Robust estimation method for determining when subsequent data processing can include sign-bit representations of full-waveform seismic traces

A method is disclosed for determining if sign-clipped variations of full-waveform traces can be used in subsequent cross-correlation processing without undue hardship. In accordance with one aspect, the present invention examines skewness of the amplitude spectrum of the full waveform. If the former significantly differs from conventional Gaussian distribution over the frequency range of interest, i.e., the amplitude spectrum is non-Gaussian, sign-clipped versions thereof can be used in subsequent cross-correlation processing without undue loss in processing accuracy. In another aspect, the present invention is used to edit a series of traces, one at a time, gathered by a large multichannel collection system. In this aspect, the different spectral noise estimates, including cross-correlation estimates generated from sign-clipped versions of the full-waveform traces, are advantageously used.

SCOPE OF THE INVENTION 
This invention relates to a method of determining if sign-bit 
representations of full-waveform seismic data can be used in subsequent 
processing of the data, say in editing traces collected by large 
multichannel seismic systems, even though the full waveform is also 
available for the same operation. 
In accordance with one aspect of the present invention, a determination of 
the robustness of the process algorithm is used to suggest that sign-bit 
representations of conventional recordings (i.e., where only the algebraic 
sign is retained) can be used in editing traces of large multichannel 
seismic systems. 
That is to say, the present invention has been found to have surprising 
usefulness in providing cross-correlation estimates of sign-bit data in 
editing traces collected by large multichannel systems such as set forth 
in a copending application Ser. No. 429,358 "Method Of Editing Seismic 
Traces, As, Say Gathered By Large Multichannel Collection Systems", 
Raymond A. Ergas and assigned to the assignee of the present application. 
[In the above application, now Pat. No. 4,479,183 correlations estimates 
were used to determine if predetermined editing standards were met for 
each trace of the 500 or so traces collected each cycle of operation. If 
such standards were attained, full trace waveforms (of non-eliminated 
traces) were then used in subsequent processing operations even though the 
precursor testing was carried out over only a small time increment (say 
the last second of a six-second trace)]. 
BACKGROUND OF THE INVENTION 
Martin et al., U.S. Pat. No. 4,058,791, for "Method And Apparatus For 
Processing Seismic Signals From Low Energy Sources", describes an effort 
to solve the growing problem of handling information collected in modern 
seismic surveying in which events of interest could be preserved if only 
the algebraic signs of the incoming signals (and not the full waveforms) 
were recorded. Using information channels that need to handle only 
sign-bits makes it possible to use several times as many channels for the 
same recording and processing capacity. 
Also, Martin et al. observed that in some of their vibratory seismic work, 
that when sign-bit representations of the source waves were 
cross-correlated with sign-bit representations of the received waves, the 
resulting cross-correlation functions appear to be similar to 
cross-correlation functions from full-waveform inputs, provided that the 
resulting correlation functions are "common depth point stacked" to a high 
multiplicity ("the CDP fold is at least 40"). 
While Martin et al. recognized that high order stacked correlograms of 
sign-bit data (CDP folds of at least 40) may have acceptable processing 
qualities to allow their utilization in seismic interpretation, they did 
not anticipate the special circumstances that single-fold seismic data (or 
folds where n=`for that matter) could be processed advantageously in a 
sign-bit form, or more importantly where, in a more general application to 
seismic processing, the "robust" property of the underlying sign-bit 
process could be ascertained. 
On the general subject of robust modeling, see "Robust Modeling With 
Erratic Data", J. F. Claerbout and Francis Muir, Geophysics, Vol. 38, No. 
5, pp. 826-844, 1973. In the paper, the question of the judicious use of 
robuts estimators vis-a-vis seismic data is raised but specific 
applications of such estimators are not discussed, especially in the area 
of spectral requirements for sign-clipped processing of such data. 
