Empirical Green's Function Method to synthesis strong ground motion complete in Python
- Why ? Easy to Modify and Visualize
- IO: readTH
- Data fromat transfer: zoo2th (CSMNC2TH, CSMNC2SAC)
- Vectorize to quickly run: calWeight
- Filter and FFT by numpy
- Initialize variables;
- Read parameters from input file;
- Read time histories of main and after shocks;
- Process time histories: input -> rmean-> integrate -> bandpass filter -> rmean -> output;
- Calculate factor for propagation correction, function: calRdn;
- Calculate weight for superpose in time domain and synthetize (calWeight, synt);
- Rmean and visualize
One component in Miyake, 2003, BSSA
- Main Schock: 1997 March, Kagoshima-ken Hokuseibu
$M_{JMA}$ 6.5 - Station: K-NET, KGS002
- After Schock: 1997 March, Kagoshima-ken Hokuseibu
$M_{JMA}$ 4.7
Table Parameters of main and after shocks
| Date(JST) | Latitude (deg) | Longitude (deg) | Depth (km) | |
|---|---|---|---|---|
| 1997/03/26 17:31 | 31.970N | 130.380E | 8.2 | 6.5 |
| 1997/03/26 17:39 | 31.968N | 130.362E | 11.1 | 4.7 |
Fig. Amplitude spectra of mainshock and synthetic ground motions
cd ./example
from egfm import *
zoo2th('mainShock.zoo', 'mainTH.txt')
zoo2th('afterShock.zoo', 'egfTH.txt')
%run egfm_pc.py(enter input file name) 1997KKH.txt
