Seismic diffractions for gas leakage detection in the shallowwaters of the Gulf of Mexico
Alexander Klokov1, Ramon H. Trevino1, Timothy A. Meckel1
1Bureau of Economic Geology, The University of Texas at Austin
(September 7, 2017)
Running head: Diffraction imaging with P-Cable data
ABSTRACT
Successful carbon capture and storage (CCS) requires secure CO2 confinement within a
geologic reservoir. Examination of the shallow subsurface for hydrocarbon leakage allows
conclusions about the sealing capability of the underlying confining systems (caprocks or
seals). Numerous seismic signatures have been reported to be hydrocarbon indicators. The
interpretation can be advanced by using of seismic diffractions, which could indicate subtle
hydrocarbon accumulations not detectable by conventional techniques. We investigate the
possibility of using seismic diffractions to detect shallow gas. We extract diffractions from
the ultra-high-resolution 3D P-Cable seismic dataset acquired along the Gulf of Mexico
inner continental shelf. Interpretation of this dataset revealed numerous seismic signatures
associated with hydrocarbon accumulations. In particular, a prominent gas chimney was
identified. We analyze scattering features of the detected hydrocarbon accumulations and
confirm the correlation between diffractions and hydrocarbon saturation. Based on these
observations, we interpret seismic diffractions to predict other hydrocarbon accumulations
— those that are not detectable by conventional techniques. In addition, we discuss other
subsurface features (salt dome, channel morphology, Chenier plain) that appear strong
1
INTRODUCTION
Effective implementation of a carbon capture and storage (CCS) program requires a superior
reservoir. In addition to high capacity and high permeability, the reliable CO2 storage
should be securely sealed to ensure keeping the carbon dioxide in place over tens of thousands
of year. Therefore, detailed investigation of confining systems (caprocks or seals), their
integrity and sealing properties are an important component of prospecting for potential
CO2 storage sites.
Miocic et al. (2016) evaluated natural CO2 reservoirs, in which the carbon dioxide has
been trapped for million years, and discussed the main factors determining the storage
security, which included thickness of the confining system, reservoir depth, and gas density.
Fault and fractures were reported as the main conductors for migration of CO2 within the
subsurface. Preliminary assessment of storage security can be done by seismic data analysis.
In addition to identifing reservoirs and evaluating of stratal thicknesses, seismic can detect
faults in the confining system and overlying strata (Juhlin et al., 2007; Alcalde et al., 2013).
In combination with geomechanics, this information is critical for determining the optimal
configuration of a potential storage site (Vidal-Gilbert et al., 2010; Teatini et al., 2014;
Ward et al., 2016; White et al., 2016).
If associated with a depleted hydrocarbon reservoir, confining properties may be de-
termined by investigating the overlying strata for hydrocarbon accumulations. Minimal
indication of hydrocarbons suggests reasonbable sealing properties. Conversely, an increase
of deteceted gas concentrations above the reservoir indicates poor confining properties.
Løseth et al. (2009) presented an extended overview of seismic features associated with
hydrocarbon leakage. Hydrocarbon saturation causes amplitude anomalies (bright spots,
3
polarity reversals, dim spots), which have been exploited as a hydrocarbon indicators for
decades (Brown and Abriel, 2014). These features are mostly related to reservoir units.
However, they may be observed in rocks with low permeability as well (Løseth et al., 2009).
One prominent seismic signature associated with hydrocarbon leakage is a gas chimney,
which is a vertical zone with discontinous reflectors (Heggland, 1998; Singh et al., 2016).
The migrating gas causes irregular changes in the compressional velocity field that yields
scattering and degradation of reflected waves (Arntsen et al., 2007). Zhu et al. (2012)
investigated seismic imaging of a target in offshore China and concluded that wave scattering
caused by shallow gas is the primary phenomenon causing the reflected waves degradation.