SUMMARY OF THE INVENTION 
In discovering special circumstances where the sign-clipped versions of 
full-waveform traces can be useful in seismic processes, systematic 
preprocessing of the seismic data of interest over a predetermined time 
window (or the equivalent) may be in order in some cases and is 
specifically related to the robustness of the process algorithms. 
While robustness is typically determined by substituting different 
combinations of input data and observing the process results as the 
algorithm is repeated (the classical definition of the Monte Carlo 
method), the method of the present invention limits such testing to only 
those algorithms that involve cross-correlation estimates using seismic 
data in which distribution of the dependent variable of interest with 
respect to the independent variable (or series of variables) is 
non-Gaussian. Assuming a seismic trace in which the dependent variable is 
amplitude and the independent variable is frequency, a peak frequency 
shift must be of the order of at least 50% to meet these requirements. For 
shifts less than the above, the penalty extracted by the use of 
sign-clipped data is usually too great. 
In other cases, sign-clipped versions of the seismic data can sometimes be 
used based on analogous application of the above standard so as to provide 
results that parallel those set forth above. E.g., in the copending 
application, op. cit., describing editing of seismic traces based on 
spectral estimates of the noise spectrum over a very short time window, 
standards are set forth to determine if the trace should be allowed to 
undergo further processing. In the circumstances set forth in the above 
application, sign-clipped versions of the data have been found to be 
useful in generating the required cross-correlation estimates. 
The present invention hence contemplates a method for editing traces of 
large multichannel collection systems in partial sign-bit form through a 
pattern analysis of the associated noise spectrum that occurs only over a 
small time increment of each trace, say, the last second of the trace, and 
even though the full waveform is available for such operations. 
Since other seismic processes of which I am aware also involve 
cross-correlation estimates of data having similar or analogous spectral 
characteristics, use of the present invention in such processes is also 
contemplated, e.g., cross-correlation estimates involving static 
corrections of certain seismic recordings come to mind. 
DEFINITIONS 
In this application, certain key terms are used in the description of the 
present invention and for ease of understanding several aspects of the 
invention, these terms are defined below. 
The term "sign-bit data" refers to seismic traces in which only the 
algebraic sign of the signal is retained, as either a ONE or MINUS ONE. 
The term "robustness" is used to indicate the degree of forgiveness, or 
tolerance characteristic, of a given process for satisfactory performance 
under unusual operating conditions. With specific reference to processing 
seismic data in sign-bit form, it refers to the degree that the algorithm 
of interest performs its task. Where the algorithm relates to editing 
traces based on certain noise spectral characteristics in partial sign-bit 
form, robustness is keyed to the fact that changes in the dependent 
variable, e.g., amplitude, as a function of the independent variable, 
i.e., frequency, are unrelated to the performance of the algorithm. 
The term "color" pertains to the degree that the noise spectrum, by 
corollary, can be equated to the visible spectrum. E.g., true random noise 
measured over sufficiently long time periods will contain equal amounts of 
all frequencies, and by corollary with the visible spectrum is termed 
"white" (because a uniform amount of each frequency over the visible 
spectrum is seen as white light). 
The term "skewed", as relating to the energy distribution of the noise 
spectrum, means that such distribution is also a function of the 
collection system and the associated environment where the seismic data is 
collected. If the latter have nonlinear response characteristics, then 
parameters can be imposed upon the collected data, causing the dominant 
"hue" of the noise spectrum to be shifted from "white" toward another 
"color". The degree that the system and the environment have imposed such 
nonlinear characteristics on the recorded traces (i.e., the degree of 
shift) can be related directly to the analogous dominant hue associated 
with the noise spectrum. 
The term "time window" is defined as the time span of each trace over which 
the amplitudes thereof are examined or processed.

DETAILED DESCRIPTION OF THE INVENTION 
Referring to FIG. 1, there is illustrated in schematic form, a 
preprocessing method 10 for determining the robustness of a selected 
cross-correlation process. 
