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Structural Geophysics Tool v0.3.5

Simple Potential Field and other Geophysical Grid Calcs to assist WAXI/Agate Structural Geophysics Course
https://waxi4.org and https://agate-project.org

This plugin is available directly within QGIS using the Plugin Manager, however the latest ArcGIS Pro version or QGIS version with all new bugs will always be at this site.

Cheat Sheet (Thanks Chops)  |    Download Basic Help Document    |    Ctrl-click on link to watch demo video

SGTools image

changelog=0.3.5

  * Redo GUI so it removes need for .ui file and is now scrollable
  * redo RTE code following suggestion from google AI   
  * Almost complete ARCGIS Pro version now available with full GUI   
  * Translations of GUI for French, Spanish, Portuguese (Portugal and Brazil) and Mandarin
  0.3.4
  * Fix bug with inclination declination signs for RTE_P now that images always north  
  * Check for commas as decimal separators and replace with decimal points     
  0.3.3
  * change to single decimal versions
  * Don't add mean back for THG calcs
  * Replace raster copy code so it works for negative and positive delta z on linux/macos
  * Add networkx, shapely and geopandas  as requirement so linux is happy
  0.3.01   
  * Fix median removal and resoration for FFT filters   
  0.3.00   
  * Add Spatial stats and convolutions to ArcGIS Pro toolbox
  * Move ArcGIS Pro files to their own directory    
  * Update IGRF to allow up to 2030    
  * Move Euler and IGRF to calcs directory   
  * Compatibility with both QGIS4/QT6 and QGIS3/QT5   
  * Add RGB triplets as LUT definition for RGB to greyscale convertor

Full changelog Metadata

Installation

QGIS:

  1. Either:
  • Download the zip file from the green <> Code button and install the zip file in QGIS using the plugin manager for the version on github or
  • Install directly from the QGIS plugin manager from the plugin repository

  1. If you get an warning of the type The following Python packages are required but not installed: scikit-learn or any other module name the best way to manage this is to install the QGIS Plugin called qpip and open it. It will tell you which libraries are missing and allow you to install the correct versions.

    The packages required for specific functions are:
    matplotlib WTMM, Radial Power Spectrum
    scikit-learn BSDWorms, PCA, ICA
    PyWavelets WTMM

    If you don't use these functions, there is no need to install the extra packages.

  2. For BSpline Gridding you need to install the plugin Processing Saga NextGen Provider

ArcGIS Pro:

  1. Download and unzip this respository and store somewhere safe.
  2. In order to run python code in ARCGIS Pro you will need to manually add some python libraries so SGTool has everything it needs:
  • a) Open Package Manager: Click the Project tab on the ribbon and select Package Manager from the side menu. Access Environment Manager (Gear icon to right of active environemnt):
    • i) Click the Environment Manager button (top right) to open the management dialog.
    • ii) Clone the Environment: Locate the environment you wish to copy (usually arcgispro-py3) and click the Clone button next to it.
    • iii) Set Destination: In the Clone Environment dialog, provide a name and path for your new environment or leave it as the default.
    • iv) Finalize: Click OK. The cloning process may take several minutes.
  • b) Once the cloned environment has been created, search for and install the following packages, if they are not already installed:
    • scipy
    • matplotlib
    • scikit-learn
    • pyproj>=3.7.2
    • networkx
    • shapely
    • geopandas
  • c) In the ArcGIS Pro Catalogue area, go to Add toolbox and select the file GeophysicalProcessor.pyt in the ArcGIS directory in this repository.
  • d) Double click on the new Geophysical Processing Toolbox to get the list of functions that can be run, and select Launch then run SGTool GUI . Alternatively you can access individual tools from the same Toolbox.

Inputs

Capabilities

Grav/Mag Filters

Reduction to the Pole
$H_{RTP}(k_x, k_y) = \frac{k \cos I \cos D + i k_y \cos I \sin D + k_x \sin I}{k}$
Converts magnetic data measured at any inclination and declination to what it would be if measured at the magnetic pole. Where

  • kx and ky : The wavenumber components in the x and y directions.
  • k = The total wavenumber magnitude = sqrt{kx2 + ky2}
  • I : Magnetic inclination (in radians).
  • D : Magnetic declination (in radians).
  • i : Imaginary unit.