Since local hydrocarbon accumulations, as any local heterogeneities in terms of acoustic
properties, scatter seismic energy, they can be identified by analysis of seismic diffrac-
tion waves. The linkage between hydrocarbon accumulations and seismic diffractions has
been documented in various case studies (Rauch-Davies et al., 2014; Klokov et al., 2014;
Ogiesoba and Klokov, 2015; Schoepp et al., 2015; Klokov et al., 2015). In contrast to conven-
tional methods, diffraction analysis allows operating with much weaker seismic signals and,
thereby, identification of subtle hydrocarbon accumulations or even just increases in hydro-
carbon concentration. Klokov et al. (2017) analyzed seismic diffractions from ultra-high-
resolution 3D (UHR3D) seismic data acquired on the inner shelf of the Gulf of Mexico (near
Bolivar Peninsula, Figure 1) using a P-CableTM acquisition system (Petersen et al., 2010;
Lippus, 2014) to evaluate fluid migration above potential reservoirs. Diffraction anomalies
have been reported and interpreted as evidence of hydrocarbon migration. Finally, poor
sealing capabilities of underlying confining zones has been concluded.
In this work, we further investigate the potential of seismic diffractions for use in shallow
4
gas detection. We utilize a P-Cable dataset acquired in the Texas State Waters near the San
Luis Pass (Figure 1). Interpreting the same dataset, Meckel and Mulcahy (2016) described
various seismic anomalies associated with hydrocarbon accumulations. We compare these
anomalies with seismic diffraction signatures to examine scattering features of hydrocarbon
saturated zones. Then, we interpret weaker diffraction signals to identify subtle hydrocarbon
accumulations not imagible with conventional seismic attribute analysis.
GEOLOGICAL SETTINGS
Addressing seismic attribute analysis of the same dataset used in the current study, Meckel
and Mulcahy (2016) mapped and interpreted two Quaternary age sequence boundaries and
related incised valley systems that were associated with two glacial lowstands (approxi-
mately 140 ka and 20 ka, respectively). They based their interpretation of the local geology
on the many preceding studies (Berryhill et al., 1987; Paine, 1991; Anderson et al., 1996;
Abdullah et al., 2004; Simms et al., 2007). Following from the interpretation of Abdullah
et al. (2004), a significant portion of the Pleistocene section of the study area is composed
of a delta lobe of the paleo Brazos River. The lobe is interpreted to have been deposited
during oxygen isotope stage 5e early in the Wisconsin interstadial and have aggradational to
progradational clinoform configurations. The 20 ka sequence boundary identified by Meckel
and Mulcahy (2016) is above lobe 5e; whereas, the 140 ka sequence boundary is below. The
incised valleys correlated with the two sequence boundaries include seismic facies typical
of fluvial channels (e.g., scours, point bars, lateral accretions). The interfluvial deposits
outside the incised valleys comprise seismic facies, which were interpreted as coarse-grained
channel scour deposits of a meandering channel and transgressive estuarine to marine, fine-
grained mud fill. The natural gas accumulations in the coarser grained deposits are the
5
focus of the current study.
DATA ACQUISITION AND PROCESSING
The P-Cable technology provides a specific acquisition configuration that allows imaging of
near subsurface with extremely high resolution (Petersen et al., 2010; Lippus, 2014). The
UHR3D dataset utilized for the current study was acquired in the Texas State Waters in
close proximity to San Luis Pass. The acquisition system utilized 12 streamers spaced at
12.5 m and armed with 8 channels each. Shots were spaced at 12.5 m and the source-receiver
offset was 110 m. Seismic impulse was generated by a single 90 cubic inch GI air gun, which
produced seismic data with a dominant frequency of 150 Hz. The P-Cable survey produced
high-resolution seismic imaging (bin size of 6.25 m) over the area of about 32 square km.
The acquisition parameters described in detail by Meckel and Mulcahy (2016). The data
were processed at the Bureau of Economic Geology, The University of Texas at Austin as
discussed in detail by Hess et al. (2014).
VELOCITY MODEL BUILDING
P-Cable surveys are featured by short offsets. This causes non-significant velocity depen-
dence of CMP gathers. And the traditional investigation of reflection arrivals along offset
(NMO analysis) does not produce a robust velocity estimation.
