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hcorzopolahcorzopola
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adding folder with reproduced figures from Yilmaz' (#311)
* added SConstruct with real data example * added SConstruct with Ray Abma's synthetic data example * Colab Notebook for seismic trace interpolation using machine learning * added google colab notebook for seismic trace interpolation using machine learning * adding folder with reproduced figures from Oz Yilmaz' Seismic Analysis
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‎book/hackathon/2024soundfx/real/SConstruct‎

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from rsf.proj import *
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# Fetch & plot dataset
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Fetch('elf-stk2.rsf','masha')
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Flow('elf','elf-stk2','dd form=native | put unit1=s')
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Result('elf','grey')
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# Decimate
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Flow('decimated','elf','window j2=4 | put d2=53.3333')
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Result('decimated','grey')
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#Result('original_n_decimated',['elf','decimated'],'SideBySideAniso')
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# Zoom-in
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Flow('original_w','elf','window max1=1.5 n2=600 | costaper nw1=40')
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Flow('decimated_w','decimated','window max1=1.5 n2=150 | costaper nw1=40')
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Result('original_w','grey title="Original data"')
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Result('decimated_w','grey title="Decimated data"')
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#Result('original_n_decimated_w',['elf_w','decimated_w'],'SideBySideAniso')
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# T-X interpolation
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Flow(['pad_w','mask_w'],'decimated_w','lpad jump=4 mask=${TARGETS[1]}')
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Flow(['pef_w','lag_w'],'decimated_w','lpef lag=${TARGETS[1]} a=17,5 jump=4')
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Flow('interpolated_w',['pad_w','mask_w','pef_w'],
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'miss padin=4 filt=${SOURCES[2]} mask=${SOURCES[1]} prec=n')
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Result('interpolated_w','grey')
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Flow('pef_aw','pad_w','apef a=15,4 jump=4 rect1=20 rect2=5 niter=200 verb=y')
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Flow('interpolated_aw',['pad_w','pef_aw','mask_w'],'miss4 filt=${SOURCES[1]} mask=${SOURCES[2]} verb=y')
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Result('interpolated_aw','grey')
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## Interpolation error
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# F-X interpolation
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## Interpolation error
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End()

‎book/hackathon/2024soundfx/synthabma/SConstruct‎

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from rsf.proj import *
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from rsf.recipes.beg import server as private
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###########################################################################
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for dat in ('curve','linear','random','marm'):
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input = 'input.%s.segy' % dat
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Fetch(input,'ray',private)
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Flow([dat,'./A'+dat,'./B'+dat],input,
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'''
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segyread tape=$SOURCE read=d hfile=${TARGETS[1]} bfile=${TARGETS[2]}
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''',stdin=0)
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Flow('linear2','linear','window min1=0.5 max1=2.7 | bandpass fhi=60')
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Plot('linear','linear2',
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'''
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grey yreverse=y transp=y poly=y label2=Position
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title=Input
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''')
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Plot('jlinear','linear2',
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'''
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window n2=11 f2=23 n1=150 min1=1.35 |
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put d1=1 o1=675 label1=Sample unit1= |
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wiggle yreverse=y transp=y poly=y label2=Position wherexlabel=t
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title=Input wheretitle=b clip=0.0451806 labelsz=5. titlesz=7
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labelfat=2 font=2 titlefat=2 screenratio=1.2
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''')
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# nonstationary PWD
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Flow('lindip','linear2','twodip2 order=3 nj1=4 nj2=4 eps=10 gauss=n')
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Flow('lindip2','lindip',
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'transp | spline n1=240 o1=0 d1=0.25 | transp')
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Flow('lin4 linones4','linear2','lpad jump=4 mask=${TARGETS[1]}')
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Flow('lindeal','lin4 lindip2 linones4',
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'planemis2 dip=${SOURCES[1]} mask=${SOURCES[2]} order=3 verb=y')
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Plot('lindeal','grey yreverse=y transp=y poly=y title=Interpolated')
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Result('linear-deal','linear lindeal','SideBySideAniso')
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# Stationary T-X PEFs
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Flow('lpef llag','linear2','lpef lag=${TARGETS[1]} a=10,4 jump=4')
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Flow('lscov','lpad lmask lpef',
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'miss padin=4 filt=${SOURCES[2]} mask=${SOURCES[1]} prec=n')
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Plot('lscov',
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'grey yreverse=y transp=y poly=y title="Stationary PEF"')
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Result('linear-scomp','linear lscov','SideBySideAniso')
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# Nonstationary T-X PEFs
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Flow('lpad lmask','linear2','lpad jump=2 mask=${TARGETS[1]}')
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Flow('lapef','lpad','apef a=15,4 jump=2 rect1=20 rect2=5 niter=200 verb=y')
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Flow('lacov','lpad lapef lmask',
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'miss4 filt=${SOURCES[1]} mask=${SOURCES[2]} verb=y')
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Plot('lacov',
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'''
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grey yreverse=y transp=y poly=y label2=Position
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title="Adaptive PEF"
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''')
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Plot('jlacov','lacov',
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'''
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window n2=22 f2=46 n1=150 min1=1.35 |
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put d1=1 o1=675 label1=Sample unit1= |
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wiggle yreverse=y transp=y poly=y label2=Position wherexlabel=t
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title="Adaptive PEF" wheretitle=b clip=0.0225903 labelsz=5. titlesz=7
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labelfat=2 font=2 titlefat=2 screenratio=1.2
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''')
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Result('linear-comp','linear lacov','SideBySideAniso')
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# Stationary F-X PEFs
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Flow('lfx','lpad',
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'''
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spitz norm=n verb=y
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''')
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Plot('lfx',
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'''
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grey yreverse=y transp=y poly=y label2=Position
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title="Adaptive PEF"
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''')
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Result('linear-fxcomp','linear lfx','SideBySideAniso')
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End()

