3. Tutorial¶

This tutorial is designed to give you an overview of the capabilities and implementation of the PyMPA Python package.

3.1. Downloading Seismological Data¶

To download seismological data from EIDA (European Integrated Data Archive) servers: Waveform broad band data from european broad band seismic stations are available from many others European Institutions. To download seismological data from EIDA (European Integrated Data Archive) servers and inventory data in STATIONXML format many examples can be found in ObsPy examples.

PyMPA requires, continuous data and stations inventories. EIDA servers can easily release continuous data from permanent networks and the corresponding inventories. The examples in the subdirectory input.download_data.dir show the python scripts that allows the download.

In the case your data come from other sources, PyMPA through ObsPy libraries is able to manage most of the seismological data formats (MSEED, SAC, SEISAN, SEGY, etc..). An inventory data file including station information needs to be created by modifying an existing StationXML file and read that with ObsPy to perform the rest.

PyMPA does not use databases and prefers to store single channel daily continuous data in archieves.

(download_data) download data

3.2. Create Templates¶

(create_template) create templates

Needed files 1. Events in a catalog: e.g. templates.zmap (quakeml or zmap formats) see ObsPy for admitted formats

cat templates.zmap

Days to process: one column file including days to process e.g. lista1 cat lista1 120103 120104
Set parameters: e.g. trim.par cat trim.par #Line 1 – list of stations #Line 2 – list of channels #Line 3 – list of networks #Line 4 – Lowpass frequency #Line 5 – Highpass frequency #Line 6 – Trimmed Time before S-wave #Line 7 – Trimmed Time after S-wave #Line 8 – UTC precision #Line 9 – Continuous data dir #Line 10 – Template data dir #Line 11 – Processing days list #Line 12 – Zmap catalog #Line 13 – Starting template #Line 14 – Stopping template #Line 15 – Taup Model AQU CAMP CERT FAGN FIAM GUAR INTR MNS NRCA TERO BHE BHN BHZ IV MN 2.0 8.0 2.5 2.5 6 24h template lista1 templates.zmap 26 27 aquila_kato

3.3. Directories¶

./24h = include daily continuous time series ./template = output trimmed templates

3.4. Velocity Model use to compute travel times¶

cat aquila_kato.tvel aquila depth P vel. S vel. density older density

0.000 3.7500 2.1650 2.4500 1.500 3.7500 2.1710 2.4500 1.510 4.9400 2.8520 2.7800 4.510 4.9400 2.8580 2.7800 4.520 6.0100 3.2790 2.7600

14.520 6.0100 3.2850 2.7600 14.530 5.5500 3.3950 2.9100 29.530 5.5500 3.4010 2.9100 29.540 5.8800 4.0990 3.1000 43.540 5.8800 4.1050 3.1000 43.550 5.8800 4.5610 3.1000 57.500 5.8800 3.3600 3.1000 57.500 7.1100 4.0100 3.2300 93.000 7.1100 4.0100 3.2300 93.000 7.1000 3.9900 3.3000

3.5. Check Template Quality¶

(template_check) select good templates

3.6. Calculate Travel Times¶

(calculate_ttimes) calculate travel times

3.7. Running PyMPA¶

(main.pympa_chunks_channel_limit) run pympa

3.8. Output Processing¶

(output.process_detections) controls multiple detections in short time windows

3.9. Verify Detections¶

(output.verify_detection) visual verification of events

3.10. References¶

Shelly, D. R., G. C. Beroza, and S. Ide (2007). Non-volcanic tremor and low frequency earthquake swarms, Nature 446, 305–307.

Peng, Z., and P. Zhao (2009). Migration of early aftershocks following the 2004 Parkfield earthquake, Nature Geosci. 2, 877–881.

Yang, H., L. Zhu, and R. Chu (2009). Fault-plane determination of the 18 April 2008 Mount Carmel, Illinois, earthquake by detecting and relocating aftershocks, Bull. Seismol. Soc. Am. 99, 3413–3420.

Kato, A., K. Obara, T. Igarashi, H. Tsuruoka, S. Nakagawa, and N. Hirata (2012). Propagation of slow slip leading up to the 2011 Mw 9.0 Tohoku-Oki earthquake, Science 335, 705–708.

Zhang, M., and L. Wen (2015). An effective method for small event detection: Match and locate (M&L), Geophys. J. Int. 200, 1523–1537.

Krischer, L., T. Megies, R. Barsch, M. Beyreuther, T. Lecocq, C. Caudron, and J. Wassermann (2015). ObsPy: A bridge for seismology into the scientific Python ecosystem, Comput. Sci. Discov. 8, no. 1, 014003, doi: 10.1088/1749-4699/8/1/014003.