# Does the procedure of progressively adding noise have a name?

I want to make a qualitative estimate of the amount of noise in my dataset $$X$$. For that purpose, I use a certain information estimator $$F(X)$$ (for example, mutual information between two different variables in a dataset)

I have designed the following procedure:

1. Generate a fake dataset $$Y$$ with well-understood properties. Normalize it
2. Progressively add noise using $$G(Y, \alpha) = (1 -\alpha) Y + \alpha N$$, where $$N \sim \mathcal{N}(0,1)$$ and $$\alpha$$ is sampled within the interval $$[0, 1]$$
3. Evaluate $$F(G(Y, \alpha))$$, plot it as a function of $$\alpha$$.
4. Repeat the steps 2,3 for real data to obtain the plot of $$F(G(X, \alpha))$$
5. Make a prediction of the amount of noise $$\alpha$$ in $$X$$ by evaluating the real data slope $$s(X, 0) = \frac{\partial F(G(X, \alpha))}{\partial \alpha} \biggr |_{\alpha=0}$$ and solving $$s(Y, \alpha) = s(X, 0)$$ for $$\alpha$$. If the plot of $$F$$ wiggles too much (which it does), I low-pass-filter the plot before evaluating the slopes numerically.

Question:

• Does this procedure of "blind inference of SNR" have a name?
• What do people usually do in such scenario?