Marine 3D seismic volumes from 2D seismic survey with large streamer feathering
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Strong ocean current influences a marine seismic survey and forces the streamer off-course from the survey line. The sideway drift of the streamer results in that the reflection data are no longer distributed in common midpoint gathers along the survey line but become swath distribution on one side of the ship track. This effect is known as “streamer feathering” which degrades the profile image of the 2D processed seismic data. However, if we have long streamer or closely spaced parallel 2D seismic survey lines, we may turn this deleterious effect into a good opportunity to generate 3D seismic volumes with swath distributed reflection data. We present two case studies in which 2D seismic data were collected offshore eastern Taiwan where the strong Kuroshio Current heavily influenced the ship speed and caused large streamer feathering. The first case is a large-offset 2D seismic profiling data collected using a 6-km long streamer. We processed the swath part of the reflection data in 3D that not only avoids the inappropriate smearing effect in 2D data processing but also generates a 3D seismic volume to help the seismic interpretation. In the second case, we adjusted our 2D survey strategy when realizing that strong Kuroshio Current was causing significant streamer feathering, and collected a set of closely spaced parallel 2D seismic lines. This multi-swath dataset covers a broad area which enables us to generate a 3D seismic volume. Since our datasets are not real 3D seismic data, we have tailored our processing flows to deal with different data configurations and limitations of each dataset. Our results show that not only we have enhanced 2D seismic images of the originally-interested survey lines, but also provide information on 3D geometry of the geological features imaged. The benefits and limitations of utilizing the streamer feathering effect to generate 3D seismic volumes from 2D seismic profile data are reported. Overall, this approach is a considerable way to handle 2D seismic data with large streamer feathering for both avoiding unreliable 2D seismic images and obtaining information on 3D geometry of the geological features imaged.
KeywordsMulti-channel seismic data 3D seismic volume Streamer feathering Seismic data processing
Most geological features are 3-dimensional in nature. For better understanding the subsurface geometries of the geological complex, 3D seismic survey is an effective way. The 3D seismic technology is widely used in industrial hydrocarbon exploration for studying the relations between regional geology and hydrocarbon resources that reduces the risk and improves the success rate for exploration drilling (Alfaro et al. 2007). In general, industrial marine 3D seismic surveys are achieved by using multiple streamers (as many as 8–12 streamers) with single or multiple source arrays. However, conducting 3D seismic survey is prohibitively expensive for academic research, thus few educational and research institutions could keep up with the 3D revolution (Biondi 2006).
In this study, 2 datasets from seismic surveys with a single source and a single multichannel streamer are used to test our idea. In the first case, we processed a strong feathered 2D large-offset seismic profile (a single swath) dataset in 3D point of view. We generated an elongate seismic image cube that not only improves 2D profile image but also provides information which help to better understand the 3D configurations of the substrata along this profile. In the second case, we tested 3D processing of a pseudo-3D dataset consisting of multiple parallel 2D seismic profile data with large streamer feathering angles (multi-swaths) that cover a wide area, and generated a 3D seismic image cube which reveals details of the geological features such as faults, volcanic intrusions and hydrothermal vents, etc., in the survey block. As these datasets are not true 3D seismic data, the processing feasibility is limited. We need to modify the processing flow based on the problems and limitations of the acquired datasets during 3D seismic data processing. The benefits, problems and limitations of this processing approach are presented.
Two datasets are used in this study; both were collected in the area offshore eastern Taiwan where Kuroshio Current flows by. The seismic surveys were conducted to investigate the tectonics and crustal structures of the Ryukyu subduction-backarc extension system. In this area, the Philippine Sea Plate subducts northward beneath the Eurasia Plate to form the Ryukyu Subduction System (Fig. 1a). The Ryukyu subduction system in offshore eastern Taiwan consists of Ryukyu Trench, Yaeyama Ridge (accretionary wedge), Hoping, Nanao, and East Nanao Basins (forearc basins), Ryukyu arc, and Southern Okinawa Trough (SOT) backarc basin from south to north (Fig. 1b). Our 1st case is a single 2D seismic survey line running in NW–SE direction across the Hoping Basin collected by a 6-km long streamer. The 2nd case is a pseudo-3D seismic survey consists of a series of N–S trending parallel 2D seismic lines in the rifting center of SOT. Since both surveys were conducted with survey lines running across the strong Kuroshio Current with high oblique angles, the strong ocean current caused large feathering angles on the streamers so that the source-receiver midpoints (reflection points) are scattered in a swath to one side of the survey lines. 3D processing of these two seismic datasets with large streamer feathering are presented below respectively for their regional geological backgrounds, data acquisition information, data processing flows, and results of the constructed 3D seismic cubes.
