Visualization tips for geoscientists: Matlab, part III


Last weekend I had a few hours to play with but needed a short break from writing about color palettes, so I decided to go back and finish up (for now) this series on geoscience visualization in Matlab. In the first post of the series I expanded on work by Steve Eddins at Mathworks on overlaying images using influence maps and demonstrated how it could be used to enhance the display of a single geophysical dataset.

Using transparency to display multiple data sets an example

At the end of the second post I promised I would go back and show an example of using transparency and influence maps for other tasks, like overlaying of different attributes. Here’s my favorite example in Figure 1. The image is a map in pastel colors of the Bouguer Gravity anomaly for the Southern Tuscany region of Italy, with three other layers superimposed using the techniques above mentioned.

It is beyond the objectives of this post to discuss at length about gravity exploration methods or to attempt a full interpretation of the map. I will go back to it at a later time as I am planning a full series on gravity exploration using this data set, but if you are burning to read more about gravity interpretation please check these excellent notes by Martin Unsworth, Professor of Physics at the Earth and Atmospheric Sciences department, University of Alberta, and note 4 at the end of this post. Otherwise, and for now, suffice it to say that warm colors (green to yellow to red) in the Bouguer gravity map indicate, relatively speaking, excess mass in the subsurface and blue and purple indicate deficit of mass in the subsurface.

The black and grey lines are lineaments extracted from derivatives of the Bouguer gravity data using two different methods [1]. The semitransparent, white-filled polygons show the location of some of the  basement outcrops (the densest rocks in this area).

Lineaments extracted from gravity data can correspond to contacts between geological bodies of different density, so a correlation can be expected between basement outcrops and some of the lineaments, as they are often placed in lateral contact with much lesser dense rocks. This is often exploited in mineral exploration in areas such as this where mineralization occurs at or in the vicinity of this contacts. As an example, I show in Figure 2 the occurrences (AGIP – RIMIN, unpublished industry report, 1989) of silicization (circles) and antimony deposits (triangles), superimposed on the distance from one of the set of lineaments (warm colors indicate higher distance) from Figure 1.

The fact that different methods give systematically shifted results is a known fact, often due the trade-off between resolution and stability, whereby the more stable methods are less affected by noise, but often produce smoother edges over deeper contacts, and their maxima may not correspond. This is in addition to the inherent ambiguity of gravity data, which cannot, by themselves, be interpreted uniquely. To establish which method might be more correct in this case (none is a silver bullet) I tried to calibrate the results using basement outcrops (i.e. does either method more closely match the outcrop edges?). Having done that, I would have more confidence in making inferences on possible other contacts in the subsurface suggested by lineament. I would say the black lines do a better overall job in the East, the gray perhaps in the West. So perhaps I’m stuck? I will get back to this during my gravity series.

Figure 1


Figure 2

Matlab code

As usual I am happy to share the code I used to make the combined map of Figure 1. Since the data I use is in part from my unpublished thesis in Geology and in part from Michele di Filippo at the University of Rome, I am not able to share it, and you will have to use your own data, but the Matlab code is simply adapted. The code snippet below assume you have a geophysical surface already imported in the workspace and stored in a variable called “dataI”, as well as the outcrops in a variable called “basement”, and the lineaments in “lnmnt1” and “lnmnt2”. It also uses my cube1 color palette.

% part 1 - map gravity data
figure; imagesc(XI,YI,dataI); colormap(cube1); hold on;
% part 2 - dealing with basement overlay
white=cat(3, ones(size(basement)), ones(size(basement)),...
ttt=imagesc(Xb,Yb,white); % plots white layer for basement
% part 3 - dealing with lineaments overlays
black=cat(3, zeros(size(lnmnt1)), zeros(size(lnmnt1)),...
kkk=imagesc(XI,YI,black); % plots black layer for lineament 1
sss=imagesc(XI,YI,gray); % plots gray layer for lineament 2
hold off
% part 4 - set influence maps
set(ttt, 'AlphaData', basement_msk); % influence map for basement
set(kkk, 'AlphaData', lnmnt1); % influence map for linement 1
set(sss, 'AlphaData', lnmnt2); % influence map for linement 2
% making it pretty
axis equal
axis tight
axis off
set(gcf,'Position',[180 150 950 708]);
set(gcf,'OuterPosition',[176 146 958 790]);

Matlab code, explained

OK, let’s break it down starting from scratch. I want first to create a figure and display the gravity data, then hold it so I can overlay the other layers on top of it. I do this with these two commands:


hold on;

The layer I want to overlay first is the one showing the basement outcrops. I make a white basement layer covering the full extent of the map, which is shown in Figure 3, below.

