Geophysical Prospecting最新文献

Considering the anisotropy of the earth media is helpful in reducing the depth error between seismic and drilling and providing reliable imaging data for seismic interpretation and inversion. Transversely isotropic media with a vertical axis of symmetry are the most common type of anisotropic media and have been under constant study. The separation of the P- and S-wavefields, which affects the accuracy of elastic wave imaging and the inversion in transversely isotropic media with a vertical axis of symmetry, is a hot research topic. Among the commonly used wavefield decoupling methods, the S-wave is typically obtained by subtracting the P-wave from the total wavefield. However, this kind of wavefield decoupling method often leads to severe P-wave crosstalk in the separated S-wavefield; thus, it needs further development. In this paper, the principle that the divergence of the S-wave is zero is employed to solve the unresolved S-wave elastic parameters of the S-wave stiffness matrix by utilizing the modified zero-order pseudo-Helmholtz decomposition operator. The obtained S-wave elastic parameters are employed to construct the qS-wave stress and facilitate the derivation of the relationship between the qS-wave particle velocity and the qS-wave stress. Furthermore, a qS-wave decoupling first-order velocity–stress equation, which matches the qP-wave decoupling first-order velocity–stress equation, is derived. By jointly using the decoupling equations of qS- and qP-wave, the separation of the P- and S-wavefields in transversely isotropic media with a vertical axis of symmetry is realized. The efficiency of the proposed method is demonstrated via numerical tests conducted to assess the wavefield separation. Moreover, a complex model is employed to perform elastic reverse-time migration, resulting in the acquisition of accurate imaging results for PP, PS, SP, and SS waves, without the presence of significant artefacts. The correctness of the qS-wave decoupling equation in transversely isotropic media with a vertical axis of symmetry is confirmed by the comparative tests using decoupling methods in isotropic media.

Detailed knowledge of the subsurface elastic properties might provide much-needed insight into the subduction-zone structure and the estimation of reservoir parameters. Compressional and shear wave velocities can be inverted by elastic full-waveform inversion using multicomponent ocean-bottom seismometer data. However, this process is computationally intensive, requiring massive, repeated simulations scaling with the number of sources. Although source–receiver reciprocity can streamline elastic full-waveform inversion, it cannot be applied by directly interchanging the positions of sources and ocean-bottom seismometers. To reduce the computational cost, we develop a source–receiver reciprocal elastic full-waveform inversion, in which the reciprocity in multicomponent data can be accomplished by transforming the data matching from the original geometry to a specific form under the reciprocal geometry. This approach significantly reduces computational costs from twice the number of sources, as seen in conventional elastic full-waveform inversion, to a scale with the count of ocean-bottom seismometers. The tests on synthetic data verify that the proposed reciprocal elastic full-waveform inversion maintains accuracy while improving computational efficiency, and further application on the field data from East China Sea also showcases the efficiency of the proposed method, resulting in a speed-up of 10 times.

The basic assumption of low-rank methods is that noise-free seismic data can be represented as a low-rank matrix. Effective noise reduction can be achieved through the low-rank approximation of Hankel matrices composed of the data. However, selecting the appropriate rank parameter and avoiding expensive singular value decomposition are two challenges that have limited the practical application of this method. In this paper, we first propose symplectic geometric decomposition that avoids singular value decomposition. The symplectic similarity transformation preserves the essence of the original time sequence as well as the signal's basic characteristics and maintains the approximation of the Hankel matrix. To select an appropriate rank, we construct the symplectic geometric entropy according to the distribution of eigenvalues and search for high-contributing eigenvalues to determine the needed rank parameter. Therefore, we provide an adaptive approach to selecting the rank parameter by the symplectic geometric entropy method. The synthetic examples and field data results show that our method significantly improves the computational efficiency while adaptively retaining more effective signals in complex structures. Therefore, this method has practical application value.

