The lithospheric folding model applied to the Bighorn uplift during the Laramide orogeny

ABSTRACT The Bighorn uplift, Wyoming, developed in the Rocky Mountain foreland during the 75–55 Ma Laramide orogeny. It is one of many crystalline-cored uplifts that resulted from low-amplitude, large-wavelength folding of Phanerozoic strata and the basement nonconformity (Great Unconformity) across Wyoming and eastward into the High Plains region, where arch-like structures exist in the subsurface. Results of broadband and passive-active seismic studies by the Bighorn EarthScope project illuminated the deeper crustal structure. The seismic data show that there is substantial Moho relief beneath the surface exposure of the basement arch, with a greater Moho depth west of the Bighorn uplift and shallower Moho depth east of the uplift. A comparable amount of Moho relief is observed for the Wind River uplift, west of the Bighorn range, from a Consortium for Continental Reflection Profiling (COCORP) profile and teleseismic receiver function analysis of EarthScope Transportable Array seismic data. The amplitude and spacing of crystalline-cored uplifts, together with geological and geophysical data, are here examined within the framework of a lithospheric folding model. Lithospheric folding is the concept of low-amplitude, large-wavelength (150–600 km) folds affecting the entire lithosphere; these folds develop in response to an end load that induces a buckling instability. The buckling instability focuses initial fold development, with faults developing subsequently as shortening progresses. Scaled physical models and numerical models that undergo layer-parallel shortening induced by end loads determine that the wavelength of major uplifts in the upper crust occurs at approximately one third the wavelength of folds in the upper mantle for strong lithospheres. This distinction arises because surface uplifts occur where there is distinct curvature upon the Moho, and the vergence of surface uplifts can be synthetic or antithetic to the Moho curvature. In the case of the Bighorn uplift, the surface uplift is antithetic to the Moho curvature, which is likely a consequence of structural inheritance and the influence of a preexisting Proterozoic suture upon the surface uplift. The lithospheric folding model accommodates most of the geological observations and geophysical data for the Bighorn uplift. An alternative model, involving a crustal detachment at the orogen scale, is inconsistent with the absence of subhorizontal seismic reflectors that would arise from a throughgoing, low-angle detachment fault and other regional constraints. We conclude that the Bighorn uplift—and possibly other Laramide arch-like structures—is best understood as a product of lithospheric folding associated with a horizontal end load imposed upon the continental margin to the west.

Effective diffraction separation using the improved optimal rank-reduction method

Seismic diffractions encoding subsurface small-scale geologic structures have great potential for high-resolution imaging of subwavelength information. Diffraction separation from the dominant reflected wavefields still plays a vital role because of the weak energy characteristics of the diffractions. Traditional rank-reduction methods based on the low-rank assumption of reflection events have been commonly used for diffraction separation. However, these methods using truncated singular-value decomposition (TSVD) suffer from the problem of reflection-rank selection by singular-value spectrum analysis, especially for complicated seismic data. In addition, the separation problem for the tangent wavefields of reflections and diffractions is challenging. To alleviate these limitations, we propose an effective diffraction separation strategy using an improved optimal rank-reduction method to remove the dependence on the reflection rank and improve the quality of separation results. The improved rank-reduction method adaptively determines the optimal singular values from the input signals by directly solving an optimization problem that minimizes the Frobenius-norm difference between the estimated and exact reflections instead of the TSVD operation. This improved method can effectively overcome the problem of reflection-rank estimation in the global and local rank-reduction methods and adjusts to the diversity and complexity of seismic data. The adaptive data-driven algorithms show good performance in terms of the trade-off between high-quality diffraction separation and reflection suppression for the optimal rank-reduction operation. Applications of the proposed strategy to synthetic and field examples demonstrate the superiority of diffraction separation in detecting and revealing subsurface small-scale geologic discontinuities and inhomogeneities.