Figure 3

I create it and plot it with the commands:

white=cat(3, ones(size(basement)), ones(size(basement)), ones(size(basement)));


The handle  ttt is to be used in combination with the basement influence map to produce the partly transparent basement overlay: remember that I wanted to display the outcrops in white color, but only partially opaque so the colored gravity map can still be (slightly) seen underneath. I make the influence map, shown in Figure 4, with the command:


Since the original binary variable “basement” had values of 1 for the outcrops and 0 elsewhere, whit the command above I assign an opacity of 0.6 to the outcrops, which will be applied when the next command, below, is run, achieving the desired result.

set(ttt, ‘AlphaData’, basement_msk); % uses basement influence map

Figure 4

For the lineaments I do things in a similar way, except that I want those plotted with full opacity since they are only 1 pixel wide.

As an example I am showing in Figure 5 the black layer lineament 1 and in Figure 6 the influence map, which has values of 1 (full opacity) for the lineament and 0 (full transparency) for everywhere else.

Figure 5

Figure 6

Now a few extra lines to make things pretty, and this is what I get, shown below in Figure 7: not what I expected!

Figure 7

The problem is in these two commands:

white=cat(3, ones(size(basement)), ones(size(basement)), ones(size(basement)));


I am calling the layer white but really all I am telling Matlab is to create a layer with maximum intensity (1). But the preceding colormap(cube1) command assigned a salmon-red color to the maximum intensity in the figure, and so that is what you get for the basement overlay.

Again, to get the result I wanted, I had to come up with a trick like in the second post examples. This is the trick:

I create a new color palette with this command:

cube1edit=cube1; cube1edit(256,:)=1;  

The new color palette has last RGB triplet actually defined as white, not salmon-red.

Then I replace this line:

figure; imagesc(XI,YI,dataI); colormap(cube1); hold on;

with the new line:

figure; imagesc(XI,YI,dataI, [15 45]); colormap (cube1edit); hold on;

The highest value in dataI is around 43. By spreading the color range from [15 43] to [15 45], therefore exceeding max(dataI) I ensure that white is used for the basement overlay but not in any part of the map where gravity is highest but there is no basement outcrop. In other words, white is assigned in the palette but reserved to the overlay.

Please let me know if that was clear. If it isn’t I will try to describe it better.


[1] One method is the total horizontal derivative. The other method is the hyperbolic tilt angle – using Matlab code by Cooper and Cowan (reference). This is how I produced the two overlays:  first I calculated the total horizontal derivative and the tilt angle, then I found the maxima to use as the overlay layers. This is similar to Figure 3e in Cooper and Cowan, but I refined my maxima result by reducing them to 1-pixel-wide lines (using a thinning algorithm).


Cooper, G.R.J., and Cowan, D.R. (2006) – Enhancing potential field data using filters based on the local phase  Computers & Geosciences 32 (2006) 1585–1591

Related posts (MyCarta)

Visualization tips for geoscientists: Surfer

Visualization tips for geoscientists: Matlab

Visualization tips for geoscientists: Matlab, part II

Image Processing Tips for Geoscientists – part 1

Visualization tips for geoscientists: Matlab, part II


In my previous post on this topic I left two loose ends: one in the main text about shading in 3D, and one in the comment section to follow-up on a couple of points in Evan’s feedback. I finally managed to go back and spend some time on those and that is what I am posting about today.

Part 1 – apply shading with transparency in 3D with the surf command

I was trying to write some code to apply the shading with transparency and the surf command. In fact, I’ve been trying, and asking around in the Matlab community for more than one year. But to no avail. I think it is not possible to create the shading directly that way. But I did find a workaround. The breakthrough came when I asked myself this question: can I find a way to capture in a variable the color and the shading associated with each pixel in one of the final 2D maps from the previous post? If I could do that, then it would be possible to assign the colors and shading in that variable using this syntax for the surf command:


where data is the gravity matrix and c is the color and shading matrix. To do it in practice I started from a suggestion by Walter Robertson on the Matlab community in his answer to my question on this topic.

