Geophysical Prospecting最新文献

Audun Bakk, Marcin Duda, Xiyang Xie, Jørn F. Stenebråten, Hong Yan, Colin MacBeth, Rune M. Holt. Third-order elasticity of transversely isotropic field shales. Geophysical Prospecting. 2024; 72:1049–1073. https://doi.org/10.1111/1365-2478.13446

The wet bulk densities of the tested shale samples were not included in the original article. These values, along with the measurement method, are provided here for clarity.

Wet bulk densities of tested shales:

The wet bulk density was determined as the ratio of the sample's weight to its total volume, measured under ambient conditions prior to testing, including the native fluid content of the well-preserved sample.

Song H, Liu Y, Yang J. (2024) Elastic full waveform inversion for tilted transverse isotropic media: A multi-step strategy accounting for a symmetry axis tilt angle. Geophysical Prospecting, 72(7), 2486-2503.

On the left side of the first page, in the Funding information, the text “National Natural Science Foundation of China, Grant/Award Numbers: 42374136, 41930105, 42004096; Fundamental Research Funds for the Central Universities of China” is incorrect. This should read: “National Natural Science Foundation of China, Grant/Award Numbers: 41930105, 42374126, 42374136, 42004096; Fundamental Research Funds for the Central Universities of China”.

In the Acknowledgements section, the text “We thank the editors and three reviewers for their constructive comments. We are also grateful to Professor Chao Huang and Professor Liangguo Dong for their help. This work was supported by grants 41930105, 42004296 and 42374126 of the National Natural Science Foundation of China, as well as Fundamental Research Funds for the Central Universities of China.” is incorrect. This should read: “We thank the editors and three reviewers for their constructive comments. We are also grateful to Professor Chao Huang and Professor Liangguo Dong for their help. This work was supported by grants 41930105, 42374126, 42374136, and 42004096 of the National Natural Science Foundation of China, as well as Fundamental Research Funds for the Central Universities of China.”

We apologize for this error.

Deep learning-based methods have performed well in seismic waveform inversion tasks in recent years, while the need for velocity models as labels has somewhat limited their application. Unsupervised learning allows us to train the neural network without labels. When inverting seismic velocity models from observed data, labels are often unavailable for real data. To address this problem and improve network generalization, we introduce a multi-scale strategy to enhance the performance of unsupervised learning. The first ‘multi-scale’ is derived from the conventional full waveform inversion strategy, in which the low-, middle- and high-frequency inversion results are successively predicted during the network training. Another ‘multi-scale’ is to introduce multi-scale similarity as an additional data loss term to improve the inversion results. With 12,000 samples from the overthrust model, our method obtains comparable results with the supervised learning method and outperforms unsupervised methods that rely only on the mean square error as a loss function. We compare the performance of the proposed method with multi-scale full waveform inversion on the Marmousi model, and the proposed method achieves better results at low- and middle-frequencies, and, as a result, it provides good initial models for further full waveform inversion updates.

Seismic responses from a horizontal poroelastic layer provide chances to detect fluids and characterize reservoirs. The poroelastic layer can be considered a thin poroelastic bed if the layer's thickness is less than about one-eighth of the P-wave wavelength. Most previous theoretical studies on the reflection and transmission of waves in a model containing a thin poroelastic bed employ fluid or poroelastic medium as the overlying media. Existing approximate formulas of PP-wave reflection coefficients are given for P-wave normal-incidence. Thus, this paper derived the wave reflection and transmission approximate formulas of a thin poroelastic bed between two elastic half-spaces with P-wave oblique incident. First, we illustrated the exact reflection and transmission matrix equations for P-wave incidents based on poroelasticity theory and the boundary conditions. Assuming the poroelastic bed's thickness is far less than wavelengths of S- and P-waves, approximate reflection and transmission formulas are expanded in Taylor series centred at value of the parameter defined as the product of angular frequency, thickness and slowness. Numerical results show that the thinner the poroelastic layer, the closer the approximate reflection and transmission coefficients are to the exact ones. The approximate formulas are valid for small and medium angles. Approximated PP-wave reflection and transmission coefficients are closer to the exact values than those of the converted waves, which is caused by the fact that P-wave has a lower slowness than S-wave.

Seismic datasets acquired in onshore basins typically present a great challenge for seismic depth imaging due to the poor quality of the prestack data caused by inherent acquisition difficulties and, in particular, the distortion of the seismic signal caused by topography and heterogeneity of the weathering zone. In this seismic data, the standard static correction does not give satisfactory results in depth imaging. Wave-equation-based redatuming is an alternative solution to this problem, as it correctly moves the data measured on the terrain surface to a new flat datum. But most redatuming techniques require knowledge of an accurate near-surface velocity model. A new workflow for depth imaging of land data is proposed by combining Kirchhoff single-stack redatuming and prestack data enhancement by common-reflection surface stack method. A major advantage of this redatuming method is that it only requires a good approximation of the near-surface velocity model to properly remove distortions in the seismic signal caused by the rugged topography and weathering zone. A new algorithm is introduced to apply Kirchhoff single-stack redatuming to multi-coverage land seismic data. Common-reflection surface-based prestack data enhancement is applied after redatuming to attenuate radon noise and enhance coherent event. The proposed workflow has been successfully applied to land seismic data from the Parnaíba Basin in northeastern Brazil, transforming surface-referenced prestack data to a new flat datum. By applying common-reflection surface-based enhancement to the redatumed prestack data, a significant improvement in signal-to-noise ratio and enhancement of reflections was achieved. The depth-migrated image confirms the great improvement in quality, where the reflectors show strong enhancement and better continuity throughout the section, compared to the migrated image obtained from the only redatumed data. This improvement in quality was important for the interpretation of the main reflectors of the geological formations.

