Thanks to BayHunter, now one can apply the transdimensional MCMC inversion to receiver function and/or surface-wave dispersion conveniently. I find that in applying the inversion to a local-scale seismic data, it would be sometimes necessary to make the model halfspace consistent with the regional reference model. Thus I was thinking if it would be useful to add such an input, the halfspace parameters, to the inversion. Meanwhile, I come up with two ways to implement this:
In the get_vp_vs_h function in Models.py, one could modify the last component of the vp and vs arrays. By doing this, one modifies the model halfspace directly during the inversion.
One could also add the halfspace parameters to the thkm, vpm, vsm, and rhom arrays in the get_modelvectors function of surf96_modsw.py. One also needs to add 1 to nlayer in run_model. This only works for surface-wave dispersion and actually adds a layer to the inversion model. Thus I do not recommend this way.
I am not sure if these implementing would be added. I wrote these down in case someone will need to freeze the model halfspace in BayHunter.
Thanks to BayHunter, now one can apply the transdimensional MCMC inversion to receiver function and/or surface-wave dispersion conveniently. I find that in applying the inversion to a local-scale seismic data, it would be sometimes necessary to make the model halfspace consistent with the regional reference model. Thus I was thinking if it would be useful to add such an input, the halfspace parameters, to the inversion. Meanwhile, I come up with two ways to implement this:
I am not sure if these implementing would be added. I wrote these down in case someone will need to freeze the model halfspace in BayHunter.