Case 1: a single-swath dataset collected by a large offset seismic system
Hoping Basin is the westernmost forearc basin of the Ryukyu subduction system (Fig. 1b). Unlike other E–W trending forearc basins located farther away from Taiwan, Hoping Basin is strongly deformed and trends in NNW–SSE direction, indicating that this basin is located in an unstable junction area between Ryukyu Trench, Longitudinal Valley Fault, and the transform zone along which the Ryukyu Arc moves southward (Lallemand et al. 1997). Structural complex of strike slip faults and frequent earthquake activities are reported (e.g. Lallemand et al. 1997; Kao et al. 1998; Lallemand et al. 2001; Theunissen et al. 2012). Underlying the Hoping Basin sediments, thick Suao strata are observed that was suggested to be the sediments deposited in the old Suao Basin and later deformed and subsided with the interaction of subduction and collision processes in this area (Lallemand et al. 1997).
Seismic data acquisition parameters of the datasets used in this study
MCS1175-3D (19 2D lines)
Marcus G. Langseth
Ocean Researcher 1
Streamer channel (maximum offset)
468 Channels (6 km)
120 Channels (1.65 km)
Shot interval (nominal)
Marcus G. Langseth
Ocean Researcher 1
Streamer channel (maximum offset)
468 Channels (6 km)
120 Channels (1.65 km)
Shot interval (nominal)
MGL0906-22N ran across the three forearc basins, namely Eastern Nanao Basin, Nanao Basin, and Hoping Basin from east to west (Fig. 1b). In the section over eastern part of the Hoping Basin, the streamer was strongly affected by the northward flowing Kuroshio Current, and rotated around 30° anticlockwise. The source-receiver midpoints, or the reflection points, thus were scattered in a swath on the eastern side of the ship track (Fig. 2). The width of the midpoint swath in this section is about 1650 m, and those seismic data were selected to generate the 3D seismic data volume.
Seismic data processing
Next, we build true 3D geometry of the reflection midpoints based on the actual shot and receiver locations. To set the grids of common-cell gathers (bins) of 3D geometry, the range, size, and numbers of bins in inline and crossline directions were carefully designed considering the actual midpoint distribution. To optimize the result, we try to strike a balance between increasing the resolution (smaller bin size) and reducing the number of 0-fold (empty) bins (keep bin size large enough to have reflection point in the bin). For this study, we choose a bin size of 25 m × 25 m and a rectangular area of 27.5 km × 1.625 km to cover the scattered reflection midpoints. By doing so, we have 65 inline and 1100 crossline profiles for the 3D seismic volume constructed.
After the zero-offset seismic traces were allocated into their respective 3D geometry bins, bad traces were picked and removed, and a band pass filter was applied. Then, the zero-offset traces in each bin were stacked directly because NMO corrections had been applied already. After stacking, we employed trace equalization to reduce the inhomogeneous amplitude variations caused by irregular fold number distribution. Since we do not have 3D velocity information, we could not perform a full 3D migration. We tested two-pass migration (migrate the data in inline direction and crossline direction, respectively) with water velocity, but as the cross-line profiles are too short, the results were not good. In the end, we migrated the data only along inline direction for a better final result.
Results of the constructed 3D seismic cube
Case 2: a multi-swath dataset in the Southern Okinawa Trough
The Southern Okinawa Trough (SOT) located offshore northeastern Taiwan is a backarc basin behind the Ryukyu arc where rifted crustal structures, magmatic and hydrothermal activities are widely observed (Sibuet et al. 1998; Lin et al. 2007; Shyu and Liu 2001; Klingelhoefer et al. 2009; Gena et al. 2013). In 2017, a seismic survey was carried out to investigate the E–W trending central rift zone of SOT. The N–S running seismic lines which were designed to go perpendicularly to the structural trend are in high oblique angle with the ENE flowing Kuroshio Current in the survey area. As the streamer feathering became large, we decided to reduce the originally designed 2D survey lines to conduct a pseudo-3D seismic survey. This approach provides us the opportunity to generate a 3D seismic image block from our 2D seismic data acquisition system onboard R/V Ocean Researcher 1.
