Post-stack Seismic Data Research Articles

Qualitative and quantitative comparison of the genetic and hybrid genetic algorithm to estimate acoustic impedance from post-stack seismic data of Blackfoot field, Canada

SUMMARY In this study, seismic inversion is carried out using a genetic algorithm (GA) as well as a hybrid genetic algorithm (HGA) approach to optimize the objective function designed for the inversion. An HGA is a two steps coupled process, where a local optimization algorithm is applied to the best model obtained from each generation of the GA. The study aims to compare the qualitative as well as the quantitative performance of both methods to delineate the reservoir zone from the non-reservoir zone. Initially, the developed algorithm is tested on synthetic data followed by its application to real data. It is found that the HGA for synthetic data is providing more accurate and high-resolution subsurface information as compared with the conventional GA although the time taken later is less as compared with the former methods. The application to real data also shows very high-resolution subsurface acoustic impedance information. The interpretation of the impedance section shows a low impedance anomaly zone at (1055–1070) ms time interval with impedance ranging from (7500 to 9500) m s−1*g cc−1. The correlation between seismic and well data shows that the low impedance zone is characterized as a clastic glauconitic sand channel (reservoir zone). In seismic inversion using an HGA, one can delineate the areal extent of the reservoir zone from the non-reservoir zone more specifically as compared to the GA-derived impedance. The convergence time of HGA is 4.4 per cent more than GA and can be even more for larger seismic reflection data sets. Further, for a more detailed analysis of the reservoir zone and to cross-validate inverted results, an artificial neural network (ANN) is applied to data, and porosity volume is predicted. The analysis shows that the low impedance zone interpreted in inversion results are correlating with the high porosity zone found in ANN methods and confirm the presence of the glauconitic sand channel. This study is important in the aspect of qualitative as well as quantitative comparison of the performance of the GA and HGA to delineate sand channels.

Deep physics-aware stochastic seismic inversion

Seismic inversion allows the prediction of subsurface properties from seismic reflection data and is a key step in reservoir modeling and characterization. With the generalization of machine learning in geophysics, deep learning methods have been proposed as efficient seismic inversion methods. However, most of these methods lack a probabilistic approach to deal with the uncertainties inherent in the seismic inversion problem and/or rely on complete and representative training data, which often is partially or scarcely available. We have explored the ability of deep convolutional neural networks to extract meaningful and complex representations from spatially structured data, combined with geostatistical simulation, to efficiently invert poststack seismic data directly for high-resolution models of acoustic impedance. Our model incorporates physics constraints and sparse direct measurements while leveraging the use of imprecise but widely distributed indirect measurements as represented by the seismic data. The models generated with geostatistical simulation provide additional information with higher spatial resolution than the original seismic data and allow assessing uncertainty in the model predictions by generating multiple realizations of the subsurface properties. Our method can (1) provide an uncertainty assessment of the predictions, (2) model the complex and nonlinear relationship between data and model, (3) extend the seismic bandwidth at low and high ends of the frequency parameters spectrum, and (4) lessen the need for large, annotated training data. Our method is applied to a 1D synthetic example and a real 3D application example from a Brazilian reservoir. The predicted impedance models are compared with those obtained from a full iterative geostatistical seismic inversion. Our method allows retrieving similar models but has the advantage of generating alternative solutions in greater numbers, providing a larger exploration of the model parameter space in less time than the iterative geostatistical seismic inversion.

