Surface-related Multiple Elimination Research Articles

OBC shallow water de-multiple based on the principle of Fresnel diffraction

Abstract In shallow-water ocean-bottom cable (OBC) seismic data, the ineffectiveness of conventional surface-related multiple elimination (SRME) methods due to poor seabed records is addressed. This research utilizes the seismic wavefield received by multiple cables from a single shot gather to predict shallow water multiple models for that shot gather. Initially, the seismic data within a finite aperture around a seismic trace in the time domain shot gather is treated as the known seismic wavefield. This seismic wavefield is then extrapolated along the water layer to this seismic trace, following the Fresnel diffraction principle. The extrapolated data becomes the shallow water multiple model for this seismic trace. This process is repeated for each trace in the shot gather to obtain the shallow water multiple model of the entire shot gather. Forward modeling tests have shown that smaller data apertures can effectively avoid the impact of spatial aliasing on multiple model prediction. To address the overlap of primary waves and shallow water multiples in deep seismic data, which have lower dominant frequencies, the multiple model data is used as a known seismic wavefield and extrapolated along the water layer again. This produces second-order and higher-order multiple models. Applying this model to suppress multiple waves can minimize primary waves loss. This entirely data-driven approach necessitates solely water depth information, imposing no additional conditions. Both forward modeling and real seismic data testing validate the efficacy of this method in shallow water.

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.

Improved Seismic Residual Diffracted Multiple Suppression Method Based on Object Detection and Image Segmentation

Seismic multiple is one of the most common noises in marine seismic data, which heavily affects subsequent processing and interpretation. To eliminate the influence of seismic multiples, many methods have been developed, while surface-related multiple elimination (SRME) is one of the most widely deployed methods. However, results of SRME always contain a few strong residual diffracted multiples (RDMs) in practice because of the unprecise prediction of diffracted multiples compared to reflection multiples. If we try to apply further multiple suppression methods to SRME results, it not only tends to damage the signals, but also spends lots of unnecessary computations where there is no RDM. In this article, we propose an improved RDM suppression method based on object detection and image segmentation. First, we employ an object detection network to locate bounding boxes containing RDMs in the SRME results. Then a threshold-based image segmentation method is utilized to identify regions of strong RDMs in the detected boxes. According to the segmentation results, parameters for weak multiples and strong multiples are provided for the adaptive multiple subtraction (AMS) in different regions to generate different results. At last, we combine the suppression results of strong RDMs and weak RDMs as the final results. Application on field data demonstrates that our method is able to suppress RDMs with little loss of signal.

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.

Efficient 2D multiple attenuation using SRME with curvelet-domain subtraction

Surface Related Multiple Elimination (SRME) usually suffers the issue of either over-attenuation that damages the primaries or under-attenuation that leaves strong residual multiples. This dilemma happens commonly when SRME is combined with least-squares subtraction. Here we introduce a more sophisticated subtraction approach that facilitates better separation of multiples from primaries. Curvelet-domain subtraction transforms both the data and the multiple model into the curvelet domain, where different frequency bands (scales) and event directions (orientations) are represented by a finite number of curvelet coefficients. When combined with adaptive subtraction in the time–space domain, this method can handle model prediction errors to achieve effective subtraction. We demonstrate this method on two 2D surveys from the TAiwan Integrated GEodynamics Research (TAIGER) project. With a careful parameter determination flow, our result shows curvelet-domain subtraction outperforms least-squares subtraction in all geological settings. We also present one failed case where specific geological condition hinders proper multiple subtraction. We further demonstrate that even for data acquired with short cables, curvelet-domain subtraction can still provide better results than least-squares subtraction. We recommend this method as the standard processing flow for multi-channel 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.

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.