The 120-channel seismic data acquisition system does not have sophisticated navigation system; the calculations of acquisition configuration for each shot are thus simplified. The source positions are calculated based on a fixed lay back distance from the GPS antenna in the middle of the ship, and the positions of the receivers for each shot are calculated based on the near-offset from source position, channel interval, the GPS positions on tailbuoy, and streamer featuring angle. The 120-channel streamer is generally straight during surveying, confirmed by the compass headings in the streamer depth control units and by the computed distance between the ship and the tailbuoy GPSs, except when ship turns.
Seismic data processing
For this dataset, as they were collected using pseudo-3D seismic data acquisition approach, the distribution of reflection midpoints covers a large area, we processed this dataset following standard 3D seismic data processing procedures (Fig. 7). First, we set the 3D geometry after choosing a proper bin size. The range, size, and numbers of bins in inline and crossline directions all need to be considered based on the actual midpoint distribution to provide a high resolution reflection grid with less 0-fold (empty) bins. We decided to set the bin size to be 50 m × 12.5 m as we have much larger cross-line spacing than inline spacing. We chose a rectangular grid covers an area of 15.5 km × 6.2 km, i.e. 124 inlines and 1240 crosslines. After traces were allocated into their respective 3D bins, 3D velocity analyses were performed on supergathers at every 6 inline bins by 100 crossline bins. As the data are irregularly distributed, supergathers for velocity analysis are designed to combine 7 inline bins and 11 crossline bins. The final velocity model consists of 240 velocity control nodes in the 96.1 km2 area. NMO corrections were performed with those velocity results. Before stacking, bad traces had been picked and removed and bandpass filter applied, then traces in each bin were stacked and trace equalization was applied to reduce the inhomogeneous amplitude distribution led by irregular fold number distribution. As the resolution in crossline direction (50 m) may result in spatial aliasing on migration, we interpolated the poststack traces in crossline direction that not only fills the 0-fold bins but also doubles the inline numbers (from 124 to 247 inlines). 3D phase-shift migration method was selected to construct a migrated data volume with less spatial aliasing effect (Yilmaz 2001). Migration of the 3D seismic data volume was performed using our 3D velocity information.
Results of the constructed 3D seismic cube
Seismic attribute analyses have been performed to enhance the 3D images of some specific geological features that can be displayed on top view or perspective view (Fig. 10). The distributions of normal faults, strong reflections surrounding the volcanos and the fluid-related blanking zones can be easily observed on the time slices (Fig. 10). This case study demonstrates the advantage of having a 3D seismic volume on studying the geometries and relations between the faulted blocks, volcanic extrusions, and hydrothermal vents in this part of SOT.
Our two case studies demonstrate the benefits of turning large feathered 2D seismic datasets into 3D seismic volumes. After 3D geometry process with rigorously-calculated source-receiver midpoint locations and carefully designed binning grids, the seismic signals are allocated to the processing bins where they actually should be. This step reduces the distances between the processing bins and the actual reflection point locations, and keeps the seismic signals representing the subsurface at their actual locations. In addition, we have images from all three dimensions to reveal the lateral variations of geological features. In the first case, we turn a 2D seismic profile into an elongated seismic cube. Observations can be made not just on a single 2D profile with many pitfalls. Once seeing the relief of the base of Hoping Basin and the structural features in the third dimensions, we realize that we should not just to stack lateral variations into a 2D profile. In the second case, we turn 19 2D seismic profile data into a 3D seismic volume with 124 inline profiles after 3D seismic data processing. This approach increases the lateral resolution by about 6 times. Poststack trace interpolation helps not only reducing the aliasing on migration, but also increases the number of crossline bins by two folds on the final result. Therefore, we cannot have seismic images on all three dimensions to see the real geometries of geological features. The time slice images created are effective to present features on map view that intuitively displaying the spatial information like structural strikes and horizontal offsets of the faults.