3-D quantitative seismic imaging of tectonically-influenced Middle-Eocene carbonates stratigraphic traps of SE-Asian basins using spectral decomposition: Implications for hydrocarbon exploration

Carbonates form energetic stratigraphic petroleum systems. The tectonic uplifting and subsidence significantly affect the overall reservoir configurations. During these stratigraphic scenarios, the prediction of accurate thickness for subsidence and uplifting zones becomes challenging. The band-limited seismic data interpretation tools fail to detect the zones of thinning and thickening of sediments along with uplifting and subsidence. The band-limited seismic does not have the tuning frequency, that can be used to predict the accurate vertical thickness of the thin-bedded carbonates. The constant wavelet-based transformation tool (WT) and inverted reservoir simulations have robust implications for the quantification of any depositional system. Especially, when the reservoir is shallow marine, the application of WT has resolved a variety of stratigraphic traps in terms of their seismic and sedimentological aspects. The stratigraphic traps are comprised of thin beds, which are not resolvable on conventional seismic. These traps require a specific tuning frequency, which is the key ingredient for the prediction of oil and gas inside them and can only be obtained by the implementation of the WT tool. Therefore, the WT, static stratigraphic thickening wedge models (SSTWM), and WT–based instantaneous spectral thickness modeling (ISTM) tools are applied on a gas field in Indus Basin, Pakistan. The 43-Hz CWT images the densely fractured and faulted network within the shoal reservoir limestone lenticular lens (SRLL) with an aerial extent of 208 km2 in southwest-northeast orientations. The band-limited attributes revealed poor imaging of sub-surface carbonate systems. The 43-Hz wavelet-based SSTWM predicts a 15-m-thick coarse-grained SRLL along subsidence and a 12-m-thick fine-grained SRLL along uplifting zones. Post-stack processing at 43-Hz CWT tuning frequency predicts the 18 and 10 m thick sediments for SRLL along the subsidence and uplifting zone. The ISTM predicts a thickness of 31 m during subsidence within the SRLL and 14 m for uplifting zones. The 43-Hz tuning frequency-based processing at ISTM predicts enhanced thicknesses of 41 and 18 m for subsiding and uplifting sediments that are trapped inside the shoal limestone petroleum systems. The quantitative visualization of AI with the predicted thicknesses at ISTM has provided robust clues for imaging the internal stratigraphic architecture of the SRLL. The linear regression analysis remained a very successful tool for assessing the quantitative aspects of SRLL with a strong R2 > 0.95, which has implicated the real-time imagining of sea-level fall and rise. Only post-stack seismic data processing at 43-Hz found a very effective exploration tactic in predicting the enhanced thicknesses, and lateral continuity of the reservoir, and improved the S/N by removing the tuning effect of ambiguous lithology/fluids, which have accurately predicted the exact location along the subsidence zones. This workflow can act as an analogue stratigraphic exploration of remaining gas fields within Asian Basins and similar geological settings.

RETRACTED ARTICLE: Improvement of seismic data quality and recognition of fault discontinuities through seismic data conditioning applications: a case study of Issaran oil field, Gulf of Suez, Egypt

Obtaining high-quality seismic imaging in shallow heterogeneous carbonate reservoirs with complicated structural regimes, such as the Issaran field, is difficult. Issaran field is a heavy oil shallow heterogeneous fractured carbonate reservoirs located in the Gulf of Suez of Egypt. It has many geological factors that affect image quality and pose numerous challenges. In addition, the seismic data was acquired more than 12 years ago, with narrow azimuth and short offsets. As a result, the fault zones are not sharply defined. Furthermore, the seismic data was processed about 10 years ago. The signal-to-noise ratio is relatively poor due to the random and coherent noises. A robust data conditioning workflow for noise suppression and fault discontinuity sharpening is used to improve the post-stack seismic data quality. In this context, a steering dataset was generated, and a dip-steered median filter (DSMF), a dip-steered diffusion filter, and a fault enhancement filter (FEF) were applied to sharpen the discontinuities. Structural attributes were evaluated, to investigate how the newly applied data conditioning affects the clarity of fault patterns. Furthermore, multiple physical attributes were extracted, including instantaneous phase, instantaneous frequency, and RMS amplitude to better understand the reservoir stratigraphic heterogeneity. The application of DSMF is useful in removing the residual random noises. The FEF-similarity attribute revealed small-scale faults with a 50-foot vertical throw. The physical attributes proved that the Issaran carbonate facies is controlled by structure. Moreover, the RMS amplitude attribute helped in distinguishing between porous and non-porous dolomite facies. As imaging quality has significantly improved, the applied seismic data conditioning workflow is beneficial for the field development in Issaran field. It is also suggested that this data conditioning workflow be applied in other heterogeneous carbonate reservoirs with complex structures around the world.

