EGFM-Python

Empirical Green's Function Method to synthesis strong ground motion complete in Python

Emprical Green's Function Method

• EGFM

EGFM by Python

• Why ? Easy to Modify and Visualize

Codes

egfm.py

• IO: readTH
• Data fromat transfer: zoo2th (CSMNC2TH, CSMNC2SAC)
• Vectorize to quickly run: calWeight
• Filter and FFT by numpy

egfm_pc.py

1. Initialize variables;
2. Read parameters from input file;
3. Read time histories of main and after shocks;
4. Process time histories: input -> rmean-> integrate -> bandpass filter -> rmean -> output;
5. Calculate factor for propagation correction, function: calRdn;
6. Calculate weight for superpose in time domain and synthetize (calWeight, synt);
7. Rmean and visualize

Example

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)	$M_{JMA}$
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

Run follow script in jupyter notebook

from egfm import *
zoo2th('mainShock.zoo', 'mainTH.txt')
zoo2th('afterShock.zoo', 'egfTH.txt')
%run egfm_pc.py

(enter input file name) 1997KKH.txt

Fig. The timehistories of mainshock, aftershock and synthetic ground motions