Additional benefits include that 3D seismic attribute analyses using multiple traces can be applied on these data volumes (Fig. 10). The seismic attributes highlight geological or geophysical events. Combining these techniques, the seismic expression of faults and stratigraphic features can be revealed even clearer. In our study, we have tried some edge detection attributes to highlight faults and some reflection strength attributes for identifying structures. The results are helpful for interpreters to see their targets effectively.
Limitations and problems
Since the datasets are acquired by 2D seismic systems, there are limitations and problems that need to be carefully considered during 3D seismic data processing.
The positioning of source and receiver should be as accurate as possible. Misplaced reflection signals will not create reliable 3D seismic images. For example, seismic data acquired during the ship turns should be removed if the positioning method cannot accurately calculate the positions of sources and receivers.
Insufficient number of traces and lack of uniformity on fold coverage are the major limitations for generating a 3D volume by using 2D dataset. In our cases, the streamers cannot be controlled manually, the degree of streamer feathering depends on in situ ocean current and ship heading and speed so that the width of the midpoint distribution varies, and there could be some no-data areas and high data density areas (Figs. 6, 7). After 3D binning, 0-fold bins become holes in seismic images. These gaps or holes can be filled by trace interpolation methods or just by migration methods if the gaps are not too large. In addition, trace amplitude after stack may be influenced by irregular fold distribution. If so, trace equalization should be applied for standardizing the amplitude before migration.
Another limitation is that velocity analysis cannot be performed on range limited data. In our first case, we could analyze the 3D velocity structure from the single swath dataset by using near-offset traces for near-offset area and far-offset traces for far-offset area, but the results would not be reliable, so we chose to apply NMO correction with the results from 2D velocity analysis. In the second case, we performed 3D velocity analysis on constructed supergathers which may contain far-offset traces from one midpoint swath and near-offset traces from the next swath in many places.
The size of the binning grid for 3D geometry defines the resolution of the seismic cube and final images. Depending on the distribution of reflection midpoints, we may need to choose a larger bin size in crossline direction to reduce the number of 0-fold bins. This choice concurrently reduces lateral resolution of poststack volume and also creates aliasing problems when doing migration due to the large poststack trace interval. The applications of trace interpolation or choosing a proper migration method may help to improve the images.
After our two case studies, the streamer feathering seems no longer to be just a deleterious effect that makes 2D seismic processing inappropriate and profile images unreliable. In this paper, both benefits and problems on converting 2D seismic data with large streamer feathering into 3D seismic volume are presented. Constructing 3D seismic volume with data acquired by 2D system is beneficial on academic research, as we may view the imaged geological features from 3D perspectives and also reducing the pitfalls caused by conventional 2D data binning on large streamer feathered data. However, there are problems and limitations on applying this approach. Comparing to a conventionally processed 2D seismic profile, the 3D seismic block provides continuous inline profiles, crossline profiles and horizontal slices which help to reveal additionally information on spatial variations of the geological structures imaged. The lateral variation of geological features observed in our case studies also shows how careful we should be when binning the feathered 2D data, especially in areas where strong cross-track ocean current presents. However, limitations for this approach are experienced during 3D data processing in our studies. Fine and accurate positioning of source and receiver is a fundamental requirement for this approach. The insufficient trace numbers and inhomogeneous distribution of reflection midpoints are major limitations when generating a 3D seismic volume using 2D seismic dataset. This limitation leads to many extended problems, for example, the traces from different offsets with different dominate frequency bands and amplitude ranges are irregularly distributed. These limitations need to be dealt carefully to avoid problems on 3D seismic data processing.
We would like to thank the Central Geologic Survey, Ministry of Economic Affairs and the National Science Council (now the Ministry of Science and Technology), ROC for their supports to the projects in which the datasets analyzed in this paper were collected. The captains, crew and technical and scientific personnel onboard the R/Vs Ocean Researcher I and Marcus G. Langseth are appreciated for their efforts in collecting the seismic data used in this study. We also thank former members of Seismic Exploration Laboratory in Institute of Oceanography, National Taiwan University for developing the seismic data processing system and techniques. The comments and suggestions from the reviewers help to improve the quality of this paper. Financial supports of this study are from Central Geologic Survey (106-5226904000-05-01; 107-5226904000-05-01) and Ministry of Science and Technology (NSC-98-2611-M-002-007).
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