Processing of Single Channel Air and Water Gun

Data for Imaging an Impact Structure at the

Chesapeake Bay

Myung W. Lee

U.S. Geological Survey Bulletin 2169

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

Bruce Babbitt, Secretary

U.S. Geological Survey

Charles G. Groat, Director

This report is only available on-line at:

http://greenwood.cr.usgs.gov/pub/bulletins/b2169/index.html

Any use of trade, product or firm names is for

descriptive purposes only and does not imply

endorsement by the U.S. Government.

Published in the Central Region, Denver, Colorado

Manuscript approved for publication July 28, 1999

Graphics by the author and Gayle M. Dumonceaux

Photocomposition by Gayle M. Dumonceaux

Edited by Lorna Carter
Contents

Abstract ......................................................................................................................................... 1

Introduction .................................................................................................................................. 1

Acknowledgments ....................................................................................................................... 1

Data Processing ........................................................................................................................... 1

Air Gun (GI Gun) Data .................................................................................................... 1

Static Correction and Direct Arrival Correction............................................. 1

Gain, Deconvolution............................................................................................ 1

Wavelet Deconvolution and Matched Filtering ............................................. 2

F-X Deconvolution............................................................................................... 4

Water Gun Data .............................................................................................................. 4

Discussion...................................................................................................................................... 5

Summary ........................................................................................................................................ 7

References Cited .......................................................................................................................... 7

Figures

1. Location map of the single channel seismic lines in Chesapeake Bay .................... 2

2. Processing flowchart ........................................................................................................ 2

3–7. Images showing: 

3. Examples of seismic data processing ....................................................................... 3

4. Effect of signal enhancement filter (F-X deconvolution) ....................................... 4

5. Wavelet deconvolution for the water gun data ....................................................... 5

6. Result of water gun data processing ........................................................................ 6

7. Final processing for line 7, acquired by GI (air) gun ............................................... 6

III
1

Processing of Single Channel Air and 

Water Gun Data for Imaging an Impact 

Structure at the Chesapeake Bay

By Myung W. Lee

Abstract

Processing of 20 seismic profiles acquired in the Chesapeake Bay area aided in analysis of the details of an impact 

structure and allowed more accurate mapping of the depression 

caused by a bolide impact. Particular emphasis was placed on 

enhancement of seismic reflections from the basement. Application of wavelet deconvolution after a second zero-crossing 

predictive deconvolution improved the resolution of shallow 

reflections, and application of a match filter enhanced the basement reflections. The use of deconvolution and match filtering 

with a two-dimensional signal enhancement technique (F-X 

filtering) significantly improved the interpretability of seismic 

sections.

Introduction

In 1996, a single channel seismic data set was acquired in 

the Chesapeake Bay area, near Norfolk, Virginia, using a generator injector (GI) air gun and a water gun. The purpose of the 

data acquisition was to accurately map the impact structure by 

resolving the basement surface, the basement structure, internal 

disruptions in stratigraphy, and possible faulting. These lines 

were shot around the impact structure, supplementing the existing regional seismic lines in the area. The majority of these 

data (19 lines) were acquired using a GI gun (45 in.3 generator 

chamber and 45 in.3 injector chamber) with a 50-m long recording streamer; lines 13a and 15a were shot with a 15 in.3 watergun and a solid-element streamer about 4.5 m long. Line 

locations are shown in figure 1.

Important processing considerations were as follows: (1) 

For the GI gun data, the distance from gun to streamer center is 

generally much greater than the water depth, so it will be hard to 

image the shallow section due to interference between reflection 

and direct arrival. (2) Gas emanating from sediments was obvious in many places, and its presence was confirmed with a highfrequency system (chirp sonar). In areas where gas was present, 

the quality of the section was locally degraded by the frequency 

shift of the reflections, strong reverberations and “time sag.” (3) 

Despite the excellent tuning of the gun, the computer firing the 

gun sometimes failed to synchronize properly, so small static 

shifts occurred in the data. Therefore, the processing strategy 

was tailored to minimize the problems associated with data 

acquisition and to enhance the signal-to-noise ratio of the 

basement reflections as well as the increased temporal 

resolution of internal reflections.

Acknowledgments

I thank D.R. Hutchinson and C.W. Poag for providing the 

map for Chesapeake Bay Cruise 1996 and relevant information 

for data processing. I thank J.J. Miller and W.F. Agena for 

helping to improve the clarity of the paper.

