BOUGUER

Geophysical Bouguer Analysis — User Manual

Overview

BOUGUER is a graphical tool for working with geophysical point data that includes gravity measurements. It covers the preparation, density correction, and analysis of Bouguer gravity anomaly data: clipping data to an area of interest, recalculating the Bouguer anomaly for a different crustal density, and finding the optimal density by two independent methods — Nettleton's minimum-correlation method and the minimum fractal-dimension method.

The sidebar organises all commands into two sections: Data (prepare and clip the input data) and Calculate (analysis and density calculations). The result of each command is displayed in the Plot Preview panel on the right.

File formats

BOUGUER works with ASCII point data files. Two column layouts are supported:

Format	Columns	Description
3-column (XYZ)	`X Y Z`	A simple point file with X/Y position and a Z value (e.g. elevation). Used by coordinates and fractal dimension.
6-column (full)	`X Y A S F B`	Full gravity station file. A = elevation (m), S = station correction (mGal), F = free-air anomaly (mGal), B = Bouguer anomaly (mGal). Required by bouguer density, min correlation, and min dimension.

In both formats, comment lines beginning with # and blank lines are ignored. Up to 20 000 points are read from each file; additional points are silently discarded.

Interface

Toolbar buttons

Button	Action
Save Plot	Save the current preview image as PNG or JPG at a chosen pixel size (current size, a preset, or custom dimensions).
Clear Log	Erase the output log panel.

Typical workflow

coordinates — clip data to the area of interest; optionally apply a coordinate projection.
fractal dimension — inspect the roughness of the data to assess data quality and choose variogram settings.
bouguer density — recalculate the Bouguer anomaly if a different reference density is needed.
min correlation and/or min dimension — find the density that best removes topographic influence from the Bouguer anomaly.

coordinates

Clips a 3-column (X, Y, Z) or 6-column (X, Y, A, S, F, B) point dataset to a rectangular bounding box. The output file preserves the same number of columns as the input.

When to use: Before any analysis, to restrict the dataset to the area of interest or to remove points that fall outside the study region.

Option	Description
Input data file	Source file of 3-column or 6-column whitespace-delimited data.
Output data file	Destination file for the clipped dataset. Optional — if left blank, the clipped points are kept in memory and displayed without saving to disk.
Bounding limits — X min, Y min, X max, Y max	The rectangle to retain. Points whose XY coordinates fall within the rectangle are kept. Auto-filled from the data extents when an input file is selected.
Remove duplicate XY points	If ticked, points sharing the same XY coordinates (to 6 decimal places) are reduced to one.
Show selected area	Re-plots the data points in the preview panel and overlays four lines marking the chosen bounding rectangle. Use this to verify the limits before running.

Coordinate conversion (Lam19, x East)

The form includes two buttons for converting between geographic (longitude/latitude) and projected (Cartesian kilometre) coordinates using the Lambert Conformal Conic projection centred on 19°W, 65°N (International 1924 ellipsoid). The conversion operates on the in-memory data and updates the bounding limits automatically.

Button	Effect
lon / lat → x, y	Converts column 1 (latitude °N) and column 2 (longitude °W) to Cartesian kilometres. Column 1 becomes x (km, East-positive), column 2 becomes y (km, North-positive).
x, y → lon / lat	Converts column 1 (x km, East-positive) and column 2 (y km, North-positive) back to geographic coordinates. Column 1 becomes latitude (°N), column 2 becomes longitude (°W, positive).

Coordinate conversion is applied to the currently loaded in-memory dataset. Load a file first, then convert. After conversion, the bounding-limit fields and the preview scatter plot are both updated. The conversion is not written to disk until you click Run Command and specify an output file.

fractal dimension

Estimates the fractal dimension D of a scalar field from the variogram of irregularly-spaced point data. The variogram measures how the variance of the field value increases with separation distance. A log-log regression of variance on distance gives the slope from which D is derived:

D = 3 − slope / 2

D values near 2 indicate a smooth surface; values approaching 3 indicate a maximally rough (uncorrelated) surface.

When to use: To characterise the roughness of elevation, free-air, or Bouguer data, and to compare roughness across different density choices when used alongside min dimension.

Option	Description
Input data file	Source file. Accepts both 3-column and 6-column formats.
Value	Which data column to analyse. Elevation uses column A (or Z for 3-column files). Free Air uses column F. Bouguer Value uses column B. The Free Air and Bouguer options are only enabled when a 6-column file is loaded.
Distance classes	Number of distance bins for the variogram. More bins give finer resolution but require more data pairs per bin. Range: 10–200; default: 50.
Min pairs / class	Minimum number of data pairs a distance bin must contain to be included in the regression. Bins with fewer pairs are excluded. Range: 1–9999; default: 32.
Fit range — Min, Max	The distance range over which the log-log regression is fitted. Only variogram bins whose distance falls in this range are used. Auto-populated from the actual data range when a file is selected. Adjust to focus the fit on the linear part of the variogram.

