Surface-related Multiples Research Articles

Full-Wavefield Migration Using an Imaging Condition of Global Normalization Multi-Order Wavefields: Application to a Synthetic Dataset

In marine seismic exploration, seismic signals comprise primaries that undergo first-order scattering, as well as multiples resulting from multi-order scattering events. Surface-related multiples involve multi-order scattering at the free surface interface between seawater and air and exhibit a smaller reflection angle and broader illumination compared to primaries. Internal multiples, originating from multi-order scattering among stratified layers, provide additional illumination compensation beneath the reflecting interface. However, in conventional primary migration, different-order wavefields may result in crosstalk artifacts. To address this issue, we developed a least-squares migration (LSM) method based on the multi-order wavefield global normalization condition. This methodology investigates the illumination effects and crosstalk artifacts associated with different-order surface-related and internal multiples, and then modifies the global normalization condition by incorporating an illumination compensation perspective. Virtual sources, represented by surface-related multiples and internal multiples, are integrated into the source compensation term, ultimately yielding a multi-order wavefield normalization condition. This normalization condition is subsequently combined with least-squares full-wavefield migration (LSFWM). Numerical experiments demonstrate that the normalization condition of multi-order wavefields can resolve the problem of weak deep imaging energy and promote the suppression of multiple crosstalk artifacts in the least-squares algorithm.

Surface-related multiple attenuation based on a self-supervised deep neural network with local wavefield characteristics

Multiple suppression is a very important step in seismic data processing. To suppress surface-related multiples, we develop a self-supervised deep neural network method based on a local wavefield characteristic loss function (SDNN-LWCLF). The first and second input data and the output data of the self-supervised deep neural network (SDNN) are the predicted surface-related multiples, the full-wavefield data, and the estimated true surface-related multiples, respectively. The role of the SDNN is to replace the convolutional filter part of adaptive subtraction. Although there are differences in amplitudes and phases between the predicted and true surface-related multiples, the predicted surface-related multiples correspond kinematically to the true surface-related multiples and can be mapped to the estimated true surface-related multiples by the SDNN. The SDNN-LWCLF uses a local wavefield characteristic (LWC) loss function with physical properties to constrain the nonlinear optimization process. The LWC loss function is composed of the mean-absolute-error (MAE) and local normalized crosscorrelation (LNCC) loss functions. LNCC can measure the local similarity between the estimated multiples and the estimated primaries. By minimizing the LWC loss function, the MAE loss function corrects amplitudes and phases of the predicted surface-related multiples to their true values, and the LNCC loss function automatically checks and reduces the leaked multiples and residual primaries in the estimated true surface-related multiples. Our SDNN-LWCLF method does not need label data, such as true primaries and true surface-related multiples, which are usually unavailable in real-world applications. Therefore, the SDNN-LWCLF solves the problem of missing training data. Synthetic and field data examples demonstrate that our method can well suppress the surface-related multiples, and its suppression effect is better than the traditional L1-norm adaptive subtraction method and the SDNN method based on only the MAE loss function.

Surface-Related Multiples Elimination for Waterborne GPR Data

Ground-penetrating radar (GPR) is a well-respected, effective, and efficient geophysical technique. However, for underwater engineering detection and underwater archaeology, the measured B-scan profiles typically contain surface-related multiple waves, which can reduce the signal to noise ratio and interfere with the interpretation of results. SRME is a feedback iteration method based on wave equation, which is frequently utilized in marine seismic explorations but very rarely in GPR underwater engineering detection. To fill this gap, we applied SRME to suppress multiples that appear in GPR underwater images. When we compared the effectiveness of the underwater horizontal layered model and the underwater undulating interface model, we found a high match rate between the predicted and the real-world multiples. In addition, the addition of the Gaussian random noise level with a 4% maximum amplitude to the B-scan profile of the horizontal stratified model yielded satisfactory multiple suppression results. Finally, we applied this method to the B-scan GPR section of actual underwater archaeological images to achieve multiple suppression, which can more effectively weaken and inhibit the surface-related multiples. Both numerical simulations and actual field data show that the SRME method is highly suitable for interpreting waterborne GPR data, and more accurate interpretation can be obtained from the GPR profile after multiples suppression.