The velocity model building issue can be resolved by analysis of a diffraction image. Any
scatterer is imaged with the highest focusing if migrated with correct velocity. Using lower
or higher migration velocity, the scatterer smears along a hyperbola oriented down or up
respectively. Klokov et al. (2017) used the diffraction focusing/defocusing feature to build
a migration velocity model for the Bolivar P-Cable dataset collected approximately 80 km
6
northeast of the current area of study. Evaluation of numerous scatterers was integrated
to obtain a one-dimensional velocity profile. The intent of that study was to evaluate
hydrocarbon distribution instead of accurate locating of subsurface objects. Therefore,
the approximate velocity model was acceptable. Similar objectives and the short distance
between the two surveys make it reasonable to use the velocity model developed for the
Bolivar dataset for the current work.
DIFFRACTION ANALYSIS
To extract diffractions from the 3D stack volume and obtain a 3D diffraction image, we
followed the approach developed by Klokov and Fomel (2012). In addition to reflection
elimination, this method allows effective segregation of diffractions from background noise
that, in turn, favors interpretation of subtle diffraction signals. To facilitate interpretation,
the diffraction image was transformed into a diffraction energy attribute volume by energy
integration over a time gate of 4 ms. In contrast to many conventional seismic attributes,
diffraction energy is directly linked to acoustic properties of the subsurface. Thus, in addi-
tion to locating subsurface heterogeneities, a diffraction image can be used to characterize
the scatterers. In terms of hydrocarbon detection, higher diffractivity often appears as-
sociated with higher hydrocarbon saturation (Rauch-Davies et al., 2014; Schoepp et al.,
2015).
Next we consider some seismic signatures associated with hydrocarbon saturation and
investigate how they appear in the diffraction image.
7
Bright spot
When hydrocarbon replaces brine in a sandstone rock, it reduces the local acoustic impedance
because of lower velocity and density. If the acoustic impedance of the sandstone was ini-
tially lower than the acoustic impedance of the enclosing shales (which is typical for young
clastic sediments), such reduction leads to increased acoustic contrast between the reservoir
rock and sealing formations (Brown and Abriel, 2014). The higher contrast, in turn, is ex-
pressed in higher seismic amplitudes. These amplitude anomalies (bright spots) have been
routinely used for hydrocarbon detection. Since P-Cable surveys target the shallow sub-
surface, prospecting for bright spots is a reasonable strategy for hydrocarbon distribution
assessment.
Figure 2 shows a group of bright spots (solid outline) detectable at inline 154 and a time
of 92 ms. In the diffraction image, these bodies appear as strong anomalies as well. The
scattering features of bright spots can be easily explained when the heterogeneity (caused
by hydrocarbon saturation) is small and can be considered as a point scatterer. However, we
observe large bright spots: the largest one exceeds 140 m (which is 14 wavelengths) in cross
section. Therefore, we associate the diffractivity with irregular hydrocarbon distribution in
the formation. This likely causes local velocity and density anomalies that favor scattering
of seismic energy.
Diffraction imaging suggests that two detected bright spot areas are hydraulically con-
nected by a narrow link indicated by red arrows in the diffraction energy time slice. Fig-
ure 3 displays the cross section AA’, which reveals a vertical diffraction anomaly extended
up from the fluvial channel (blue arrow). This feature can be interpreted as a fault (or
fracture zone) caused by differential compaction associated with the channel (Allen and
8
Allen, 2005). Note that the supposed faulting/fracturing is not clearly detectable in the
conventional image time slice (Figure 2c). This can be explained by low diffraction energy
suppressed by stronger reflections and background noise.
Thus, bright spots, which are traditionally considered as a direct hydrocarbon indicator,
are detected as amplitude anomalies in the diffraction image. However, zones of high
diffractivity appear more extended in the diffraction energy time slice (Figure 2d). This
diffractivity can be caused by faulting and fracturing, which provides migration paths for
the hydrocarbons manifested as bright spots.
Gas chimney
Gas chimneys are another set of prominent seismic features used for hydrocarbon detection
(Heggland, 1998; Singh et al., 2016). They are characterized by scattering of seismic energy
and reflection wave degradation caused by hydrocarbon saturation. Meckel and Mulcahy
(2016) interpreted a gas chimney in the southwest part of the San Luis Pass P-Cable survey.
