Synthetic Seismic Sections Research Articles

Synthetic seismic forward modeling of a high-frequency depositional sequence: The example of the Tiber depositional sequence (Central Italy)

This research presents the results of a synthetic seismic forward modeling carried out by generating stacked sections of the stratigraphic-depositional architecture in the high-frequency Tiber Depositional Sequence (TDS) (Central Italy). The stacked sections were created using the seismo-acoustic approach provided in the geophysical codes developed by the CREWES consortium and modeled in Matlab. The models reproduce the seismic response of a sedimentary succession showing the upward transition from a wave-dominated estuary to a wave-dominated delta. The synthetic seismic sections reveal the subsurface structure and sedimentary architecture of the TDS, demonstrating the complexity of the sedimentary environments as well as the variation in lithology and petrophysical characteristics across the sections. The results demonstrate the ability of the seismo-acoustic approach to accurately model the seismic response of sedimentary facies in fluvial, coastal, and shelf environments, providing valuable information for exploring potential reservoir systems, with the TDS serving as an analog. In addition, the models identify several seismic facies and reflectors that can be interpreted as boundaries and surfaces of sequence-stratigraphic significance.

Seismic forward modeling for investigating and interpreting thin beds in a carbonate reservoir in SW Iran

High-frequency contents of reflections are essential in the investigation and interpretation of thin-bed reservoirs. These beds can be even more complicated in carbonate rocks, as pore geometries influence final seismic responses. To address these complexities, we propose a seismic forward modeling workflow to investigate several thin-bed reservoirs in a carbonate oilfield with variable pore geometries. The new workflow enhances the existing forward models for the investigation of thin beds by integrating seismic petrophysics, geological model building, and 2D finite-difference elastic modeling. We used seismic petrophysics to ensure the consistency between petrophysical well logs and seismic data using rock physics modeling. Then, we introduced a new high-resolution workflow for velocity modeling to build a reliable geological model. Finally, the 2D finite-difference elastic modeling is employed to generate synthetic traces based on our geological model to obtain seismic responses for the existing thin-bed reservoirs. The forward models used in this study are a powerful tool for investigating thin layers because they enable high-resolution investigation of the given geological model in distinguishing lateral and vertical lithofacies changes. The new velocity modeling workflow, implemented in this research, is more reliable and effective than the conventional velocity property modeling approaches, which resulted in synthetic seismic sections with increased lateral and vertical resolutions and enhanced data from a thin bed. The main features of this workflow are the incorporation of well-log data into geological model building, combining the high-resolution data of horizontal seismic stacking velocity with vertical well logging, and the incorporation of a residual model to improve the seismic stacking velocity. We produced a more coherent section resembling the acquired 3D seismic data by applying the proposed workflow to data from an oil carbonate reservoir in the Fahliyan Formation within the Abadan Plain in SW Iran. It is concluded that the higher frequency synthetic sections from the proposed workflow can assist in resolving the seismic interpretation challenges. By applying the proposed workflow to the current data set, four thin-bed carbonate reservoirs were investigated with corresponding thicknesses of approximately 25, and 17 m at peak frequencies of 60, and 90 Hz, respectively.

Interpretation method of tight sandstone based on seismic forward modeling: a case study of Permian in Southeast Ordos Basin

Based on Permian data and the reflection coefficient convolved 0° and 90° phase ricker wavelets form different phases of synthetic seismic sections. This paper theoretically discusses three aspects: one is Permian sandstone thickness and seismic amplitude, the second is tight sandstone (tuning) thickness and seismic wave frequency, and the last is tight sandstone and seismic wave phase. The result shows that when sandstone thickness is greater than seismic wavelength λ/4, the sandstone thickness can be determined through the time difference between the sandstone’s top and bottom Peaks and troughs. When sandstone thickness is less than λ/4, only the seismic reflection amplitude of the top surface of sandstone was used to determine the thickness of sandstone. In the 0° phase seismic model, peaks were located on the thin layer’s upper part and trough in the lower half, with no correspondence between the polar and lithology. In the 90° phase synthesis record, the thin sandstone layer roughly corresponds to the seismic reflection trough. 0° and 90° phase synthesis have the exact vertical resolution; the frequency variation can reflect sand body distribution. The optimal frequency makes the thickest layer of sandstone reach amplitude-frequency tuning (tuning frequency).

