Prior Probability

Truncated uniform distributions are adopted for all model parameters:

$P(k,\sigma_1, \cdots, \sigma_m,z_1,\cdots, z_k,\delta V_P_1, \cdots, \delta V_P_{k+1},\delta V_S_1, \cdots, \delta V_S_{k+1} ) \\ = P(k)\prod_{j=1}^{m}P(\sigma_i)\prod_{i=1}^{k}P(z_i|k)\prod_{i=1}^{k+1}P(V_P_i|k)\prod_{i=1}^{k+1}P(V_S_i|k)\\ = k!\left( \frac{1}{k_{max}-k_{min}}\right )\left(\frac{1}{\sigma_{max} - \sigma_{min}}\right)^{m} \left(\frac{1}{z_{max} - z_{min}}\right)^{k}\left(\frac{1}{V_P_{max} - V_P_{min}}\right)^{k+1}\left(\frac{1}{V_S_{max} - V_S_{min}}\right)^{k+1}$ , where x_max and x_min are upper and lower limit of the prior bounds for a parameter x, respectively.

RF_INV: A Fortran Program for Trans-Dimensional Inversion of Receiver Functions

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Before Use

Theory

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RF_INV: レシーバ関数Trans-dimensional inversionのためのFortranプログラム

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Prior Probability

Prior Probability

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RF_INV: A Fortran Program for Trans-Dimensional Inversion of Receiver Functions

Before Use

Theory

How to Use

RF_INV: レシーバ関数Trans-dimensional inversionのためのFortranプログラム

使用前の準備

理論

使用方法

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