Preparations for inversion

This part prepares necessary files to run this examples, including:

Download input file for source and receiver locations raw link
Build a 1-D isotropic initial model.
Build a 1-D checkerboard true model by adding perturbations to the initial model.
Calculate the traveltimes in the true model (checkerboard model) as the observed traveltimes.

Users can directly run the following codes to generate all necessary files automatically.


python prepare_input_files.py
mpirun -n 8 TOMOATT -i 3_input_params/input_params_1dinv_signal.yaml

Here, we demonstrate the preparation step-by-step. First, please load necessary modules in Python Script:


import os
import numpy as np
import sys
from pytomoatt.model import ATTModel
from pytomoatt.checkerboard import Checker
from pytomoatt.src_rec import SrcRec

Prepare the initial model

The initial model is built as an isotropic 1D model, satisfying:

a constant velocity of 5.0 km/s above 0 km depth.
a constant velocity gradient from 5.0 km/s at 0 km to 8.0 km/s at 40 km.
a constant velocity of 8.0 km/s below 40 km depth.

Users can build the initial model by


class BuildInitialModel():
    def __init__(self, par_file="./3_input_params/input_params_signal.yaml", output_dir="2_models"):
        """
        Build initial model for tomography inversion
        """
        self.am = ATTModel(par_file)
        self.output_dir = output_dir
 
    def build_initial_model(self, vel_min=5.0, vel_max=8.0):
        """
        Build initial model for tomography inversion
        """
        self.am.vel[self.am.depths < 0, :, :] = vel_min
        idx = np.where((0 <= self.am.depths) & (self.am.depths < 40.0))[0]
        self.am.vel[idx, :, :] = np.linspace(vel_min, vel_max, idx.size)[::-1][:, np.newaxis, np.newaxis]
        self.am.vel[self.am.depths >= 40.0, :, :] = vel_max
 
    def build_ckb_model(self):
        """
        Build checkerboard model for tomography inversion
        """
        nr = self.am.n_rtp[0]
        for ir in range(nr):
            dep = self.am.depths[ir] 
            self.am.vel[ir, :, :] = (1 + 0.05 * np.sin(np.pi * dep / 10.0)) * self.am.vel[ir, :, :]
 
if __name__ == "__main__":
 
    # build initial model
    output_dir = "2_models"
    os.makedirs(output_dir, exist_ok=True)
    bim = BuildInitialModel(output_dir=output_dir)
    bim.build_initial_model()
    bim.am.write('{}/model_init_N{:d}_{:d}_{:d}.h5'.format(bim.output_dir, *bim.am.n_rtp))

The initial model file is generated as 2_models/model_init_N61_61_61.h5

Prepare the true model (checkerboard model)

The 1D true model is built by adding positive and negative 5% velocity perturbations with the vertical size of 10 km are assigned to the 1D initial model:


V_true = V_init * (1 + 0.05 * sin(pi * dep/10))

Users can build the true model by


if __name__ == "__main__":
    bim.build_ckb_model()
    bim.am.write('{}/model_ckb_N{:d}_{:d}_{:d}.h5'.format(bim.output_dir, *bim.am.n_rtp))

The true model file is generated as 2_models/model_ckb_N61_61_61.h5

Prepare observed traveltime file

The observed traveltimes are generated by calculating the traveltimes in the true model (checkerboard model).

First, we can download the data file here , by using


url = 'https://zenodo.org/records/14053821/files/src_rec_config.dat'
path = "1_src_rec_files/src_rec_config.dat"
os.makedirs(os.path.dirname(path), exist_ok=True)
if not os.path.exists(path):
    sr = SrcRec.read(url)
    sr.write(path)

Then, we can run TomoATT in bash to calculate the traveltimes in the true model


mpirun -n 8 TOMOATT -i 3_input_params/input_params_1dinv_signal.yaml

Here we use 8 processors in parallel.

If users want to change the number of used processors, please correspondingly change the parallel setting n_sims in the YAML file 3_input_params/input_params_1dinv_signal.yaml:


parallel: # parameters for parallel computation
    n_sims: 8 # number of simultaneous runs (parallel the sources)

After calculating the traveltimes, we can find the calculated data file in OUTPUT_FILES/OUTPUT_FILES_1dinv_signal/src_rec_file_step_0000.dat