While robustness is typically determined by solving overdetermined linear 
simultaneous equations (by squared error minimization) and then observing 
the minimization of absolute values of errors (see, Fundamentals of 
Geophysical Data Processing, J. F. Claerbout, McGraw-Hill, 1976, p. 123), 
the preprocessing method 10 of the present invention is limited first in 
scope to testing algorithms that generate cross-correlation estimates of 
seismic data and second, to steps of minimal mathematical complexity. 
As shown in FIG. 1, a typical seismic trace is analyzed via a series of 
instructions 11-18 that control the operations of a digital computer such 
as an IBM Model 3033 to determine robustness of the cross-correlation 
algorithm under analysis. In this regard, the method 10 is set forth in 
sufficient detail to enable a computer programmer of ordinary skill in the 
seismic processing art to program a general purpose computer using a 
conventional programming language, such as FORTRAN, in accordance with the 
teachings of the present invention. 
Briefly, the method 10 determines if a single condition of the algorithm 
can be met: that the dependent variable of interest varies in a 
non-Gaussian manner with respect to the independent variable (as depicted 
in instruction 12) over a representative interval. 
After the method 10 determines that each trace meets the standard, the 
traces that do not meet the standard are flagged at instruction 14; a 
comparison instruction then assumes command at 15. All traces (and 
comparative results) are next stored at 16. That is to say, instruction 16 
asumes control after the Gaussian distributional characteristics of all of 
the traces have been determined. These results are stored on tape 19. The 
method 10 termintes when instruction 17 is answered in the affirmative at 
18. Alternative iteration of the instruction 17 is via loop 20. 
[The fact that Gaussian probability functions are so frequently encountered 
in nature (not just in geophysics or in physics in general, but also in 
the biological and social sciences) is explained in detail in Claerbout, 
op. cit., in his classical discussion of the central-limit theorum of 
probability, Chapter 4, pp. 83 et seq.] 
An interpretation of the results of the comparison instruction 15 (of 
flagged and unflagged traces on tape 19) next occurs; that comparison 
determining whether or not the particular cross-correlation process is (or 
is not) to be used in association with sign-bit versions of the data 
without payment of undue penalties. 
FIG. 2 illustrates a cross-correlation process 30 in which a determination 
has been made--that sign-bit data could be used in the cross-correlation 
of traces 31 and 32, even though the full waveform is available for such 
processing. 
As shown, not that process 30 involves sign clipping of the traces 31 and 
32 at clipper 33 and 34 followed by cross-correlating the sign-clipped 
traces at cross-correlator 35. That is to say, the traces 31 and 32 
(previously indicated to have a non-Gaussian dependent variable) are sign 
clipped at the clippers 33 and 34 whereby only the algebraic signs of 
their amplitudes are retained. At cross-correlator 35, estimates of the 
trace similarities of the sign-bit traces are generated. These estimates 
in turn are stored in buffer 36 after passing through editor 37 which 
converts the integer outputs of the cross-correlator 35 into floating 
point estimates. 
Although traces 31 and 32 of FIG. 2 are seen to be somewhat similar in 
terms of amplitude vs. time variations, they are not identical. If the 
amplitude vs. time characteristics of such traces are identical, their 
processing via cross-correlator 35 is usually termed "autocorrelation". 
But for purposes of interpretation of this application, if traces 31 and 
32 are identical, the term "cross-correlation" will be used to describe 
the latter type of processing inasmuch as autocorrelation is merely 
"cross-correlating" a trace with itself. 
In a copending application, op. cit., there is described a method for 
editing traces of a large multichannel collection system based on 
cross-correlation estimates of the noise spectrum over a very short time 
window. 
