Morris Jesup Spur and Rise north of Greenland – exploring present seabed features, the history of sediment deposition, volcanism and tectonic deformation at a Late Cretaceous/early Cenozoic triple junction in the Arctic Ocean

The narrow Morris Jesup Spur and an adjacent broader western rise extend 220 km into the Eurasia Basin from the shelf edge north of Greenland. We have used a hovercraft platform drifting with the sea to collect the first seismic reflection transect across an area postulated to be a former triple junction between the Greenland, Eurasian and North American plates. The narrow, flat-topped Morris Jesup Spur is a succession of west-dipping (? 3°) sediments overlying a basal volcanic unit truncated at the top by an unconformity. The Morris Jesup Rise is formed by intensely deformed sediments and volcanic rocks with a deformation front to the northwest. The basin between theMorris Jesup Rise and the Lomonosov Ridge has a sediment thickness of >3 km with a large submarine channel/levee complex in the upper part and repeated volcanic units present in the deeper stratigraphy below 1.0 sec. sub-bottom. Volcanism on the Morris Jesup Spur is considered to be Late Cretaceous–early Cenozoic in age, and continued into the late Miocene on the Morris Jesup Rise and possibly into early Oligocene in the SW Amundsen Basin. The western slope of the Morris Jesup Spur represented the continental slope north of Svalbard in the Late Cretaceous. A block which included the Morris Jesup Spur and Yermak Plateau rifted off during the initial opening of the Eurasia Basin and moved as part of Greenland until about Chron 22. The architecture of the Morris Jesup Rise is a result of plate convergence possibly including a former extensional plate boundary segment which connected the Gakkel spreading centre to the Hornsund Fault between Chron 22 and Chron 13. The Morris Jesup Rise may be a northern tectonic outlier of a more extensive Eurekan tectonic domain hidden below the Lincoln Sea continental shelf. The Morris Jesup Spur remained subaerial until latest Miocene and submergence of the spur most likely intensified the East Greenland Current.publishedVersio

Sediment deformation atop the Lomonosov Ridge, central Arctic Ocean: Evidence for gas-charged sediment mobilization

We have used a hovercraft platform drifting with the sea ice to acquire the first digitally recorded seismic reflection data transects across the Canada/Greenland (89°N-85°N) section of the Lomonosov Ridge, central Arctic Ocean. The flat-lying, laterally uniform Cenozoic sediment package on top of the ridge at 87°N, 60° W shows at least four sites with local seismic amplitude anomalies. The common feature is a column (<600 m wide) of partly discontinuous or chaotic bright reflection events at the center of a <1.5 km wide dome (amplitude <25 m) terminating at the seabed in a 8–12 m deep depression. The amplitude anomalies are interpreted as gas-charged fluid escape pipes marked by a pockmark at the seabed. Gas and fluids introduced from below have mobilized the overlying high porosity, low density Eocene bio-siliceous ooze causing the doming. The gas and fluids appear to originate from the top of rotated fault blocks and sub-basalt sediments of Mesozoic or older age deposited when the Lomonosov Ridge was part of the pre-Late Cretaceous continental margin north of Franz Josef Land.publishedVersio

Digital superresolution in seismic AVO inversion

The sparseness promoted by the total variation norm is utilized to achieve superresolution amplitude-variation-with-offset (AVO) inversion. The total variation norm promotes solutions that have constant values within unspecified regions and thus are well suited for an earth model consisting of layers bounded by faults and erosion surfaces. Algorithmic developments from digital image and video restoration are utilized to solve the geophysical problem. A spatial point spread function is used to model the resulting effect of wave propagation, migration, and processing. The methodology is compared to current alternatives and discussed in the context of AVO inversion. Good results are obtained in a Barents Sea test case

