\chapter{Conclusions}\label{chapter:Concl}
Passive seismology is a very broad field that basically recapitulates
the entire field of seismology with unconventional sources.  Correlating 
traces from a receiver array calculates the time differences recorded
in an active survey.  The central caveat to successfully synthesizing
active data from passive data is that wavefields from many sources
from a wide distribution of subsurface locations are available.  If
so, the synthesized shot gathers are, at best, reproductions of a data
volume that has been collected and analyzed extensively for decades.  As 
such, all of the same limitations, inconveniences, and problems are
included in the processed data volume.  In practice, utilizing the ambient
sound field of the Earth will lead to synthesized data that have even
more difficulties than active data.
\par
Preprocessing passive data is a challenging task.  At its simplest,
short time windows can be extracted when a seismologist is sure that
useful energy is investigating the subsurface.  After events are
identified, there are a range of options that lead to the standard
processing strategies seen in global seismology and the exploration
industry.  If identifiable events are not present in the raw data, the
only preprocessing strategies available are those based on known
quantities such are the array geometry and sensor recording
bandwidth.  After synthesizing shot gathers, a host of signal
enhancing algorithms are likely applicable to the data.  However, if a
complete distribution of subsurface sources was not captured to synthesize
the gathers, it may be impossible to correctly identify signal and
noise components in the gathers.
\par
For subsurface imaging exploiting body waves, a distribution of
subsurface sources directly beneath the recording array is sufficient
to synthesize interpretable shot gathers.  The results have artifacts
that are not present in active seismic data, but the bulk of the
events in the volume are accurate for further analysis with
conventional processing algorithms.  When only a small number of
subsurface sources are captured, or when multiple subsurface sources
are captured in the same time window, the correlation results can be
completely uninterpretable.  There is still ample subsurface
information contained within the data volume however that can be
uncovered by further processing with a migration algorithm.  Migrating
passive data due to single or summed sources produces stable,
interpretable images.  The image domain provides a convenient, smaller
sized volume of information from which to judge if the data collected
contain informative reflection events or just noise.
\par
Direct migration of passive seismic data is an alternate processing
strategy to produce a subsurface image without first correlating the
traces from a receiver array at the acquisition surface.  Instead, the
raw data can be extrapolated to all subsurface depth levels before
correlation with a shot profile depth migration algorithm.  Redatuming
the data before correlation does not change the underlying seismic
experiment as long as sufficiently accurate propagators and velocity
models are used.  Direct migration is a new definition of
appropriate initial conditions for a specific data acquisition
strategy.  After defining the correct initial conditions for the
wavefields used in migration, the same shot profile migration
algorithms developed for active seismic data can be used to process
the passive data.
\par
With modeled transmission data, I found very little difference in
results obtained by direct migration compared to images produced by
conventional migration of synthesized data.  Substantial computational
cost can often be saved by processing transmission wavefields with
direct migration rather than first synthesizing shot gathers.  This is
because migration is such an expensive processing algorithm, and
correlating every trace with every other trace squares the size of the
data volume to migrate.  Therefore, these cost considerations are more
important for passive data collected with large arrays with more than
several thousand sensors.
\par
Passive seismic data recorded over 36~hours was processed to produce
images in this thesis.  I used approximately 2300 hydrophone pressure
sensors from the Valhall array in the Norwegian North Sea.  Correlation 
processing to synthesize shot gathers did not reveal subsurface
information.  Instead, the results were dominated by a direct arrival
through the water column from production noise at a facility 40~km
away.  That facility flares produced gas whereas the Valhall
production facility does not.  I believe this is the energy source
that gives rise to the only interpretable event on the correlated
gathers.  The event is stable in results produced with passive data
volumes collected one year apart.
\par
Direct migration of the Valhall passive data produced noisy, but
interpretable subsurface images.  Images produced with active seismic
data were available for direct comparison.  Events as deep as 2~km
were present in the passive images that seemed to correspond to events
in the active image.  However, migrating modeled data based on the
far-field noise extracted from the synthesized gathers produced almost
identical images.  Identifying the dominant seismic energy in the
records was accomplished by analysis of deconvolved synthesized shot
gathers.  Without modeling the effects of this noise in all of the
processing schemes used with this data, results would have been
over interpreted.  The coherent noise events caused artifacts with all
the character of real subsurface events seen in the active data.  The
image volumes produced by synthesizing shot gathers followed by
migration were quite different from direct migration images.  Because
these two processing options are mathematically equivalent for data
from a broad distribution of subsurface sources, the difference
between the results indicates that the field data do not contain the
distribution of subsurface sources required for successful imaging.
