TONG Ping

Appointment: Associate Professor

Research topics:

Numerical methods, inverse problems, seismic imaging (migration+tomography), time-series analysis.

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131 to 140 of 204 Results

SoCal.ATT.Vp+Az.WuEtAl2022.p001.dat Sep 2, 2022 - Replication Data for: Complex Patterns of Past and Ongoing Crustal Deformation in Southern California Revealed by Seismic Azimuthal Anisotropy Fixed Field Text Data - 112.0 MB - MD5: 256a7de7a9773f1ce2e09163b7b998cc Please refer to Readme
Adjoint-state traveltime tomography software package (Training version) Aug 21, 2022 Tong, Ping, 2021, "Adjoint-state traveltime tomography software package (Training version)", https://doi.org/10.21979/N9/YPGQRF, DR-NTU (Data), V4 The source code for adjoint-state traveltime tomography (ATT). This simple version is mainly for training purpose. The details of the algorithm can be found at Tong, P., 2021. Adjoint‐state traveltime tomography: Eikonal equation‐based methods and application to the Anza area in...
velocity3d_final_lontitude_latitude_depth Aug 21, 2022 - Adjoint-state traveltime tomography software package (Training version) Plain Text - 939.8 MB - MD5: 71ff4deb52a563cf6193f71ee8ece826
velocity3d_ini_lontitude_latitude_depth Aug 21, 2022 - Adjoint-state traveltime tomography software package (Training version) Plain Text - 939.8 MB - MD5: bad63d40ba1720e04e1f8c0e345bfc65
Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California Jul 14, 2022 Tong, Ping, 2022, "Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California", https://doi.org/10.21979/N9/0O380V, DR-NTU (Data), V2 Southern California PmP phase data expanded by a deep learning technique PmPNet.
PmP_Net.ipynb Jul 14, 2022 - Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California Jupyter Notebook - 186.6 KB - MD5: 1e01a932f0f967ab1b5174496538e470 Source code for PmPNet
PmP_traveltime_Net.ipynb Jul 14, 2022 - Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California Jupyter Notebook - 72.9 KB - MD5: 51090bb7844388c19d8252f685ef5cb2 Source code for PmPTraveltimeNet
PmP_data.txt Mar 22, 2022 - Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California Plain Text - 2.5 MB - MD5: fb5fd27e721d1b2b41c5d93d8f3e603d
Readme.txt Mar 22, 2022 - Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California Plain Text - 681 B - MD5: 16488b8ee1a37a3e0fc2944c2b320a1e Documentation
Replication Data for: Imaging the upper 10 km crustal shear-wave velocity structure of central Myanmar via a joint inversion of body wave polarizations and receiver functions Feb 24, 2022 Tong, Ping, 2021, "Replication Data for: Imaging the upper 10 km crustal shear-wave velocity structure of central Myanmar via a joint inversion of body wave polarizations and receiver functions", https://doi.org/10.21979/N9/FD5DEV, DR-NTU (Data), V2 Data and Model for "The upper 10 km crustal shear-wave velocity structure of central Myanmar via a joint inversion of body wave polarizations and receiver functions." The original receiver functions can be downloaded via ftp://159.226.119.161/data/CMGSMO-1/RF_mainprofile/.

SoCal.ATT.Vp+Az.WuEtAl2022.p001.dat

Sep 2, 2022 - Replication Data for: Complex Patterns of Past and Ongoing Crustal Deformation in Southern California Revealed by Seismic Azimuthal Anisotropy

Fixed Field Text Data - 112.0 MB -

Please refer to Readme

Adjoint-state traveltime tomography software package (Training version)

Aug 21, 2022

Tong, Ping, 2021, "Adjoint-state traveltime tomography software package (Training version)", https://doi.org/10.21979/N9/YPGQRF, DR-NTU (Data), V4

The source code for adjoint-state traveltime tomography (ATT). This simple version is mainly for training purpose. The details of the algorithm can be found at Tong, P., 2021. Adjoint‐state traveltime tomography: Eikonal equation‐based methods and application to the Anza area in...

velocity3d_final_lontitude_latitude_depth

Aug 21, 2022 - Adjoint-state traveltime tomography software package (Training version)

Plain Text - 939.8 MB -

velocity3d_ini_lontitude_latitude_depth

Aug 21, 2022 - Adjoint-state traveltime tomography software package (Training version)

Plain Text - 939.8 MB -

Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California

Jul 14, 2022

Tong, Ping, 2022, "Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California", https://doi.org/10.21979/N9/0O380V, DR-NTU (Data), V2

Southern California PmP phase data expanded by a deep learning technique PmPNet.

PmP_Net.ipynb

Jul 14, 2022 - Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California

Jupyter Notebook - 186.6 KB -

Source code for PmPNet

PmP_traveltime_Net.ipynb

Jul 14, 2022 - Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California

Jupyter Notebook - 72.9 KB -

Source code for PmPTraveltimeNet

PmP_data.txt

Mar 22, 2022 - Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California

Plain Text - 2.5 MB -

Readme.txt

Mar 22, 2022 - Replication Data for: Deep neural networks for creating reliable PmP database with a case study in southern California

Plain Text - 681 B -

Documentation

Replication Data for: Imaging the upper 10 km crustal shear-wave velocity structure of central Myanmar via a joint inversion of body wave polarizations and receiver functions

Feb 24, 2022

Tong, Ping, 2021, "Replication Data for: Imaging the upper 10 km crustal shear-wave velocity structure of central Myanmar via a joint inversion of body wave polarizations and receiver functions", https://doi.org/10.21979/N9/FD5DEV, DR-NTU (Data), V2

Data and Model for "The upper 10 km crustal shear-wave velocity structure of central Myanmar via a joint inversion of body wave polarizations and receiver functions." The original receiver functions can be downloaded via ftp://159.226.119.161/data/CMGSMO-1/RF_mainprofile/.

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