Seismic forward modeling of acoustic surface-related order-separated multiples

Seismic surface-related multiples have become a hot topic of great significance due to the buried geological information provided by broader illumination areas than primaries. In recent years, researchers attempt to extract the hidden hint of multiples rather than treating them as noise and eliminating them directly. The elimination methods, e.g., the surface-related multiple elimination (SRME) and the inverse scattering series free-surface multiple elimination (ISS-FSME), may be affected by the overlapping or proximity of primaries and multiples. Typical imaging methods, e.g., the reverse time migration (RTM) and the least-square reverse time migration (LSRTM), suffer severe crosstalk artifacts from multiples of inappropriate order and smooth migration velocities. To study the characteristics of primaries and surface-related multiples, whether for elimination or imaging, we propose a forward modeling method of acoustic surface-related order-separated multiples established on the areal/virtual source assumption. The free surface is replaced with an absorbing surface under the dipole source approximation and the ghost creation approach. We present two reflection operators to approximate the reflection at the free surface and apply them to the areal source to obtain ideal results. Numerical experiments on three models prove the effectiveness of the proposed forward modeling method of acoustic surface-related order-separated multiples.

Seismic Multiple Attenuation in the Continent–Ocean Transition Zone of the Northern South China Sea

In this study, we process four new multichannel reflection seismic profiles acquired in 2015 and 2016 in the continent–ocean transition zone (COT) of the northern South China Sea (SCS). We apply a multi-domain, progressive, and seabed-controlled denoising technique and obtain a good denoising effect. Combining velocity analysis in the multi-round time domain and forward modeling, we analyze the types and characteristics of multiples in the study area and formulate an effective demultiple technique to attenuate strong seabed multiples, diffracted multiples from rough seafloor, and other multiples from deep reflectors. The processing results show that the sea surface-related multiple elimination technique predicts the sea surface-related multiples accurately by data convolution, and has a good effect in attenuating seabed multiples. Diffracted multiple attenuation method extracts high-frequency and high-energy diffracted multiples, and suppresses multiples by the energy ratios of multiples to primary events. To attenuate deep multiples, we select predictive deconvolution to attenuate periodic deep multiples after many trials and detailed analysis. The combination of these different techniques in sequence proves to be quite effective in attenuating different seismic multiples in the COT. The imaged crustal structures near the COT often show strong magmatism and/or basement uplifting. The faulted and thinned continental crust adjacent to the COT corresponds to the lowest free-air gravity anomalies. Gravity anomalies often increase from the COT to the oceanic crust. An exception is to the northeast of the SCS, where the relatively wide COT shows very high gravity anomalies, likely induced by mantle upwelling and serpentinization.

An improved method for internal multiple elimination using the theory of virtual events

Compared with first-order surface-related multiples from marine data, the onshore internal multiples are weaker and are always combined with a hazy and occasionally strong interference pattern. It is usually difficult to discriminate these events from complex targets and highly scattering overburdens, especially when the primary energy from deep layers is weaker than that from shallow layers. The internal multiple elimination is even more challenging due to the fact that the velocity and energy difference between primary reflections and internal multiples is tiny. In this study, we propose an improved method which formulates the elimination of the internal multiples as an optimization problem and develops a convolution factor T. The generated internal multiples at all interfaces are obtained using the convolution factor T through iterative inversion of the initial multiple model. The predicted internal multiples are removed from seismic data through subtraction. Finally, several synthetic experiments are conducted to validate the effectiveness of our approach. The results of our study indicate that compared with the traditional virtual events method, the improved method simplifies the multiple prediction process in which internal multiples generated from each interface are built through iterative inversion, thus reducing the calculation cost, improving the accuracy, and enhancing the adaptability of field data.

