@Andiguys 

The things you will have to adjust for future data are pink written in the attached file Scalling_issue_fixsd.mw
.

@acer 

When I posted my answer I have no clue at all about what the OP wanted.
I simply guessed he wanted to identify the regions where some TM dominates the others: so I proposed him this notional inequal plot without knowing if it asked or not its question (what puzzed me by the time was the image he joined at the end if its question).
And doing so I didn't pay attention to the phrase "(TM1, TM2, TM3 are all positive)"

So you're likely right that the conditions TMs > 0 should be included in some inequal plot... which I see you did in your answer.

@acer 

Thanks for your reply. 

See you soon on another thread

@Andiguys 

... implies not the same code to apply.

Indeed in your initial question you had L := something = 0, now you have L := M1=M2 (with M2 <> 0).
So you have to replace the line

A := expand(lhs(L)):

by the line

A := expand(M1-M2):

to be in the same conditions than previously.

Here is your file corrected.mw

@acer 

I vote up

@acer 

By the way, do you know whether modern Maple versions enable giving an axis a color, and the associated label another one, or yet, enable using attributes 'linestyle' or 'transparency' for an axis?

@AHSAN 

As I use Maple 2015 I wasn't capable to find any simple way to add the contour lines to the projected surface.
Maybe more recent versions have such feature?

Meanwhile there is a workaround:
With_Maple_2015.mw



By the way, have a look to the posts  @acer  wrote in the last two years and to the answers he gave to, if I'm not mistaken, quite similar problems.

@acer 



All_plots_Combined_sand15.mw

@WD0HHU 

You can write all the quantities with the units you are the most at ease with and, before defining your system to solve, convert all of them in SI units.
For instance you can define Mars armospheric density in g/m³ , Mars mass in Kg, distances in Km and convert all of them in [Km, Kg, s].

@C_R 

Shouldn't you send a SCR?

This won't answer your question, but using `#mi("&#8240;")` instead fixes the issue.
Don't ask me why...
(I always use this kind of syntax which enables managing font and color.)

Download mi.mw

@dharr 

EDITED 4:00 PM CET
 

See Equifinality

(in a few words the model  E(𝝊; A, C, E0, Em) is  non identifiable in the sense that different sets of parameter values (A, C, E0, Em) give essentially the same result...which is pretty obvious given there are only 3 observations and 4 parameters).

Nevertheless it is intructive to observe what are the 4-tuples (A, C, E0, Em) which give essentially the same result than the full paramolic model F := 𝝊 → a +b 𝝊 + c 𝝊²  in the (min..max)(nuvals) range.
ML_sand15_(MC).mw

Let ℱ(A) the family of models E(𝝊, A, C, E0, Em) defined by

C=-1.9e-19, 
E0=exp(21.17115-ln(A)), 
Em=-0.068883

whatever A in the range 1e-2 .. 1e5

All model in ℱ(A) are identical to the full parabolic fit F := 𝝊 → a +b 𝝊 + c 𝝊²  in the sense that 
evalf( Int( (F(𝝊) - E(𝝊, A, C, E0, Em))², 𝝊 = (min..max)(nuvals) ) ) < 1e-6.

 

... or in simpler form

Download igcd.mw

@dharr 

The "not of full rank" thing is a numerical issue as shown (Maple inputs in normal black font) in the attached file.
Conversion of nuvals and Evals into rationals removes this "not of full rank issue" and leads to a parabolic model a+b𝝊² which is now quite good (in addition a fullparamolic model including 𝝊 is constructed, simply to verify that the system is of fullrank too).
MLfit_full_rank.mw

By the way I did some stuff around this full parabolic model a +b 𝝊 + c 𝝊² .
The idea is basically the following:

1. Let F := 𝝊 → a +b 𝝊 + c 𝝊²  the fitted model. 
2. Find the 4-uple (A, C, E0, Em) which minimizes
          evalf( Int( (F(𝝊) - E(𝝊, A, C, E0, Em))², 𝝊 = (min..max)(nuvals) ) )
   My first (unsuccessfull) attempt was to use  NLPSolve. So I thought using some user-made genetic algorithm instead but I don't know if it's worth spending time on it until the OP clarifies its needs ?

@MichaelVio 

"What is the value of A that I should start with so that the abs|Em| is the minimum possible, and the plot looks like shown above?"
Do you mean like the parabola shape you provide in your question?
If it is so just remove the axis[1]=[mode=log] in my plot command: ML_sand15_(2).mw (I use this log mode to separate the obervations)

case 1) I gave up the negative Em condition?
case 2) I gave up all conditions except E(nu) positive for nu = [~10^4 to ~2.8*10^10]?
Giving them up or not has no effect because they are not explicitely accounted for in the fitting process