P-P imaging

uIntroduction

uFigures 1-2

uAcquisition / processing

uApplications

P-SV imaging

uComment

uFigures 3-4

uWave length

uIncreasing frequency

uReducing velocity

uAppendix

P-P imaging

uIntroduction

uFigures 1-2

uAcquisition / processing

uApplications

P-SV imaging

uComment

uFigures 3-4

uWave length

uIncreasing frequency

uReducing velocity

uAppendix

P-P imaging

uIntroduction

uFigures 1-2

uAcquisition / processing

uApplications

P-SV imaging

uComment

uFigures 3-4

uWave length

uIncreasing frequency

uReducing velocity

uAppendix

P-P imaging

uIntroduction

uFigures 1-2

uAcquisition / processing

uApplications

P-SV imaging

uComment

uFigures 3-4

uWave length

uIncreasing frequency

uReducing velocity

uAppendix

P-P imaging

uIntroduction

uFigures 1-2

uAcquisition / processing

uApplications

P-SV imaging

uComment

uFigures 3-4

uWave length

uIncreasing frequency

uReducing velocity

uAppendix

P-P imaging

uIntroduction

uFigures 1-2

uAcquisition / processing

uApplications

P-SV imaging

uComment

uFigures 3-4

uWave length

uIncreasing frequency

uReducing velocity

uAppendix

Figure Captions

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Acquisition and Processing 

In deepwater multicomponent seismic data acquisition, there is a large elevation difference between source stations (an air gun at the sea surface) and receiver stations on the seafloor. Conventional processing of deepwater 4-C seismic data involves a wave-equation datuming step that transforms the data to a domain in which sources and receivers are on the same depth plane. This step effectively removes the water layer and allows the data to be processed as if the source was on the seafloor. This adjustment of source-receiver geometry also allows deepwater multicomponent data to be processed with software already developed for shallow-water environments where marine multicomponent data acquisition technology was originally developed and applied. 

An example of a good-quality, deepwater P-P image of near-seafloor geology made with this wave-equation datuming approach is shown as Figure 1a. This image shows local geology associated with a fluid-gas expulsion chimney that extends to the seafloor. 

If a person wishes to study near-seafloor strata, a new approach to P-P imaging of deepwater multicomponent seismic data is to not eliminate the large elevation difference between sources and receivers but to take advantage of that elevation difference. The objective is to process deepwater multicomponent data similar to the way vertical seismic profile (VSP) data are processed, because VSP data acquisition also involves large elevation differences between sources and receivers (Figure 2). 

Users of VSP technology know VSP data provide high-resolution images of geology near downhole receiver stations. That same logic leads to the conclusion that deep-water multicomponent seismic data processed with VSP-style techniques should yield higher resolution images of geology near deep seafloor receivers. 

The P-P processing illustrated here can be done with either 2-C or 4-C seafloor sensors. The fundamental requirement is to acquire data with a sensor having a hydrophone and a vertical geophone. The seafloor hydrophone response (P) and the seafloor vertical-geophone response (Z) are combined to create downgoing (D) and upgoing (U) P-P wavefields as:

D=P+Z/cos(F)

U=P--Z/cos(F)

“F” defines the incident angle at which the downgoing compressional wave arrives at the seafloor. Once this wavefield separation is done, deepwater multicomponent seismic data are defined in terms of downgoing and upgoing wavefields, just as are VSP data. 

Having access to downgoing (D) and upgoing (U) wavefields means sub-seafloor reflectivity can be determined by taking the ratio U/D. This reflectivity wavefield is then segregated into stacking corridors, and data inside these corridors are summed to create image traces just like VSP data have been processed for the past 20-plus years. 

Figure 1b shows a P-P image made with this technique using the same deep-water data displayed in Figure 1a. The improvement in resolution is obvious.

Applications and Constraints 

Applying this VSP-style imaging technique to deepwater multicomponent seismic data is proving to be invaluable for gas hydrate studies, geomechanical evaluations of deepwater seafloors and other applications where it is critical to image near-seafloor geology with optimal resolution. 

Every seismic data-processing technique, however, has constraints and pitfalls. Two principal constraints of the technology described here are:

• There has to be a significant difference between the elevations of sources and receivers. The technique is not appropriate for multicomponent seismic data acquired in shallow water.

• The improvement in image resolution over that of production processing of marine multicomponent seismic data diminishes as the image space extends farther (deeper) from the receivers. At significant sub-seafloor depths, production-style, wave-equation-datuming-based, P-P imaging (Figure 1a) is equivalent or superior to the VSP-style imaging described here.