Seismic reservoir characterization of the Gassum Formation in the Stenlille aquifer gas storage, Denmark — Part 1

Seismic reservoir characterization plays an important role in carbon capture and storage analysis. The Havnsø anticlinal structure in Denmark is a prospective CO2 storage site due to its proximity to two large emission sources—a coal-fired power station and a nearby refinery. Although legacy 2D seismic lines over the area outline the anticlinal structure, their quality is insufficient for quantitative interpretation. Earlier studies have shown that the natural gas stored in the Stenlille aquifer exhibits a seismic response similar to the modeled CO2 fluid in the Havnsø structure. Thus, seismic reservoir characterization carried out on the Stenlille aquifer gas storage in terms of identifying spatial distribution of gas and outlining faults would provide insight regarding value addition that seismic data can bring into the proposed CO2 storage at Havnsø. Using the available poststack seismic data, we apply an integrated reservoir characterization analysis. After performing the adequate data conditioning, the impedance of the target Stenlille Formation is estimated through generation of an accurate low-frequency model. Thereafter, multiattribute analysis was used to generate volumetric estimates of porosity, gamma ray, and water saturation within the target formation so that the spatial distribution of gas can be mapped. The resulting porosity and gamma-ray volumes indicate encouraging results and were used for Bayesian classification to predict the probability of the more important lithofacies, namely, sand, shale, moderate-porosity sand, and moderate-porosity shaly sand, which enabled the mapping of high-porosity/facies zones in the two aquifer storage levels. Independently, we make use of unsupervised machine learning applications for seismic facies prediction and compare them at the two storage levels, which will be presented in the part 2 of this paper.

A practical approach to develop a proper anisotropic rock physics model for media comprising of multiple fracture sets in the absence of sophisticated laboratory/wireline data

For economical production from a fractured reservoir, a characteristic analysis of the fracture parameters like its density and orientation within the reservoir is essential to improve the fluid flow during extraction. This study deals with the development of a proper anisotropic rock physics model for a media with multiple fracture sets to study the spatial distribution of important fracture parameters i.e., fracture density and orientation in the absence of sophisticated laboratory/wireline and pre-stack seismic data. The crest of hydrocarbon producing fault-bounded Balkassar Anticline in Northern Potwar, Upper Indus Basin, Pakistan is selected as a case study representing a potential zone for development of fractures at reservoir level (Sakesar Limestone). The methodology consists of the interpretation of 3D post-stack seismic and conventional wireline log data to demarcate the reservoir containing fractures. The Ant-tracking discrete fracture network (DFN) attribute is applied on 3D post-stack seismic data to obtain an initial estimate about the presence of fracture corridors and their orientations. Based on this initial estimate, a proper rock physics model has been developed utilizing inverse Gassmann relations, T-matrix approximation, and Brown and Korringa relations. The output from the developed rock physics model has been displayed in the form of 13 effective independent elastic stiffness constants (monoclinic symmetry–representing media comprising of multiple fracture sets) as a function of fracture densities and azimuthal fracture orientations. A clear decreasing trend in effective elastic stiffness constants with increasing fracture densities can be observed. Similarly, a periodic trend of effective elastic stiffness constants with fracture orientations can be observed. These trends are more or less expected, but they would have been difficult to quantify without a proper rock physics model. The use of independent effective elastic constants for the generation of synthetic seismic amplitude versus angle and azimuth (AVAZ) data and its correlation with observed seismic AVAZ data in a geostatistical sense has been discussed.