The full code to do that is below here, followed by an explanation including 3 figures. As for the other post, since the data set I use is from my unpublished thesis in Geology, I am not able to share it, and you will have to use your own data, but the Matlab code is simply adapted.

%% cell 1
shadedpcolor(x,y,data,(1-normalise(slope)),[-5.9834 2.9969],[0 1],0.45,cube1,0);
axis equal; axis off; axis tight
shadedcolorbar([-5.9834 2.9969],0.55,cube1);

In cell 1 using again shadedpcolor.mnormalise.m, and cube1 color palette I create the 2D shaded image, which I show here in Figure 1.

Figure 1

Continue reading

Visualization tips for geoscientists – Matlab


In my last post I described how to create a powerful, nondirectional shading for a geophysical surface using the slope of the data to assign the shading intensity (i.e. areas of greater slope are assigned darker shading). Today I will show hot to create a similar effect in Matlab.

Since the data set I use is from my unpublished thesis in Geology, I am not able to share it, and you will have to use your own data, but the Matlab code is simply adapted. The code snippets below assume you have a geophysical surface already imported in the workspace and stored in a variable called “data”, as well as the derivative in a variable called “data_slope”.

Method 1 – with a slope mask and transparency

Some time ago I read this interesting Image Processing blog post by Steve Eddins at Mathworks on overlaying images using transparency. I encourage readers to take a look at this and other posts by Steve, he’s great! That particular blog post gave me the idea to use transparency and the slope to create my favorite shading in Matlab.

In addition to the code below you will need normalise.m from Peter Kovesi‘s website, and to import the color palette cube1.

%% alpha transparency code snippet
black = cat(3, zeros(size(data)), zeros(size(data)), ...
    zeros(size(data)));             % make a truecolor all-black image
gray=black+0.2;                     % make a truecolor all-gray image
alphaI=normalise(data_slope);       % create transparency weight matrix
                                    % using data_slope

imagesc(data);colormap(cube1);      % display data
hold on
h = imagesc(gray);                  % overlay gray image on data
hold off
set(h, 'AlphaData', alphaI);        % set transparency of gray layer using
axis equal;                         % weight matrix
axis tight;
axis off;

And here is the result in Figure 1 below – not bad!

Figure 1. Shaded using transparency

Continue reading

Visualization tips for geoscientists: Surfer

I have been thinking for a while about writing on visualization of geophysical data. I finally got to it, and I am now pleased  to show you a technique I use often.  This tutorial has shaped up into 2 independent posts: in the first post I will show how to implement the technique with Surfer, in the second one with Matlab (you will need access to a license of Surfer 8.08 or later, and Matlab 2007a or later to replicate the work done in the tutorial).

I will illustrate the technique using gravity data since it is the data I developed it for. In an upcoming series of gravity exploration tutorials I will discuss in depth the acquisition, processing, enhancement, and interpretation of gravity data (see [1] and [4]). For now, suffice it to say that gravity prospecting is useful in areas where rocks with different density are laterally in contact, either stratigraphic or tectonic, producing a measurable local variation of the gravitational field. This was the case for the study area (in the Monti Romani of Southern Tuscany) from my thesis in Geology at the University of Rome [2].

In this part of the Apennine belt, a Paleozoic metamorphic basement (density ~2.7 g/cm3) is overlain by a thick sequence of clastic near-shore units of the Triassic-Oligocene Tuscany Nappe (density ~2.3 g/cm3). The Tuscan Nappe is in turn covered by the Cretaceous-Eocene flish units of the Liguride Complex (density ~2.1 g/cm3).

During the deformation of the Apennines, NE verging compressive thrusts caused doubling of the basement. The tectonic setting was later complicated by tensional block faulting with formation of horst-graben structures generally extend along NW-SE and N-S trends which were further disrupted by later and still active NE-SW normal faulting (see [2], and reference therein, for example [3]).