Note in the above-mentioned application that the noise spectral 
characteristic sought for verification involve a noise amplitude spectrum 
skewed toward the lower end of the frequency range, peaking at say 25 Hz., 
then decreasing therefrom in a smooth manner with increasing frequency up 
to about 200 Hz., that is to say, it is non-Gaussian. Hence, usage of 
sign-bit versions of such unstacked traces in the above cross-correlation 
process is contemplated. That is, since the peak frequency shift is 
greater than 50%, vis-a-vis, a Gaussian distribution of such spectrum 
(being in fact equal to a 75% peak shift), then cross-correlation 
estimates by definition involve a dependent variable, viz., amplitude, 
that varies in non-Gaussian function as a function of an independent 
variable, viz., frequency. Employing sign-bit data in such processing has 
been found to be especially rewarding in the environment of the 
above-identified application, viz., in the processing of data involving 
500 or more channels of data collected each collection cycle. 
Referring to collection of 500 or more traces in the above manner, 
reference should now be made to FIG. 3. 
A large multichannel exploration system 50 is there shown. It includes a 
vessel 51. Vessel 51 tows a super multichannel seismic cable assembly 52. 
Seismic cable assembly 52 includes a shock-absorbing elastic section 56, a 
lead-in 57, and a short terminator section 58. 
A typical seismic cable assembly 52 also consists of 50 or more active 
cable sections 60 (each of the latter being about 60 meters long). The 
assembly 52 may have a total length of 10,000 feet or more. Each cable 
section 60 may contain ten elemental sensors 61; a group of such sensors 
constitutes a single channel. A connector module 53, contained inside a 
transceiver unit connects active cable sections 60 together, electrically 
and mechanically. The entire cable assembly 52 produces output signals 
from about 500 individual channels, the resulting signal outputs from the 
sensors 61 and being coupled to the transceiver unit which transmits the 
signal to a central station 62 on vessel 51. The central station 62 
includes control circuitry 64. In that way, interrogation, command, power, 
and test signals can be transmitted. Station 62 also includes a recording 
apparatus 66 to receive and record digital data words from the data link. 
At intervals, a seismic sound source 59, such as an air gun or a gas 
exploder, generates acoustic waves in the water. The acoustic waves 
propagate downwardly along ray path 70 impinging upon water bottom 72 and 
become refracted along path 73 due to the velocity differences between the 
water and earth formation 74. Continuing along refracted ray path 73, the 
waves next become reflected from a subsurface earth layer 76. The 
reflected acoustic waves return along ray path 78 and thence continue 
upwardly along ray path 79 to be detected by a group of sensors which 
convert the reflected acoustic waves to electric signals. Acoustic waves 
which take other ray paths, such as 81-84, are detected by other sensors 
such as 61, in similar fashion. Result: 500 or so traces are recorded 
aboard the vessel each time source 59 is activated. Subsequent processing 
can yield multiple-fold CDP gathers, say where n is a range of 24 to 200 
with n=50 being preferred. 
FIG. 4 illustrates--in analog form--a series of traces that could be 
generated from the digital records at recorder 66 of FIG. 3 using 
conventional methods. 
On the left side of the traces T1-T5 is a time scale. Time t=0 corresponds 
to a time value migratable to optimum surface level. Time t-tmax (say, 
equal to 6 seconds in actual practice) represents the maximum record time 
for receiving events from the earth. Amplitude excursions along the traces 
T1 and T2 indicate events occurring during the collection process, e.g., 
time at which the source 59 of FIG. 3 was activated (viz., between time t1 
and t2); reflections from the horizon of strata 76 all indicated at time t 
and t+L. 
Traces T1 and T2 are obviously symbolically suitable for further 
processing. 
Traces T3, T4 and T5, however, represent sensor outputs that make such 
traces unsuitable for further enhancement. In this regard, trace T3 is a 
"dead" trace, having the output of an inoperative group of sensors 61 
within an active cable section 60; traces T4 and T5 are outputs from 
groups of sensors 61 which have experienced extremely large system or 
environmental noise. 