Organic-rich shales contain large amounts of oil and gas and are anisotropic because of fine-scale layering and the partial alignment of organic matter and anisotropic clay minerals with the bedding. An example is the Wolfcamp Shale in the Permian Basin. Elastic anisotropy needs to be accounted for in the characterization of such formations using seismic data and plays a role in hydraulic fracturing and in the evaluation of stress changes and geomechanical effects resulting from production. Using extensive well log data acquired in the Midland Basin, the eastern sub-basin of the Permian Basin, we estimate and compare the elastic anisotropy in the Middle and Upper Wolfcamp Shale by combining data from a vertical pilot well with two lateral wells, one (6SM) drilled in the Middle Wolfcamp and one (6SU) drilled in the Upper Wolfcamp. The data used were acquired at the Hydraulic Fracture Test Site 1, located in the eastern part of the Midland Basin. Thomsen's anisotropy parameter $�\gamma$ calculated from the fast and slow shear sonic is higher on average for the 6SM lateral than for 6SU, consistent with there being less carbonate content in 6SM than in 6SU. However, the anisotropy parameter $�\gamma$ in some regions with higher carbonate content in well 6SU is higher than in well 6SM. This may indicate the influence of natural fractures. The primary set of steeply dipping fractures observed in the lateral wells at Hydraulic Fracture Test Site 1 acts to increase $�\gamma$ if the ratio of the normal-to-shear fracture compliance is less than about 0.5. Sub-horizontal fractures may also increase $�\gamma$ and could affect the vertical extent of hydraulic fractures. Relations between elastic moduli C₃₃ and C₅₅ in the Upper and Lower Wolfcamp in a vertical pilot well allow C₃₃ to be predicted in a lateral well using measurements of C₅₅ in that well. Comparison of Thomsen's anisotropy parameters $�\gamma$ and $�\epsilon$, with $�\gamma$

Controlled-source electromagnetic methods are applied to survey the electrical resistivity distribution of the subsurface. This work compares normalized electromagnetic field components and transfer functions such as impedance tensors and vertical magnetic transfer functions generated by two independent source polarizations as input data for three-dimensional inversion. As most other available inversion codes allow for inverting only one of the mentioned input data types, it is unclear which data type is preferable for controlled-source electromagnetic inversion. Our three-dimensional non-linear conjugate gradient inversion code can handle both input data types, facilitating a comparison of normalized electromagnetic field components and transfer functions inversion. Examining inversion results for a three-dimensional synthetic model with two anomalies, we infer that the transfer functions inversion is favourable for recovering the overall resistivity distribution below the receiver sites in fewer iterations. The inversion of normalized electromagnetic field components produces a sharper image of the anomalies and may be capable of detecting the resistivity distribution below the extended sources, which comes at the price of introducing a more heterogeneous background resistivity in the model.

Marine seismic data is often missing near offset information due to separation between the source and receiver cables. To solve this problem, a convolutional neural network is trained on synthetic seismic data to reconstruct the near offset gap. The synthetic data is created using a two-dimensional finite difference method within a heterogeneous velocity model. These synthetics are generated with a source-over-receiver acquisition geometry so that they contain complete near offset data. The convolutional neural network is then trained on input-target synthetic pairs where the inputs are common midpoint gathers with the near offset section removed, and the targets are the same gathers with the near offset section retained. Following training, the robustness of the method is investigated with regards to common midpoint data sorting, normal moveout correction and changes in the velocity model. It is found that training on common midpoint-sorted data results in 2.8 times lower error than training on shot gathers, that normal moveout correction of the training data makes no significant difference in error levels, and that the model can reconstruct realistic near offsets on synthetic data generated 10 km away within the heterogeneous velocity model. In field data testing, first a dataset with source-over-cable acquisition geometry from the Barents Sea is used to compare the reconstructed wavefields to ground truth values. Although the reconstructed amplitudes require minor scaling to match the true values, predictions on this dataset yield 2.5 times lower near offset reconstruction error compared to a simple Radon transform interpolation method. Furthermore, amplitude versus offset gradient and intercept sections from the Barents Sea dataset are estimated with half the error when including the convolutional neural network-predicted near offset data, compared to only using the conventionally-acquirable portion of the data (beyond 112.5 m of offset). In a secondary field data test, a conventional northern North Sea dataset is used to demonstrate how the method may be applied in practice. Here, the convolutional neural network generates more realistic predictions than the Radon method, and the gradient and intercept sections calculated using the convolutional neural network-predicted traces have higher signal-to-noise ratios than the sections calculated using only the original data. The combination of high-quality synthetic training data and interpolation in the common midpoint domain enables near offset reconstruction at significant depth (1 s of two-way traveltime or more), which is demonstrated in both synthetic and field examples.

Delimiting salt inclusions from migrated images during the velocity model building flow is a time-consuming activity that depends on highly human-curated analysis and is subject to interpretation errors or limitations of the images and methods available. We propose a supervised deep learning based method to include three-dimensional salt geometries in the velocity models. We compare two convolutional networks – based on the U-Net architecture – which can process three-dimensional seismic data. One architecture uses three-dimensional convolutional kernels, and the other has convolutional long short-term memory units. Each architecture requires specific preprocessing steps which affects their training and predictive performance. Both proposed architectures use subsurface offset gathers obtained from reverse time migration with an extended imaging condition as input and are trained to predict the salt inclusions. The velocity model used in migration is a reasonable approximation of sediment velocity but without salt inclusions. Thus, the migration model and, consequently, the migrated images are inaccurate due to the absence of the salt inclusion in the model using just the sediment velocity information for the segmentation. A similar salt inclusion methodology was previously validated for two-dimensional approaches; we extend it to the three-dimensional case. Our approach relies on subsurface common image gathers to focus the sediments' reflections around the zero offset and spread salt reflections' energy over large subsurface offsets. The results show that both proposed network models can accurately delineate the salt bodies in the test models, but when evaluating the trained networks for the three-dimensional SEG/EAGE salt model, the architecture with convolutional long short-term memory units has proven to generalize better.

The accuracy and computational efficiency of full waveform forward modelling in poroelastic media are crucial for microseismic monitoring. It enables intuitive, precise and efficient simulation of subsurface responses, thereby improving the reliability of moment tensor inversion and seismic source mechanism interpretation. Additionally, it reflects the role of fluid effects in waveform evolution. In this paper, based on the Biot mechanism, we derived the first-order velocity–stress equation of poroelastic media and discretized the model using a rotated staggered grid. The rotated staggered grid can well adapt to anisotropic media with high contrast parameters. We provide the finite difference formula based on graphic process unit–acceleration and moment tensor and also provide the graphic process unit workflow for forward modelling of anisotropic poroelastic media. First, two models with different grid sizes were run based on single graphic process unit, 1-thread Central Processing Unit (CPU) and 16-thread CPU. The results show that the speedup factors are approximately 14.3 and 3.7, respectively, compared with the running time of 1-thread CPU and 16-thread CPU. Then, we compare and evaluate the response of three basic source mechanisms (isotropic expansion, double couple and compensated linear vector dipole) in the model. The comparison of analytical and numerical results verifies the effectiveness of the method. Furthermore, wavefield snapshots of two typical anisotropic media (vertical transversely isotropic and horizontal transversely isotropic) are analysed to correspond to different moment tensor sources. The results showed that the source mechanism does not change the isotropic and anisotropic plane and the wave travel time, but it does change the polarization amplitude of the velocity component. The attenuation of slow qP-wave increases along with the increase of the value of viscosity. The effect of permeability on wavefield appears with the opposite effect of viscosity. Finally, the seismic waveform differences between multi-layer elastic media and poroelastic media are compared and analysed. The results showed that the seismic wavefield and waveform of poroelastic media are more complex, the propagation speed of seismic waves is faster, but the attenuation of seismic waves is stronger.

When compared to traditional seismic data acquisition, irregular blended acquisition significantly promotes the acquisition efficiency. Yet, the blending noise of subsampled blended data introduces new obstacles for the subsequent processing of seismic data. Due to the predictability of linear events in the frequency–space domain, the constructed Hankel matrices exhibit low-rank characteristics. However, the blending noise of subsampled blended data increases the rank, so deblending and interpolation can be implemented via rank-reduction algorithms such as the singular spectrum analysis. The significant computing cost of the singular value decomposition, however, makes the traditional singular spectrum analysis inefficient. An alternative algorithm, known as the randomized singular spectrum analysis, employs the randomized singular value decomposition instead of the traditional singular value decomposition for rank-reduction. The randomized singular spectrum analysis significantly enhances the efficiency of the decomposition process, particularly when dealing with large Hankel matrices. There still remains some random noise when using the singular spectrum analysis or randomized singular spectrum analysis for subsampled blended data, because the noise subspace and signal subspace are coupled together. Thus, we incorporate a damping operator into the randomized singular value decomposition and propose a novel damped randomized singular spectrum analysis method. The damped randomized singular spectrum analysis combines the advantages of the randomized singular value decomposition and the damping operator to enhance the computational efficiency and suppress the remaining noise. Moreover, an iterative projected gradient descent strategy is introduced to achieve deblended and interpolated seismic data for subsequent processing. Examples from synthetic data and field data are used to demonstrate the effectiveness and superiority of the proposed damped randomized singular spectrum analysis method, which enhances the accuracy and efficiency during simultaneous deblending and interpolation.