Data from: 3D shear-wave velocity model of central Makran using ambient-noise adjoint tomography
Data files
Aug 30, 2023 version files 1.04 GB
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Initial_Model.zip
20.74 MB
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M21_Final_Model_smoothed.zip
72.03 MB
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M21_Final_Model.zip
23.63 MB
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README.md
4.84 KB
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RGF.zip
922.46 MB
Abstract
The Makran subduction zone is unique in its wide onshore thick accretionary prism, and a volcanic arc not parallel to the E-W trend of the Makran accretionary prism. To investigate the internal structure of the accretionary prism, the crustal nature of Jaz Murian Depression, and the trend of the buried trench we have calculated a 3D shear-wave velocity model for a region around the border between eastern and western Makran using ambient-noise adjoint tomography and data from IASBS/CAM Makran temporary seismic network. In close agreement with previous works, our velocity model shows that the onshore accretionary prism consists of a low-velocity zone in the south and a high-velocity zone in the north with an average thickness of accreted sediments of 22 and 30 km, respectively. The young age of the surface rocks of the high-velocity part of the prism suggests the presence of a significant volume of igneous rocks scraped from the subducting oceanic slab. The velocity model indicates a continental crust of ~40 km with a thick sedimentary cover of ~20 km for the eastern part of Jaz Murian Depression. The presence of a NE-SW trending low-velocity region at a depth interval of 40-60 km subparallel with the trend of the volcanic arc, intermediate-depth earthquakes, and geometry of the overriding plate might be related to the trend of the buried trench. This implies that the observed NE-SW trending volcanic arc might be related to the geometry of the buried trench and not the eastward reduction of the subduction angle.
README: Replace 'your_file_path.sac' with the actual path to your SAC file
3D Shear-wave Velocity Model of Central Makran using Ambient-noise Adjoint Tomography Dataset
The dataset includes zip files: Initial_Model.zip M21_Final_Model_smoothed.zip M21_Final_Model.zip RGF.zip
each of them contains files as described below :
Initial_Model.zip : Initial Model from Irandoust et. al., (2022). A four-column text file with corresponding longitude, latitude, depth (km), and shear wave velocity (km/s).
M21_Final_Model.zip : Final refined model of our study. A four-column text file with corresponding longitude, latitude, depth (km), and shear wave velocity (km/s).
M21_Final_Model_smoothed.zip : Final refined model of our study which is horizontally smoothed with 75 km wide Gaussian function (result represented at this study). A four-column text file with corresponding longitude, latitude, depth (km), and shear wave velocity (km/s).
RGF.zip : A directory for each of the 38 stations used in this study that contains the following content:
ADJOINT_SOURCES_T008_T020: Adjoint sources for the final model for the period band of 8-20 seconds. These adjoint sources are functions that are constructed by the time derivative of synthetic waveforms weighted by misfit values calculated between synthetic and empirical green functions and reversed in the time domain. Adjoint sources are time-dependent signals and each file contains two columns of time and amplitude of signal.
ADJOINT_SOURCES_T015_T030: Adjoint sources for the final model for the period band of 15-30 seconds.
ADJOINT_SOURCES_T025_T050: Adjoint sources for the final model for the period band of 25-50 seconds.
ADJOINT_SOURCES : This directory contains the name and the location of used stations for constructing adjoint sources
The following contents are in .sac format. Sac files are well-known seismic file formats and their content can be accessed by different programming languages like Python (using obspy package) and also by the Seismic Analysis Code (SAC) software represented on the IRIS web page. To download the sac program people need to pull a request to http://ds.iris.edu/ds/nodes/dmc/forms/sac/
Full manual to sac usage is available at http://ds.iris.edu/files/sac-manual/
DATA: Empirical green functions for each station
M00_SYN: Synthetic green function built using Initial model
M21_SYN: Synthetic green function built using Final model (not smoothed version of the final model)
As a fast guide to review .sac files contents, after installing the SAC program following Linux commands will result in showing the content of each file (\n at the end of the following commands is equal to the enter command in the Linux terminal, r will read sac file, p will plot signal trace and lh will results files related headers):
sac \n
r filename.sac \n
p \n
lh \n
q \n
As a python user one can perform following thing to see .sac files contents:
pip install obspy
import obspy
from obspy import read
file_path = 'your_file_path.sac'
Read the SAC file
st = read(file_path)
Print the header information
print("Header information:")
print(st[0].stats)
Plot the signal trace
st.plot()
Just replace 'your_file_path.sac' with the actual path to your SAC file, and then run the script. It will read the SAC file, print its header information, and display a plot of the signal trace.
A full description of sac files headers can be found at:
http://ds.iris.edu/files/sac-manual/manual/file_format.html
Most of the available headers are not required to be filled in the case of the green function. Here we summarize some of the important header values that have been used in our study as follows:
NPTS : number of points
B : begin time (s) with respect to reference time of signal
E : end time (s)
Delta: time step
Depmin: minimum amplitude
Depmax: maximum amplitude
KZDATE: reference time of signal
KSTNM : station name (In the case of ambient noise adjoint tomography, a station referred to the receiver station)
STLA : station latitude
STLO : station longitude
KEVNM : event name (In the case of ambient noise adjoint tomography, an event referred to the reference station)
EVLO : event longitude
EVLA : event latitude
Dist : epicentral distance of station and event
Az : azimuth
KCMPNM: component (Z N E)
KNETWK: knetwork name
Sharing/Access information
Links to other publicly accessible locations of the data:
enayat, mohammad (2023), “ambient noise adjoint tomography in makran region ”, Mendeley Data, V2, doi: 10.17632/9pb6fjtdgy.2
reference
Irandoust, M. A., Priestley, K., & Sobouti, F. (2022). High‐Resolution Lithospheric Structure of the Zagros Collision Zone and Iranian Plateau. Journal of Geophysical Research: Solid Earth, 127(11). https://doi.org/10.1029/2022JB025009
Methods
The dataset is related to Makran IASBS/CAMB temporary seismic network. The network worked during June 2016 to Sep 2020. The network was installed by Institute for Advanced studies in Basic Sciences, Zanjan, Iran (iasbs.ac.ir). The data comprises of the following items,
1) Ambient noise green functions between all possible stations used in the study
2) Initial and final 3D shear-velocity model
3) Adjoint sources for the final iteration
Usage notes
Sac files are well-known seismic file formats and their content can be accessed by different programming languages like Python (using obspy package) and also by the Seismic Analysis Code (SAC) software represented on the IRIS web page. To download the sac program people need to pull a request to http://ds.iris.edu/ds/nodes/dmc/forms/sac/
Full manual to sac usage is available at http://ds.iris.edu/files/sac-manual/
As a fast guide to review .sac files contents, after installing the SAC program following Linux commands will result in showing the content of each file (\n at the end of the following commands is equal to the enter command in the Linux terminal, r will read sac file, p will plot signal trace and lh will results files related headers):
sac \n
r filename.sac \n
p \n
lh \n
q \n
As a python user one can perform following thing to see .sac files contents:
pip install obspy
import obspy
from obspy import read
# Replace 'your_file_path.sac' with the actual path to your SAC file
file_path = 'your_file_path.sac'
# Read the SAC file
st = read(file_path)
# Print the header information
print("Header information:")
print(st[0].stats)
# Plot the signal trace
st.plot()
Just replace 'your_file_path.sac' with the actual path to your SAC file, and then run the script. It will read the SAC file, print its header information, and display a plot of the signal trace.