lskk / ecn-synth

Synthesize low-cost dense accelerometer seismic network signals from IRIS network seismometer signals

ecn-synth

Synthesize low-cost dense accelerometer seismic network signals from IRIS network seismometer signals

Supported input files are miniSEED files downloaded from USGS (usually IRIS Wilber3).

miniSEED manipulation uses obspy.mseed library.

Python 3.6 is required.

Create the virtual environment using python -m venv venv

Make sure to pip install -r requirements.txt

Overview

Slides

Avicenna Dataset: Earthquakes used as initial case study

The datasets are named alphabetically (A..Z) using scientist names, starting from Avicenna.

Hendy stores this dataset in E:\project_amanah\S3\earthquake\dataset-avicenna.

Each event contains:

1. QuakeML
2. miniSEED waveforms for selected stations (up to nearest 5 stations with signals)

No.	Description	Time	Location	Depth	MMI
1	M 5.9 - 29km SW of Panyaungan Timur, Indonesia	2018-01-23 06:34:54 UTC	7.092°S 105.963°E	48.2 km	V
2	M 5.2 - 50km S of Sinarharapan, Indonesia	2017-12-16 00:22:30 UTC	7.884°S 106.820°E	44.0 km	III
3	M 6.5 - 1km E of Kampungbaru, Indonesia	2017-12-15 16:47:58 UTC	7.492°S 108.174°E	90.0 km	VII
4	M 5.7 - 109km SSW of Cibungur, Indonesia	2017-06-11 23:15:06 UTC	8.321°S 106.259°E	7.0 km	IV
5	M 5.2 - 0km S of Cipatujah, Indonesia	2017-04-23 18:01:13 UTC	7.740°S 108.019°E	72.2 km	V

Signal Processing

1. Differentiate from displacement/velocity into acceleration
2. Attenuate (based on linear distance from hypocenter)
3. Add noise - different levels of noise