The chimney was clearly detectable in attribute sections and confirmed by gas analysis of
cores collected in the shallow sequence.
The gas chimney can be clearly seen in Figure 4 as a zone in which reflectors appear
strongly corrupted. The hydrocarbon migration is provided by a system of faults and
fractures, which are sources of diffraction waves. The scattering power of these faults is
boosted by hydrocarbon saturation because of higher acoustic impedance contrast between
fault zones and accommodating rocks. Therefore, the gas chimney is observed as a strong
amplitude anomaly in the diffraction image.
Using the similarity attribute, which is a response of coherency between neighboring
9
traces (de Rooij and Tingdahl, 2002), on a time slice from 130 ms (Figure 5), the gas
chimney can be delineated on three sides where the attribute intensity changes significantly.
On the fourth side, the gas chimney feature is demarcated by a fault plane (F1).
In addition to the prominent fault plane F1, the diffraction imaging reveals two strong
linear scattering features F2 and F3. Because of the azimuth consistency with F1, we also
interpret these features as fault planes. Note that they are not easily detectable on the
similarity attribute. This may be explained by high gas saturation around the fault zones;
thus, a significant amount of seismic energy is scattered that obscures imaging and analysis
of the reflection boundaries. Diffraction imaging, in turn, allows collecting the scattered
energy and utilizing it for subsurface imaging. Feature F4 probably results from the same
phenomenon.
Gas chimneys confined by faults and associated with gas migrating along the faults
are classified as Type I (Heggland, 2005). The faults providing migration paths are easily
detectable in the diffraction image as vertical high-amplitude features (some are indicated
by arrows in Figure 4).
Fault trap
The observed correlation between confidently interpreted gas accumulations and seismic
scattering adds validity to using seismic diffractions as hydrocarbon indicators. Figure 6
shows a high-amplitude diffraction anomaly (solid outline), which is associated with a dip-
ping structure and trapped against a fault plane. The fault, in contrast to those shown
in Figure 4, does not appear as a strong scatterer. This can be explained by minor fluid
saturation within the fault zone, which means a sealing fault with no fluid migration along
the fault plane in this location. So, the diffraction anomaly can be interpreted as a hy-
10
drocarbon (likely methane) accumulated in a structural trap. Low diffractivity above the
anomaly suggests low permeability sediments sealing the trap.
The interpreted gas accumulation does not stand out in the conventional image (Fig-
ure 6a). Note also that the conventional image looks seriously corrupted by acquisition
footprint (Figure 6c). Diffraction imaging allows noise suppression that facilitates interpre-
tation of the data.
OTHER SCATTERING FEATURES
In the previous section, we discussed the linkage between seismic diffractions and hydrocar-
bon saturation. In addition, seismic scattering can be caused by abrupt changes in lithology.
Below, we review some subsurface scattering features detected by diffraction imaging.
Salt dome
The San Luis Pass P-Cable survey partially illuminated the San Luis Pass Salt Dome
(Nettleton, 1957). The salt body is located in the southeast part of the survey; it is
easily detectable in the conventional seismic image and appears as a strong anomaly in
the diffraction image (Figures 7 and 8). First, this is caused by high acoustic contrast
between the salt and adjacent clastic sediments. Second, salt domes can have sharp edges
which scatter seismic energy. Note that some scattering points are detected inside the salt
body, which possibly indicates heterogeneous structure of the salt. In addition, diffraction
imaging illuminates some radial faults associated with the salt extension (Figure 7b). High
diffraction energy observed at these faults can be interpreted as indicators of fluid migrating
along the fault planes.
11
Channel morphology
Meckel and Mulcahy (2016) mapped an unconformity (referred as UC2) and interpreted
it as an incised valley associated with the OIS 6 lowstand, which was attributed to about
140 ka (Simms et al., 2007). Figure 9 shows the UC2 horizon slice. It reveals a channel
system with multiple ravines roughly orthogonal to the main channel.
In the diffraction image, the ravines are associated with strong diffraction anomalies.