Seismic de-multiple strategy in the submarine slope of Taiwan accretionary wedge

Reducing multiple contaminations in reflection seismic data remains one of the primary challenges in marine seismic data processing. Besides geological settings, its effectiveness is also dependent on the multiple removal methods. In this study, we undertook two legacy 2D multi-channel seismic data crossing the accretionary wedge off SW Taiwan to test the efficiency of various multiple-attenuation scenarios. The tectonic domain has resulted from the incipient arc-continent collision between the northern rifted margin of the South China Sea and the Luzon volcanic arc. The wedge extends from shallow water to deep water bathymetries, hence promoting both short-period and long-period multiples within the seismic records. A cascade of de-multiple methods was tested to attenuate multiple energy under various seafloor bathymetry and tectonic areas. The first step relies on the periodicity nature of multiples. Spatial dependent predictive deconvolution in the x-t domain was performed to attenuate reverberations and improve temporal resolution in the time domain. Wave-equation multiple attenuation (WEMA) was applied to suppress the water layer multiples based on a combination of numerical wave extrapolation in the shot domain through water layer and water bottom reflectivity. Surface-related multiple elimination (SRME) aimed to attenuate the residual water bottom multiple and peg-leg multiple by assuming surface-related multiples can be kinematically predicted via convolution of pre-stack seismic traces at possible surface multiple reflection locations. The second step exploits the spatial move-out difference behavior between primaries and multiples. Parabolic Radon transforms far-offset multiples by subtracting noise energy in the τ-p domain, whereas the frequency-wave number (F-K) filter aimed to eliminate any residual multiples energy in the F-K domain. Predictive deconvolution improved seismic resolution and suppressed sea-bottom reverberation energy in the continental and lower wedge slopes, but not in the upper wedge slope. WEMA, Radon filter, and F-K filter reduced multiples energy both at the continental slope and wedge slope; whereas SRME made minimal impact on both areas. Since the reflection seismic datasets stretch diverse tectonic environments and water depth, there was no single multiple attenuation method capable to suppress multiples in all tectonic environments and bathymetry.

Surface-Related and Internal Multiple Elimination Using Deep Learning

Multiple elimination has always been a key, challenge, and hotspot in the field of hydrocarbon exploration. However, each multiple elimination method comes with one or more limitations at present. The efficiency and success of each approach strongly depend on their corresponding prior assumptions, in particular for seismic data acquired from complex geological regions. The multiple elimination approach using deep learning encodes the input seismic data to multiple levels of abstraction and decodes those levels to reconstruct the primaries without multiples. In this study, we employ a classic convolution neural network (CNN) with a U-shaped architecture which uses extremely few seismic data for end-to-end training, strongly increasing the neural network speed. Then, we apply the trained network to predict all seismic data, which solves the problem of difficult elimination of global multiples, avoids the regularization of seismic data, and reduces massive amounts of calculation in traditional methods. Several synthetic and field experiments are conducted to validate the advantages of the trained network model. The results indicate that the model has the powerful generalization ability and high calculation efficiency for removing surface-related multiples and internal multiples effectively.

High-Efficiency Prediction of Surface-Related Multiples by Inverse Scattering Series in the Curvelet Domain

High-Efficiency Prediction of Surface-Related Multiples by Inverse Scattering Series in the Curvelet Domain

Elimination of free-surface-related multiples by combining Marchenko scheme and seismic interferometry

The removal of free-surface-related multiples plays an important role in seismic data processing. We have developed a novel scheme for predicting free-surface-related multiples by combining the revised Marchenko equations with free-surface effects and convolutional seismic interferometry. This data-driven method can create the free-surface-related multiple prediction using only the reflection response recorded at the free surface and a macrovelocity model estimated in the first layer. By setting only one reference boundary in the first layer, we can stably and efficiently retrieve the one-way upgoing and scattered downgoing Green’s functions at virtual focusing points and then predict all orders of free-surface-related multiples. Numerical experiments on a 2D model and a subset of the Marmousi model indicate that this method can accurately and efficiently predict the traveltimes of all orders of free-surface-related multiples. After applying least-squares matched filtering, all orders of the surface-related multiples can be effectively eliminated from the raw shot gathers.

Impacts of model resolution on multiple prediction

Despite theoretical advancements in multiple identification and elimination, the application and success of these advancements are questionable in some cases and are limited to marine environments, especially deepwater. In land seismic, however, a clear understanding of the internal multiple generators is not readily available; thus, efforts to attenuate them often are quite ineffective. We have analyzed, in the case of many thin layers, how the primaries and multiples are affected by fine-scale variations in the velocity model. Cross-coherence is used to measure the similarity of the original primaries/multiples and the ones generated from upscaled velocity models. The combined use of kurtosis and the cross-coherence method enables us to precisely quantify the impact of fine layering on multiples. Test results demonstrate that surface-related multiples and internal multiples are much more sensitive than the primaries to thickness variations of the velocity model. As the thickness of each upscaled layer varies from 1 to 21 m, multiples change rapidly, whereas primaries are almost the same. The high sensitivity of internal multiples on fine layering suggests that the detailed model information should be considered in model parameterization in the internal multiple removal, especially for model-driven methods.

