118 posts in this topic

Geo - I was talking about airborne EM surveys - where did I say I wasn't?

You are don't seem to be aware of how geophysics is used in the search for disseminated gold deposits, so let me help you understand how this works. Generally speaking, large hydrothermal systems (like those which give rise to many disseminated type gold and gold-silver deposits, especially in Nevada) lower the bulk resistivity of the country rocks in the areas they affect by adding clay minerals and increasing porosity, permeability and pore salinity. Within these systems, the core is often more intensely silicified, and the silica infusion can increase apparent resistivity in the area affected by that silicification. The broad altered zone often forms a halo around the more intensely silicified and mineralized core. You had said earlier that you don't know what geologists contribute to the exploration process, and here you can see that the understanding of these geologic models and how these geologic systems affect the surrounding rocks, helps leads to a better understanding of how geophysics can be used to discover them.

Much of geophysics is used to detect deposits indirectly. You yourself referred in this thread to buried plutons that are likely sources of gold detected by their magnetics.

Clay and Tad R like this

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I've got my hip waders on and I'm going to give a shot at bringing some of my cartographic experience to this subject.

 

We find it much easier to understand complex discreet interactive data as visual images. It's just our nature as human beings.

 

As such it's important to realize that the maps you have been viewing are not the data. These are only an attempt on the part of the mapmaker to interpret the data in a visual form.

 

This has led to some basic misunderstandings of the quality, quantity and nature of the data being displayed. Some of the most obvious I'll explain below.

 

1. This data is not being displayed in three dimensions.

These mapping methods often appear to be three dimensional. This is a common misunderstanding due to our experiences with multi dimensional mapping. If the data is 2D the mapping is 2D. There is no effective way to describe the z axis when all the data is captured in the x and y axis.

 

Surface grid samples whether by air or on the ground are x & y axis only. The only effective way to extend a grid under the earths surface is by drilling or some other form of digging. Just because a sample value varies, whether magnetic, sound or gravity based, it does not allow you to see actual z values below the earth's surface.

 

2. The data is not smooth but the presentation is.

Values vary greatly in any type of single point sampling. The cartographer tries to make sense of the data by "smoothing" the actual values. This smoothing is based on some pretty complex mathematics but they are all related to a few, less than simple, concepts. The basic formulas are usually directly calculated from r.flow or one of it's many mathematical brothers, sisters or cousins.

 

Suffice it to say the way the data is manipulated within this math is up to the cartographer. Usually that decision is based on their understanding of the intent of the map and the appearance - not the actual data. If you want to understand the basics of how that data is smoothed look into the math concepts of spline and tension.

 

3. The data resolution is artificially increased by a large factor.

The images you see as a map are almost completely interpolated values. They are interpolated in at least two ways. By the previously mentioned smoothing and by creating artificial color/density gradients between adjacent values.

 

The space between any two data points is usually shown as a gradient from light to dark or visa versa. Color is used to visually define different data classes or predefined levels of values. Of course real maps are dealing with many more data point interactions than two. This can lead to a misunderstanding about the quality or density of the data.

 

The color gradient methods used on these and many other interpretative maps are what are called "heat maps". These heat maps give generalized visuals of single point data that appears to be much more complex than the data actually is. Data points 1/4 mile apart are visualized as a continuous color and density ramp with a resolution about 60 - 300 (or more) times than that of the actual data.

 

Looking at the map already discussed in a previous post the area represented by the outline has a total of about 33 - 35 samples (at 1/4 mile resolution) represented by about 18,000 color dots of about 160 different color combinations. This visual representation has mathematically smoothed data values and artificially created filler values of at least 800:1 in relation to the actual sampled data.

 

post-14528-0-50733200-1418189571.jpg

 

This doesn't mean that these maps have no value. They do have a probative value if you understand the limitations of the medium. As I stated in the beginning we are a visually orientated species. This type of map is our best shot at a quick understanding of the meanings behind the numbers.

 

My point is the information you are discussing has already been interpreted by a visual artist (cartographer). The data you are looking at is not the data to be found in that 2D grid sample. Your own understanding of what the map is showing you is going to be influenced by your understanding of the presentation method, and intent, and the prior interpretation of the cartographer.

 

Dig deeper, much deeper, than the maps if you want to understand the real value of the data being presented.

 

Barry

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Taking your offered values gives us these figures.

 

There are 117,376 pixels in that little image.  I suspect the original is much higher resolution (higher pixel density per ground unit) but I'll base the equations on the image as presented.

 

That comes out to 6.9 pixels for each of 17,000 sample points.

 

The original sample points have no colors - they are only number values.

 

The smoothed and color graduated values on the map representing the sample data use about 160 cartographer defined colors to represent the smoothed values of the survey data. The number of input values from the survey may be greater or less than the number of color values used - that's the representative nature of a color ramp.

 

17,000 samples made in an evenly spaced grid over the 11 square miles on the map comes out to one sample per 289.44 linear meters (949.6 linear feet - 4.55 square acres per sample). Not one per 3.3 meters.

 

If your calculation of 3.3 meters is based on a straight line survey sample and the survey lines are 1/4 mile apart the resolution is 1/4 mile (402.336 meters) - not 289.44 meters  or 3.3 meters. Sample resolution is calculated on the greatest value not the least.

 

_________________________________

 

Resistivity depth is not a measure of the z axis it is only relative to the time domain measurement. This is properly classified as apparent depth. Actual 3D resistivity studies are done with a receiver used at known depths in a physical bore hole in the earth (z axis). See my comment above about the necessity to actually dig before 3D subsurface data can be imaged.

 

There is no z axis in the data. There is an apparent depth assumption in relation to other readings from the survey. With a few accurate physical bore samples the apparent depth can be extrapolated to actual depth to a fair degree but not without physical corroboration. The x & y values are complimented only by the actual calculated survey value. A z axis represents the third dimension, not a survey value. The survey does not produce a three dimensional map.

 

Barry

 

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The contours you are describing are surface contours overlaid on the graphic representation of the data. The contours, gulches, sections and trails were not produced by the survey but were added later to give a point of reference to the viewer.

 

contour_maps.jpg

 

Barry

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Research is certainly one of the most important efforts for the prospector, and successful prospectors do lots of research, then take their knowledge out into the field and see what they can find.

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So how does one sort through thousands of potential targets and identify the actual valuable deposits? It is like the search for a needle in a haystack, but there are tools to assist one in choosing the better and more likely valuable targets as opposed to the hundreds of potential targets which are not valuable mineral deposits, but some other geophysical condition which is not a valuable deposit.

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How do you separate junk from gold?

 

Metal detection is actually pretty simple. Almost anyone with a metal detector can detect a beer can.

 

Separating trash from gold is the "finesse" that separates winners from losers!

 

- Geowizard 

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"Finesce" ? What language is that and what does it mean?

 

As I continue to point out, there are hundreds of potential geophysical targets which are not valuable mineral deposits, but some other geophysical condition which is not a valuable deposit. That's why geologists, soil sampling, drilling and many other methods are needed to find the few valuable deposits in among the thousands of potential geophysical targets.

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Finesse is a noun;

 

The origin is from Middle English and Middle French and is derived from the word "fineness". :)

 

Meaning "Degree of excellence or purity".

 

Definition: 1. Extreme delicacy or subtlety in action, performance, skill, discrimination, taste, etc.

 

- Geowizard

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