The main channel, in turn, reveals low-to-moderate scattering power. The ravines can
accumulate less sorted coarser sediments that favor creation of local velocity and density
anomalies. These zones have higher scattering features, which is detectable by diffraction
imaging. The similar interpretation can be applied to point bar deposits identified within
the channel (Meckel and Mulcahy, 2016). Figure 10 reproduces an example in which dipping
reflections were interpreted as coarse-grain bar deposits. This zone demonstrates high
scattering features indicating high acoustic contrast to adjacent sediments.
Chenier plain
Figure 11 is interpreted as an example of a strand plain comprising multiple beach ridges
subparallel to the ancient shoreline (Otvos and Price, 1979; Augustinus, 1989). In the
diffraction energy slice, the strand plain comprises long and narrow high-amplitude features
interpreted as beach ridges. Because of wave-winnowing, beach ridges are often composed of
coarser material. This makes them lithologically heterogeneous that favors seismic energy
scattering. High porosity and fluid saturation can enhance the scattering power of the beach
ridges.
12
RESULTS AND DISCUSSION
A P-Cable survey acquired along the inner shelf of the Gulf of Mexico revealed numerous
seismic signatures associated with hydrocarbon accumulations. We compared these signa-
tures with seismic diffractions. Observed correlation between diffractivity and hydrocarbon
saturation supports utilizing seismic diffractions for shallow gas detection. The ability to
validate small accumulations or low concentrations of natural gas could, in turn, increase
reliability of CO2 storage assessment by identifying areas of active natural fluid migration.
Two conventional hydrocarbon indicators, gas chimney and bright spot, were associated
with high-amplitude anomalies in the diffraction image. Strong diffractions attributed to
the gas chimney is caused by significant faulting and fracturing. The observed bright spots
occupied large areas that significantly exceeded the seismic wavelength. Therefore, it is
not likely that they were acting as point diffractors. The scattering phenomenon could be
explained by irregular distribution of migration pathways, which are faulted and fractured
zones.
The correlation between hydrocarbon saturation and seismic scattering allows using
seismic diffractions for locating shallow gas not easily detectable by conventional seismic
attributes. We interpreted a high-amplitude diffraction anomaly, which was associated
with dipping structure and confined by a fault plane. Diffraction imaging also suggested a
fractured zone that could provide hydraulic connections between observed bright spots.
In addition to hydrocarbon saturated zones, we discussed other subsurface scattering
objects that were exposed by diffraction imaging. We observed high diffractivity from point
bar deposits, channel ravines, salt dome, and Chenier plain. For correct interpretation of a
diffraction image, these features should be taken into consideration.
13
ACKNOWLEDGMENTS
Research was supported by the U.S. Department of Energy, National Energy Technology
Laboratory, DE-FE0026083. The P-Cable data were processed by Thomas Hess. We thank
Vladimir Moskalev and Osareni Ogiesoba for helpful discussions. We thank Caroline Breton
for assistance with the graphics. Seismic attribute analysis was performed in the OpendTect
environment.
Disclaimer: “This report was prepared as an account of work sponsored by an agency
of the United States Government. Neither the United States Government nor any agency
thereof, nor any of their employees, makes any warranty, express or implied, or assumes any
legal liability or responsibility for the accuracy, completeness, or usefulness of any informa-
tion, apparatus, product, or process disclosed, or represents that its use would not infringe
privately owned rights. Reference herein to any specific commercial product, process, or ser-
vice by trade name, trademark, manufacturer, or otherwise does not necessarily constitute
or imply its endorsement, recommendation, or favoring by the United States Government or
any agency thereof. The views and opinions of authors expressed herein do not necessarily
state or reflect those of the United States Government or any agency thereof.”
14
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LIST OF FIGURES
1 Map of the Texas Coastal Bend and Texas State Waters showing P-Cable survey
location. Clemente-Tomas Fault outline is from Seni et al. (1997); fault located where it
offsets the top of the Miocene geologic section.
2 Bright spots (solid outline) detected in the conventional time-migrated image (a,
c). In the diffraction energy attribute (b, d), these signatures appear as high-amplitude
anomalies. Red arrows indicate possible linkage between two bright spots.
3 Conventional time-migrated image section (a) and diffraction energy section (b)
for profile AA’ indicating a channel (blue arrow) and associated fracture zone (red arrow),
which provides hydraulic connection between two bright spots. The channel is clearly seen
in the conventional image time slice (c).