FIGS. 5A-5C illustrate different aspects of seismic noise and its 
relationship with the present invention. 
In analyzing seismic signals that include system and/or environmental 
noise, certain conditions of time domain signal characteristics may be 
difficult to observe in that domain, but may be clearer in the frequency 
domain. 
In FIG. 5A, the time domain and frequency domain characteristics of system 
and/or environmental noise associated with seismic traces having favorable 
editing qualities, are illustrated. 
Note on the left side, the amplitude vs. time signature of an acceptable 
noise trace is seen. Its characteristics are rather difficult to analyze 
but on the right side, its amplitude vs. frequency characteristics are 
seen to set forth a smooth amplitude vs. frequency curve 80. That is, the 
curve rises smoothly from 0 Hz., peaks at about 25 Hz., and then decreases 
as a function of increasing frequency up to about 200 Hz. The pattern of 
curve 80 permits its description in alternative ways: 
(i) by corollary with the visible spectrum, it can be described as "pink 
noise" since the wavelengths of the lower spectrum dominate; 
(ii) because the peak of curve 80 has been skewed toward the lower 
frequencies, the distribution of the amplitude spectrum can be termed 
non-Gaussian since the curve 80 peaks at about 25 Hz., rather than at the 
mid-frequency of response, viz., at 100 Hz. That is to say, a peak 
frequency shift of approximately 75% has occurred. 
FIGS. 5B and 5C illustrate white noise characteristics in both the time and 
frequency domains in an "ideal" and in a known collection response state, 
respectively. 
The term "white" as pertaining to the noise spectrum is used by way of 
analogy to the visible spectrum. That is to say, random noise measured 
over sufficiently long time periods will contain equal amounts of all 
frequencies. 
Hence, amplitude vs. frequency curves 81 and 82 of FIGS. 5B and 5C are seen 
to be horizontal with respect to frequency. That is to say, the curve 81 
of FIG. 5B is flat over all ranges of frequency in an "idealized" manner, 
but in FIG. 5C the curve 82 is only horizontal over a frequency range of 
about 0 to to 200 Hz. because the collection system is band-limiting. By 
corollary with the visible spectrum, such noise is termed "white" in both 
circumstances. 
FIG. 6 illustrates in detail the large multichannel editing process in 
accordance with the present invention. It represents an overall viewpoint. 
Assume that the editing process is carried out on-shore using, e.g., a 
conventional computing system in which pattern analysis of the noise 
spectrum occurs as a function of window length per individual trace, i.e., 
occurs on a trace-by-trace basis over only an incremental time window of 
the total trace record time. Conventional static and dynamic time 
corrections are of course omitted. 
In FIG. 6, the flow chart sets forth the desired sequence of steps 
controlling the operation of the digital computer, such as an IBM Model 
3033, to achieve the desired result of editing signals, viz., in order to 
provide seismic signals of greater intelligibility and clarity for 
geophysical analysis and interpretation. The flow chart sets forth the 
process steps of the present invention in sufficient detail to enable a 
computer programmer of ordinary skill in the seismic signal processing art 
to program a general purpose computer, using a conventional programming 
language such as FORTRAN, in accordance with the present invention. 
An instruction 100 causes the computer system to read in the data record 
traces to be processed and the requisite input parameters to control the 
system. The input parameters include the following parameters: 
N--defining the location of the time window; 
M--the number of traces in the field data; 
L--the length of the time window; 
NB--the number of digital amplitude samples within the time window, 
equaling the input parameter L divided by the sample interval at which the 
digital amplitude samples are taken in the seismic data. 
In practice, the minimum length of the window is 10 wavelengths of the 
mid-band of the noise spectrum; there is also a critical upper limit; if 
the window is too long, the process time savings can be lost; if too 
short, the value can be weighted by individual amplitudes rather than the 
noise spectrum as a whole. In practice, a one-second length window near 
the end of a conventional six-second trace has been found to be adequate. 
