Step 1: Understanding apparent resistivity in a double-dipole system.
In a double-dipole system, apparent resistivity is calculated using the voltage measured between the potential electrodes, the current injected, and the geometric factor (dependent on electrode spacing and array configuration). The formula is: \[ \rho_a = K \cdot \frac{V}{I} \] where \(K\) is the geometric factor, \(V\) is the measured potential difference, and \(I\) is the injected current.
Step 2: Analyzing each factor:
(A) Electrode spacing affects the geometric factor \(K\), so it affects apparent resistivity.
(B) The true subsurface resistivity influences the voltage measured, affecting apparent resistivity.
(C) The distance between the dipole centers changes the sensitivity and geometry of the measurement, affecting \(K\).
(D) Telluric currents are naturally occurring geoelectric currents in the Earth, but in controlled source resistivity measurements, their influence is negligible or eliminated through signal processing.
Hence, the apparent resistivity is not affected by the telluric current.
Consider a medium of uniform resistivity with a pair of source and sink electrodes separated by a distance \( L \), as shown in the figure. The fraction of the input current \( (I) \) that flows horizontally \( (I_x) \) across the median plane between depths \( z_1 = \frac{L}{2} \) and \( z_2 = \frac{L\sqrt{3}}{2} \), is given by \( \frac{I_x}{I} = \frac{L}{\pi} \int_{z_1}^{z_2} \frac{dz}{(L^2/4 + z^2)} \). The value of \( \frac{I_x}{I} \) is equal to 
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)
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)
The following table provides the mineral chemistry of a garnet. All oxides are in weight percentage and cations in atoms per formula unit. Total oxygen is taken as 12 based on the ideal garnet formula. Consider Fe as Fetotal and Fe\(^{3+}\) = 0. The Xpyrope of this garnet is _.