Diffraction pattern recognition using deep semantic segmentation

ABSTRACTDiffraction imaging can help better understand small‐scale geological structures. Due to their often‐weak signal, in order to image them, it is necessary to separate diffraction signals from the rest of the wavefield. Many different methods have been developed for diffraction wavefield separation, and the newest trend includes the application of artificial neural networks and deep learning. Available case studies with a deep‐learning approach for diffraction separation show good results when applied to synthetic and sedimentary setting datasets where diffraction signals are either strong or have pronounced characteristics. Examples, however, are missing from crystalline or hardrock geological settings where the signal‐to‐noise ratio is by far lower and diffraction signals are usually within a complex reflectivity medium, have steep tails and are usually incomplete. In this study, we showcase the application of a deep semantic segmentation model on synthetic seismic, real ground‐penetrating radar, and hardrock seismic datasets. Synthetic seismic sections were generated using different random noise levels and coherent noise resembling a complex reflectivity pattern interfering with diffraction tails. For the real GPR dataset, diffraction signals were successfully delineated, although in some locations reflections were picked up because of their similar pixel values as the apex of the diffractions. As for the real seismic dataset, through a number of approaches, we were able to completely delineate a single diffraction within several inlines that was generated from a massive sulphide body. The algorithm also enabled us to recognize an incomplete diffraction, at the edge of the seismic cube, which was never labelled. This diffraction originated from outside of the seismic volume and may be a target for future mineral exploration programmes, thanks to the deep semantic segmentation algorithm providing this possibility.

An improved variational mode decomposition for seismic random noise attenuation using grasshopper optimization via shape dynamic time warping

In seismic signal processing, attenuation of noise is one of the most difficult challenges. Variational mode decomposition (VMD) is a recently developed adaptable signal processing technique that subdivides a signal into bandlimited intrinsic mode functions (BIMFs). In VMD, the key elements are the number of intrinsic modes and the quadratic penalty factor for better denoising. In this paper, we have applied the Grasshopper optimization algorithm (GOA) to determine the key elements of VMD and decomposes the signal into optimized BIMFs. Then, the Shape dynamic time warping (shapeDTW) is applied for determining the relevant modes. Finally, the relevant modes are deal with Wavelet thresholding (WT) to obtain a better result of signal to noise ratio (SNR). Initially, the analysis is made on the synthetic signals to represent the dominance of the new approaching method with Empirical mode decomposition (EMD) based and VMD based methods. We also applied new approaching method to a 1D real seismic signal from the Pacific Earthquake Engineering Research Center (PEER) ground motion database to suppress the random noise interferences. The proposed approach is also tested in a 2D synthetic seismic section corrupted by random noise, and also with a 2D real Forrest seismic survey in South Australia's resources information gateway. Application of the proposed GOA-VMD-shapeDTW-WT method on both the synthetic and the real seismic signal shows supremacy with other denoising techniques.

Evaluation of 3D seismic survey design parameters through ray-trace modeling and seismic illumination studies: a case study

This paper describes a case study of wavefront construction-based ray-trace modeling to access the 3D seismic exploration parameters that significantly impact achieving the exploration target in seismic data acquisition. The traditional methods assessment is based on the horizontal reflector concept, which does not consider the subsurface's inhomogeneities. This case study provides a methodology by considering the effect of subsurface variations on the estimation of seismic survey parameters. As the first step in this methodology, an elastic earth model is created to propagate theoretical seismic rays from a 3D seismic survey to generate seismic ray properties. Then illumination maps and synthetic seismic sections are generated from these seismic ray attributes to evaluate seismic survey performance in seismic imaging targets. The results proved that the method helps estimate seismic survey efficiencies in seismic target imaging. Therefore, the ray-trace modeling methodology can help obtain the target fold coverage in complex geological settings by designing and verifying the seismic survey parameters.