Reduction to the Equator
$H_{RTE}(k_x, k_y) = \frac{k \cos I \cos D + i k_y \cos I \sin D + k_x \sin I}{k \cos I \cos D - i k_y \cos I \sin D + k_x \sin I}$
Converts magnetic data measured at any inclination and declination to what it would be if measured at the magnetic equator. Where

  • kx and ky : The wavenumber components in the x and y directions.
  • k = The total wavenumber magnitude = sqrt{kx2 + ky2}
  • I : Magnetic inclination (in radians).
  • D : Magnetic declination (in radians).
  • i : Imaginary unit.

Continuation
$H(k) = e^{-k h}$
Where
h > 0 for upward continuation.
h < 0 for downward continuation.

Vertical Integration
$H(k_x, k_y) = \frac{1}{k}$
When applied to an RTE or RTP image provides the so called Pseudogravity result
Where
k = sqrt{kx2 + ky2} .

Frequency Filters

High Pass Filter

$$H(k) = \begin{cases} 0 & \text{if } k \leq k_{low} \\ \frac{1}{2}\left(1 - \cos\left(\pi \frac{k - k_{low}}{k_{high} - k_{low}}\right)\right) & \text{if } k_{low} < k < k_{high} \\ 1 & \text{if } k \geq k_{high} \end{cases}$$

The high-pass filter removes low-frequency components (long wavelengths) while retaining high-frequency components (short wavelengths) with a smooth transition to reduce ringing artifacts.

Where:

  • $k$ : Current wavenumber magnitude $\sqrt{k_x^2 + k_y^2}$
  • $k_{low} = \frac{2\pi}{\lambda_c + w/2}$ : Lower transition boundary
  • $k_{high} = \frac{2\pi}{\lambda_c - w/2}$ : Upper transition boundary
  • $\lambda_c$ : Cutoff wavelength
  • $w$ : Transition width (in same units as wavelength)

Low Pass Filter

$$H(k) = \begin{cases} 1 & \text{if } k \leq k_{inner} \\ \frac{1}{2}\left(1 + \cos\left(\pi \frac{k - k_{inner}}{k_{outer} - k_{inner}}\right)\right) & \text{if } k_{inner} < k < k_{outer} \\ 0 & \text{if } k \geq k_{outer} \end{cases}$$

The low-pass filter removes high-frequency components (short wavelengths) while retaining low-frequency components (long wavelengths). Optional smooth transition reduces potential ringing artifacts.

Where:

  • $k$ : Current wavenumber magnitude $\sqrt{k_x^2 + k_y^2}$
  • $k_{inner} = \frac{2\pi}{\lambda_c + w/2}$ : Inner transition boundary
  • $k_{outer} = \frac{2\pi}{\lambda_c - w/2}$ : Outer transition boundary
  • $\lambda_c$ : Cutoff wavelength
  • $w$ : Transition width (optional, in same units as wavelength)

Band Pass Filter

$$H(k) = H_{high}(k) \times H_{low}(k)$$

Where:

High-pass component: $$H_{high}(k) = \begin{cases} 0 & \text{if } k \leq k_{h,low} \ \frac{1}{2}\left(1 - \cos\left(\pi \frac{k - k_{h,low}}{k_{h,high} - k_{h,low}}\right)\right) & \text{if } k_{h,low} < k < k_{h,high} \ 1 & \text{if } k \geq k_{h,high} \end{cases}$$

Low-pass component: $$H_{low}(k) = \begin{cases} 1 & \text{if } k \leq k_{l,inner} \ \frac{1}{2}\left(1 + \cos\left(\pi \frac{k - k_{l,inner}}{k_{l,outer} - k_{l,inner}}\right)\right) & \text{if } k_{l,inner} < k < k_{l,outer} \ 0 & \text{if } k \geq k_{l,outer} \end{cases}$$

The band-pass filter isolates features within a specific wavelength range by combining high-pass and low-pass components with smooth transitions.

