# Concatenating daily seismic traces into one MiniSeed File (codes included)

I concatenate the daily seismic traces for 15 days into one miniseed file for further analysis. Then I obtained the spectrogram of the 15 days seismic data. Codes included.

I have daily seismic traces for several days. I need to concatenate them into one for analysis. This post will show you how you can use Obspy to achieve that. Following is the list of steps to achieve that.

## Import necessary libraries

First step is to import necessary libraries.

```
import glob, os
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [10,6]
plt.rcParams.update({'font.size': 18})
plt.style.use('seaborn')
from obspy import read, Stream
```

I will use the `glob`

and `os`

to find the data files, `read`

to read the mseed file into memory. `Stream`

will be used to create a new obspy “stream” object for the final concatenated mseed file.

## Concatenate the traces

In the below script snippet, `mseed_data`

is the list of all the mseed file paths, `datarange`

is the indexes of the files that will be concatenated. Please note that if your file paths are not sorted then you will have to sort it first. I loop through each of the 15 files and append it to the `mytrace`

object iteratively.

```
mseed_data = glob.glob(os.path.join("data", "*.mseed"))
downsample = True
datarange = np.arange(0,15, dtype=int)
stZ = read(mseed_data[datarange[0]])
mytrace = stZ[0]
starttime0 = mytrace.stats.starttime
endtime0 = mytrace.stats.endtime
for i,trdata in enumerate(mseed_data[datarange[1]:datarange[-1]]):
st = read(trdata)
if i==0:
oldend = endtime0
print(f"Reading trace {i+2}: {trdata}, range: {st[0].stats.starttime}-{st[0].stats.endtime}, jump: {st[0].stats.starttime-oldend} second")
tr = st[0]
mytrace+=tr
oldend = tr.stats.endtime
mystream = Stream(traces=[mytrace])
for tr in mystream:
if isinstance(tr.data, np.ma.masked_array):
tr.data = tr.data.filled(0)
if downsample:
## I want the final sampling rate to be 1Hz
mystream.interpolate(sampling_rate=mystream[0].stats.sampling_rate/mystream[0].stats.sampling_rate,starttime=starttime0) #to deal with missing data
#abort downsampling in case of changing end times (strict_length=true)
# mystream[0].decimate(factor=10, strict_length=False, no_filter=False) #lowpass filter is applied to ensure no aliasing artifacts are introduced
# mystream[0].decimate(factor=4, strict_length=False, no_filter=False)
mystream.write(mseed_data_comb, format="MSEED")
```

Finally, the missing values are masked by filling the `0`

s. Sometimes, you would like to downsample the traces for the further analysis. My traces are 40 Hz originally, and I downsampled it to 1Hz (1 samples/second). Please note that the above script shows two ways of downsampling - interpolation, or decimate. You can select either one based on your needs.

Finally, I wrote the stream into a mseed file.

## Plot the traces

I read the concatenated mseed file and plotted its data with respect to its time.

```
newMseed = read(mseed_data_comb)
print(newMseed)
fig, ax = plt.subplots(1,1)
ax.plot(newMseed[0].times('matplotlib'), newMseed[0].data, lw=0.5, color='k')
fig.autofmt_xdate()
plt.tight_layout()
plt.savefig('new-mseed-data.png', bbox_inches='tight', dpi=300)
plt.close('all')
```

## Plot the spectrogram using Obspy

For the fft window length, I used the 12th of the total length of the traces (arbitrarily). The output amplitude is in the dbscale. I clip the x axis to show the obtained values only (remove the masked values)

```
## Plot spectrogram
fig, axx = plt.subplots(1,1, figsize=(10,4))
total_length = newMseed[0].data.shape[0]
wlength = int(total_length/12) #window length for fft
newMseed[0].spectrogram(log=True, wlen=wlength,show=False, axes=axx, cmap='jet', dbscale=True) #wlen for the trade off between freq and time
ymin, ymax = axx.get_ylim()
xmin, xmax = axx.get_xlim()
print(xmin, xmax, ymin, ymax)
axx.set_xlim([xmin+wlength, xmax-wlength])
axx.set_title('Spectrogram')
axx.set_xlabel('Time in sec')
axx.set_ylabel('Frequency (Hz)')
plt.tight_layout()
plt.savefig('spectrogram_plot.png', bbox_inches='tight', dpi=300)
plt.close('all')
```

## Plot custom spectrogram

I modfied the Obspy’s spectrogram function to obtain the spectrogram computation results and then I plotted it. This provides us more control on the plotting and customization.

```
from matplotlib.colors import Normalize
from spectrogram_obspy_modified import compute_spectrogram
## Plot spectrogram
fig, axx = plt.subplots(1,1, figsize=(10,4))
total_length = newMseed[0].data.shape[0]
wlength = int(total_length/12) #window length for fft
specgram, freq, time = compute_spectrogram(newMseed[0].data,samp_rate=newMseed[0].stats.sampling_rate, wlen=wlength, dbscale=True) #wlen for the trade off between freq and time
## Normalize the color map
vmin = specgram.min()
vmax = specgram.max()
if vmin<0:
vmin = 0
norm = Normalize(vmin, vmax, clip=True)
axx.set_yscale('log')
cscale = axx.pcolormesh(time, freq, specgram, norm=norm, cmap='jet')
# Log scaling for frequency values (y-axis)
axx.set_title('Spectrogram')
axx.set_xlabel('Time in seconds')
axx.set_ylabel('Frequency (Hz)')
cbar = fig.colorbar(cscale, extend='max')
cbar.set_label('Amplitude in dbscale')
plt.tight_layout()
plt.savefig('spectrogram_plot2.png', bbox_inches='tight', dpi=300)
plt.close('all')
```

The modified script can be downloaded from here.

The complete script for this post can be downloaded from here.

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