Data Processing

The processing flowchart is shown in figure 2. As indicated in figure 2, the processing steps for the water gun data are 

slightly different from those of the air gun data.

Air Gun (GI Gun) Data

Static Correction and Direct Arrival Correction 

As mentioned in the Introduction, inaccurate computer 

firing of the gun introduced a small static shift in the data. The 

shifts were measured visually on the screen display and were 

corrected to align the zero-time.

The offset between the GI gun and the center of the 

streamer was 50 m, and direct arrivals to the streamer interfered 

with reflections from the shallow water bottom. To suppress 

the direct arrival, we tried subtracting the running sum of 10 

direct arrivals from the data. Figure 3A shows the original raw 

data with automatic gain control (AGC); figure 3B shows the 

same data after deconvolution (to be mentioned later) and 

suppression of direct arrivals. Strong arrivals in the beginning 

of the section in figure 3A are direct arrivals superimposed with 

weak reflections. Although these direct arrivals were reduced in 

figure 3B, the process did not enhance any interpretable shallow 

reflections. Therefore, we did not attempt to suppress direct 

arrivals in this processing.

Gain, Deconvolution 

Because the primary concern in the data processing was to 

map the basement structure, preserving relative amplitudes was 
2 Processing of Single Channel Air and Water Gun Data, Chesapeake Bay

283°20' 283°40' 284°00' 284°20'

37°40'

37°20'

37°00'

0 5 10 KILOMETERS

INPUT

AGC

AGC

ALIGN FIRST ARRIVAL

WAVELET

DECONVOLUTION

SECOND ZERO-CROSSING

PREDICTIVE DECONVOLUTION

WAVELET

DECONVOLUTION

MATCHED

FILTERING

F-X DECONVOLUTION

BAND PASS FILTERING

AIR GUN DATA WATER GUN DATA

SECOND

ZERO-CROSSING

PREDICTIVE

DECONVOLUTION

not considered important. Therefore using an AGC with a 150 

ms (millisecond) window is acceptable for the gain function. 

The raw data with the 150 ms AGC are shown in figure 3A.

Figure 3A reveals strong reverberations, particularly 

between 200 and 400 ms. Also tails of strong basement reflections near 520 ms due to the ringing of the air gun wavelet are 

evident in the seismic section. In order to suppress these reverberations, second zero-crossing deconvolution was applied. 

Second zero-crossing deconvolution is a type of predictive 

deconvolution where the prediction distance is the second zerocrossing of the auto-correlation of the trace. A single window 

with 120 ms or 180 ms operator length was used. The reason 

for using second zero-crossing deconvolution instead of spiking 

deconvolution comes from the consideration of signal-to-noise 

ratio as well as suppressing wavelet ringing in the processing. 

The result of applying predictive deconvolution is shown in 

figure 3B. As indicated in this view, most of the reverberations 

were suppressed and the ability to interpret primary reflections 

was significantly enhanced.

Wavelet Deconvolution and Matched Filtering

To further improve temporal resolution and to correct the 

Figure 2. Processing flowchart. AGC, automatic gain correction. phase of the wavelet (ideally resulting in a zero-phase wavelet), 

Figure 1. Location map of the single channel seismic lines in Chesapeake Bay acquired in 1996. Line 3a and line 15a 

were shot using a water gun energy source; all other lines were shot using a GI gun (air gun) energy source.
Data Processing 3

In equation (1), it is assumed that the reverberation is 

already suppressed.

The purpose of wavelet deconvolution is used to remove 

the wavelet effect in equation (1), so the wavelet deconvolved 

section Sw(t) can be written as

 

where N’(t) is the random noise deconvolved with the wavelet. 

As can be seen from equation (2), the wavelet deconvolution 

increases the temporal resolution by removing the wavelet 

effect, and at the same time it may increase the random noise 

component because the noise component (N(t)) itself was 

deconvolved. This property is not optimum in mapping the low 

signal-to-noise ratio basement reflections. To overcome this 

shortcoming of the wavelet deconvolution, we applied the 

matched filtering to the data for the basement reflections. 

Mathematically, this can be written as

 

where Sm(t) is the output with a match filtering applied and 

indicates a cross-correlation operator. As indicated in equation 

(3), the match filtering is a process that seismic data are crosscorrelated with a wavelet (Claerbout, 1992), so that this processing method increases the spectral component of the wavelet. 

