadata, outlier resistivity values, and EDI quality before interpreting the difference as geology.

11.21.15. Reading The Results

Use these rules of thumb:

• Treat Bostick depth as a fast transform, not as an inversion result.

• Prefer broad station-period patterns over one-frequency extremes.

• Check rho_a_ohmm when depth_m looks surprisingly large.

• Compare measured vertical resolution with a fixed rho_override if you need to separate survey-design spacing from resistivity effects.

• Use neighboring lines as sanity checks before making absolute depth claims.

11.21.16. Common Failure Modes

Empty tables

No valid impedance tensors were loaded. Check the EDI path and make sure files contain usable Z data.

Unreachable target depths

frequency_schedule drops target depths that map outside the requested frequency band. Compare input target count with output schedule count.

Very large depth estimates

Bostick depth grows with apparent resistivity and decreases with frequency. High-resistivity, low-frequency outliers can produce large depths that should be treated as qualitative indicators.

Negative or surprising resolution gaps

With measured data, adjacent depths are computed from adjacent measured resistivities. Noise or strong resistivity variation can make the sequence less smooth than the analytical fixed-resistivity formula.

11.21.17. Saving A Reproducible Bundle

For reporting, save the planning assumptions, measured tables, and key figure together.

>>> survey = Path("data/AMT/WILLY_DATA/L18PLT")
>>> out = Path("outputs/csumt_l18plt")
>>> out.mkdir(parents=True, exist_ok=True)
>>> targets_m = np.array([10.0, 20.0, 35.0, 50.0])
>>> schedule = frequency_schedule(targets_m, rho_estimate=300.0)
>>> np.savetxt(out / "planned_frequency_schedule_hz.txt", schedule)
>>> bostick_depth(survey).to_csv(out / "bostick_depth.csv", index=False)
>>> vertical_resolution(survey).to_csv(
...     out / "vertical_resolution.csv", index=False,
... )
>>> depth_coverage_table(survey).to_csv(
...     out / "depth_coverage.csv", index=False,
... )
>>> fig, ax = plt.subplots(figsize=(10, 5))
>>> _ = plot_depth_section(survey, ax=ax)
>>> fig.tight_layout()
>>> fig.savefig(out / "bostick_depth_section.png", dpi=200)

schedule preserves the design assumptions, the three to_csv calls save the measured data products behind every table in this page, and the saved figure is the same depth pseudo-section used throughout – together they let a reviewer retrace any claim back to the row that produced it.

11.21.18. Worked Example

The gallery example applies the Bostick transform to L18PLT and L22PLT from data/AMT/WILLY_DATA/. It starts with the pure planning equations, then moves to measured station curves, station coverage, pseudo-sections, and a two-line comparison.

Open the rendered example here: Bostick depth and CSUMT survey design (pycsamt.emtools.csumt).