Blind inversion of multidimensional seismic data using sequential Tikhonov and total variation regularizations

The acoustic impedance (AI) model is key data for seismic interpretation, usually obtained from its nonlinear relation with seismic reflectivity. Common approaches use initial geologic and seismic information to constrain the AI model estimation. When no accurate prior information is available, these approaches may dictate false results at some parts of the model. The regularization of ill-posed underdetermined problems requires some constraints to restrict the possible results. Available seismic inversion methods mostly use Tikhonov or total variation (TV) regularizations with some adjustments. Tikhonov regularization assumes smooth variation in the AI model, and it is incurious about the rapid changes in the model. TV allows rapid changes, and it is more stable in the presence of noisy data. In a detailed realistic earth model that AI changes gradually, TV creates a staircasing effect, which could lead to misinterpretation. This could be avoided by using TV and Tikhonov regularization sequentially in the alternating direction method of multipliers and creating the AI model. The result of implementing our algorithm, sequential Tikhonov and total variation regularizations (STTVR), on 2D synthetic and real seismic sections indicates that the smaller details in the lithologic variations are accounted for as well as the general trend. STTVR can calculate major AI variations without any additional low-frequency constraints. The temporal and spatial transition of the calculated AI in real seismic data is gradual and close to a real geologic setting.

Seismic Data Denoising Based on Tensor Decomposition With Total Variation

In order to remove random noise in seismic data, this letter proposes a seismic data denoising method based on tensor decomposition and total variation (TDTV). Based on the self-similarity of seismic data, this method first groups similar patches into a stack, then utilizes the low-rank tensor approximation strategy to restore the structural effective information of the seismic section. Considering that the approximate seismic section obtained after CANDECOMP/PARAFAC (CP) decomposition and patch aggregation is unsmooth, this letter introduces the total variation (TV) constraint to perform anisotropic diffusion, and protects edge information while smoothing. Finally, the gradient descent method is used to solve the whole model. The TDTV method proposed in this letter can not only effectively denoise synthetic and field seismic section but also restore structural and edge information. Experimental results show that the proposed method outperforms many state-of-the-art denoising methods.

Nonlocal Weighted Robust Principal Component Analysis for Seismic Noise Attenuation

Seismic data are usually contaminated by various noises. Noise suppression plays an important role in seismic processing. In this article, we propose a new denoising method based on the nonlocal weighted robust principal component analysis (RPCA). First, seismic data are divided into many patches and grouped based on the nonlocal similarity. For each group, then, we establish a similar block matrix and set up the objective function of the RPCA. Next, we introduce the iterative log-thresholding algorithm into the augmented Lagrangian method to solve the problem. Furthermore, varying weights are specified to different singular values when minimizing the objective function. Finally, aggregating all recovered matrices can obtain the denoised seismic data. The proposed method considers the nonlocal similarity and adaptively sets weights with local noise variance. It performs well also owing to the superiority of the iterative log-thresholding method. The presented method is assessed using a synthetic seismic section with several crossover events. We also apply this novel approach to a real seismic data, which shows good results. Comparison with other approaches reveals the effectiveness of the proposed approach.

Thickness Distribution Prediction for Tectonically Deformed Coal with a Deep Belief Network: A Case Study

Thickness of tectonically deformed coal (TDC) has positive correlations with the susceptible gas outbursts in coal mines. To predict the TDC thickness of the coalbed, we proposed a prediction method using seismic attributes based on the deep belief network (DBN) and dimensionality reduction. Firstly, we built a DBN prediction model using the extracted attributes from a synthetic seismic section. Next, we transformed the possibly correlated seismic attributes into principal components through principal components analysis. Then, we compared the true TDC thickness with the predicted TDC thicknesses to evaluate the prediction accuracy of different models, i.e., a DBN model, a support vector machine model, and an extreme learning machine model. Finally, we used the DBN model to predict the TDC thickness of coalbed No. 8 in an operational coal mine based on synthetic experiments. Our studies showed that the predicted distribution of TDC thickness followed the regional characteristics of TDC development well and was positively correlated with the burial depth, coalbed thickness, and tectonic development. In summary, the proposed DBN model provided a reliable method for predicting TDC thickness and reducing gas outbursts in coal mine operations.

