# Analyzing MiniSEED seismic data in MATLAB (codes included)

We will learn how to convert a mseed data file into mat format and then read and analyze it using MATLAB

I have a MiniSEED (or mseed) file and I want to analyze it in MATLAB. But unfortunately, MATLAB can’t read mseed file. So, let us figure out how can we read and analyze it using MATLAB.

## What is MiniSEED format?

IRIS uses SEED as a data format intended primarily for the archival and exchange of seismological time series data and related metadata. MiniSEED is a stripped down version of SEED containing only waveform data. There is no station and channel metadata included. See here for more.

## Utitlity program to convert MiniSEED into MAT format

I wrote a utility program that uses the Obspy library in Python to convert the mseed file to mat format. You can download the utility from here.

usage: convert_mseed_mat.py [-h] -inp INPUT_MSEED [-out OUTPUT_MAT]

Python utility program to convert mseed file to mat (by Utpal Kumar, IESAS, 2021/04)

optional arguments:
-h, --help            show this help message and exit
-inp INPUT_MSEED, --input_mseed INPUT_MSEED
input mseed file, e.g. example_2020-05-01_IN.RAGD..BHZ.mseed
-out OUTPUT_MAT, --output_mat OUTPUT_MAT
output mat file name, e.g. example_2020-05-01_IN.RAGD..BHZ.mat


Let us see an example:

python convert_mseed_mat.py -inp example_2020-05-01_IN.RAGD..BHZ.mseed


### Output data structure

• stats contains all the meta data information corresponding to each trace and
• data contain the time series data
mat_file.mat -> stats, data
stats -> stats_0, stats_1, ...
data -> data_0, data_1, ...


## Read mat file in MATLAB

Now, let us read the mat file containing the seismic time series data. We start by the usual initializing the MATLAB and reading the file name.

clear; close all; clc;

wdir='.\';

fileloc0=[wdir,'example_2020-05-01_IN.RAGD..BHZ'];
fileloc_ext = '.mat';
fileloc = [fileloc0 fileloc_ext];


### Plot time series

We now check if the mat file exists, and the read the meta data stored in stats_0. We get the sampling_rate, delta, starttime, endtime. For plotting, we create the datetime_array.

if exist(fileloc,'file')
disp(['File exists ', fileloc]);

all_stats = fieldnames(stats);
all_data = fieldnames(data);

%     for id=1:length(fieldnames(data))
for id=1
stats_0 = stats.(all_stats{id});
data_0 = data.(all_data{id});

sampling_rate = getfield(stats_0,'sampling_rate');
delta = getfield(stats_0,'delta');
starttime = getfield(stats_0,'starttime');
endtime = getfield(stats_0,'endtime');
t1 = datetime(starttime,'InputFormat',"yyyy-MM-dd'T'HH:mm:ss.SSS'Z'");
t2 = datetime(endtime,'InputFormat',"yyyy-MM-dd'T'HH:mm:ss.SSS'Z'");
datetime_array = t1:seconds(delta):t2;

%% plot time series
fig = figure('Renderer', 'painters', 'Position', [100 100 1000 400], 'color','w');
plot(t1:seconds(delta):t2, data_0, 'k-')
title([getfield(stats_0,'network'),'-', getfield(stats_0,'station'), '-', getfield(stats_0,'channel')])
axis tight;
print(fig,['docs/',fileloc0, '_ts', num2str(id),'.jpg'],'-djpeg')

%         close all;
end
end


### Plot spectrogram

We used the spectrogram function from MATLAB to plot the spectrogram (can be improved further). We divide the signal into sections of length 128, windowed with a Kaiser window with shape parameter (\beta = 18) and specify 120 samples of overlap between adjoining sections. We evaluate the spectrum at 65 frequencies and ((length(x)−120)/(128−120)=235) time bins.

if exist(fileloc,'file')
disp(['File exists ', fileloc]);

all_stats = fieldnames(stats);
all_data = fieldnames(data);

%     for id=1:length(fieldnames(data))
for id=1
stats_0 = stats.(all_stats{id});
data_0 = data.(all_data{id});

sampling_rate = getfield(stats_0,'sampling_rate');

fig2 = figure('Renderer', 'painters', 'Position', [100 100 1000 400], 'color','w');
data_0_double = double(data_0);

spectrogram(data_0_double,kaiser(128,18),120,128,sampling_rate,'yaxis')

%% if you want to normalize the frequency axis in range 0 to 1
%         yticks([0 sampling_rate/4 sampling_rate/2])
%         yticklabels({'0','0.5','1'})
%         ylabel('Normalized Frequency');

title([getfield(stats_0,'network'),'-', getfield(stats_0,'station'), '-', getfield(stats_0,'channel')])
print(fig2,['docs/',fileloc0, '_spectrogram', num2str(id),'.jpg'],'-djpeg')
%         close all;
end
end


## Conclusions

Converting Miniseed into mat format allows us to easily read the seismic time series data in MATLAB. Once we load the data in MATLAB, we can make use of all the avilable MATLAB commands and tools.

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