jrfapp 0.0.6

Joint inversion of Receiver function and Apparant velocity

Jrfapp Package

Introduction:

The Jrfapp stands for joint inversion of the Receiver Function and apparent velocity data. This is a python package to perform joint inversion of these datasets and outputs an estimated shear velocity model. For more info see "manuscript title".

Installation:

To run this code, you will need the following software and tools:

• Computer Program in Seismology
• Python 3.8
• matplotlib
• numpy
• obspy
• rf

1. You can install Computer Program in Seismology (CPS) from here.

You need to path the binary of CPS in your .bashrc. Before proceeding to the next step make sure that this program is installed correctly. This package depends on the hrftn96. If you installed CPS correctly and included it in your .bashrc the output of hrftn96 in the terminal should look like this: `

Model not specified USAGE: hrftn96 [-P] [-S] [-2] [-r] [-z] -RAYP p -ALP alpha -DT dt -NSAMP nsamp -M model -P (default true ) Incident P wave -S (default false) Incident S wave -RAYP p (default 0.05 ) Ray parameter in sec/km -DT dt (default 1.0 ) Sample interval for synthetic -NSAMP nsamp (default 512 ) Number samples for synthetic -M model (default none ) Earth model name -ALP alp (default 1.0 ) Number samples for synthetic H(f) = exp( - (pi freq/alpha)**2) Filter corner ~ alpha/pi -2 (default false) Use 2x length internally -r (default false) Output radial time series -z (default false) Output vertical time series -2 (default false) use double length FFT to avoid FFT wrap around in convolution -D delay (default 5 sec) output delay sec before t=0 -? Display this usage message -h Display this usage message SAC header values set by hrftn96 B : delay USERO : gwidth KUSER0: Rftn USER4 : rayp (sec/km) USER5 : fit in % (set at 100) KEVNM : Rftn KUSER1: hrftn96 The program creates the file names hrftn96.sac This is the receiver fucntion, Z or R trace according to the command line flag `

2. All the python package requires for Jrfapp and this package can be installed by pip install jrfapp.

Tip: I highly recommend creating a conda environment and installing the package in this environment.

Examples:

I have included four tutorials on the GitHub page that explain the main usage of the package.