Random noise attenuation of 2D seismic data based on sparse low-rank estimation of the seismic signal

Attenuation of noise in seismic data is a crucial and difficult task in analyzing seismic data. We propose a new noise suppression algorithm in which the noise can be suppressed; meanwhile, the seismic signal is preserved. In the proposed algorithm, first we analyze each seismic trace using a linear time–frequency method, i.e., the short-time Fourier transform, then we estimate the sparse low-rank matrix from the obtained noisy matrix. To this aim, an objective function comprising of a data-faithfulness term and two parameterized non-convex penalty functions is proposed. Finally, the denoised signal is synthesized using the obtained semi-low-rank matrix. We assess the proposed technique considering a synthetic seismic trace and seismic section. We have also assessed the proposed technique using pre-stack real data collected from a hydrocarbon field located in the southwest area of Iran. The performance of the proposed method is evaluated and compared with other state-of-the-art techniques such as the synchrosqueezed wavelet transform and low-rank signal matrix approximation by Semi-Soft GoDec (WSST-GoDec) algorithm, classical f−x singular spectrum analysis (f−x SSA) and the sparse signal estimation and extraction low-rank component by optimum singular value estimation (SLR-OptShrink). The results indicate the advantage of the proposed method over the selected methods.

Seismic Random Noise Attenuation Using Sparse Low-Rank Estimation of the Signal in the Time–Frequency Domain

Suppression of random noise in seismic data is a challenging preprocessing task. We propose a new denoising method, which includes the following steps. First, the short-time Fourier transform of the noisy seismic signal is computed. Then, a sparse low-rank matrix is estimated based on solving a nonconvex penalty function. A threshold function associated with the penalty function based on the optimal shrinkage of the singular values is employed to solve the penalty function. The nonconvex penalty function induces sparsity to the time–frequency (TF) matrix, and the optimal shrinkage optimizes the thresholding function to extract the singular values. Finally, the seismic signal is synthesized based on the estimated sparse low-rank TF matrix. We evaluate the proposed method by using a synthetic seismic section contaminated by random noise and a prestack real seismic dataset from an oil field in the southwest of Iran. The synchrosqueezed wavelet transform-OptShrink, synchrosqueezed wavelet transform-Go Decomposition, and classical $f-x$ singular spectrum analysis methods are selected to compare the performance of the proposed method. The results indicate the superiority of the proposed method compared to the other state-of-the-art noise suppression methods.

The synthetic seismic expression of the Messinian salinity crisis from onshore records: Implications for shallow‐ to deep‐water correlations

AbstractThe onshore–offshore correlation of sedimentary successions is a common problem in basin analysis, but it becomes critical for the full understanding of the Messinian salinity crisis (MSC), a complex array of palaeoenvironmental events which affected the Mediterranean basin at the end of the Miocene. The outcrop records show that the Messinian stratigraphic architectures may be highly complex as the deposits of the different MSC evolutionary stages can be lithologically similar and separated by erosional surfaces and/or morphostructural highs. The correct definition of the nature and stratigraphic position of Messinian deposits in offshore areas through seismic data may be almost impossible, especially where core data are sparse. To bridge the gap between onshore and offshore records, we have built synthetic seismic sections from well‐constrained outcrop successions. Our results provide useful insights and warnings for the interpretation of offshore data, pointing out that MSC units having different age, nature and depositional settings, may show similar seismic facies and geometries. Conversely, the same deposit may result in different seismic facies, either with parallel and high‐amplitude reflections or even transparent or chaotic due to interference patterns of seismic reflections related to dominant frequency. It follows that a correct interpretation of the nature and age of deep‐seated Messinian deposits can only be obtained through the integration of seismic and core data, and considering the onshore record. The application of our approach to the Balearic Promontory results in an alternative interpretation with respect to previous models. We show that this offshore area has good analogues in the onshore of the Betic Cordillera and includes both shallow and intermediate depth sub‐basins that underwent a strong post‐Messinian subsidence.