An Unsupervised Deep Neural Network Approach Based on Ensemble Learning to Suppress Seismic Surface-Related Multiples

Surface-related multiples are generally removed as noise. To suppress surface-related multiples, we propose an unsupervised deep neural network approach based on ensemble learning (UDNNEL). The unsupervised deep neural network (UDNN) has excellent nonlinear mapping ability, which maps the predicted surface-related multiples to true surface-related multiples, thereby completing the separation and estimation of multiples and primaries. UDNN consists of three deep neural networks (DNNs), one input data, six outputs, and six pseudo-labels (PLs). In practical use, input data are the predicted surface-related multiples, PLs consist of full-wavefield data and 0 matrices, and outputs are the desired results of estimated true surface-related multiples and differences between these desired results. Input data, one DNN, and corresponding output data are combined into a single base learner. Each base learner corrects amplitudes and phases of the predicted surface-related multiples and maps predicted multiples to true surface-related multiples under the minimization of the total loss function. The principle ensures that our UDNNEL method does not need true primaries or true multiples as training data and solves the problem of missing training datasets. Ensemble learning combines the advantages of three base learners and integrates the nonlinear optimization capabilities of three DNNs to achieve better multiple suppression effectiveness than a single base learner. Therefore, UDNNEL is better than UDNN based on a single base learner (UDNNSBL). Two synthetic data examples verify that our proposed method has good surface-related multiple suppression effectiveness. Another field data example demonstrates that our proposed method can efficiently suppress multiples under complex conditions.

Adaptive Surface-Related Multiple Subtraction Based on Convolutional Neural Network

Adaptive surface-related multiple subtraction is an important step of surface-related multiple elimination (SRME) method. Generally, the traditional methods match the predicted multiples with the original data using obtained filter. In this letter, we propose a matching algorithm based on convolutional neural network (CNN) to strike a balance between the attenuation of multiples and the protection of primaries. Taking the predicted surface-related multiples as input, CNN’s output can better match with the original data. From the processing results of synthetic data, compared with the traditional <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 or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> -norm method, CNN method has lower calculation cost and the signal-to-noise ratio (SNR) of the primaries obtained after matching and subtraction is increased by 3 dB. The test of Pluto data and physical simulation data show that the proposed method can effectively remove surface-related multiples in seismic data.

Seismic Surface-Related Multiples Suppression Based on SAGAN

Suppressing multiples from seismic records is necessary to improve imaging quality. Deep neural networks (DNNs) can automatically mine features from data. Once a network is successfully trained, it can process data with extremely high efficiency. In this letter, a generative adversarial network (GAN) framework is proposed to remove surface-related multiples in both synthetic and field datasets, where the generator is U-Net with Markov discriminator. Adding self-attention (SA) blocks to GAN improves processing precision. Improved signal noise ratio (SNR), and accurate reverse time migration (RTM) images implemented by network’s outputs of synthetic datasets, jointly support that this network is effective on surface-related multiple suppression. Based on the results from field application, deep learning method in this letter is comparable to conventional adaptive surface-related multiple elimination (SRME) method but time-saving. By constructing an end-to-end workflow for seismic surface-related multiples suppression, small batches dataset can be used to train the network, and large batches of datasets can be processed accurately and efficiently.