Also, the spectral component outside the wavelet is suppressed 

in the output. This is an excellent signal-to-noise ratio enhancement filter, because the matched filter is designed such that ideally the presence of a signal is indicated by a single large 

amplitude in the output (Robinson and Treitel, 1980).

Figure 3D shows the result of applying the matched filter to 

the section shown in figure 3B. Comparing views B, C, and D, 

0

400

800

TIME, IN MS

A B C D

a variable norm deconvolution method by Gray (1979) was 

used in the processing. This aids in the interpretation of internal disruptions of stratigraphy. About 300 random windows 

(window length is about 16 ms or 18 ms) were selected between 

about 150 and 450 ms in the deconvolved data, and wavelets 

were estimated using different norms, window lengths, and 

design areas. Deconvolved outputs were compared visually, 

and optimum parameters were chosen. A single deconvolution 

operator was derived from the optimum parameters and applied 

to the whole line. 

Figure 3C shows an example of this wavelet deconvolution 

process for the data shown in figure 3B. Comparing figure 3C

with figure 3B indicates that the wavelet processing increased 

the overall temporal resolution of the data. Notice the sharp 

reflections between about 350 ms and 550 ms in figure 3C. 

However, the continuity and strength of the basement reflections 

(reflections near 500 ms) are somewhat degraded in figure 3C. 

Therefore wavelet deconvolution is not an optimum processing 

method for the enhancement of basement reflections, so we used 

a matched filtering for the enhancement of the basement reflections. This approach can be easily understood if a simple convolution model for the reflection seismogram is employed.

The reflection seismogram can be approximated in the following simple convolution model:

 ¬ 

where S(t): seismogram,

 W(t): wavelet,

 R(t): reflectivity of the medium and assumed to be a 

random white sequence,

 N(t): noise, which is assumed to be random and 

uncorrelated with R(t), 

 * : a convolution operator

Figure 3. Examples of seismic data processing. A, Raw data with automatic gain correction applied. B, Result with a second zerocrossing deconvolution applied to data shown in A. C, Result with a wavelet deconvolution applied to data shown in B. D, Result with a 

matched filter applied to data shown in B.

S t( ) = W t( )*8R t( ) + N t( ) (1)

Sw( )t = R t( ) + N t ,’( ) (2)

Sm( )t = W t( )∅×W t( )8R t * ( ) + W t( )∅×N t( ) (3)

×
4 Processing of Single Channel Air and Water Gun Data, Chesapeake Bay TIME IN MS

a

0

400

800 A B

0

400

800

TIME, IN MS

A B

we concluded that the matched filtering enhances basement 

reflections (events near 520 ms) for this line.

F-X Deconvolution 

Common noise in the seismic section is random noise. To 

reduce this random noise, we used a F-X deconvolution method 

(Canales, 1984; Gulunay, 1986) as a final step of signal enhancement. This process uses the concept that the signal can be 

defined in terms of a simple model that separates the time and 

space variable, so that the signal can be formed by a sum of 

complex exponentials in the frequency-distance (F-X) domain. 

This is easily envisioned if we consider that arbitrary waves can 

be decomposed into many plane waves which can be predicted 

accurately in the F-X domain. To estimate the underlying signal, 

we implemented Wiener filter theory to derive a complex prediction filter using eight traces and five temporal samples. One of 

the advantages of the F-X deconvolution method is that the continuity of the reflections is improved without much smearing 

effect common to many mixing methods (for example, running 

sum).

Figure 4A shows a section without the application of F-X 

deconvolution, and figure 4B shows the result of F-X deconvolution applied to the data shown in view A. Notice that a noise 

trace in the middle of the section in figure 4B is substituted with 

signals predicted from the adjacent traces. In F-X deconvolution, an isolated trace is treated like random noise, because the 

single trace cannot be predicted in the F-X deconvolution (there 

is no spatial correlation). One of the concerns of applying 

multi-channel signal enhancement techniques is the reduction of 

spatial resolution due to the adverse mixing effect. Comparing 

reflection characters shown in figure 4, views A and B, clearly 

shows that the mixing effect is not problematic when using F-X 

deconvolution. Therefore, the processing strategy using F-X 

deconvolution would not hamper the ability of mapping faults, 

which is one of the primary concerns of data interpretation.

