GEO 365N/384S Seismic Data Processing Computational Assignment 4

Teapot Dome data

Next, we return to processing the Teapot Dome data. Our task is to estimate the surface-consistent amplitude normalization, similar to how it was done with the two previous datasets.

1. Change directory to hw4/teapot.
2. Run

   scons -c
   

   to remove (clean) previously generated files.
3. The process should look familiar by now. Figure 14 shows the two amplitude factors ($L_s$ and $L_r$) estimated by least-squares inversion.
4. To visualize how the amplitude correction depends on the surface coordinates, we can display it by interpolating to a regular grid from the shot locations. To display the result (Figure 15), run

   scons sint.view
   

5. Your task: modify the SConstruct file to interpolate in receiver coordinates (gx and gy) instead of shot coordinates (sx and sy). Do you notice any interesting differences?

tshot,treceiver
Figure 14. Estimated shot and receiver surface-consistent amplitude terms for the Teapot Dome dataset.

sint
Figure 15. Surface-consistent amplitude interpolated using the shot coordinates.

from rsf.proj import *

# Download Teapot Dome field data
#Fetch('npr3_field.sgy','teapot',
#      server='http://s3.amazonaws.com',top='')
Fetch('npr3_field.sgy','TeapotDome3D',
      top='/home/p1/seismic_datasets/SeismicProcessingClass',
      server='local')

# Convert to RSF
Flow('traces header header.asc','npr3_field.sgy',
     'segyread tfile=${TARGETS[1]} hfile=${TARGETS[2]}')

# Seismic data corresponds to trid=1
Flow('trid','header','headermath output=trid | mask min=1 max=1')

# Average trace amplitude
Flow('arms','traces trid',
     '''
     mul $SOURCE | headerwindow mask=${SOURCES[1]} | 
     stack axis=1 | math output="log(input)" 
     ''')
Flow('theader','header trid','headerwindow mask=${SOURCES[1]}')

# shot/receiver indeces: fldr and tracf
Flow('index','theader','window n1=2 f1=2 | transp')

prog = Program('../alaska/surface-consistent.c')
sc = str(prog[0])

Flow('model',['arms','index',sc],
     '${SOURCES[2]} index=${SOURCES[1]} verb=y')

Flow('sc',['arms','index',sc,'model'],
     '''
     conjgrad ${SOURCES[2]} index=${SOURCES[1]} 
     mod=${SOURCES[3]} niter=50
     ''')

Result('tshot','sc',
       '''
       window n1=850 | put o1=1 d1=1 | 
       graph title="Shot Term" 
       label1="Shot Number" unit1= label2=Amplitude unit2=
       ''')
Result('treceiver','sc',
       '''
       window f1=850 n1=1063 | put o1=1 d1=1 | 
       graph title="Receiver Term" 
       label1="Receiver Number" unit1= label2=Amplitude unit2=
       ''')

# Surface-consistent Log Amplitude for each trace
Flow('scarms',['sc','index',sc],
     '${SOURCES[2]} index=${SOURCES[1]} adj=n')

# Interpolate to a regular grid

# !!!! MODIFY BELOW !!!

# Using sx and sy 
Flow('scoord','theader',
     'window n1=2 f1=21 | dd type=float | scale dscale=1e-6')
Flow('sint','scarms scoord',
     '''
     nnshape coord=${SOURCES[1]} rect1=10 rect2=10 
     o1=785 d1=0.1 n1=251 o2=935 d2=0.1 n2=451 niter=10 
     ''')
Result('sint',
       '''
       grey color=j title="Log-Amplitude in Shot Coordinates" 
       transp=n label1=X label2=Y unit1=kft unit2=kft 
       bias=-17 clip=3
       ''')

End()

GEO 365N/384S Seismic Data Processing Computational Assignment 4