17

2.2.4 First Examination of the Data:

i. The Process

After the trace headers have been correctly entered, the processor should always take the

time for a detailed first examination of the data to identify specific problems, obvious

reflections, and coherent noise. This sounds easy, but correctly identifying reflections (signal)

from the onset of data processing is not always straightforward, and misidentification will lead

to an incorrect seismic section.

ii. Applying the Process

Kansas Data

Figure 2.5 A first examination of the Kansas data, with some phases identified.

A first look at a typical shot gather (unprocessed) from the Kansas data (Fig. 2.5) shows

several distinct features. First, noisy traces are evident (see Section 2.3.1). The second

prominent feature is the high-amplitude ground roll. Ground roll, which in vertical-component P-

0.1

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Tim

e (s

)

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NoisyTrace

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GroundRoll

Reflection

Air Wave

Reflection

18

wave seismic data is typically composed of Rayleigh waves, is identified by two main

characteristics. First, ground roll has a slow phase velocity (steep slope). Wave-equation

physics constrains the propagation velocity of Rayleigh waves as being slower than the

direct S-wave, which in turn must be slower than direct P-waves. The propagation velocity

of ground roll for a Poisson’s ratio of 1/4 is 54% of the P-wave velocity for a homogeneous,

isotropic medium.

The second characteristic of ground roll is that it is dispersive (i.e., shingled or ringy). Ground

roll propagates along the surface, and the depth of material affected is directly dependent on

the frequency of the ground roll. The high-frequency component of the ground roll interacts

with the very-near-surface material, whereas lower-frequency ground roll interacts with deeper

material as well as with shallow material. Therefore, ground roll will be dispersive when the

near-surface velocity structure is variable with depth (typically increasing with depth)

because different frequencies of ground roll will travel with varying velocities, depending on

the particular average velocity being sampled.

The third characteristic of ground roll is that it typically has a lower dominant frequency than

near-surface refractions or reflections. Ground roll has a different frequency-dependent rate of

attenuation than S-waves or P-waves. Therefore, for a given propagating distance, the high-

frequency component of ground roll is attenuated much faster than the P-wave reflections or

refrations and is recorded with a lower frequency content.

The final two important features to identify are coherent noise and reflections. These will be

discussed in the Pitfalls section.

19

England Data

Figure 2.6 A first examination of the England data.

A first look at a typical unprocessed shot gather from the England data (Figure 2.6) shows

features similar to the Kansas data (noisy traces and strong reflections), but it also shows a

very strong refracted arrival and air wave. The air wave is a typical problem in shallow

reflection data (see Section 2.3.2) and is identified because its velocity will always be 330 to

340 m/s (with variations due to elevation, air pressure, temperature, and wind). Because of

the differences between the Kansas and England data, special considerations during

processing will be necessary. The most critical step for both, however, is correctly identifying

the reflections.

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20

When first examining data, the initial step is to identify the main features (described for both

example data sets above). The next step is to examine the data using various filters and

gains to get a sense of features that might not be obvious on the raw data and to determine

the frequency content of the signal (which will be useful when resampling; see Section 2.2.2).

Following are several panels of the same field file from the Kansas data with various filters and

gains applied, demonstrating the importance of this step.

Figure 2.7 Various filters and gains applied to a single field file from the Kansas data.The top-left panel is the same raw, unprocessed data shown in Fig. 2.6. The top-right panelis unfiltered data with an AGC gain applied. The remaining panels have the same AGC gainapplied, but with different band-pass filters. Details of the newly observed features areshown in Fig. 2.8. Note the frequency content of the noisy traces.

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)

AG

C G

ain

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Low

-Pas

s F

ilter

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.-P

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erG

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+ V

. Hig

h-P

ass

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Gai

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Hig

h-P

ass

Filt

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21

Figure 2.8 Field file from the Kansas data with detailed identification of phases after filteringand gaining. The field file and processing are identical to Fig. 2.7, right-center panel. Thesource pulse in this data appears as a doublet (i.e., two positive peaks per phase), and the firstpeak is picked for interpretation. This is most evident on the direct wave, reflection, andrefraction, and with reversed polarity in the first multiple reflection.

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Refraction

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Direct Wave

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1st MultipleReflection

22

iii. Pitfalls Associated with the Process

When identifying reflections, the processor must always remember that other forms of

coherent noise such as aliased ground roll or air wave, diffraction energy, or random coherency

may all look like reflection events. There are several checks to increase confidence in an

apparent reflection event:

1) Reflections should be visible on several records without much processing. If the

processor identifies a reflection-like event on only one shot gather and cannot find it on other

shot gathers, it should be discounted. Often a noise event at the time of recording may

generate an apparent reflection. It should be discounted, but not forgotten. Remember that a

48-trace shot gather will have one contributing trace in 48 CMP gathers. If the apparent

reflection has a high enough amplitude (or is incorrectly enhanced by processing), it may stack

and show up on 48 different traces on the final seismic section!

