Ordinary Kriging Animation

This animation shows the math behind the ordinary kriging interpolation.
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[@anakkuliahan14]

Really nice video. Nice animation. I hope further you can make a isotropy and anisotrophy geostatistic for estimation. Thanks

[@MiningGeologist]

Thank you for the suggestion!

[@GeoSciful]

That's a really nice intuition. Congratulations.

[@MiningGeologist]

Thank you !

[@luizalbertopersonal]

I love it, specially the song

[@MiningGeologist]

haha thank you !

[@renaldyjohan1541]

Please make sure, in the matrix of covariance between data points, which is on the bottom righ, you wrote 1 1 1 1 and 0.8.. are you sure is that correct for 0.8?

[@renaldyjohan1541]

Because, I have checked, when I put 0.8 in that matrix, I get the result 0.1 for the lambda, instead of you get the result 0.03... and then when I change the value 0.8 to 0.2 in the that matrix, I get 0.03 for the lambda, it is the same as you write

[@MiningGeologist]

@@renaldyjohan1541
matrix = np.array([
[0.80, 0.73, 0.59, 0.61, 1],
[0.73, 0.80, 0.60, 0.66, 1],
[0.59, 0.60, 0.80, 0.68, 1],
[0.61, 0.66, 0.68, 0.80, 1],
[1, 1, 1, 1, 0.80]
])
Vector = np.array([0.69, 0.71, 0.69, 0.70, 1])
weights = np.linalg.solve(matrix, vector)
Result :
array([0.18837377, 0.28487231, 0.29616708, 0.20566864, 0.03114775])
it is also slightly different here because in the animation it is showing rounded values but in the background it is using float values without decimal limitation, as you can see the values in the above code snippet are also slightly different from the animation but still the LaGrange multiplier is 0.03

[@kemalcizik5096]

Yeeah bro. You are crazy.

[@kemalcizik5096]

ua-cam.com/video/QmjWwufuAog/v-deo.htmlfeature=shared

[@MiningGeologist]

hahaha i'll take that as a compliment