Step 1: Understanding upward continuation.
Upward continuation is a mathematical transformation applied to potential field data (e.g., magnetic or gravity data) to simulate measurements at a higher elevation than the actual observation level.
Step 2: Effects on magnetic sources. It reduces the influence of shallow magnetic sources because their field strength decays rapidly with height.
In contrast, it relatively enhances the contribution from deeper sources, as their field strength decays more slowly.
Step 3: Eliminating incorrect options.
(C) is incorrect because upward continuation suppresses, rather than enhances, near-surface anomalies.
(D) is incorrect; it’s a misinterpretation — sources do not move, only their apparent influence changes.
A color model is shown in the figure with color codes: Yellow (Y), Magenta (M), Cyan (Cy), Red (R), Blue (Bl), Green (G), and Black (K). Which one of the following options displays the color codes that are consistent with the color model?
While doing Bayesian inference, consider estimating the posterior distribution of the model parameter (m), given data (d). Assume that Prior and Likelihood are proportional to Gaussian functions given by \[ {Prior} \propto \exp(-0.5(m - 1)^2) \] \[ {Likelihood} \propto \exp(-0.5(m - 3)^2) \]
The mean of the posterior distribution is (Answer in integer)