2) A true reflection should remain visible over a band of frequencies. Always use several

frequency filters with slight variations in pass-band frequencies on a questionable reflection.

If the apparent reflection is a product of aliasing, it will noticeably change its appearance for

different frequency ranges.

3) Reflections should be hyperbolic, and this can be checked directly by fitting a hyperbolic

curve through the event or picking three points on the event and calculating the fit. However,

reflection events will not be truly hyperbolic when they are generated by an undulating

surface, traveling through a strong, laterally-varying-velocity medium, or when severe

elevation statics problems exist. Therefore, deviations from hyperbolic moveout can be

observed. But remember, a non-hyperbolic reflection event from one of the aforementioned

causes should also be visible on adjacent shot gathers.

The most common error during the initial examination of the data is misinterpreting refractions as

reflections. When this is done, the processor will typically process the data to enhance what

is believed to be a reflection. Thus, correct segregation of reflections and refractions from the

onset is perhaps the most critical process in all of shallow seismic data processing.

23

2.3 Improving the Signal-to-Noise Ratio (S/N)

The goal of seismic processing is to enhance the signal-to-noise ratio (S/N) of the data. Three

ways to improve S/N are:

1) Attenuating noise information in a domain in which signal and noise c

separated. Muting is a way of attenuating noise that has different traveltime and offset

positions than reflections in the time-offset (t-x) domain. Frequency-wavenumber filtering is a

way of attenuating noise that has a different spatial relationship (slope) than reflections. It is

performed in the f-k domain. Frequency filtering is a way of attenuating noise that has a

different frequency content than the reflections and is done in the amplitude-frequency (or

frequency) domain. Each of these techniques assumes that S/N of the selection of data that

is being muted is significantly lower than the remaining information.

2) Correcting for spatial or temporal shifts of the traces. Spatial shifts in the data are

caused when the conditions in the subsurface violate the layered-earth assumption. These

spatial shifts can be corrected using migration when sufficient velocity information about the

region is known (see Section 3.1). Additionally, lateral velocity variations in the region above

the water table (the weathered zone) create temporal shifts in the shot gathers such that a

hyperbolic reflection event is distorted. Several correction techniques exist to compensate for

this effect. However, seismic processing for shallow data typically is used to retain

information from the weathered zone because it is within the region of interest. One type of

temporal static that needs to be corrected in shallow processing is due to source and receiver

elevation differences. Elevation statics are used to correct for temporal shifts caused by

deviations from the datum plane of the source and receivers during the recording process.

3) Stacking. Theoretically, S/N increases as the square-root of the fold of the seismic data.

This is based on the assumption that reflection information is embedded in random noise.

Thus, during stacking, the signal will increase in amplitude by a factor equal to the fold due to

constructive interference, and the random noise will sum to random noise with only slightly

higher amplitude. The higher the fold of the seismic data, the higher the S/N. However, this

assumption is typically violated by the addition of nonrandom (coherent) noise to the seismic

data, in which case the S/N ratio will not increase as rapidly as the square-root of the fold and,

in some cases in which the coherent noise is not properly removed, S/N will not increase at all

or will decrease with increasing fold. Stacking is covered in Section 2.5.

24

2.3.1 Killing Noisy Traces

i. The Process

Simple but important, killing noisy traces should be one of the first processes applied to the

data (see Pitfalls, below). The process of “killing” refers to setting to zero all of the amplitude

values in a specified trace.

ii. Applying the Process

The noisy traces seen in Figures 2.5 and 2.6 could be selected and muted one at a time, but in

most cases a noisy trace will be due to a bad connection or bad geophone at a particular

receiver location that was not identified in the field. In this case, most processing packages

allow for all of the traces from a particular receiver location to be zeroed quickly and easily.

This was true for the England and Kansas data.

iii. Pitfalls Associated with the Process

Noisy traces must be killed for two reasons. First, even when a noisy trace appears to

contain some reflection information, it still has a lower S/N than the rest of the data and will

therefore only serve to decrease S/N of the final section. Removing any trace with a lower

S/N is almost always better than assuming that important information will be lost if the trace is

removed.

The second and most important reason noisy traces should be killed is more subtle. Some

noisy traces can contain data “spikes” in which a single sample has the maximum amplitude

and the adjacent samples are much smaller. This creates two problems: First, the spike will

appear to have infinite frequency and may cause frequency-related processes to behave

badly. When frequency filtering is applied, the spike will be convolved with the filter operator

and appear as a high-amplitude wavelet with the same frequency characteristics as the filter

operator. Second, because the amplitude of the spike is anomalously high, it will not “stack

out” under the assumption that it is random noise. Thus, if any process is applied that

produces spatial effects on the data (trace mixing, f-k filtering, migration, etc.), the single spike

will contaminate much more of the data; it may even appear as a continuous coherent event

on a stacked section.

