The numpy array object
In [2]:
import numpy as np
import matplotlib.pyplot as plt
% matplotlib inline
Exercise:
This exercise requires the file called seismic_cube.txt (120 MB). Download it and place it in your data folder,
a) Use np.loadtxt(...) to load the text file named data/seismic_cube.txt into an numpy array called data
b) How many dimensions does this data array have?
c) How many elements does data have in each dimension (np.shape)?
d) How much space does this object take up in memory?
e) How much time did it take? (seconds) (import time)
In [ ]:
In [6]:
import time
start = time.time()
data = np.loadtxt('data/seismic_cube.txt')
end = time.time()
elapsed = end - start
print("Time taken to read volume: {:.2f} seconds.".format(elapsed))
Some information about the seismic cube:
Number of inlines: 194
Number of crosslines: 299
Number of samples per trace: 463
Sample rate in seconds: 0.004
I'm giving you this information, but in practice, this would probably come from the file's header or meta-data section
In [4]:
nIL = 194 # number of inlines
nXL = 299 # number of crosslines
nt = 463 # number of samples per trace
dt = 0.004 # sample rate in seconds
Exercise: Use numpy's reshape function to turn this this array into a 3D array. Print the shape attribute of the reshaped object to verify that this object has the correct number of inlines and crosslines
In [ ]:
# enter your code here
Exercise: Get rid of some bad data for the last few samples on each trace; take the first 450 samples (of the last dimension)
In [8]:
# enter your code here
Exercise: create a new object called xline find the middle trace on crossline 150
xlinexline using plt.imshow(xline)imshow function change the ugly default to your favourite colourbar! figure function to make a figure object, figExercise: grab the trace in the middel of xline to answer the following questions
a) How many time samples are in this trace?
b) What is the range of peak to peak values of the trace?
c) what is the maximum value along this trace? And where is it located?
d) what is the minumum value along this trace? And where is it located?
e) Seismic traces should have a mean value close to zero. Does this trace have seismic trace that is close to zero?
In [30]:
s = xline[int(line.shape[0]/2),...]
s.shape
Out[30]:
In [31]:
%matplotlib inline
t = np.arange(0, 450 * 4, 4)
a = 12
#
fig = plt.figure(figsize=(3,9), facecolor='w')
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax.plot(s,t, 'g--')
ax.set_xlim(-a*s.std(), a*s.std())
ax.grid()
fig.savefig('dotted_green.png', dpi=300)
#plt.plot(s, t) #, 'g*', lw=2.0, alpha=0.25)
#plt.xlim(-2.0*s.std(), 2.0*s.std())
Exercise: Plot this seismic trace (vertically!)
- make a "time axis": `np.arange(first_sample, last_sample, sample_rate)
- use matplotlib's `plt.plot` function (documentation!)
- other `plt` functions to check out: `plt.xlim`, `plt.title`, `plt.ylabel`, `plt.invert_yaxis()`, 'plt.grid`Exercise: use the plt.hist(s, bins) function to create a histogram with 100 bins
As suspected, most of the data values are close to zero, and fan outward (more or less symmetrically) to larger positive and negative values.
To be sure, we can include the values from the entire line to build up better statitics. However, in order to pass a 2D array to the plt.hist function, we have to unravel it first,
Let's look at the frequency content of this trace. To do this we will need to use the the fast fourier transform function from the Scipy FFT module. It helps to know that the sample rate of the trace is 0.04 s
In [32]:
from scipy import fft
S = abs(fft(s))
power = 20*np.log10(a)
faxis = np.fft.fftfreq( len(power), d = dt)
Exercise: Plot the power spectrum (frequency on the time axis). For bonus points, plot it on a log base 10 scale (Decibel)
In [ ]:
Exercise: write a loop to plot the power spectrum all the traces
In [ ]:
Exercise: plot the 2D line (any 2D matrix can be shown as an image)
plt.imshow(...)
In [ ]: