scholarly journals Reverse Time Migration of Vertical Cable Seismic Data to Image Hydrate-Bearing Sediments With High Resolution

2021 ◽  
Vol 9 ◽  
Author(s):  
Linfei Wang ◽  
Huaishan Liu ◽  
Zhong Wang ◽  
Jin Zhang ◽  
Lei Xing ◽  
...  
Keyword(s):  
High Resolution ◽  
Natural Gas ◽  
Gas Hydrates ◽  
Velocity Analysis ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Natural Gas Hydrates ◽  
High Resolution Images ◽  
Hydrate Bearing Sediments

Marine vertical cable seismic (VCS) is a promising survey technique for submarine complex structure imaging and reservoir monitoring, which uses vertical arrays of hydrophones deployed near the seafloor to record seismic wavefields in a quiet environment. Recently, we developed a new type of distributed VCS system for exploration and development of natural gas hydrates preserved in shallow sediments under the seafloor. Using this system and air-gun sources, we accomplished a 3D VCS yield data acquisition for gas hydrates exploration in the Shenhu area, South China Sea. In view of the characteristics of VCS geometry, we implement reverse time migration (RTM) on a common receiver gather to obtain high-resolution images of marine sediments. Due to the unique acquisition method, it is asymmetrical for the reflection path between the sources and the receivers in the VCS survey. Therefore, we apply accurate velocity analysis to common scatter point (CSP) gathers generated from common receiver gathers instead of the conventional velocity analysis based on common depth point gathers. RTM with this reliable velocity model results in high-resolution images of submarine hydrate-bearing sediments in deep water conditions. The RTM imaging section clearly shows the bottom simulating reflector (BSR) and also the reflection characteristics of the hydrate-bearing sediments filled with consolidated hydrates. Moreover, its resolution is relative to that of acoustic logging curves from the nearby borehole, and this imaging section is well consistent with the synthetic seismogram trace generated by the logging data. All these results reveal that VCS is a great potential technology for exploration and production of marine natural gas hydrates.

scholarly journals Deep learning for enhancing multi-source reverse time migration

2022 ◽  
Author(s):  
Yaxing Li ◽  
Xiaofeng Jia ◽  
Xinming Wu ◽  
Zhicheng Geng
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Plane Wave ◽  
Computational Efficiency ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Numerical Examples ◽  
Time Migration ◽  
High Resolution Images ◽  
Wave Migration

<p>Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built 505 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.</p>

scholarly journals Deep learning for enhancing multi-source reverse time migration

2022 ◽  
Author(s):  
Yaxing Li ◽  
Xiaofeng Jia ◽  
Xinming Wu ◽  
Zhicheng Geng
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Plane Wave ◽  
Computational Efficiency ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Numerical Examples ◽  
Time Migration ◽  
High Resolution Images ◽  
Wave Migration

<p>Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built 505 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.</p>

Mirror reverse time migration using vertical cable seismic data for methane hydrate exploration

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. B447-B460
Author(s):  
Ehsan Jamali H. ◽  
Masafumi Katou ◽  
Kenji Tara ◽  
Eiichi Asakawa ◽  
Hitoshi Mikada
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Methane Hydrate ◽  
The Sea Of Japan ◽  
Velocity Analysis ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Shallow Sediments

Gas hydrates are located in the permafrost and in deepwater shallow sediments, where low temperature and high pressure satisfy the stability conditions of methane clathrates to remain as solid compounds. Hydrates are found in a form of fine-layered or altered-layered structure with hiatuses and necessitate high-resolution surveys, which may not be achieved by conventional marine acquisition using towed streamers. We have developed a recent case study in which the vertical cable seismic (VCS) method has been used for high-resolution subseafloor imaging using a set of buoyed vertical-arrayed receivers that are anchored to the seafloor. The observation close to the target in the deepwater environment provides a higher signal-to-noise ratio and higher resolution. The primary reflections, however, could not achieve reliable depth images in the data processing due to their limited subsurface coverage. We used a reverse time migration (RTM) implementation of mirror imaging to extend the spatial subsurface coverage by using receiver ghost reflections. Because conventional velocity analysis methods are not applicable to the VCS survey due to the asymmetrical reflection path between the source and receiver, we implemented seismic interferometry and generated virtual surface seismic data from VCS data for velocity analysis. To preserve the resolution, amplitudes, and phase characteristics, we applied mirror RTM on the ghost reflections in the original VCS data rather than imaging the virtual data. The introduced case study using a VCS survey for identifying the methane hydrate system of the Umitaka Spur in the Sea of Japan led to high-resolution images, which suggest that a large gas chimney exists beneath a pockmark and is responsible for transferring methane gas from a deep hydrocarbon source to the shallow sediments. A bottom-simulating reflector as the base of the gas hydrate stability zone was also imaged.

Near-Borehole Imaging Using Full-Waveform Sonic Data

2021 ◽  
Author(s):  
Hala Alqatari ◽  
Thierry-Laurent Tonellot ◽  
Mohammed Mubarak
Keyword(s):  
Seismic Data ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Full Waveform ◽  
Time Migration ◽  
Imaging Results ◽  
High Resolution Images ◽  
Recorded Data ◽  
The Impact ◽  
Processing Steps

Abstract This work presents a full waveform sonic (FWS) dataset processing to generate high-resolution images of the near-borehole area. The dataset was acquired in a nearly horizontal well over a distance of 5400 feet. Multiple formation boundaries can be identified on the final image and tracked at up to 200 feet deep, along the wellbore's trajectory. We first present a new preprocessing sequence to prepare the sonic data for imaging. This sequence leverages denoising algorithms used in conventional surface seismic data processing to remove unwanted components of the recorded data that could harm the imaging results. We then apply a reverse time migration algorithm to the data at different processing stages to assess the impact of the main processing steps on the final image.

Angle-domain common-image gathers from plane-wave least-squares reverse time migration

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Chuang Li ◽  
Zhaoqi Gao ◽  
Jinghuai Gao ◽  
Feipeng Li ◽  
Tao Yang
Keyword(s):  
Inverse Problem ◽  
Plane Wave ◽  
Least Squares ◽  
Migration Velocity ◽  
Velocity Analysis ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Migration Velocity Analysis ◽  
Amplitude Versus

Angle-domain common-image gathers (ADCIGs) that can be used for migration velocity analysis and amplitude versus angle analysis are important for seismic exploration. However, because of limited acquisition geometry and seismic frequency band, the ADCIGs extracted by reverse time migration (RTM) suffer from illumination gaps, migration artifacts, and low resolution. We have developed a reflection angle-domain pseudo-extended plane-wave least-squares RTM method for obtaining high-quality ADCIGs. We build the mapping relations between the ADCIGs and the plane-wave sections using an angle-domain pseudo-extended Born modeling operator and an adjoint operator, based on which we formulate the extraction of ADCIGs as an inverse problem. The inverse problem is iteratively solved by a preconditioned stochastic conjugate gradient method, allowing for reduction in computational cost by migrating only a subset instead of the whole dataset and improving image quality thanks to preconditioners. Numerical tests on synthetic and field data verify that the proposed method can compensate for illumination gaps, suppress migration artifacts, and improve resolution of the ADCIGs and the stacked images. Therefore, compared with RTM, the proposed method provides a more reliable input for migration velocity analysis and amplitude versus angle analysis. Moreover, it also provides much better stacked images for seismic interpretation.

scholarly journals Imaging Complex Subsurface Structures for Geothermal Exploration at Pirouette Mountain and Eleven-Mile Canyon in Nevada

2021 ◽  
Vol 9 ◽  
Author(s):  
Yunsong Huang ◽  
Miao Zhang ◽  
Kai Gao ◽  
Andrew Sabin ◽  
Lianjie Huang
Keyword(s):  
High Resolution ◽  
Least Squares ◽  
Seismic Data ◽  
Seismic Wave Propagation ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Geothermal Exploration ◽  
Subsurface Structures ◽  
Time Migration ◽  
Ground Roll

Accurate imaging of subsurface complex structures with faults is crucial for geothermal exploration because faults are generally the primary conduit of hydrothermal flow. It is very challenging to image geothermal exploration areas because of complex geologic structures with various faults and noisy surface seismic data with strong and coherent ground-roll noise. In addition, fracture zones and most geologic formations behave as anisotropic media for seismic-wave propagation. Properly suppressing ground-roll noise and accounting for subsurface anisotropic properties are essential for high-resolution imaging of subsurface structures and faults for geothermal exploration. We develop a novel wavenumber-adaptive bandpass filter to suppress the ground-roll noise without affecting useful seismic signals. This filter adaptively exploits both characteristics of the lower frequency and the smaller velocity of the ground-roll noise than those of the signals. Consequently, this filter can effectively differentiate the ground-roll noise from the signal. We use our novel filter to attenuate the ground-roll noise in seismic data along five survey lines acquired by the U.S. Navy Geothermal Program Office at Pirouette Mountain and Eleven-Mile Canyon in Nevada, United States. We then apply our novel anisotropic least-squares reverse-time migration algorithm to the resulting data for imaging subsurface structures at the Pirouette Mountain and Eleven-Mile Canyon geothermal exploration areas. The migration method employs an efficient implicit wavefield-separation scheme to reduce image artifacts and improve the image quality. Our results demonstrate that our wavenumber-adaptive bandpass filtering method successfully suppresses the strong and coherent ground-roll noise in the land seismic data, and our anisotropic least-squares reverse-time migration produces high-resolution subsurface images of Pirouette Mountain and Eleven-Mile Canyon, facilitating accurate fault interpretation for geothermal exploration.

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