Attenuation of long period multiple using F-k filter and surface-related multiple elimination methods on 2D broadband seismic data from Morowali Waters, Sulawesi

Wide range frequency bandwidth on seismic data is a necessity due to its close relation to resolution and depth of target. High-frequency seismic waves provide high-resolution imaging that defines thin bed layers in shallow sediment, while low-frequency seismic waves can penetrate into deeper target depth. As a result of broadband seismic technology, its wide range of frequency bandwidth is a suitable geophysical exploration method in the oil and gas industry. A major obstacle that is frequently found in marine seismic data acquisition is the existence of multiples. Short period multiple and reverberation are commonly attenuated by the predictive deconvolution method on prestack data. Advanced methods are needed to suppress long period multiple in marine seismic data. The 2D broadband marine seismic data from deep Morowali Waters, Sulawesi, contains both short and long period multiples. The predictive deconvolution, which is applied to the processing sequences, successfully eliminates short period multiple on prestack data. The combination of F-k filter and Surface Related Multiple Elimination (SRME) methods are successful in attenuating long period multiple of the 2D broadband marine seismic data. The Prestack Time Migration section shows fine resolution of seismic images.

Fast local primary-and-multiple orthogonalization for surface-related multiple leakage estimation and extraction

Surface-related multiple elimination (SRME) has proven to be a robust surface multiple and primary estimation tool for decades. However, surface-related multiple leakage is still commonly observed in SRME-processed results due to imperfect multiple predictions. Usually, adaptive subtraction cannot fully correct for these effects without primary damage. Local primary-and-multiple orthogonalization (LPMO) has recently been proposed to partially mitigate surface multiple leakage, by multiplication of the estimated primaries with a weight function that scales down residual multiples while preserving primaries. The weight function is determined by shaping regularization followed by thresholding and median filtering. Although effective leakage extraction can be achieved, LPMO has a large computational cost due to many conjugate-gradient iterations within the shaping regularization-based inversion framework. Using information on the typical local coherency length of primaries and multiples, a spatially constrained scaled point-by-point division can be used to avoid the iterative inversion within the LPMO method. Based on this, we have adopted a fast LPMO (FLPMO) for surface-related multiple leakage estimation and extraction. Applications on two different field data sets demonstrate the very similar surface multiple leakage extraction performance for LPMO and FLPMO, while showing that the scaled point-by-point division in FLPMO is approximately 40 times faster on real data sets than the shaping regularization-based inversion in LPMO.

Robust estimation of primaries by sparse inversion and Marchenko equation-based workflow for multiple suppression in the case of a shallow water layer and a complex overburden: A 2D case study in the Arabian Gulf

Suppression of surface-related and internal multiples is an outstanding challenge in seismic data processing. The former is particularly difficult in shallow water, whereas the latter is problematic for targets buried under complex, highly scattering overburdens. We have developed a two-step, amplitude- and phase-preserving, inversion-based workflow that addresses these problems. We apply robust estimation of primaries by sparse inversion (R-EPSI) to solve simultaneously for the surface-related primaries Green’s function and the source wavelet. A significant advantage of the inversion approach of the R-EPSI method is that it does not rely on an adaptive subtraction step that typically limits other demultiple methods such as surface-related multiple elimination. The resulting Green’s function is used as the input to a Marchenko equation-based approach to predict the complex interference pattern of all overburden-generated internal multiples at once. In this approach, no a priori information about the subsurface is needed. In theory, the interbed multiples can be predicted with correct amplitude and phase and, again, no adaptive filters are required. We illustrate this workflow by applying it on an Arabian Gulf field data example. It is crucial that all preprocessing steps are performed in an amplitude-preserving way to restrict any impact on the accuracy of the multiple prediction. In practice, some minor inaccuracies in the processing flow may end up as prediction errors for which corrections will be needed. Hence, we conclude that the use of conservative adaptive filters were necessary to obtain the best results after interbed multiple removal. The obtained results indicate promising suppression of surface-related and interbed multiples.

Adaptive Multiple Subtraction Based on an Accelerating Iterative Curvelet Thresholding Method.