High-quality seismic data play a crucial role in accurately interpreting tectonic and lithologic features such as small faults, river margins and thin beds. Over the past decades, researchers have developed numerous methods to enhance seismic resolution and signal-to-noise ratio. However, the benefits of quality-improved seismic data for seismic interpretation have received limited attention. In response, we propose a generative adversarial network–based algorithm to enhance seismic quality and assess how this algorithm improves the accuracy of both machine learning–based and manual fault identification. For machine learning–based fault identification, we integrate a resolution enhancement and noise attenuation neural network (HRNet) with a fault identification neural network (FaultNet). A raw seismic image is first fed into the trained HRNet to obtain a resolution-enhanced and noise-suppressed image, which is then input into the trained FaultNet to produce the high-resolution fault probability map. For manual fault identification, we enlisted three interpreters with geophysical backgrounds to annotate faults on seismic images both before and after HRNet enhancement. Comparison experiments on three field seismic samples show that our method generates more accurate, cleaner and sharper fault probability maps than directly feeding raw seismic images into FaultNet. In addition, our workflow outperforms both the milestone fault identification method and state-of-the-art Transformer-based neural networks, particularly in detecting small-scale faults. Furthermore, the HRNet-enhanced seismic images help interpreters identify small- and medium-scale faults with reduced uncertainty. In the future, HRNet-enhanced seismic data can be applied to a broader range of high-precision seismic interpretation tasks, including horizon picking, channel boundary detection and attribute inversion.

We develop a three-dimensional inversion code to image the resistivity distribution of the subsurface from frequency-domain controlled-source electromagnetic data. Controlled-source electromagnetic investigations play an important role in many different geophysical prospecting applications. To evaluate controlled-source electromagnetic data collected with complex measurement setups, advanced three-dimensional modelling and inversion tools are required.

We adopt a preconditioned non-linear conjugate gradient algorithm to enable three-dimensional inversion of impedance tensor and vertical magnetic transfer function data produced by multiple sets of two independent active sources. Forward simulations are performed with a finite-element solver. Increased sensitivities at source locations can optionally be counteracted with a weighting function in the regularization term to reduce source-related anomalies in the resistivity model. We investigate the capabilities of the inversion code using one synthetic and one field example. The results demonstrate that we can produce reliable subsurface models, although data sets from single pairs of independent sources remain challenging.

In order to improve the identification and characterization of underground fractured reservoirs, seismic inversion for elastic properties and fracture indicators is required. To improve the accuracy of seismic inversion, model constraints are necessary. Model constraints of P- and S-wave moduli can be provided by well logging data; however, model constraints of fracture weaknesses are often unavailable. To obtain model constraints of fracture weaknesses, we propose a two-stage inversion method, which is implemented as (1) estimating azimuthal elastic impedance (AEI) and fracture facies using partially incidence-angle-stacked seismic data at different azimuths; and (2) using the estimated azimuthal elastic impedance to predict P- and S-wave moduli, density and fracture weaknesses, which is constrained by models constructed using the estimated fracture facies. In the first stage, we use Gaussian mixture prior distribution to obtain azimuthal elastic impedance of different incidence angles and azimuths, and we also predict fracture facies combining the obtained azimuthal elastic impedance and seismic data. In the second stage, we implement the Bayesian maximum a posterior inversion for estimating unknown parameter vectors. We apply the proposed inversion method to noisy synthetic seismic data, which illustrates the inversion method is robust even in the case of a signal-to-noise ratio of 1. Tests on real data reveal that reliable results of P- and S-wave moduli and fracture weaknesses are obtained, which verifies that the inversion method is a valuable tool for generating reliable fracture indicators from azimuthal seismic data for identifying underground fractured reservoirs.

Fault detection is a crucial step in seismotectonic interpretation and oil–gas exploration. In recent years, deep learning has gradually proven to be an effective approach for detecting faults. Due to complex geological structures and seismic noise, detection results of such approaches remain unsatisfactory. In this study, we propose a hybrid network (NRA-SANet) that integrates a self-attention mechanism into a nested residual attention network for a three-dimensional seismic fault segmentation task. In NRA-SANet, the nested residual coding structure is designed to fuse multi-scale fault features, which can fully mine fine-grained fault information. The two-head self-attention decoding structure is designed to construct long-distance fault dependencies from different feature representation subspaces, which can enhance the understanding of the model regarding the global fault distribution. In order to suppress the interference of seismic noise, we propose a fault-attention module and embed it into the model. It utilizes the weighted and the separate-and-reconstruct channel strategy to improve the model sensitivity to fault areas. Experiments demonstrate that NRA-SANet exhibits strong noise robustness, while it can also detect more continuous and more small-scale faults than other approaches on field seismic data. This study provides a new idea to promote the development of seismic interpretation.