Linear inversion-imaging method based on joint primary reflected waves and surface-related multiples

Linear inversion-imaging method based on joint primary reflected waves and surface-related multiples

First-order multiples imaging aided by water bottom

Surface-related multiples frequently propagate into the subsurface and contain abundant information on small reflection angles. Compared with the conventional migration of primaries, migration of multiples offers complementary illumination and a higher vertical resolution. However, crosstalk artifacts caused by unrelated multiples during reverse time migration (RTM) using multiples severely degrade the reliability and interpretation of the final migration images. Therefore, we proposed RTM using first-order receiver-side water-bottom-related multiples for eliminating crosstalk artifacts and enhancing vertical resolution. We first backward propagate the first-order receiver-side water-bottom-related multiples using a water-layer model, followed by saving the upper boundary wavefield. Then we produce the source wavefield using a seismic wavelet and the receiver wavefield by back-extrapolating the saved boundary. Finally, the cross-correlation imaging condition is applied to generate the final image. This method transforms the receiver-side multiples into primaries, followed by the conventional migration processing procedures. Numerical examples using synthetic datasets demonstrate that our method significantly enhances the imaging quality by eliminating crosstalk artifacts and improving the resolution.

Horizon Picking from SBP Images Using Physicals-Combined Deep Learning

Horizon picking from sub-bottom profiler (SBP) images has great significance in marine shallow strata studies. However, the mainstream automatic picking methods cannot handle multiples well, and there is a need to set a group of parameters manually. Considering the constant increase in the amount of SBP data and the high efficiency of deep learning (DL), we proposed a physicals-combined DL method to pick the horizons from SBP images. We adopted the DeeplabV3+ net to extract the horizons and multiples from SBP images. We generated a training dataset from the Jiaozhou Bay survey (Shandong, China) and the Zhujiang estuary survey (Guangzhou, China) to increase the applicability of the trained model. After the DL processing, we proposed a simulated Radon transform method to eliminate the surface-related multiples from the prediction by combining the designed pseudo-Radon transform and correlation analysis. We verified the proposed method using actual data (not involved in the training dataset) from Jiaozhou Bay and Zhujiang estuary. The positions of picked horizons are accurate, and multiples are suppressed.

3D surface-related multiples elimination based on improved apex-shifted Radon transform

3D surface-related multiples elimination based on improved apex-shifted Radon transform

Migration using sea surface-related multiples: Challenges and opportunities

In marine geophysical exploration using seismic data, sea surface-related multiples can be used for imaging and producing extra illumination. However, the imaging of multiples can involve challenges related to acquisition and migration. The acquisition offset, trace spacing, and towing direction are all key factors that control the quality of imaging using multiples. The migration algorithm also affects its successful implementation. Ultimately, the objective of imaging using multiples is not to replace imaging using primaries; instead, imaging using multiples augments conventional migration when the imaging of primaries suffers from illumination issues. The imaging of multiples has the potential to mitigate shallow acquisition footprints and provide high-resolution shallow images, allowing the identification of drilling hazards and a reduction in drilling risks. Additionally, the imaging of multiples can provide high-resolution illumination of complex structures along salt boundaries, which is valuable for velocity model building. Moreover, this technique has the potential to reduce exploration expenses when applied with ocean-bottom seismic data.

Training deep networks with only synthetic data: Deep-learning-based near-offset reconstruction for (closed-loop) surface-related multiple estimation on shallow-water field data

Accurate removal of surface-related multiples remains a challenge in shallow-water cases. One reason is that the success of surface-related multiple estimation (SRME)-related algorithms is sensitive to the quality of the near-offset reconstruction. When it comes to a larger missing gap and a shallower water bottom, the state-of-the-art near-offset gap construction method — the parabolic Radon transform — fails to provide reliable recovery of the shallow reflections due to the limited information from the data and highly curved events at near offsets with strong lateral amplitude variations. Therefore, we have developed a novel workflow that first deploys a deep-learning-based reconstruction of the shallow reflections and then uses the reconstructed data as the input for the subsequent surface multiple removal. In particular, we use a convolutional neural network architecture — U-net that was developed from convolutional autoencoders with extra direct skip connections between different levels of encoders and the corresponding decoders. Instead of using field data directly in network training, the training set is carefully synthesized based on the prior water-layer information of the field data; thus, a fully sampled field data set, which is difficult to obtain, is not needed for training in our workflow. An inversion-based approach — closed-loop SRME — is used for the surface multiple removal, in which the primaries are directly estimated via full-waveform inversion in a data-driven manner. Finally, the effectiveness of our workflow is determined based on 2D North Sea field data in a shallow-water scenario (92.5 m water depth) with a relatively large minimum offset (150 m).