Mapping Cretaceous faults using a convolutional neural network – A field example from the Danish North Sea

The mapping of faults provides essential information on many aspects of seismic exploration, characterisation of reservoirs for compartmentalisation and cap-rock integrity. However, manual interpretation of faults from seismic data is time-consuming and challenging due to limited resolution and seismic noise. In this study, we apply a convolutional neural network trained on synthetic seismic data with planar fault shapes to improve fault mapping in the Lower and Upper Cretaceous sections of the Valdemar Field in the Danish North Sea. Our objective is to evaluate the performance of the neural network model on post-stack seismic data from the Valdemar Field. Comparison with variance and ant-tracking attributes and a manual fault interpretation shows that the neural network predicts faults with more details that may improve the overall geological and tectonic understanding of the study area and add information on potential compartmentalisation that was previously overlooked. However, the neural network is sensitive to seismic noise, which can distort the fault predictions. Therefore, the proposed model should be treated as an additional fault interpretation tool. Nonetheless, the method represents a state-of-the-art fault mapping tool that can be useful for hydrocarbon exploration and CO2 storage site evaluations.

Machine-learning-based porosity estimation from multifrequency poststack seismic data

Estimating porosity models via seismic data is challenging due to the low signal-to-noise ratio and insufficient resolution of the data. Although impedance inversion is often used in combination with well logs, to obtain subseismic scale porosity data, several problems must be addressed. Alternatively, we have proposed a machine learning-based workflow to convert seismic data into porosity models. A residual U-Net++ (ResUNet++)-based workflow is designed to take multiple poststack seismic volumes with different frequency bands as input and estimate a corresponding porosity model as output. This workflow is demonstrated in a 3D channelized reservoir to estimate the porosity model, and the R2 score of more than 0.9 is achieved for training and validation data. Moreover, a stress test is performed by adding noise to the seismic data to verify the expandability of applications, and the results find a robust estimation with 5% added noise. The additional two ResUNet++ are trained to only take the lowest or highest resolution seismic data as input to estimate the porosity model, but they exhibit underfitting and overfitting, respectively, supporting the importance of using multiscale seismic data for the porosity estimation problem. We mainly use experimental cases with simulated data. Therefore, scaling ResUNet++ for real data is needed in future research, such as considering coherent noise in seismic data, allowing uncertainty in petrophysical parameters, and expanding the size of ResUNet++ to the practical reservoir extent.

Parameters test on time migration with Kirchhoff method on 2D synthetic data with dipping event and its reflector structural changes

This paper describes the effect of parameter variation with Kirchhoff migration in post-stack seismic data. Parameters tested covering velocity, aperture, and maximum dip. The data used in this work was generated from velocity and density models with gaussian syncline and anticline layer boundaries. This work is the effect of parameter changes on the model and conclude best applied parameters for Kirchhoff migration in certain model created.

Recurrent autoencoder model for unsupervised seismic facies analysis

Machine learning-based automatic seismic facies analysis has increased significantly over the past few decades. The key is to select the most representative features (such as the commonly used poststack amplitude data or the induced seismic attributes) as the input of the different machine learning algorithms. As an advanced branch of machine learning, deep learning can be used to extract the discriminatively deep features from seismic data similar to those used in image classification. In this study, we havedeveloped an unsupervised seismic facies analysis method by using a recurrent autoencoder model. First, we have constructed and trained an autoencoder architecture combined with long short-term memory-based recurrent operation. Its main aim is to learn the deep discriminative features by taking the windowed poststack seismic data as the input time series data. This type of unsupervised learning takes advantage of the no labeling requirement of seismic data. In addition, the recurrent operation is beneficial in delineating the time-sequential characteristics of seismic data. Second, we have taken the learned features as the input of simple K-means clustering and analyzed the corresponding seismic facies. In other words, the clustering is executed in the learned feature space (learned feature-based clustering). Real data results have demonstrated that our method reveals more details than the original amplitude-based K-means clustering, depending on the cluster calibration. In particular, according to the known natural gamma-ray logs and lithological descriptions of five wells, different amounts of sandstone and mudstone deposit are more accurately discriminated, which is substantially informative in reservoir prediction and hydrocarbon exploration. Furthermore, the estimated average silhouette scores have quantitatively shown the effectiveness of our method.