Automatic fault detection in seismic data using Gaussian process regression

Compartmentalization of hydrocarbon reservoirs, change in fluid contacts, effects of high permeable fractures around faults, and hydrocarbon traps created by seal faults make fault detection and extraction as a necessity in the analysis of seismic data. Faulting disrupts the smoothness trend of geological layers (reflections in seismic sections) and displaces layers along its plane. Thus, a fault could be considered as an abnormal phenomenon that globally deviates normal behavior of layers around its plane. In the present study, faults are considered as sparse global anomalies in a seismic section that can be extracted using Gaussian process regression. The Gaussian process regression is a nonparametric probabilistic model based on Bayesian statistics that can be used to model spatial properties as a regression problem. The Gaussian process usually is used to extract and describe normal interactions from the data set using smooth functions.The main idea of this study is to detect the global anomaly using Gaussian process regression. For this purpose, we considered geological layers as smooth normal events in seismic sections. Therefore, the location of the fault plane is where the Gaussian process gets an error during describing the layers. Abnormalities such as faults cause the Gaussian process to suffer an error near the anomaly. We used these errors and analyzed them to detect probable locations of fault edge. Finally, we used a consistent connection algorithm to separate most probable fault points and to connect them to an edge using morphological reconstruction algorithm. The proposed algorithm was evaluated based on the receiver operating characteristics analysis. Several synthetic seismic sections with different levels of signal to noise ratios were used to evaluate the algorithm in the presence of random noise. The results showed that all points predicted by a diagnostic test fell into the area above the diagonal of the receiver operating characteristics space, which represents a good diagnostic classification.

Pattern Recognition Applied to Attenuation of Multiples in Subsalt Imaging

It is well known that high impedance contrasts between salt bodies and their surroundings yield blurriness in seismic images which does not allow adequate interpretation; some of the artifacts generated by this process are multiple reflections and diffractions. In this work we propose a novel method of detection and attenuation of multiples from seismic gathers which is based on pattern recognition and fuzzy logic. In order to corroborate the effectiveness of this new approach, we use synthetic models which mimic a region intensely affected by salt tectonics in deep-water Gulf of Mexico. We obtained synthetic seismic sections by simulating wave propagation with an FDTD–CPML formulation. Detection of seismic events is performed through the mapping of parabolic shapes in shot gathers that approximate the recorded wavefronts. Undesired multiple events are then surgically attenuated by using fuzzy threshold criteria. Results show an optimal attenuation of multiple events, even when they are not directly related to salt structures.

Improving seismic QP estimation using rock-physics constraints

Our goal is to accurately estimate attenuation from seismic data using model regularization in the seismic inversion workflow. One way to achieve this goal is by finding an analytical relation linking [Formula: see text] to [Formula: see text]. We derive an approximate closed-form solution relating [Formula: see text] to [Formula: see text] using rock-physics modeling. This relation is tested on well data from a clean clastic gas reservoir, of which the [Formula: see text] values are computed from the log data. Next, we create a 2D synthetic gas-reservoir section populated with [Formula: see text] and [Formula: see text] and generate respective synthetic seismograms. Now, the goal is to invert this synthetic seismic section for [Formula: see text]. If we use standard seismic inversion based solely on seismic data, the inverted attenuation model has low resolution and incorrect positioning, and it is distorted. However, adding our relation between velocity and attenuation, we obtain an attenuation model very close to the original section. This method is tested on a 2D field seismic data set from Gulf of Mexico. The resulting [Formula: see text] model matches the geologic shape of an absorption body interpreted from the seismic section. Using this [Formula: see text] model in seismic migration, we make the seismic events below the high-absorption layer clearly visible, with improved frequency content and coherency of the events.

Parameters effective on estimating a nonstationary mixed-phase wavelet using cumulant matching approach