Water Gun Data

One difference in the data processing of air gun data and 

water gun data is the wavelet processing step. This is due to the 

fact that whereas the wavelet from the GI gun is nearly minimum phase and has a compact wave form, the wavelet from the 

water gun has a low-frequency precursor (Grow and others, 

1986) and is a well-known mixed-phase wavelet. Variable norm 

deconvolution (Gray, 1979) can handle the mixed-phase wavelet, but these data presented difficulty in extracting a mixedphased wavelet for the reasons described following. 

In the air gun data processing, the wavelet deconvolution 

was applied after second zero-crossing deconvolution, and a 

wavelet was estimated from 300 random windows 16 or 18 ms 

long. This type of wavelet deconvolution using a random window works well for compact wave forms where there is no precursor, as demonstrated in Lee and others (1998). In the water 

gun data, the wavelet was extracted from the raw data using a 

fixed window, 34 ms long, and 300 traces. Even though the frequency content of water gun data is higher than that of the air 

gun data, a longer design window was used for the water gun 

data, because the precursor should be included in the wavelet 

extraction. The fixed window starts at 24 ms, near the onset of 

the precursor of the reflection.

Figure 5A shows the first 100 ms of water gun data without 

any gain correction. As indicated in the figure, directed arrivals 

(A-A′) and water bottom reflections (B-B′) interfere with each 

Figure 4. Example showing effect of signal enhancement filter (F-X deconvolution). A, Input (wavelet processed section). Portions of 

these data are shown in figure 3C. B, Result with the application of F-X filter. Filters were designed using eight traces and five time samples 

using complex Wiener filter.
Discussion 5

A B

0

100

TIMES, IN MS

other in such a way that the precursor of the reflection occurs 

between the precursor and main pulse of the direct arrival. 

Because of the interference of different wave types, extracting a 

reliable wavelet was difficult. The result of one of the many 

attempted wavelet deconvolutions is shown in figure 5B. In this 

case, direct arrivals were surgically muted to minimize the effect 

of mixing of the direct arrival with the water bottom reflections 

in the estimation of the water gun wavelet. The result shown 

in figure 5B indicates that the reflections near 40 ms are 

enhanced significantly and the interfering low-frequency tails 

near 50 ms in figure 5A were suppressed by the wavelet processing. At this stage, whether or not wavelet deconvolution 

was adequate for this data set is difficult to evaluate, but the 

advantage of the wavelet processing over predictive deconvolution can be drawn from the fully processed section. 

Figure 6A shows the raw data with AGC; figure 6B shows 

the section processed using the method identical to the air gun 

data processing without wavelet deconvolution; and figure 6C

shows the section processed using the wavelet processing 

described in this section. Figure 6A indicates the presence of 

much interference not only owing to the reverberatory nature of 

the reflections but also coming from the precursors of the reflections, so that interpreting the primary reflections is not easy. 

Figures 6B and 6C show significantly improved sections. However, as far as the clarity and continuity of the reflections are 

concerned, the result shown in figure 6C, processed with the 

wavelet deconvolution, shows better results than those shown in 

figure 6B, processed with the predictive deconvolution.

Discussion

Figure 7 shows the result of the processing of the air gun 

data for a portion of line 7, which was acquired using the GI 

gun. The result of minimal processing, AGC, and band pass filtering is shown in figure 7A, and the result of processing steps 

given in figure 2 with the application of matched filtering is 

shown in figure 7B. The strategy, using a second zero-crossing 

deconvolution and matched filtering, enhanced the basement 

reflections and suppressed the reverberatory energy for this line. 

Because the bubble pulse is suppressed in the GI gun, the 

GI gun signature is clean as demonstrated in the first arrival 

event in figure 3A. However, the reverberatory nature of reflections due to the receiver ghosts, source ghosts, and water bottom 

multiples is evident in figure 3A. Therefore, for an accurate 

interpretation of internal disruptions of stratigraphy, deconvolution should be applied to the data. Considering the signal-tonoise ratio as well as the resolution of single channel data, we 

conclude that a predictive deconvolution with second zerocrossing predictive distance is optimum for this data set. 