\par
The passive imaging experiments described herein used a unique data
volume provided by BP.  This volume of data recorded more data with
more receiver stations than any other passive seismic experiment ever
fielded.  Because the sensors are permanently installed on the sea
floor, data can be continuously collected to continue analyzing the
effectiveness of passive imaging.  Processing 1.5~days of passive
data did not produce images of subsurface structure.  It is possible
that similar processing with more data could improve on my results.  The
failure of my experiments, however, provides little encouragement to
invest in the substantial processing required to analyze passive seismic
records.  
\par
I believe that successful subsurface imaging at Valhall
with passive seismic data is predicated on removing the contaminating
coherent noise from the gas flare at the Ardmore production 
facility.  The noise is so much more energetic than any
local seismic energy that my attempts at removing it from the data
have been unsuccessful.  To improve the chance of successful imaging,
the removal should be performed before correlation.  However, the
noise is continuous and unidentifiable on the raw records, and is not
isolated from body waves in the Radon or Fourier domains.  Data
collected when the flare is extinguished for maintenance would be ideal.  
\par
Aside from collecting data when the flare is not active, it may be
possible to remove the noise from the raw data with a signal
processing strategy.  The noise is very broad-band, and continuous
in time.  This combination will make both frequency-domain and
time-domain approaches challenging.  A large body of work exists
concerning statistic and deterministic signal processing that may
address removing the noise from the data.  Specifically, echo removal
techniques seem likely candidates for initial tests.  Many of these
techniques operate on the properties of the autocorrelation of the
signal.  This could be beneficial, or a cause for total failure since
the autocorrelation of the noise is so dominant.  Multi-dimensional
processing strategies are also available.  Because the spatial
characteristics of the noise is well known, I believe that at least
2D (time and inline direction) algorithms will be required for
success.  All of these techniques are computationally expensive, often
involving chains of cost-function minimizations.  
\par
Some form of principal component analysis (PCA) might work well to separate
the strong noise arrival from the data.  Because the flare noise is so
energetic, it should occupy the higher-order principal components from
the PCA.   The lower-order principal components contain the data
components most responsible for the variability within the data.  There 
will be a large amount of background random noise in the data that
might be suppressed by also discarding the lowest-order principal
components.  Geologic signal should lie somewhere in center of
the PCA domain.  Again, I believe that utilizing the multi-dimensional
character of the flare noise, especially in the inline direction, will
provide a substantial improve in the quality of result.
\par
An alternative use for the passive recording capabilities of the array is
as a global seismology station.  Processing data from teleseismic
arrivals to image deep structure should be possible and useful.  At
such low frequencies however, the areal footprint of the array is
likely too small to support array-based processing techniques.  However, 
it is possible that similar arrays may be installed at other
reservoirs in the region.  Recording at multiple arrays
simultaneously may provide interesting data sets to investigate
lower-frequency phenomenon.  Also, a second array may provide more
areal information to attack the gas flare, which will undoubtedly be
observed elsewhere in the region.
\par
Direct migration is an example of simple seismic processing accomplished
with the imaging condition of shot profile migration.  In this case, the
imaging condition is exactly the desired processing calculation.  However,
using accurate mathematical propagators to model the physics of wave 
propagation allows us to perform any processing step after extrapolation 
in the image space.  Statics corrections are a simple data processing
example based on this principle.   
\par
Direct migration of data due to unknown 
sources is analogous to imaging reflectors with multiple energy.  In terms of
a multiple problem, correlation models primaries from first order 
multiples.  The most common operator encountered in the literature concerning
multiples is convolution.  Convolution models first order multiples from
primaries.  Correlation and convolution are adjoint processes that either 
image primaries with multiples or predict multiples from 
primaries.  Image-space multiple prediction is the adjoint of direct 
migration of passive seismic data.  The two algorithms are fundamentally 
linked by the mathematical similarities of the two operations.  
\par
Image-space multiple prediction auto-convolves shot gathers at every 
depth level in the image domain after extrapolation and sums the 
result from all shots to produce the final result.  After extrapolation, 
the redundant time axis can be discarded to keep only the space 
axes.  Traditional data-space multiple prediction can be distilled to 
component processes: Convolve, sum.  Convolving traces after extrapolation
through the course of the shot profile migration algorithm does not 
invalidate the accuracy of the multiple prediction.  The
accuracy of the propagator is not important when producing 
a multiple prediction in the image space because the conventional 
image of reflectors is produced with the same propagator.  Events in the
multiple prediction correspond exactly to the multiples in the 
conventional image even if both volumes are inaccurate.  The events 
in the two volumes are always directly comparable since the
same kinematic modeling was used to create both image volumes.