A prediction method of suspended natural gas hydrate saturation in sea area based on P-wave impedance

A large amount of suspended natural gas hydrate (NGH) has been found in Shenhu sea area of China. At present, Wood method is considered to be a suitable method for predicting the saturation of suspended NGH. However, there are two problems in the application of this method in Shenhu sea area of China. First, this method is difficult to directly predict NGH saturation based on post-stack seismic data inversion, since it does not clarify the relationship between NGH saturation and P-wave impedance. Second, the prediction results of this method are quite different from the actual values in Shenhu sea area of China, which makes it difficult to obtain reliable resource estimation in this sea area. To clarify the relationship between NGH saturation and P-wave impedance, this paper proposes an improved Wood equation called Wood-Imp equation, which lays a foundation for predicting NGH saturation by using post-stack wave impedance data. For the second problem, the main reason for the large prediction error of Wood method is that the existing measurement technology cannot avoid the measurement errors in sediment composition, pore sizes, etc. Thus, Wood-Imp method is proposed based on non-NGH layer to reduce the of NGH saturation prediction error. Three comparison experiments are used to demonstrate the effectiveness of our proposed Wood-Imp method, including simulation data, well S data in Shenhu sea area, two-dimensional forward modeling of Shenhu sea area data. (1) Wood-Imp method has much higher accuracy in predicting NGH saturation than Wood method; (2) The predicted NGH saturation in Shenhu well S by Wood-Imp method is closer to that calculated by chloride ion method than that of Wood method; (3) Wood-Imp method has obtained better NGH saturation prediction accuracy based on P-wave impedance data obtained by post-stack inversion in two-dimensional forward modeling. Therefore, the Wood-Imp method proposed in this paper is more suitable for predicting suspended NGH saturation than the existing Wood method in Shenhu sea area.

Inversion-based attenuation compensation with dip constraint

Instability is an inherent problem with the attenuation compensation methods and has been partially relieved by using the inverse scheme. However, the conventional inversion-based attenuation compensation approaches ignore the important prior information of the seismic dip. Thus, the compensated result appears to be distorted spatial continuity and has a low signal-to-noise ratio (S/N). To alleviate this issue, we have incorporated the seismic dip information into the inversion framework and have developed a dip-constrained attenuation compensation (DCAC) algorithm. The seismic dip information, calculated from the poststack seismic data, is the key to construct a dip constraint term. Benefiting from the introduction of the seismic dip constraint, the DCAC approach maintains the numerical stability and preserves the spatial continuity of the compensated result. Synthetic and field data examples demonstrate that the proposed method can not only improve seismic resolution, but also protect the continuity of seismic data.

Unsupervised Machine Learning, Multi-Attribute Analysis for Identifying Low Saturation Gas Reservoirs within the Deepwater Gulf of Mexico, and Offshore Australia

An effective method of identifying and discriminating undersaturated gas accumulations remains unresolved, resulting in uncertainty in hydrocarbon exploration. To address this problem, an unsupervised machine learning multi-attribute analysis is performed on 3D post-stack seismic data over several blocks within the deepwater Gulf of Mexico and within the Carnarvon Basin, offshore Australia. Results reveal that low-saturation gas (LSG) reservoirs can be discriminated from high-saturation gas (HSG) reservoirs by using a combination of instantaneous attributes that are sensitive to small amplitude, frequency, and phase anomalies with self-organizing maps (SOMs). This methodology shows promise for de-risking prospects, even if it is not quantitative, particularly in frontier and exploration basins where wells may not exist or be very limited. However, this method only proved to be successful within the Gulf of Mexico and yielded limited results in the Carnarvon Basin. This difference is most likely due to the Carnarvon Basin having a different amplitude response resulting from a different burial history and fluid saturations when compared to the Gulf of Mexico. Therefore, this method is non-transferrable, and a different combination of attributes may be needed in other LSG-prone basins.