When a file is selected, the variogram is computed immediately and displayed in the preview panel, allowing inspection of the curve before running the full fit.
Switching the Value radio button recomputes the variogram preview on the fly.
The regression line, fractal dimension D, and R² value are added to the preview plot after running.
Large datasets (more than 2 000 points) are sub-sampled randomly to 2 000 points for variogram computation. The log reports when this happens.
After running, the variogram settings are remembered and offered as defaults in the min dimension form.

bouguer density

Recalculates the Bouguer anomaly column (B) in a 6-column file for a new crustal density. The new anomaly is written to a 6-column output file with the same structure as the input.

When to use: When the Bouguer anomaly in the input file was computed with a density that is not optimal for the study area, and you want to produce a corrected dataset at a specific new density.

Physics

The Bouguer plate correction is proportional to density. Given the free-air anomaly F, the old Bouguer anomaly B, and the old density ρ₀, the terrain factor is independent of density:

terrain_factor = (F − B) / ρ₀

The new Bouguer anomaly for density ρ_new is then:

B_new = F − terrain_factor × ρ_new

Option	Description
Input data file	6-column source file. A 3-column file will be rejected with an error.
Output data file	6-column output file. Required — the result cannot be kept in memory only.
Input density value	The crustal density (g/cm³) used to compute the Bouguer anomaly already in the file. Default: 2.6.
Output density value	The new crustal density (g/cm³) to use for the recalculated anomaly. Default: 2.6.

All six output columns (X, Y, A, S, F, B_new) are written; only the last column changes. Setting the input and output densities to the same value produces an identical copy of the input file.

min correlation

Implements Nettleton's method for finding the optimal Bouguer density. The method sweeps a range of test densities, recalculates the Bouguer anomaly for each density, and computes the Pearson correlation coefficient between the recalculated anomaly and the station elevation. The optimal density is the one that minimises the absolute correlation, reflecting minimal topographic influence on the gravity anomaly.

When to use: To determine the best density for Bouguer reduction in the study area, based on the assumption that the true Bouguer anomaly should be uncorrelated with topography.

Option	Description
Input data file	6-column source file. A 3-column file will be rejected with an error.
Input density value	The density (g/cm³) used to compute the Bouguer anomaly in the input file. Default: 2.6.
Min Bouguer density	Lower bound of the density sweep (g/cm³). Default: 2.2.
Max Bouguer density	Upper bound of the density sweep (g/cm³). Default: 2.6.
Density increment	Step size between successive test densities (g/cm³). Smaller values give a finer sweep but take longer. Default: 0.1.

Result: A line plot of test density versus Pearson correlation coefficient. The density of minimum absolute correlation is highlighted in red with a vertical dashed line. The log reports the minimum |r| value and the corresponding density.

The correlation is computed only over points where both elevation and recalculated Bouguer anomaly are finite. Enabling Verbose output prints the correlation value at each density step to the log.

min dimension

Finds the optimal Bouguer density by minimising the fractal dimension of the Bouguer anomaly field. The method sweeps a range of test densities, recalculates the Bouguer anomaly for each density, computes a variogram, fits a log-log regression, and derives the fractal dimension D. The optimal density is the one for which D is smallest, indicating a smoothest anomaly field with the least topographic roughness.

When to use: As an alternative or complement to min correlation. Both methods aim to find the density that removes topographic influence, but rely on different statistical criteria.

Option	Description
Input data file	6-column source file. A 3-column file will be rejected with an error.
Input density value	The density (g/cm³) used to compute the Bouguer anomaly in the input file. Default: 2.6.
Min Bouguer density	Lower bound of the density sweep (g/cm³). Default: 2.2.
Max Bouguer density	Upper bound of the density sweep (g/cm³). Default: 2.6.
Density increment	Step size between successive test densities (g/cm³). Default: 0.1.
Distance classes	Number of distance bins for the variogram at each density step. Same meaning as in fractal dimension. Default: 50.
Min pairs / class	Minimum number of data pairs per distance bin required to include a bin in the regression. Default: 32.
Fit range — Min, Max	The distance range used for the log-log regression at each density step. Auto-populated from a quick variogram of the existing Bouguer column when a file is selected. If fractal dimension was run beforehand, its settings are offered as defaults.

Result: A line plot of test density versus fractal dimension D. The density of minimum D is highlighted in red with a vertical dashed line. The log reports the minimum D value and the corresponding density.

Density steps that produce too few qualifying variogram bins (after applying Min pairs and Fit range constraints) are silently skipped. Enabling Verbose output logs the reason for each skipped step.
The variogram settings are pre-populated from the most recent fractal dimension run, so it is convenient to run fractal dimension first to identify a suitable fit range.
When a file is selected, a quick variogram of the existing Bouguer column is run automatically to suggest sensible fit-range values even if fractal dimension has not been run.