Where:

  • $k$ : Current wavenumber magnitude $\sqrt{k_x^2 + k_y^2}$
  • $k_{h,low} = \frac{2\pi}{\lambda_{low} + w_h/2}$ : High-pass lower transition boundary
  • $k_{h,high} = \frac{2\pi}{\lambda_{low} - w_h/2}$ : High-pass upper transition boundary
  • $k_{l,inner} = \frac{2\pi}{\lambda_{high} + w_l/2}$ : Low-pass inner transition boundary
  • $k_{l,outer} = \frac{2\pi}{\lambda_{high} - w_l/2}$ : Low-pass outer transition boundary
  • $\lambda_{low}$ : Low cutoff wavelength (removes longer wavelengths)
  • $\lambda_{high}$ : High cutoff wavelength (removes shorter wavelengths)
  • $w_h$ : High-pass transition width
  • $w_l$ : Low-pass transition width

Directional Band Pass
Removes combined directional and high pass filtered data from original data, with scaling function modify extent of feature suppression.

Butterworth High-Pass Filter $H(k) = \frac{1}{1 + \left(\frac{k_c}{k}\right)^{2n}}$
The Butterworth filter attenuates frequencies below the cutoff kc while preserving higher frequencies.
H(k) : Filter response as a function of wavenumber k.
k : Wavenumber (spatial frequency).
kc : Cutoff wavenumber, related to the cutoff wavelength by kc = \frac{1}{\text{cutoff wavelength}}.
n : Filter order, determining the sharpness of the transition. Higher ( n ) makes the filter more selective.

Directional Cosine Filter
$H(k_x, k_y) = \left| \cos(\theta - \theta_c) \right|^p$
The Directional Cosine Filter emphasizes or suppresses frequency components along a specific direction.
H(kx, ky): Filter response as a function of wavenumber components kx and ky.
theta = \arctan\left(\frac{k_y}{k_x}\right) : Angle of the frequency component.
thetac : Center direction (in radians), representing the direction to emphasize.
p : Degree of the cosine function. Higher ( p ) sharpens the directional emphasis.

Remove Regional
Remove a 1st order (dipping plane) or 2nd order (parabolic plane) regional from data.

Automatic Gain Control
$AGC(x, y) = \frac{f(x, y)}{\text{RMS}(f(x, y), w)}$
Where
RMS(f, w) is the root mean square of the data over a window w.

Radially averaged power spectrum (but needs testing!)
$P(k) = \frac{1}{N_k} \sum_{(k_x, k_y) \in k} |\text{FFT}(f)|^2$
Where
P(k) is the radially averaged power spectrum, and Nk is the number of samples in the radial bin.

Gradient Filters

Derivative
$\frac{\partial f}{\partial u} = \frac{\partial f}{\partial x} \cos\theta + \frac{\partial f}{\partial y} \sin\theta$
Where
theta is the angle defining the direction of the derivative (x,y or z).

Total Horizontal Gradient
$THG(x, y) = \sqrt{\left(\frac{\partial f}{\partial x}\right)^2 + \left(\frac{\partial f}{\partial y}\right)^2}$

Analytic Signal
$A(x, y) = \sqrt{\left(\frac{\partial f}{\partial x}\right)^2 + \left(\frac{\partial f}{\partial y}\right)^2 + \left(\frac{\partial f}{\partial z}\right)^2}$
Computes the total amplitude of the gradients, independent of field inclination or declination. Useful for locating edges of potential field sources (e.g., faults or contacts).

Tilt Angle
$T = \tan^{-1}\left(\frac{\frac{\partial f}{\partial z}}{\sqrt{\left(\frac{\partial f}{\partial x}\right)^2 + \left(\frac{\partial f}{\partial y}\right)^2}}\right)$
Enhances the contrast of geological features by highlighting gradients relative to the vertical component. Where
df/dz : Vertical derivative of the field. df/dx , df/dy : Horizontal derivatives of the field.

Convolution Filters

Mean Applies a mean filter using a kernel of size n x n .

Median
Applies a median filter using a kernel of size n x n .

Gaussian
Applies a Gaussian filter with a specified standard deviation.

Directional
Apply directional filter (NE, N, NW, W, SW, S, SE, E)

Sun Shading
Computes relief shading for a digital elevation model (DEM) or other 2D grids.