Cross-streamer wavefield interpolation using deep convolutional networks

Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Seismic data from conventional marine acquisition lacks near offset and wide azimuth data, which limits imaging in these settings. In addition, large streamer separation introduce aliasing of spatial frequencies across the streamers. A new marine survey configuration, known as TopSeis, was introduced in 2017 in order to address the shallow-target problem. However, introduction of near offset data has shown to be challenging for interpolation and regularization, using conventional methods. In this paper, we investigate deep learning as a tool for interpolation beyond spatial aliasing across the streamers, in the shot domain. The proposed method is based on imaging techniques from single-image super resolution (SISR). The model architecture consist of a deep convolutional neural network (CNN) and a periodic resampling layer for upscaling to the non-aliased wavefield. We demonstrate the performance of proposed method on representative broad-band synthetic data and TopSeis field data from the Barents Sea

Kinematic wavefield attributes based multidimensional prestack data reconstruction

The 4D/5D interpolation and regularization methods effectively improve the quality of seismic imaging. In addition to the Fourier domain interpolation method, 5D interpolation based on the common reflection surface (CRS) method has attracted more and more attention due to simplicity of its implementation and effectiveness of performance. However, the main challenge of this method is the heavy calculation in parameter estimation. To overcome this limitation, we introduce a kinematic wavefield attributes based prestack data interpolation and regularization method. This method uses gradient structure tensor (GST) and quadratic structure tensor (QST) methods to extract kinematic wavefield attributes (local slopes and curvatures) and use them for fast 3D zero-offset (ZO) CRS parameter estimation. The derived parameters are then used for 3D CRS based prestack interpolation and regularization. The proof of concept is demonstrated on datasets acquired by TopSeis. The corresponding results show that the improved efficiency of the GST/QST based method in kinematic wavefield attribute extraction and 3D ZO CRS parameter estimation. Moreover, the interpolated and regularized TopSeis prestack data derived from the subsequent 3D ZO CRS proves the simplicity and effectiveness of this method

Cross-streamer wavefield reconstruction through wavelet domain

Seismic exploration in complex geologic settings and shallow geologic targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Conventional marine seismic and wide-azimuth data acquisition lack near-offset coverage, which limits imaging in these settings. A new marine source-over-cable survey, with split-spread configuration, known as TopSeis, was introduced in 2017 to address the shallow-target problem. However, wavefield reconstruction in the near offsets is challenging in the shallow part of the seismic record due to the high temporal frequencies and coarse sampling that leads to severe spatial aliasing. We have investigated deep learning as a tool for the reconstruction problem, beyond spatial aliasing. Our method is based on a convolutional neural network (CNN) approach trained in the wavelet domain that is used to reconstruct the wavefield across the streamers. We determine the performance of the proposed method on broadband synthetic data and TopSeis field data from the Barents Sea. From our synthetic example, we find that the CNN can be learned in the inline direction and applied in the crossline direction, and that the approach preserves the characteristics of the geologic model in the migrated section. In addition, we compare our method to an industry-standard Fourier-based interpolation method, in which the CNN approach shows an improvement in the root-mean-square (rms) error close to a factor of two. In our field data example, we find that the approach reconstructs the wavefield across the streamers in the shot domain, and it displays promising characteristics of a reconstructed 3D wavefield

Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction

In 3D marine seismic acquisition, the seismic wavefield is not sampled uniformly in the spatial directions. This leads to a seismic wavefield consisting of irregularly and sparsely populated traces with large gaps between consecutive sail-lines especially in the near-offsets. The problem of reconstructing the complete seismic wavefield from a subsampled and incomplete wavefield, is formulated as an underdetermined inverse problem. We investigate unsupervised deep learning based on a convolutional neural network (CNN) for multidimensional wavefield reconstruction of irregularly populated traces defined on a regular grid. The proposed network is based on an encoder-decoder architecture with an overcomplete latent representation, including appropriate regularization penalties to stabilize the solution. We proposed a combination of penalties, which consists of the L2-norm penalty on the network parameters, and a first- and second-order total-variation (TV) penalty on the model. We demonstrate the performance of the proposed method on broad-band synthetic data, and field data represented by constant-offset gathers from a source-over-cable data set from the Barents Sea. In the field data example we compare the results to a full production flow from a contractor company, which is based on a 5D Fourier interpolation approach. In this example, our approach displays improved reconstruction of the wavefield with less noise in the sparse near-offsets compared to the industry approach, which leads to improved structural definition of the near offsets in the migrated sections