As mentioned previously, the effect of the direct arrival 

still remained in the processed section. In a conventional data 

set, the effect of direct arrivals interfering with the reflections is 

reduced, because the reflections usually arrive at a little later 

time. However, in this data set, the arrival times of direct and 

shallow reflections caused the waveforms to overlap. Instead of 

muting the first arrivals, we attempted a subtraction method (an 

average of 10 running sums of the direct arrivals was subtracted 

Figure 5. Example of wavelet deconvolution for the water gun data. Direct arrivals are denoted by A–A’ (A is the precursor and 

A’ is the main pulse of the direct arrival of the water gun signal); water bottom reflections are denoted by B–B’ (B is the precursor 

and B’ is the main pulse of the water bottom reflection). A, Raw water gun data. B, Water gun data after wavelet deconvolution.
6 Processing of Single Channel Air and Water Gun Data, Chesapeake Bay TIME, IN MS

0

200

400

A B C

0

400

800

TIME IN MS

AIR GUN DATA

A

A B

B

800

400

0

TIME, IN MS

Figure 6. Example showing result of water gun data processing. A, Input with 100 ms AGC applied. B, Result of second zero-crossing 

deconvolution applied to data shown in A with F-X filtering application. C, Result of wavelet deconvolution with second zero-crossing 

deconvolution and F-X filtering applied to data shown in A. 

Figure 7. Examples of final processing for line 7, acquired by GI (air) gun. A, Raw data with AGC and band-pass filtering. B, Result with 

the processing steps shown in figure 2 applied.
References Cited 7

from the seismic trace); the running sum was possible, because 

the water bottom reflection times between adjacent traces are 

nearly constant. However, this procedure did not perform well 

for this data set. Predictive deconvolution followed by a wavelet-processing scheme enhanced the shallow reflections slightly. 

The frequency shift toward lower frequencies owing to the presence of near-surface gas is somewhat compensated for by the 

deconvolution and wavelet processing. 

The result of processing the water gun data shown in figure 

6 implies that wavelet processing performed better than predictive deconvolution. However, one of the shortcomings of wavelet processing for this data set is the difficulty of extracting a 

reliable source signature because of interference of direct and 

reflected arrivals. The other drawback of wavelet processing of 

the water gun data is the problem associated with the application 

of the matched filtering. Because of the low-frequency precursor 

of the wavelet, the cross-correlation of wavelet with itself 

(matched filtering) results in three separate arrivals. Thus the 

output of the matched filtering looks much more complex than 

the input.

Summary

The employed processing steps shown in figure 2 provided 

enhanced seismic profiles for the interpretation of the impact 

structure and minimized the problems associated with the data 

acquisition. Increased temporal resolution and phase correction 

of the GI gun through the use of predictive deconvolution combined with wavelet deconvolution was found to be the optimum 

technique for improving the interpretability of the internal disruptions. On the other hand, the application of matched filter 

with predictive deconvolution was optimum in mapping the 

basement reflections due to the increased signal-to-noise ratio of 

the GI gun data.

For the water gun data set, applying wavelet deconvolution 

before predictive deconvolution may be better than applying 

predictive deconvolution, owing to the low-frequency precursor 

inherent to the water gun wavelet, but following the procedure 

this way has problems associated with the extraction of the 

wavelet and the application of wavelet matched filter.

References Cited

Canales, L.I., 1984, Random noise reduction: Society of Exploration 

Geophysicists 54th Annual International Meeting, Atlanta, Ga., 

Expanded Abstracts, p. 525–527.

Claerbout, J.F., 1992, Earth sounding analysis: Boston, Mass., Blackwell 

Scientific Publications, 304 p.

Gray, W.C., 1979, Variable norm deconvolution: Palo Alto, Calif., Stanford 

University Ph. D. thesis, 101 p.

Grow, J.A., Lee, M.W., Miller, J.J, Agena, W.F., Hampson, J.C., Foster, 

D.S., and Woellner, R.A., 1986, Multichannel seismic-reflection survey of KOA and OAK craters, in Folger, D.W., Sea-floor observations 

and subbottom seismic characteristics of OAK and KOA craters, 

Enewetak Atoll, Marshall Island: U.S. Geological Survey Bulletin 

1678, p. D1–D46.

Gulunay, N., 1986, FXDECON and complex Wiener prediction filter: Society of Exploration Geophysicists 56th Annual International Meeting, 

Houston, Tex., Expanded Abstracts, p. 279–281.

Lee, M.W., Agena, W.F., Grow, J.A., and Miller, J.J., 1998, Seismic processing and velocity analysis: U.S. Geological Survey Open-File 

Report 98-34, 29 p.

Robinson, E.A., and Treitel, A., 1980, Geophysical signal analysis: Englewood Cliffs, N.J., Prentice-Hall, Inc., 468 p.