The position of the window is determined by the fact that noise level must 
exceed that of the reflected signals. Hence, the last second of the trace 
is preferred, although the initial "mute zone" recording at long offsets 
can also be used. 
After the trace has been scanned over the window i, instruction 110 assumes 
control and a series of noise spectrum functions, F0, F1 and F2 are 
generated in sequential order. 
Initially, a power level F0 is determined for each trace of the record by 
squaring the amplitude samples in each trace segment over the common time 
window i. Each power level F0 is so determined that it can be later 
compared with a specified power level (as explained below) and those which 
do not exceed the value are then set to eliminated. In effect, such 
processing prevents low amplitude level signals of little interest from 
unduly affecting analysis, such as by causing statistical degradation 
desirable seismic signals of intermediate amplitude due to their 
prominence in comparison with these low amplitude level signals; or, such 
processing excludes traces in which the noise tends to be exceedingly 
high. 
Next, the first and second spectral estimates F1 and F2 of the noise 
spectrum of each trace are generated using sign-bit versions of the traces 
in the manner set forth in FIG. 7. 
As shown in FIG. 7, instructions 130 and 140, respectively, assume command 
and cause the computation of the first and second spectral estimates of 
the traces, preferably by cross-correlating sign-bit versions of each 
trace segment with itself, using a multiple time operator. That operator 
is preferably set at time shifts (or lags) of one and two sample points 
centered at the time window T1-T2. That is to say, processing in 
accordance with the present invention involves the determination of first 
and second spectral estimates of the normalized cross-correlation 
functions of the clipped trace i over the time window T1-T2 at time shafts 
or lags of .tau.=1 sample and .tau.=2 sample shifts. Since the delay 
operator only shifts the waveform one and two sample points, respectively, 
the process is exceedingly rapid since only the algebraic sign of the 
amplitudes of each trace segment is used. 
That is, in determining the estimates F1 and F2, the computer 
cross-correlates the sign-clipped waveform with itself over the window 
T1-T2; that is to say, cross-productizes the sums of the sign-clipped 
amplitude samples of the recording at (i) zero time shift (centered of 
course at the window i); and (ii) at time shifts =1 and =2 sample in 
serium divided by the square of the amplitude samples at zero lag, to 
produce the normalized function F1 and F2 of the clipped traces; see, R. 
E. Sheriff, "Encyclopedia of Exploration Geophysics", SEG, Tulsa, Okla., 
page 15. 
Returning to FIG. 6, it should be noted that determining the spectral 
function F0 by calculating the power level of the particular trace segment 
is equivalent to generating the zero lag cross-correlation function of the 
same waveform in non-normalized fashion. That is to say, the function F0 
can also be determined by correlating the waveform segment itself over the 
time window T1-T2 via cross-productizing the sums of the zero time shift 
amplitudes with themselves, and then ignoring the subsequent normalizing 
step related to dividing the generated numerator by the square of the 
amplitude samples. But it must be achieved before the traces are sign 
clipped for determining F1 and F2 estimates. 
Instruction 120 next assumes control and the limits of each calculated 
function F0, F1 and F2 are compared with specified minimum values for 
standard traces over a standard window based on an acceptable minimum 
level for desired subsequent signal processing, as explained below. If the 
calculated values of F0, F1 or F2 do not meet the standards, the trace is 
flagged via instruction 150 in loop 160. The computer also logs the number 
of the trace flagged by the instruction 120. 
Instruction 170 next assumes control and stores the unflagged traces, say 
by loading such data onto tape 180. The process terminates when 
instruction 190 is answered in the affirmative, alternative iteration 
occurring via loop 200. 
Removing the flagged traces from further processing removes data of little 
interest and thereby prevents confusion of valid data with invalid and 
important information. That is to say, the cross-correlation functions F1, 
F2 and F3 after being compared with default values which define acceptable 
skewness patterns of the noise spectrum, i.e., qualities that indicate 
such traces are worth further enhancement. 
