Plotting and checking the bivariate distributions of multiple Gaussian data

The geostatistical modeling of continuous variables relies heavily on the multivariate Gaussian distribution. It is remarkably tractable. The multivariate Gaussian distribution is adopted for K multiple variables (often K is between 2 and 10) and for N multiple locations (often N is in the tens of millions). Our focus is on the relationship between the K variables. Each variable is transformed to be univariate Gaussian, but the multivariate nature of the data is not necessarily Gaussian after univariate transformation. If multiple data variables are deemed non-Gaussian, then additional steps need to be taken such as linearization by alternating conditional expectation (ACE) or multivariate transformation by the stepwise conditional transformation (SCT). Although all L-variate distributions (1