In the seismic exploration, recorded data contain primaries and multiples, where primaries, as signals of interest, can be used to image the subsurface geology. Surface-related multiple elimination (SRME), one important class of multiple attenuation algorithms, operates in two stages, multiple prediction and subtraction. Due to the phase and amplitude errors in the predicted multiples, adaptive multiple subtraction (AMS) is the key step of SRME. The main challenge of this technique resides in removing multiples without distorting primaries. The curvelet-based AMS methods, which exploit the sparsity of primary and multiple in curvelet domain and the misfit between the original and estimated signals in data domain, have shown outstanding performances in real seismic data processing. These methods are realized by using the iterative curvelet thresholding (ICT), which has heavy computation burden since it includes two forward/inverse curvelet transform (CuT) pairs in each iteration. To ameliorate the computational cost, we propose an accelerating ICT method by exploiting the misfit between the original and estimated signals in curvelet domain directly. Since the proposed method only needs do one forward/inverse CuT pair, it is faster than the traditional ICT method. Considering that the error of the predicted multiple is frequency-dependent, we furthermore introduce the joint constraints within different frequency bands to stabilize and improve the multiple attenuation. Synthetic and field examples demonstrate that the proposed method outperforms the traditional ICT method. In addition, the proposed method has shown to be suitable for refining other AMS methods' results, yielding a SNR improvement of 0.5-2.8 dB.

Multiple attenuation methods in short-offset 2D marine seismic data: a case study in Cendrawasih Bay

Real marine seismic data are typically embedded with free-surface multiples energy, which are troublesome in imaging an accurate seismic cross-section. In addition, more challenging situation is to bring optimum result with a short-offset streamer due to the coherent nature of multiples. In this study, we present a comparison of three methods for attenuating free-surface multiples energy in short-offset 2D seismic data from Cendrawasih Bay. Multiple attenuation methods include F-K filter, Radon transform, and Surface Related Multiple Elimination (SRME) are processed until the final Pre-Stack Time Migration (PSTM) results. Predictive deconvolution is applied in order to suppress short period multiples prior to free-surface multiple attenuation method. Predictive deconvolution successfully identifies and removes the predictable wavelet of short period multiples. Radon transform shows poor result in short-offset seismic data even if it is combined with F-K filter method. Combination of both F-K filter and SRME are successfully attenuate free-surface multiples and should be considered as proper solution to increase signal to noise ratio.

Common reflection surface methods in low fold coverage seismic data of complex marine geological structures

The primary objective of seismic data processing is to generate seismic cross-section with an optimum signal to noise ratio to model geological structures imaging as accurately as possible with the actual condition. However, we may look onto complex geological structures with less than adequate number of fold coverage seismic data. In this study, Common Reflection Surface (CRS) method is applied to generate 2D marine seismic cross-sections with better reflector continuity of 3 (three) real data seismic lines. It is based on obtaining the suitable aperture value along the attributes namely RN, RNIP, and a. Its results are compared to conventional Common Mid-Point (CMP) ones. The final products are presented in multiple- attenuated and time-migrated cross-section with Surface Related Multiple Elimination (SRME) and Kirchhoff Migration techniques, respectively. Comparison of final time-migrated both CRS and CMP stacks show the CRS method is more effective and reliable to model accurate complex marine geological structures with high signal to noise ratio based on reflector continuity and noise reduction.

Simultaneous Imaging of Primaries and Multiples Based on Wavelet Estimation and Stereographic Imaging Condition

Multiple reflections contain abundant structural information about the subsurface because of their smaller reflection angles and wider coverage. Multiple imaging has increasingly attracted attention. However, the imaging of multiples first needs to separate the multiples, which is a time-consuming and high-cost job. Therefore, the simultaneous imaging of primaries and multiples, which can make good use of multiple reflections with no need for multiple separation, has increasingly attracted attention. However, there are still some challenges to the conventional method, including wavelet selection and image artifacts suppression. In this paper, an improved method of the simultaneous imaging of primaries and multiples is proposed by introducing two creative strategies. First, considering that the wavelet, which is usually estimated by signal analysis under certain assumptions, is crucial to the simultaneous imaging, we propose to estimate the wavelet by iterative SRME (surface-related multiple elimination) using the original data containing multiples based on wave equation theory instead of signal analysis. Second, the stereographic imaging condition is introduced to suppress the crosstalk artifacts in the image. According to the numerical examples and field data test, the feasibility and effectiveness of our approach is verified.