This complex tectonic history placed the basement in lateral contact with the less dense rocks of the younger formations and this is reflected in the residual anomaly map [4] of Figure 1. Roughly speaking, there is a high in the SE quadrant of ~3.0 mgal corresponding to the location of the largest basement outcrop, an NW-SE elongated high of ~0.5 mgal in the centre bound by lows on both the SW and NE (~-6.0 and ~-5.0 mgal, respectively), and finally a local high in the N.W. quadrant of ~-0.5 mGal. From this we can infer that in this area can infer that the systems of normal faults caused differential sinking of the top of basement in different blocks leaving an isolated high in the middle, which is consistent with the described tectonic history [2]. Notice that grayscale representation is smoothly varying, reflecting (and honoring) the structure inherent in the data. It does not allow good visual discrimination and comparison of differences, but from the interpretation standpoint I recommend to always start out with it: once a first impression is formed it is difficult to go back. There is time later to start changing the dispaly.

 Figure 1 – Grayscale residual anomalies in milligals. This version of the map was generate using the IMAGE MAP option in Surfer.

OK, now that we formed a first impression, what can we try to improve on this display? The first thing we can do to increase the perceptual contrast is to add color, as I have done in Figure 2. This is an improvement, now we are able to appreciate smaller changes, quickly assess differences, or conversely identify areas of similar anomaly. Adding the third dimension and perspective is a further improvement, as seen in figure 3. But there’s still something missing. Even though we’ve added color, relief, and perspective, the map looks a bit “flat”.

Figure 2 – Colored residual anomalies in milligals. This version of the map was generate using the IMAGE MAP option in Surfer.
Figure 3 – Colored 3D residual anomaly map in milligals. This version of the map was generate using the SURFACE MAP option in Surfer.

Adding contours is a good option to further bring out details in the data, and I like the flexibility of contours in Surfer. For example, for Figure 4 I assigned (in Contour Properties, Levels tab) a dashed line style to negative residual contours, and a solid line style to positive residual contours, with a thicker line for the zero contour. This can be done by modifying the style for each level individually, or by creating two separate contours, one for the positive data, one for the negative data, which is handy when several contour levels are present. The one drawback of using contours this way is that it is redundant. We used 3 weapons  – color, relief, and contours – to dispaly one dataset, and to characterize just one property, the shape of gravity anomaly. In geoscience it is often necessary, and desireable to show multiple (relevant) datasets in one view, so this is a bit of a waste. I would rather spare the contours, for example, to overlay and compare anomalous concentrations in gold pathfinder elements on this gravity anomaly map (one of the objectives of the study, being the Monti Romani an area of active gold exploration).

Figure 4 – Colored 3D residual anomaly map in milligals. Contours were added with the the CONTOUR MAP option in Surfer.  Figure 5 – Colored 3D residual anomaly map in milligals with lighting (3D Surface Properties menu). Illumination is generated by a point source with -135 deg azimuth and 60 deg elevation, plus an additional 80% gray ambient light, a 30% gray diffuse light, and a 10% gray specular light.

The alternative to contours is the use of illumination, or lighting, which I used in Figure 5. Lighting is doing a really good job: now we can recognize there is a high frequency texture in the data and we see some features both in the highs and lows. But there’s a catch:  we are now introducing perceptual artifacts, in the form of bright white highlight, which is obscuring some of the details where the surface is orthogonal to the point source light.

There is a way to illuminate the surface without introducing artifact – and that is really wanted to show you with this tutorial – which is to use a derivative of the data to assign the shading intensity (areas of greater gradient were assigned darker shading) [5]. In this case  I choose the terrain slope, which is the slope in the direction of steepest gradient at any point in the data (calculated in a running window). The result is a very powerful shading. Here is how you can do it in Surfer:


Result is shown in Figure 6 below:

Figure 6 – Terrain slope of residual anomaly. Black for low gradients, white for high gradients. Displayed using IMAGE MAP option.

2) CREATE COMPLEMENT OF TERRAIN SLOPE AND NORMALIZE TO [1 0] RANGE (to assign darker shading to areas of greater slope. This is done with 3 operations:

i)     GRID > MATH> B=A – min(A)

where min(A) is the minimum value, which you can read off the grid info (for example you would double click on the map above to open the Map Properties and there’s an info button next to the Input File field) .

ii)    GRID > MATH> C=B /max(B)

iii)   GRID > MATH> D= 1-C

Result is shown in Figure 7 below. This looks really good, see how now the data seems almost 3D? It would work very well just as it is. However, I do like color, so I added it back in Figure 8. This is done by draping the grayscale terrain slope complement IMAGE MAP as an overlay over the colored residual anomaly SURFACE MAP, and setting the Color Modulation to BLEND in the 3D Surface Properties in the Overlay tab. I really do like this display in Figure 8, I think it is terrific. Let me know if you like it best too.