4 Inline sections for (a) conventional time-migrated image, (b) similarity attribute,
and (c) instantaneous amplitudes detecting the gas chimney. In the diffraction energy
section (d), the gas chimney appear as high-amplitude anomaly. Arrows indicate faults
providing paths for the hydrocarbon migration.
5 Time slice from 130 ms for (a) similarity attribute and (b) diffraction energy de-
tecting the gas chimney (solid outline). Arrows indicate interpreted fault planes.
6 Inline section and time slice for (a, c) conventional time-migrated image and (b,
d) diffraction energy. The diffraction anomaly (solid outline) is interpreted as hydrocarbon
accumulation associated with a structural trap. Bold solid lines indicate a fault plane.
7 Time slice from 144 ms for (a) conventional time-migrated image and (b) diffrac-
tion energy. Salt dome is a strong source of seismic diffractions. Red arrows indicate radial
faults associated with the salt dome.
8 Inline section for (a) conventional time-migrated image and (b) diffraction energy.
19
Salt dome is a strong source of seismic diffractions.
9 Horizon view for the UC2 unconformity: (a) two-way traveltime and (b) diffrac-
tion energy. Strong diffractions are concentrated in the ravines (red arrows) suggesting less
sorted coarser sediments accumulations.
10 Inline section for (a) conventional time-migrated image and (b) diffraction energy.
Point-bar deposits (white dashed outline) corresponds to the high-amplitude diffraction
anomaly.
11 Time slice from 144 ms for (a) conventional time-migrated image and (b) diffrac-
tion energy. Chenier plain features (red arrows) appear as sources of diffracted waves.
20
Figure 1: Map of the Texas Coastal Bend and Texas State Waters showing P-Cable surveylocation. Clemente-Tomas Fault outline is from Seni et al. (1997); fault located where itoffsets the top of the Miocene geologic section.Klokov et al. –
21
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Figure 2: Bright spots (solid outline) detected in the conventional time-migrated image (a,c). In the diffraction energy attribute (b, d), these signatures appear as high-amplitudeanomalies. Red arrows indicate possible linkage between two bright spots.Klokov et al. –
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Figure 3: Conventional time-migrated image section (a) and diffraction energy section (b)for profile AA’ indicating a channel (blue arrow) and associated fracture zone (red arrow),which provides hydraulic connection between two bright spots. The channel is clearly seenin the conventional image time slice (c).Klokov et al. –
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Figure 4: Inline sections for (a) conventional time-migrated image, (b) similarity attribute,and (c) instantaneous amplitudes detecting the gas chimney. In the diffraction energysection (d), the gas chimney appear as high-amplitude anomaly. Arrows indicate faultsproviding paths for the hydrocarbon migration.Klokov et al. –
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Figure 5: Time slice from 130 ms for (a) similarity attribute and (b) diffraction energydetecting the gas chimney (solid outline). Arrows indicate interpreted fault planes.Klokov et al. –
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Figure 6: Inline section and time slice for (a, c) conventional time-migrated image and (b,d) diffraction energy. The diffraction anomaly (solid outline) is interpreted as hydrocarbonaccumulation associated with a structural trap. Bold solid lines indicate a fault plane.Klokov et al. –
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Figure 7: Time slice from 144 ms for (a) conventional time-migrated image and (b) diffrac-tion energy. Salt dome is a strong source of seismic diffractions. Red arrows indicate radialfaults associated with the salt dome.Klokov et al. – 27
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Figure 8: Inline section for (a) conventional time-migrated image and (b) diffraction energy.Salt dome is a strong source of seismic diffractions.Klokov et al. –
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Figure 9: Horizon view for the UC2 unconformity: (a) two-way traveltime and (b) diffractionenergy. Strong diffractions are concentrated in the ravines (red arrows) suggesting lesssorted coarser sediments accumulations.Klokov et al. –
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Figure 10: Inline section for (a) conventional time-migrated image and (b) diffraction energy.Point-bar deposits (white dashed outline) corresponds to the high-amplitude diffractionanomaly.Klokov et al. –
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