‎book/yilmaz/README‎

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This document aims to reproduce the synthetic data examples presented on Öz Yilmaz' Seismic Data Analysis, Volume I.

‎book/yilmaz/SConstruct‎

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from rsf.book import *
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from rsf.tex import Paper
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chapters=Split(
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'''
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fourier1d
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''')
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Book(chapters,
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author='TCCS Apocrypha',
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title="Reproducing Oz Yilmaz' Seismic Data Analysis")
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Paper('intro')
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End(options='book',
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use='graphicx,color,amsmath,amssymb,amsbsy,hyperref,listings,framed,tabularx')

‎book/yilmaz/fourier1d/SConstruct‎

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from rsf.tex import *
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End(color='all')

‎book/yilmaz/fourier1d/aliasing/SConstruct‎

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from rsf.proj import *
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"""
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This SCons script aims to reproduce Fig 1.1-7 to Fig 1.1-10, presented in Öz Yilmaz' Seismic Data Analysis, Vol 1.
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The first three frames show sinusoidal functions with different frequencies:
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* 1: Frequency - 25 Hz
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* 2: Frequency - 75 Hz
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* 3: Frequency - 150 Hz
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The fourth frame shows the composition of two sinusoids with frequencies:
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* 1: Frequency - 12.5 Hz
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* 2: Frequency - 75 Hz
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All the functions above are sampled at three different rates:
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* 1: Sampling rate - 2 ms
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* 2: Sampling rate - 4 ms
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* 3: Sampling rate - 8 ms
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The original figures exemplified aliasing: the consequence of sampling below the Nyquist rate.
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*For some reason, the original figures appear to be "smoother" than they should.
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"""
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# Sinusoid parameters
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frequency = [25,75,150] # frequencies in Hz
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samplingrate = [0.002,0.004,0.008] # sampling rates in seconds
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for i in range(3):
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for j in range(3):
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jrate = samplingrate[j]
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# Produce sinusoid
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Flow('sinusoid%g-%gms'%(i+1,jrate*1000),None,
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'''
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math n1=%g d1=%f o1=0
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output="sin(x1*%f*2*(2*asin(1)))" |
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put label1="Time" unit1="s"
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'''%(1/jrate+1,jrate,frequency[i])) # pi is defined as 2*asin(1)
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Plot('sinusoid%g-%gms'%(i+1,jrate*1000),
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'graph title="Sinusoid %g, sampling rate = %g ms" screenratio=0.75 min1=0 max1=1 min2=-1 max2=1'%(i+1,jrate*1000))
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Result('sinusoid%g-%gms'%(i+1,jrate*1000),
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'graph title="Sinusoid %g, sampling rate = %g ms" screenratio=0.5 min1=0 max1=1 min2=-1 max2=1'%(i+1,jrate*1000))
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# Recovering Amplitude spectra through FFT
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## Fourier transform
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fftwindow = int(0.4/jrate) # we window N full cycles
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Flow('fourier%g-%gms'%(i+1,jrate*1000),'sinusoid%g-%gms'%(i+1,jrate*1000),'window n1=%g | fft1'%fftwindow)
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## Amplitude spectra
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Flow('fampspectra%g-%gms'%(i+1,jrate*1000),'fourier%g-%gms'%(i+1,jrate*1000),