Finally, in Figure 9, I added a contour of the anomaly in the Gold Pathfiners, to reiterate the point I made above that contours are best spared for a second dataset.

In my next post I will show you how to do all of the above programmatically in Matlab (and share the code). Meanwhile, comments, suggestions, requests are welcome. Have fun mapping and visualizing!

Figure 7 – Complement of the terrain slope. White for low gradients, black for high gradients. Displayed using IMAGE MAP option.
Figure 8 – Complement of the terrain slope with color added back.
Figure 9 – Complement of the terrain slope with color added back and contour overlay of gold pathfinders in stream sediments.


Did you lie the colormap? In future series on perceptually balanced colormaps I will tell you how I created it. For now, if you’d like to try it on your data you can download it here:

cube1 – generic format with RGB triplets;

Cube1_Surfer – this is preformatted for Surfer with 100 RGB triplets and header line. Dowload the .doc file, open and save as plain text, then change extension to .clr;

Cube1_Surfer_inverse – the ability to flip color palette is not implemented in Surfer (at least not in version 8) so I am including the flipped version of above. Again, dowload the .doc file, open and save as plain text, then change extension to .clr.


Visualization tips for geoscientists: Matlab

Visualization tips for geoscientists: Matlab, part II

Visualization tips for geoscientists: Matlab, part III

Image Processing Tips for Geoscientists – part 1

Compare lists from text files using Matlab – an application for resource exploration


Basement structure in central and southern Alberta: insights from gravity and magnetic maps

Making seismic data come alive

Visual Crossplotting

Mapping multiple attributes to three- and four-component color models — A tutorial

Enhancing Fault Visibility Using Bump Mapped Seismic Attributes 


I would like to thank Michele di Filippo at the Department of Earth Science, University of Rome La Sapienza, to whom I owe a great deal. Michele, my first mentor and a friend, taught me everything I know about the planning and implementation of a geophysical field campaign. In the process I also learned from him a great deal about geology, mapping, Surfer, and problem solving. Michele will make a contribution to the gravity exploration series.


[1] If you would like to learn more about gravity data interpretation please check these excellent notes by Martin Unsworth Unsworth, Professor of Physics at the Earth and Atmospheric Sciences department, University of Alberta.

[2] Niccoli, M., 2000:  Gravity, magnetic, and geologic exploration in the Monti Romani of Southern Tuscany, unpublished field and research thesis, Department of Earth Science, University of Rome La Sapienza.

[3] Moretti A., Meletti C., Ottria G. (1990) – Studio stratigrafico e strutturale dei Monti Romani (GR-VT) – 1: dal Paleozoico all’Orogenesi Alpidica. Boll. Soc. Geol. It., 109, 557-581. In Italian.

[4] Typically reduction of the raw data is necessary before any interpretation can be attempted. The result of this process of reduction is a Bouguer anomaly map, which is conceptually equivalent to what we would measure if we stripped away everything above sea level, therefore observing the distribution of rock densities below a regular surface. It is standard practice to also detrend the Bouguer anomaly to separate the influence of basin or crustal scale effects, from local effects, as either one or the other is often the target of the survey. The result of this procedure is typically called Residuals anomaly and often shows subtler details that were not apparent due to the regional gradients. Reduction to rsiduals makes it easier to qualitatively separate mass excesses from mass deficits. For a more detailed review of gravity exploration method check again the notes in [1] and refer to this article on the CSEG Recorder and reference therein.

[5] Speaking in general, 3D maps without lighting often have a flat appearance, which is why light sources are added. The traditional choice is to use single or multiple directional light sources, but the result is that only linear features orthogonal to those orientations will be highlighted. This is useful when interpreting for lineaments or faults (when present), but not in all circumstances, and requires a lot of experimenting. in other cases, like this one , directional lighting introduces a bright highlight, which obscures some detail. A more generalist, and in my view more effective alternative, is to use information derived from the data itself for the shading. One way to do that is to use a high pass filtered version of the data. i will show you how to do that in matlab in the next tutorial. Another solution, which I favored in this example, is to use a first derivative of the data.