P-SV Imaging (Part 2)

General Comment

 In Part 1, we considered how to improve the seismic resolution of deepwater, near-seafloor geology using P-P data acquired with seafloor-positioned multicomponent sensors.

In Part 2, we show how P-SV (converted-shear) data acquired with these same sensors provide even greater resolution of deepwater, near-seafloor strata.

Figure Captions

Wave Length 

To achieve better resolution of geologic targets with seismic data, it is necessary to acquire data that have shorter wavelengths. The wavelength l of a propagating seismic wave is given by:

l = V/f

where V is propagation velocity and f is frequency.

This equation shows there are two ways to reduce an imaging wavelength l: either increase f, or reduce V.

Option 1: Increasing the Frequency 

If deepwater strata are illuminated with conventional air gun seismic sources towed at the sea surface, there is really no way to cause a significant increase in the frequency content of the illuminating wavefield that reaches the seafloor. A different data-acquisition strategy has to be used to acquire shorter-wavelength marine P-P data. 

An approach now used for acquiring deepwater, short-wavelength P-P data is to use an Autonomous Underwater Vehicle (AUV) system. 

An AUV travels only 50 meters or so above the seafloor and illuminates seafloor strata with chirp-sonar pulses having frequency bandwidths of 2-10 kHz. This increase in signal frequency shortens P-P wavelengths by about a factor of 100 compared to the wavelengths of an air gun signal. The result is an illuminating wavefield having wavelengths of less than a meter when P-wave velocity VP is 1500 to 1600 m/s, a common range of VP for deepwater, near-seafloor sediments across the Gulf of Mexico (GOM). 

An example of an AUV chirp-sonar image acquired in water depths of approximately 900 meters in one area of the GOM is shown in Figure 3a. The image makes the same traverse across a targeted seafloor expulsion chimney that was illustrated in last month’s article.

These high-frequency P-P signals penetrate only 40 or 50 meters into the seafloor, but they image bedding and fault throws of meter-scale dimensions across this image space.

Option 2: Reducing the Velocity 

It is not possible to acquire shorter-wavelength P-P data by reducing VP in a seismic propagation medium. The value of VP within a system of targeted strata is fixed and cannot be altered. 

A seismic imaging effort, however, can switch from the conventional approach of using the P-P seismic mode and focus on using another wave mode that does have reduced velocity within a targeted interval. That logic has great benefit for imaging deepwater, near-seafloor geology when the imaging effort focuses on P-SV data rather than on P-P data. 

Across most deep-water areas, S-wave velocity VS in near-seafloor sediments tends to be 20 to 50 times less than P-wave velocity VP. Thus, if P-P and P-SV data have equivalent frequency content, which they do for shallow penetration distances  of an illuminating P-P wavefield into the seafloor, P-SV data will have wavelengths much shorter than P-P wavelengths. 

Shown as Figure 3b is a P-SV image constructed from 4C data acquired with seafloor sensors deployed along the same profile as the AUV data in Figure 3a. The illuminating wavefield that created these  P-SV data was a 10-100 Hz P-P wavefield produced by a conventional air gun array positioned at the sea surface. 

Because VS in near-seafloor sediment along this profile is less than 100 m/s, the  P-SV data have many wavelengths less than one meter in length, just as do the high-frequency chirp-sonar data. Visual inspection of the images in Figure 3 shows the spatial resolutions of kilohertz-range P-P data and low-frequency P-SV data are equivalent in deep-water, near-seafloor geology. 

The same data are shown again in Figure 4, with depth-equivalent horizons superimposed to emphasize the amazing resolution of the low-frequency P-SV data. Horizon A shown on the AUV image is not easily seen on this particular P-SV image, so no P-SV equivalent horizon is labeled. 

Note the large magnitudes of the interval values of the VP/VS velocity ratio. Also note how easy it is to identify where stratigraphy first becomes unconformable to the seafloor in these seafloor-flattened data (Horizon B).

Unfortunately, these high-resolution P-SV images cannot be extended to great sub-seafloor depths. P-SV wavelengths increase and P-SV resolution then decreases with increasing depth below the seafloor because:

·        VS increases with depth.

·        Higher frequencies attenuate more rapidly with depth for P-SV wavefields than for their companion P-P wavefields.

At sub-seafloor depths of several kilometers, P-P and P-SV data have approximately the same resolution. However, for deepwater strata close to the seafloor, the spatial resolution of P-SV data is most impressive (Figures 3b and 4b).

Appendix/Acknowledgments 

Information about this technology is available at www.beg.utexas.edu/indassoc/egl/. 

WesternGeco provided the seismic data used in this research. Research funding was provided by Minerals Management Service (Contract 0105CT39388) and DOE/NETL (Program DE-PS26-05NT42405).

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