A New Fluid Mobility Calculation Method Based on Frequency-Dependent AVO Inversion

Fluid mobility (i.e., permeability to viscosity ratio) is a key parameter that can evaluate the reservoir permeability and delineate the fluid characteristic in hydrocarbon-saturated reservoirs. Based on the asymptotic representation for the frequency-dependent reflections in the fluid-saturated pore-elastic media and frequency-dependent AVO inversion, we propose a novel method for estimating fluid mobility from poststack seismic data. First, we establish the relationship between fluid mobility and frequency-dependent AVO analysis. Then, the fluid mobility is estimated using the theory of frequency-dependent AVO inversion. Tests on synthetic data reveal that the fluid mobility shows excellent imageability for the fluid-saturated reservoirs and can accurately delineate the spatial distribution shape of the gas-saturated reservoir. The application of field data examples demonstrates that the fluid mobility calculated by the proposed method produces less background interferences caused by elastic layers compared with the conventional frequency-dependent fluid indicator. The frequency-dependent fluid mobility takes into account the dispersion features associated with hydrocarbon reservoirs, and it provides a new way to detect the location of hydrocarbon reservoirs and characterize their spatial distribution.

Improving seismic resolution based on enhanced multi-channel variational mode decomposition

The attenuation and dispersion of seismic waves change with frequency. So, it is unreasonable to compensate absorption and attenuation using the full-frequency band seismic data directly. In this study, we propose a seismic high-resolution processing workflow based on the enhanced multi-channel variational mode decomposition (EMVMD). Frist, we aim to adopt the multi-channel variational mode decomposition (MVMD) to decompose seismic data into several band-limited intrinsic mode functions (IMFs). Then, we explain the relationship between the IMFs and the different scale structures, and propose a strategy to determine the number of IMFs by introducing the contraction operator mapping (COM) and the scale-space representation (SSR). Note that we name the MVMD with a number selection strategy as the enhanced MVMD (EMVMD). Next, we adopt the adaptive spectrum-broadening method (ASBM) to separately improve the resolution of each decomposed IMF. Afterward, we can easily obtain the enhanced seismic data with a high resolution by summing the processed IMFs. Finally, we apply the proposed workflow (EMVMD-ASBM) to synthetic and field data to testify its validity and effectiveness for enhancing the resolution of the post-stack seismic data.

Reconstructing Seismic Images and Creating Pseudo-3D Volumes: a Machine Learning Approach

We describe how machine learning can be used to solve a range of interpolation problems regularly encountered in post-stack seismic data. In all experiments we train a U-net type of deep learning model on examples extracted in good-quality data areas. The 2D or 3D input images utilized for our training set are manipulated such that the inputs exhibit the same kind of problems as observed in the areas with poor, or missing data. We repair these poor data areas by applying the trained model. We show examples of interpolating missing traces, decreasing the bin-size (quadrupling the number of traces), and replacing a bad data patch in an undershoot area. We also demonstrate that these trained models are generic interpolators that can be reused without retraining to solve similar problems in other data sets. Finally, we use the same technology to create pseudo-3D volumes from 2D data. We present two workflows: a direct transformation approach and a transformation that takes place in the flattened domain. The latter approach is more demanding as it involves interpretation followed by flattening and unflattening the data.

Assessing rock physics and seismic characteristics of the Gassum Formation in the Stenlille aquifer gas storage – A reservoir analog for the Havnsø CO[formula omitted] storage prospect, Denmark