Estimation of Holocene bedload sedimentation rate in a paleo-drowned river-valley (Middle Harbour, Sydney estuary, Australia)

Middle Harbour is a drowned-river valley located adjacent to the larger Sydney estuary, Australia. Extensive, high-resolution seismic data were correlated with borehole, land use, topographical, and geological data to calculate the mass of genetically different sediment deposits in Middle Harbour. The Harbour follows a well-defined drowned river-valley structure featuring small fluvial bedload delta deposits in the upper reaches of the embayments, a deep, central extensive mud basin overlying transgressive basal accumulations and a large flood-tide delta at the entrance. Deposits of an estimated 5,094 t of bedload, 21,143 t of suspended sediment and 5,947 t of transgressive basal material located in the estuary provided sedimentation rates of 0.68 t y-1, 1.29 t y-1, and 2.86 t y -1 respectively. These rates, determined from measured accumulations, were surprisingly low and substantially smaller than modelled rates. However, low sedimentation rates for suspended material may be due to fine sediment escaping over the top of the marine tidal delta, which effectively traps all bedload material from exiting the Harbour. Results of this study indicate that Holocene bedload sedimentation in Middle Harbour was slow and regular until a rapid increase after urbanisation commenced in the catchment. Most pre-Holocene material was eroded from Middle Harbour during the Last Glacial period with sediment currently present in the estuary having been deposited since sea-level recovery.

Estimation of Seismic Wave Attenuation from 3D Seismic Data: A Case Study of OBC Data Acquired in an Offshore Oilfield

Previous studies performed in Abu Dhabi oilfields, United Arab Emirates, revealed the direct link of seismic wave attenuation to petrophysical properties of rocks. However, all those studies were based on zero offset VSP data, which limits the attenuation estimation at one location only. This is due to the difficulty of estimating attenuation from 3D seismic data, especially in carbonate rocks. To overcome this difficulty, we developed a workflow based on the centroid frequency shift method and Gabor transform which is optimized by using VSP data. The workflow was applied on 3D Ocean Bottom Cable seismic data. Distinct attenuation anomalies were observed in highly heterogeneous and saturated zones, such as the reservoirs and aquifers. Scattering shows significant contribution in attenuation anomalies, which is unusual in sandstones. This is due to the complex texture and heterogeneous nature of carbonate rocks. Furthermore, attenuation mechanisms such as frictional relative movement between fluids and solid grains, are most likely other important causes of attenuation anomalies. The slight lateral variation of attenuation reflects the lateral homogeneous stratigraphy of the oilfield. The results demonstrate the potential of seismic wave attenuation for delineating heterogeneous zones with high fluid content, which can substantially help for enhancing oil recovery.

Innovative application of full-waveform inversion applied to extended wide-azimuth marine streamer seismic in a complex salt environment

We demonstrated the application of full-waveform inversion (FWI) guided velocity model building to an extended wide-azimuth towed streamer (EWATS) seismic data set in the Gulf of Mexico. Field data were collected over a historically challenging imaging area, colloquially called the “grunge zone” due to the formation of a compressional allosuture emplaced between two colliding salt sheets. These data had a poor subsalt image below the suture with conventional narrow-azimuth data. Additional geologic complexities were observed including high-velocity carbonate carapace near the top of salt and multiple intrasalt sedimentary inclusions. As such, improved seismic imaging was required to plan and execute wells targeting subsalt strata. Significant improvements to the velocity model and subsalt image were evident with wide-azimuth towed streamer and later EWATS data using conventional top-down velocity model building approaches. Then, high-impact improvements were made using EWATS data with an FWI velocity model building workflow; this study represented an early successful application of FWI used to update salt body geometries from streamer seismic data, in which many past applications were limited to improving sedimentary velocities. Later petrophysical data verified the new FWI-derived model, which had significantly increased confidence in the structural and stratigraphic interpretation of subsalt reservoir systems below the grunge zone.

A Natural Images Pre-Trained Deep Learning Method for Seismic Random Noise Attenuation

Seismic field data are usually contaminated by random or complex noise, which seriously affect the quality of seismic data contaminating seismic imaging and seismic interpretation. Improving the signal-to-noise ratio (SNR) of seismic data has always been a key step in seismic data processing. Deep learning approaches have been successfully applied to suppress seismic random noise. The training examples are essential in deep learning methods, especially for the geophysical problems, where the complete training data are not easy to be acquired due to high cost of acquisition. In this work, we propose a natural images pre-trained deep learning method to suppress seismic random noise through insight of the transfer learning. Our network contains pre-trained and post-trained networks: the former is trained by natural images to obtain the preliminary denoising results, while the latter is trained by a small amount of seismic images to fine-tune the denoising effects by semi-supervised learning to enhance the continuity of geological structures. The results of four types of synthetic seismic data and six field data demonstrate that our network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.