= Solution
The first binned <empirical semivariogram> rises strongly through the displayed distances, approximately quadratically, with no visible sill. The spatial data plot shows higher depth at larger $y$. A stationary residual <covariance> should not be asked to explain that deterministic drift.
For a model $Z(s)=m(s)+W(s)$ with stationary zero-mean residual field, each raw squared difference has <expectation>
$$
\frac12\mathbb E[Z(s_i)-Z(s_j)]^2=\gamma_W(\|s_i-s_j\|)+\frac12[m(s_i)-m(s_j)]^2.
$$
With linear drift $m(x,y)=\beta_0+\beta_1y$, the added term is $\tfrac12\beta_1^2(y_i-y_j)^2$. Its average within distance bins can grow with separation, producing the shape seen in the first plot. The <empirical semivariogram> uses half the average squared difference over the pairs in each bin, so removing only a common intercept does not remove this drift contribution.
The formula `depth ~ y` estimates and removes a linear drift before computing a residual <empirical semivariogram>. The second graph levels off near $0.47$ with a fitted zero nugget and a finite scale, making the Gaussian residual <covariance> a plausible working model. \b[Estimate the drift and fit spatial dependence to residuals rather than the raw depth differences.] Estimated residuals are not the true errors, so their <semivariogram> has estimation effects; sparse long-distance bins are also noisy. These graphs support the model but do not establish stationarity or isotropy.
The subpart refers to `svgm` and `svgm2`, while the code calls the objects `smvg` and `smvg2`. Also the printed code omits the line that fits `gauss.model2` before plotting or using it. The supplied output must be interpreted as the result of that missing residual-variogram fit, rather than as an executable complete script.
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