25

2.3.2 Muting Coherent Noise

i. The Process

A method for increasing S/N is to remove noise that has a different location than the signal in

the t–x (or shot) domain. Specifically, properly muting refractions, air wave, and ground roll all

increase S/N. For data in which an air-wave phase is dominant, a processor might consider

spatially (f-k ) filtering the data to remove the linear air-wave event. However, air wave is

typically a high-frequency (often 1 KHz or more), broad-band noise form, and is usually

aliased (Figure 2.9, below); thus, f-k filtering the air wave is likely to degrade the data (by

enhancing the aliased air wave) rather than improve the data. If the aliased air wave shown

in Fig. 2.9 is not removed successfully by some other means, it will stack constructively during

the stacking procedure and generate coherent noise on the final stacked section. The best

alternative is to surgically mute the air wave (see Applying the Process).

When muting in any domain (i.e, t-x, f-k, etc.) the edges of the muted region should be

tapered. A taper is used so that sequential data does is not abruptly set to zero, but rather

gradually is reduced. The size of the taper must be large enough to minimize processing

artifacts that occur at the edge of the muted region but small enough not to obscure signal.

True Air WaveVelocity

Apparent Aliased

Air Wave Velocity

Refracti

Reflecti

Figure 2.9 Example seismic data showing aliasing of air wave. The true velocity of theair wave is fairly slow (steep slope), but the aliasing of the air wave yields events with anapparent velocity closer to that of the reflection (aliased slope).

26

The key to muting is removing the portion of data in which S/N is much lower than S/N of the

rest of the data. For example, Figure 2.10 shows that the removal of information with the noise

cone where S/N is low can significantly enhance S/N of the data, even if the mute region

represents a significant portion of the data volume.

Figure 2.10 An example from Baker et al., 1998 of shallow seismic data in which all of theinformation within the noise cone is degraded by air wave of the same frequency content as thereflections and thus was muted. Additionally, refractions were muted.

The result of muting such a large portion of the data can be surprising (Figure 2.11). Note that

although some reflection information was included in the muted region, S/N of the muted region

was too low to contribute any important information. Thus, following a conservative approach

to avoid contaminating the final stacked section by coherent noise, the processor could

attempt to mute all regions with low S/N, even if it includes a significant portion of the data.

Source-to-Receiver Offset (m)

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Figure 2.11 The results of the severe noise-cone mute shown inFigure 2.10. Note that some signal is contained in the muted portion (bottompanel of the stacks) but is not of sufficiently high S/N to be worthkeeping (from Baker et al., 1998).

Distance Along the Seismic Profile (m)

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noise-cone mute

Data contained in

noise-cone mute

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28

ii. Applying the Process

England Data

The dominant coherent noise in the England data is composed of air wave and refractions.

The England data did not contain significant ground roll, and reflection information with good

S/N was observed within the noise cone.

Before Mute After Mute

Figure 2.12 A preprocessed shot gather from the England data before and aftermuting the air wave and refractions. The mute taper length is 8 ms. The two noisy traces(2 & 17) were also muted. The data are displayed with AGC (40-ms window) and a band-pass frequency filter (250-300 Hz with 12 dB/octave slopes). Note that a portion of thereflection at ~35 ms was muted at farther offsets. However, that portion of the reflectioninterferes with the first-arriving refraction and thus has a distorted shape that woulddegrade the stacking quality.

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Kansas Data

Muting coherent noise within the Kansas data was accomplished with only one top-mute per

record. As previously mentioned, air wave propagates at a velocity of ~335 m/s. At the

Kansas site, the near-surface unconsolidated material had a P-wave propagation velocity

slower than the air wave. The reflection energy of interest, therefore, occurs below the air

wave (examine Fig. 2.12 as a comparison). Thus, the coherent noise to be muted consisted

of refractions, direct wave, and air wave, and is located above the reflection of interest. Figure

2.13 shows a preprocessed common-midpoint gather before muting, during the mute-picking

process, and after muting.

Figure 2.13 A single preprocessed CMP-sorted gatherfrom the Kansas data, with mute picking shown and applied.The mute taper was 8 ms.

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30

iii. Pitfalls Associated with the Process

The pitfalls associated with muting in the t-x domain generally come from failure to mute

coherent noise either properly or entirely. Applying the mute process itself is straightforward.

Comparing the top and middle panels of Fig. 2.11 shows the effect of failing to remove

coherent noise completely in an attempt to keep all signal. Following is an example of the

England data, in which the refractions and air wave were not muted, demonstrating the

creation of coherent artifacts.

Figure 2.14 The England data processed without muting air wave or refractions.The stacked, aliased airwave is moveout related and observed on low-fold CMP gathers.

Figure 2.14 shows the significant effects of not muting the coherent energy (compare with the

muted result, Fig. 1.5). Refractions stack to form coherent events. One hint that refractions

are being stacked is than frequency does not decrease with depth (i.e., low-frequency events

are seen earlier than higher frequency reflections) as one would expect with normal frequency-

dependent attenuation. Also, note the presence of coherent and incoherent air-wave noise.

0.1

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Source-to-Receiver Offset (m)T

ime

(s)

Low-frequencystacked

refractions

High-frequencystacked aliased

air wave

High-frequencystackedair wave