Step 1: Sampling theorem.
According to the Nyquist-Shannon sampling theorem, the minimum sampling frequency \(f_s\) should be at least twice the highest frequency component of the signal.
Step 2: Relation between frequency and period.
The frequency \(f\) is the reciprocal of the period \(T\): \[ f = \frac{1}{T} \] Given the period is greater than 10 s, the frequency corresponding to the maximum period (10 s) is: \[ f = \frac{1}{10} = 0.1 \, {Hz} \] Step 3: Minimum sampling frequency.
To satisfy the Nyquist criterion, the minimum sampling frequency should be: \[ f_s = 2 \times 0.1 = 0.2 \, {Hz} \] Thus, the minimum sampling frequency should be \(0.2\) Hz.
Suppose a mountain at location A is in isostatic equilibrium with a column at location B, which is at sea-level, as shown in the figure. The height of the mountain is 4 km and the thickness of the crust at B is 1 km. Given that the densities of crust and mantle are 2700 kg/m\(^3\) and 3300 kg/m\(^3\), respectively, the thickness of the mountain root (r1) is km. (Answer in integer)
For a half space composed of 3 layers with resistivities \( \rho_1 \), \( \rho_2 \) and \( \rho_3 \), as shown in the figure, which of the following statements is/are correct about the variation of apparent resistivity with electrode spacing?
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)