Seismic inversion is a process performed to remove the effects of propagated wavelets in order to recover the acoustic impedance. To obtain valid velocity and density values related to subsurface layers through the inversion process, it is highly essential to perform reliable wavelet estimation such as cumulant matching approach. For this purpose, the seismic data were windowed in this work in such a way that two consecutive windows were only one sample apart. Also, we did not consider any fixed wavelet for any window and let the phase of each wavelet rotate in each sample in the window. Comparing the fourth order cumulant of the whitened trace and fourth-order moment of the all-pass operator in each window generated a cost function that should be minimized with a non-linear optimization method. In this regard, parameters effective on the estimation of the nonstationary mixed-phase wavelets were tested over the created nonstationary seismic trace at 0.82s and 1.6s. Besides, we compared the consequences of each parameter on estimated wavelets at two mentioned times. The parameters studied in this work are window length, taper type, the number of iteration, signal-to-noise ratio, bandwidth to central frequency ratio, and Q factor. The results show that applying the optimum values of the effective parameters, the average correlation of the estimated mixed-phase wavelets with the original ones is about 87%. Moreover, the effectiveness of the proposed approach was examined on a synthetic nonstationary seismic section with variable Q factor values alongside the time and offset axis. Eventually, the cumulant matching method was applied on a cross line of the migrated data from a 3D data set of an oilfield in the Persian Gulf. Also, the effect of the wrong Q estimation on the estimated mixed-phase wavelet was considered on the real data set. It is concluded that the accuracy of the estimated wavelet relied on the estimated Q and more than 10% error in the estimated value of Q is acceptable. Eventually, an 88% correlation was found between the estimated mixed-phase wavelets and the original ones for three horizons. The estimated wavelets applied to the data and the result of deconvolution processes was presented.

Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation

Random noise elimination acts as an important role in the seismic signal processing. Generally, noise in seismic data can be divided into two categories of coherent and incoherent or random noise. Suppression of wide-band noise which is characterized by random oscillation in seismic data over time is one of the challenging issues in the seismic data processing. This paper describes a new noise suppression algorithm for seismic data denoising. The seismic data, trace-by-trace are transformed into sparse subspace using the synchrosqueezed wavelet transform, then the obtained sparse time-frequency representation is decomposed into semilow-rank and sparse components using the Optshrink algorithm. Finally, the denoised seismic trace can be recovered by back-transforming the semilow-rank component to the time domain using inverse synchrosqueezed wavelet transform. The proposed method is assessed using a single synthetic seismic trace and a synthetic seismic section with two crossover linear and curve events with two discontinuities that are buried in the random noise. We have also evaluated the method using a prestack real seismic data set from an oil field in the southwest of Iran. A comparison is performed between the proposed method and the semisoft GoDec algorithm, classical f-x singular spectrum analysis, and prediction Wiener filter. The results visually and quantitatively confirmed the superiority of the proposed method in contrast to the other well-established noise reduction methods.

Importance of Seismic Diffractions for Fractures Imaging

The use of diffraction imaging is important and rapidly gaining momentum in the oil and gas industry as the need of the industry moves toward exploiting smaller and more complex structures to find hydrocarbon. Illumination of these small scale discrete transmissivity structures such as faults or fracture zones prior to exploration offers substantial benefits for all phases of field development. We have established a new way of seismic imaging through diffractions study using reflection seismology. A subsurface geological model is developed from one of highly productive field located in the southern part of the Malay Basin. To study the seismic component in reflection seismology, a Finite difference modelling is used to generate synthetic seismic data. Both velocity and density models are used for the explanation of wave propagation, intimating the subsurface from side to side in both one-way and two-wave wave propagation. One–way wave equation migration methods are tested to image the faults in the synthetic seismic section of the Malay basin and we demonstrated the need of diffraction imaging for highly dipping faults and complex structure.

Automatic seismic event tracking using a dynamic time warping algorithm

For seismic data interpretation, horizon picking based on seismic events in stacked or migrated seismic sections is essential for obtaining information on subsurface structures. This conventional work is time-consuming via manual implementation. In this paper, we develop an automatic seismic event tracking method of horizon interpretation using the dynamic time warping (DTW) algorithm. The proposed method consists of two steps: calculating local time shifts between adjacent traces through a pilot trace and then event tracking. In the method, the DTW algorithm is applied to calculate time shifts between two adjacent traces, and an improved multitrace DTW strategy is proposed to improve the robustness. One synthetic seismic trace is used to demonstrate the DTW algorithm, and a synthetic seismic section is used to verify the feasibility of the proposed method handling contaminated seismic data with noise. Finally, we apply the method to a 3D physical model dataset. The result indicates that the proposed method is quantitatively feasible for seismic event automatic tracking and is reasonably stable for noisy seismic section flattening, which also has the potential to extract seismic horizon slices effectively.