FIG. 8 is illustrative of the selection criteria of the present invention 
in more detail. 
Selection is based on establishing classification functions for each trace 
in the manner previously described. That is to say, two groups of traces 
(e.g., acceptable trace 210 and unacceptable trace 211) are established by 
the classification process of the present invention. 
It should also be noted that in the preferred application of the method of 
the present invention, the basis of selection is akin to projecting each 
individual nonlinear function F0, F1 or F2 onto the x, y, z plot of FIG. 
8, i.e., relative to axes 215, 216 and 217, and then determining if such 
functions fall--as a class--within (or without) standard limits of 
interest. While the latter of course are projectable as a parallelopipod 
218 in FIG. 8, in actual fact, such limits are prestored by default 
values. Comparison is hence on a field-by-field basis in pairs of such 
values. 
A program for carrying out the method of the present invention has been 
designed and is characterized by use of FORTRAN editing statements wherein 
array limits for the functions F0, F1 and F2 are as set forth below. 
"EDM--Three pairs of data limits for editing traces; each trace having 
properties outside of these bounds will be excluded from processing. The 
first pair define the limits of the power of the traces (0-Lag 
autocorrelation). The second pair of values define the limits of the 
estimated 1-lag normalized autocorrelation using sign-clipped traces. The 
third pair of values define the limits of the estimated 2-lag normalized 
autocorrelation also employing sign-clipped traces." 
Default values for the program are set forth in Table I and were 
empirically designed. 
TABLE I 
______________________________________ 
Variable 
Limits 
______________________________________ 
F0 .025-.050 
F1 .200-1.01 
F2 .050-1.01 
______________________________________ 
The above-identified default values are based in part on discriminant 
analysis involving testing of several thousand actual field traces. As a 
consequence of such analysis, the traces were classified into either 
acceptable or unacceptable categories using a series of scatter plots in 
which the above limits were produced in conventional fashion. 
Traces from the Gulf region of the United States can be edited with 
satisfactory results using default values in the ranges set forth above. 
But traces from other regions and/or involving other recording situations 
may require a slight deviation from the above limits. But in such cases, 
experience has also shown that while the range of limits associated with 
the spectral estimates F1 and F2 involving sign-clipped data usually does 
not require adjustment, in the case of the limits associated with the 
estimate of the non-normalized cross-correlation function at zero lag, 
i.e., the function F0, this is not the case. 
Since in the latter circumstance, the power level can vary as a function of 
environmental and system gathering factors, such as water depth, gain 
setting and other well-documented parameters, limits may have to be 
adjusted out the range of default values set forth above. Scatter plots 
again are helpful in this regard, viz., in re-establishing default values 
within acceptable processing standards. 
The invention is not limited to the above combinations along, but is 
applicable to other anomalous circumstances as known to those skilled in 
the art. It should thus be understood that the invention is not limited to 
any specific embodiments set forth herein, as variations are readily 
apparent. 
E.g., while on-shore processing is now preferred (because of machine 
availability) and hence represents the best mode for carrying out the 
present invention, it is contemplated that at-sea processing would be 
carried out using a microcomputer system such as described in U.S. Pat. 
No. 4,316,267, for "Method For Interpreting Events Of Seismic Records To 
Yield Indications Of Gaseous Hydrocarbons", W. J. Ostrander, issued on 
Feb. 16, 1982 and assigned to the assignee of the present application. It 
may in such cases be necessary to change the window increment and to 
adjust line position along the time scale in order that the scanned data 
contain sufficient noise. Since the S/N ratio characteristics of earlier 
parts of each trace may also be below unity, such parts could be used, 
although the last second of the six-second conventional trace is currently 
preferred, as previously mentioned. Thus, the invention is to be given the 
broadest possible interpretation within the terms of the following claims.