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'''
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math output="2*abs(input)/%g" | real | put label1="Frequency" unit1="Hz"
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'''%fftwindow) # normalized by 2/N
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Plot('fampspectra%g-%gms'%(i+1,jrate*1000),
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'graph title="Amplitude Spectrum" screenratio=1 min1=0 max1=250 min2=0 max2=1')
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Result('fampspectra%g-%gms'%(i+1,jrate*1000),
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'graph title="Amplitude Spectrum %g" screenratio=1 min1=0 max1=250 min2=0 max2=1'%(i+1))
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## Plot side by side
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Plot('subplot%g-%gms'%(i+1,jrate*1000),'sinusoid%g-%gms fampspectra%g-%gms'%(i+1,jrate*1000,i+1,jrate*1000),
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'SideBySideAniso')
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Result('subplot%g-%gms'%(i+1,jrate*1000),'sinusoid%g-%gms fampspectra%g-%gms'%(i+1,jrate*1000,i+1,jrate*1000),
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'SideBySideAniso')
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Plot('frame%g'%(i+1),['subplot%g-%gms'%(i+1,samplingrate[j]*1000) for j in range(3)],'OverUnderAniso')
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Result('frame%g'%(i+1),['subplot%g-%gms'%(i+1,samplingrate[j]*1000) for j in range(3)],'OverUnderAniso')
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# Composite signal
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for j in range(3):
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jrate = samplingrate[j]
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# Produce sinusoid
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Flow('sinusoid4-%gms'%(jrate*1000),None,
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'''
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math n1=%g d1=%f o1=0
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output="sin(x1*12.5*2*(2*asin(1)))+sin(x1*75*2*(2*asin(1)))" |
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put label1="Time" unit1="s"
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'''%(1/jrate+1,jrate)) # pi is defined as 2*asin(1)
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Plot('sinusoid4-%gms'%(jrate*1000),
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'graph title="Sinusoid %g, sampling rate = %g ms" screenratio=0.75 min1=0 max1=1 min2=-1 max2=1'%(i+1,jrate*1000))
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Result('sinusoid4-%gms'%(jrate*1000),
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'graph title="Sinusoid %g, sampling rate = %g ms" screenratio=0.5 min1=0 max1=1 min2=-1 max2=1'%(i+1,jrate*1000))
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# Recovering Amplitude spectrum through FFT
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## Fourier transform
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fftwindow = int(0.4/jrate) # we window N full cycles
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Flow('fourier4-%gms'%(jrate*1000),'sinusoid4-%gms'%(jrate*1000),'window n1=%g | fft1'%fftwindow)
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## Amplitude spectrum
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Flow('fampspectra4-%gms'%(jrate*1000),'fourier4-%gms'%(jrate*1000),
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'''
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math output="2*abs(input)/%g" | real | put label1="Frequency" unit1="Hz"
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'''%fftwindow) # normalized by 2/N
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Plot('fampspectra4-%gms'%(jrate*1000),
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'graph title="Amplitude Spectrum" screenratio=1 min1=0 max1=250 min2=0 max2=1')
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Result('fampspectra4-%gms'%(jrate*1000),
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'graph title="Amplitude Spectrum %g" screenratio=1 min1=0 max1=250 min2=0 max2=1'%(i+1))
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## Plot side by side
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Plot('subplot4-%gms'%(jrate*1000),'sinusoid4-%gms fampspectra4-%gms'%(jrate*1000,jrate*1000),
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'SideBySideAniso')
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Result('subplot4-%gms'%(jrate*1000),'sinusoid4-%gms fampspectra4-%gms'%(jrate*1000,jrate*1000),
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'SideBySideAniso')
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Plot('frame4',['subplot4-%gms'%(samplingrate[j]*1000) for j in range(3)],'OverUnderAniso')
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Result('frame4',['subplot4-%gms'%(samplingrate[j]*1000) for j in range(3)],'OverUnderAniso')
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End()

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