The rock physics and seismic characteristics of prospective CO2 storage sites are important inputs to assess the feasibility of seismic reservoir characterization and monitoring of the CO2 plume. In this paper, geophysical data from the Stenlille aquifer gas storage in Denmark are used as a reservoir analog to the Gassum Formation at the Havnsø CO2 prospect. Rock physics and pore fluid modeling of the in situ natural gas and CO2 saturation was performed to obtain an initial understanding of the seismic-reservoir relationship and to model the seismic sensitivity for various pore fluid scenarios. Then, a 3D seismic survey at Stenlille was used as input to a Bayesian inversion for litho-fluid classification to outline the injected natural gas distributions in a probabilistic manner. Although only post-stack seismic data were available, the gas injected into the different reservoir zones of the Gassum Formation yields a low acoustic impedance signature that could be interpreted from the inversion results. The predictions were consistent with well log data, reported gas withdrawal and injection volumes and conforms with the geological structure. This implies good conditions for seismic reservoir characterization and monitoring CO2 saturation changes in the Gassum Formation for geological settings similar to the Stenlille aquifer.

Estimation of the depth-domain seismic wavelet based on velocity substitution and a generalized seismic wavelet model

Depth-domain seismic wavelet estimation is the essential foundation for depth-imaged data inversion, which is increasingly used for hydrocarbon reservoir characterization in geophysical prospecting. The seismic wavelet in the depth domain stretches with increasing medium velocity and compresses with decreasing medium velocity. The commonly used convolutional model cannot be directly used to estimate depth-domain seismic wavelets due to velocity-dependent wavelet variations. We have developed a separate parameter estimation method for estimating depth-domain seismic wavelets from poststack depth-domain seismic and well-log data. Our method is based on the velocity substitution and depth-domain generalized seismic wavelet model defined by the fractional derivative and reference wavenumber. Velocity substitution allows wavelet estimation with the convolutional model in the constant-velocity depth domain. The depth-domain generalized seismic wavelet model allows for a simple workflow that estimates the depth-domain wavelet by estimating two wavelet model parameters. In addition, this simple workflow does not need to perform searches for the optimal regularization parameter and wavelet length, which are time-consuming in least-squares (LS)-based methods. The limited numerical search ranges of the two wavelet model parameters can easily be calculated using the constant phase and peak wavenumber of the depth-domain seismic data. Our method is verified using synthetic and real seismic data and further compared with LS-based methods. The results indicate that our method is effective and stable even for data with a low signal-to-noise ratio.

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

Common-reflection-point (CRP) gather is one extensively used prestack seismic data type. However, CRP suffers more noise than a poststack seismic data set. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain, but also in the time-frequency (TF) domain. Therefore, we first use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the TF domain because the SSWT has better TF resolution and reconstruction properties. Then, we use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the TF domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples indicate that our method can effectively attenuate random noise with a better attenuation performance than the commonly used principal component analysis, [Formula: see text] filter, and the continuous wavelet transform method.

A Novel Multichannel Seismic Deconvolution Method via Structure-Oriented Regularization

Seismic deconvolution is an effective approach to improve the resolution of seismic data and plays an important role in migration imaging, reservoir prediction and other fields. However, conventional deconvolution methods are usually based on sparse-type regularization (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm) and adopt a trace-by-trace inversion strategy to reconstruct the subsurface reflectivity series. Although such methods can improve the resolution of seismic records to a certain extent, the lack of spatial constraint will result in poor spatial continuity in the reconstructed reflectivity. This phenomenon is particularly obvious in regions with complicated geologic structures. For the purpose of overcoming this issue, we have developed a structure-oriented regularization-based multichannel sparse spike deconvolution (SOR-based MSSD) method. This method imposes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm regularization on the reflectivity to obtain the high-resolution subsurface reflectivity series and imposes structure-oriented regularization (SOR) on the expected high-resolution seismic data to improve the spatial continuity of the inversion result. First, we construct SOR term based on the local structural orientations which can be estimated from the poststack seismic data. Then, we integrate the seismic data misfit term, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm constraint term, and the SOR term to formulate the objective function. At last, we use the alternating direction method of multipliers (ADMMs) to efficiently solve the objective function. We compare the SOR-based MSSD with existing methods by using synthetic and field data. Both deconvolution examples illustrate the performance of proposed method in terms of improving the spatial continuity.