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## Supplementary videos to the study of Persian Gulf seismo-acoustics ##
## by Salaree et al (2023) #############################################
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## A. INCLUDED ITEMS:

The items in this bundle are supporting videos to a study of subsea 
seismo-acoustics carried out regarding an earthquake in the Persian 
Gulf. The main data used in the study is a diver's recording of the 
acoustic waves from the earthquake. The epicenter and topography data 
used in this study are publicly available as cited below.

All the included files are videos in MPEG-4 or more commonly known as 
MP4 formats. This is a common container file which may include both 
audio and video. As of 2023, MP4 files can be read in a large variety 
of platforms such as OSX, Windows, Linux distributions, Android, and 
iOS using mostly non-commercial software.

1. SV1.mp4
	The June-July 2022 earthquake sequence in southern Iran. No 
	audio is included. Earthquakes are from the Iranian Seismological 
	Center (IRSC) catalog and are shown as yellow circles the sizes 
	of which are proportional to their magnitude. The background 
	is topography obtained from GEBCO (GEBCO, 2021). The bottom 
	panel shows the seismic energy released each day. The maps are 
	created using Generic Mapping Tools or GMT (Wessel et al, 2013).

2. SV2.mp4
	The CCTV footage of the 25 June 2022 Kish Island earthquake 
	recorded at a local shop. The footage includes both audio and 
	video. The video is obtained from the Hormozgan Young Journalists 
	Club of Iran.

3. SV3.mp4
	A diver's recording of the 25 June 2022 Kish Island earthquake.
	The footage includes both audio and video. The video is obtained 
	from Saeed Parvin of the IranMarina Diving School in the Kish
	Island. The video was recorded at latitude 26.59556N and 
	longitude 54.10806E.

4. SV4.mp4
	Motion tracking of the fish in a portion of the diver's 
	recording (SV3.mp4) of the 25 June 2022 Kish Island earthquake. 
	No audio is included.

5. SV5.mp4
	Animation of the ray-tracing model of the trapped acoustic 
	signal from the diver's recording along with an audio track
	created from a time series of reflections. The graphics are 
	made using Generic Mapping Tools or GMT (Wessel et al, 2013).
	The conversion of time series to audio was carried out using 
	SoX available at https://sox.sourceforge.net

6. SV6.mp4
	3D topographic map of the Kish Island from the GEBCO model
	(GEBCO, 2021). The graphics are made using Generic Mapping Tools 
	or GMT (Wessel et al, 2013).

7. README.txt
	Description of contents (this file).


## B. DESCRIPTION:

This material accompanies a study of the seismo-acoustic signals in the 
Persian Gulf region and how they can be used to enhance the monitoring 
of offshore seismic events. The authors of this study model an acoustic 
signal recorded by local divers from a magnitude ML=5.6 earthquake near 
the Kish Island on 25 June 2022. As such, the signal is described as a 
trapped acoustic wave (converted from acoustic body waves of the shallow 
earthquake) in the shallow Persian Gulf. Numerical simulations of the 
signal using the proposed model are then used to constrain the earthquake 
location and also explain the unusual shaking from the earthquake felt 
on the far side of the Persian Gulf, more than 170 km away from the 
moderate size earthquake. Using the diver's video, the authors of this 
study propose seismic early warning instrumentation.


## C. REFERENCES:

- GEBCO, 2021. Gebco Compilation Group: GEBCO 2021 Grid, The General 
	Bathymetric Chart of the Oceans, 
	doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8

- IRSC, 2023, Iranian Seismological Center earthquake catalog, 
	available at irsc.ut.ac.ir

- Wessel, P., Smith, W.H., Scharroo, R., Luis, J. and Wobbe, F., 2013. 
	Generic Mapping Tools: Improved version released. Eos, 
	Transactions American Geophysical Union, 94(45), pp.409-410, 
	https://doi.org/10.1002/2013EO450001

## FUNDING:

Among the authors, Amir Salaree and Yihe Huang were supported at the 
University of Michigan by the National Science Foundation grant PREEVENTS 
geosciences directorate No. 1663769. Amir Salaree was also partially 
supported by National Science Foundation award EAR2022716.