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)
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)
In seismology, Born approximation of the scattered (perturbed) wavefield is given by \[ \delta u(\mathbf{r}, \mathbf{s}; t) \approx \int_V \delta r(\mathbf{x}) \left(u_0(\mathbf{x}, \mathbf{s}; t) _t u_0(\mathbf{r}, \mathbf{x}; t)\right) \, d\mathbf{x}. \] Here, \( _t \) denotes temporal convolution, \( \delta r(\mathbf{x}) \) is the strength of the scatterer at \( \mathbf{x} \) in volume \( V \), \( \delta u(\mathbf{r}, \mathbf{s}; t) \) is the scattered wavefield measured at the receiver \( \mathbf{r} \) from the source \( \mathbf{s} \), \( u_0(\mathbf{x}, \mathbf{s}; t) \) is the downgoing wavefield (to the scatterer at \( \mathbf{x} \) from the source \( \mathbf{s} \)) in the unperturbed medium, \( u_0(\mathbf{r}, \mathbf{x}; t) \) is the upgoing wavefield (to the receiver \( \mathbf{r} \) from the scatterer at \( \mathbf{x} \)) in the unperturbed medium.
Select the correct statement(s).