Spatial Statistics

Calculates 1D statistics in a windowed grid

Min
Calculate Minimum of values around central pixel for given window size

Max
Calculate Maximum of values around central pixel for given window size

Standard Deviation
Calculate Standard Deviation of values around central pixel for given window size

Variance
Calculate Variance of values around central pixel for given window size

Kurtosis
Calculate Kurtosis of values around central pixel for given window size

Skewness
Calculate Skewness of values around central pixel for given window size

DTM Curvature Classifier
Calculate DTM classification based on curvature and slope
Based on Curvature Threshold, Cliff Threshold, Window Size and Smoothing Parameter
Classified array where: -1 = concave up; 0 = flat; 1 = convex up and 2 = steep slope

Multivariate Statistical Analysis

Principal Component Analysis
Principal Component Analysis (PCA) transforms correlated variables into orthogonal components that maximize variance, creating a new coordinate system where the first component captures the most variance.

Independent Component Analysis
Independent Component Analysis separates a multivariate signal into additive, statistically independent components by maximizing non-Gaussianity, often used to recover source signals from mixed observations.

Euler Deconvolution

Euler Deconvolution
Reliable Euler Deconvolution provides estimates of depth to gravity or magnetic sources based on analysis of gradients. Code derived from Reliable Euler Deconvolution by Felipe F. Melo and Valéria C.F. Barbosa https://github.com/ffigura/Euler-deconvolution-python.

Independent Component Analysis
Independent Component Analysis separates a multivariate signal into additive, statistically independent components by maximizing non-Gaussianity, often used to recover source signals from mixed observations.

Gridding

Import points
Imports point data in csv, ASEG-GDF2 dat or xyz formats

Gridding
Grids point data using either BSpline or IDW built-in gridding algoithms

Wavelets

BSDWorms
Use wavelet transforms to build multilevel "worms", saves out a single csv file of points (for use in 3D visualisation), and optionally a shapefile (for use in QGIS). Code from Frank Horowitz's bsdwormer https://bitbucket.org/fghorow/bsdwormer/

WTMM
Use wavelet transforms to build multilevel analysis (Wavelet Transform Modulus Maxima) along a selected linestring (polyline) profile extracted from grid or for imported XYZ data.

Utilities

Threshold to NaN
Define upper or lower bound (or range) for which values will be set to NaN (i.e. excluded from display). Useful when reprojected images produce an unwanted border.

Create Clipping Polygon
Create one or more polygons outlining the available data in the grid. Useful, amongst other things, for clipping worms to grid area..

Normalise Grids
Normalise the means and standard deviations of a series of grids in a directory to minimse mismatches in merged grids. Does not consider overlaps between grids, simply standardises data and removes a first or second order regional.

Convert LUT to grayscale
Takes a 3-band registered RGB image and converts it to a monotonically increasing grayscale image if you provide the correct Look Up Table, uses matplotlib CSS Colour names: https://matplotlib.org/stable/gallery/color/named_colors.html#css-colors

How To

  1. Load a raster image from file
  • If a GRD grid (Oasis Montaj) is selected, the plugin will attempt to load CRS from the associated xml file, if this is not possible a CRS of EPSG:4326 is assumed. In any case the grid is saved as geotiff.
  1. Whatever layer is shown in the layer selector will be the one processed by whatever combination of filters are selected by check boxes, but must exist as a file, this plugin cannot process grids that are only in memory.
  • All processed files will be saved as geotiffs, and will be saved in the same directory as the original file, and will have a suffix added describing the processing step.
  • If a RTP or RTE calculation is performed, it is possible to define the magnetic field manually or the IGRF mag field parameters can be assigned based on the centroid of grid, plus survey date, or embedded geotiff metadata if the source of the tif was a Noddy grid file..
  • If a file exists on disk it will be overwritten, although QGIS plugins don't always like saving to disks other than C: on Windows, and can't overwrite a file if the grid is open in another program.
  • Length units are defined by grid properties except for Up/Down Continuation (so Lat/Long wavelengths should be defined in degrees!)
  1. If multiple processing steps are required, first apply one process, select the result and then apply subsequent steps.

Alternatives

There are several excellent Open Source or at least free alternatives to this plugin if you don't want to use QGIS, or want to do things this plugin can't:

Code development

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Simple Potential Field Calcs

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