Surface-related multiple leakage extraction using local primary-and-multiple orthogonalization

Accurate multiple removal remains an important step in seismic data processing sequences. Most multiple removal methods, such as surface-related multiple elimination (SRME), consist of a multiple prediction step and an adaptive subtraction step. Due to imperfect circumstances (e.g., coarse data sampling) or built-in assumptions (e.g., 2D method versus 3D data), multiple leakage is commonly observed in the results. More aggressive adaptive multiple subtraction can reduce the leakage problem, for example, by using small local windows and a long filter length, but at the risk of severely damaging the primaries due to overfitting. In contrast, conservative adaptive subtraction with large or global windows and a short filter length can preserve most primary energy while tending to have more multiple leakage because of underfitting. Assuming that the primaries and multiples do not correlate locally in the time-space domain, our solution to this problem is to extract the leaked multiples from the initially estimated primaries using local primary-and-multiple orthogonalization (LPMO) rather than restoring the damaged primaries. Our framework consists of two steps: an initial primary estimation step and a multiple leakage extraction step. The initial step corresponds to conservative SRME (or an equivalent method) that produces the initially estimated primary and multiple models. The second step is based on LPMO to retrieve the leaked multiples from the estimated primaries via a time- and space-varying weight function that is estimated from the local correlation of predicted multiples and residual multiples in the estimated primaries with the help of shaping regularization. In this way, we can obtain a better primary model that has much less leaked multiple energy and less primary damage at the same time. We find good performance of our framework via two synthetic data examples and one field data example.

Enhancing subsurface imaging and reservoir characterization in the Marcellus Shale play, northeast Pennsylvania, through advanced reprocessing of wide-azimuth 3D seismic data

The Marcellus Shale sits in a geologically complicated region, characterized by faults and salt-cored compressional folds. Large lateral velocity variations associated with this complex geology make seismic imaging difficult. Recently acquired 3D seismic with wide azimuthal coverage and long offsets have poorly imaged folds and faults within the Marcellus Shale. Wells drilled using the existing 3D seismic volumes often encounter incorrect bed dips and folds mistakenly interpreted as faults (or vice versa) because of the poor imaging quality. We carefully examined each processing step in the seismic processing and imaging workflow and identified a few key drivers that could potentially lead to improvements in subsurface imaging. Starting from field seismic records, we identified and corrected geometry errors. To improve signal-to-noise ratio, we applied advanced noise attenuation practices including land surface-related multiple elimination. We carefully tested and validated an extension patch in the 5D seismic data interpolation that improved the offset coverage in the crossline direction. Furthermore, we performed orthorhombic prestack time migration (PSTM). An orthorhombic velocity model fits the geology better than the traditional vertical transverse isotropic (VTI) model for layered subsurface because a dominant set of orthogonal fracture sets is present in the basin. We achieved a significant improvement of subsurface imaging by implementation of these processing steps as key drivers. The new orthorhombic PSTM correctly images steeply dipping (75° and above) faults that were elusive or absent on the previous VTI migration. Additionally, previously interpreted faults are now clearly imaged as folds, small and large in scale. These imaging improvements have enabled the accurate drilling of lateral wells in the target that otherwise would have been drilled out of zone. Some large faults, which pass through the Marcellus, Mahantango, and Tully Formations, are now well-imaged but were previously barely visible. Successful imaging of such large faults helps identify and avoid geohazards. The dramatic improvement in the subsurface imaging and amplitude-preserving processing enhance the reservoir description of the Marcellus Shale.

Comparison of the inverse scattering series free-surface multiple elimination (ISS FSME) algorithm with the industry-standard surface-related multiple elimination (SRME): Defining the circumstances in which each method is the appropriate toolbox choice

The industry-standard surface-related multiple elimination (SRME) method provides an approximate predictor of the amplitude and phase of free-surface multiples. This approximate predictor then calls upon an energy-minimization adaptive subtraction step to bridge the difference between the SRME prediction and the actual free-surface multiple. For free-surface multiples that are proximal to other events, the criteria behind energy-minimization adaptive subtraction can be invalid. When applied under these circumstances, a proximal primary can often be damaged. To reduce the dependence on the adaptive process, a more accurate free-surface multiple prediction is required. The inverse scattering series (ISS) free-surface multiple elimination (FSME) method predicts free-surface multiples with accurate time and accurate amplitude of free-surface multiples for a multidimensional earth, directly and without any subsurface information. To quantify these differences, a comparison with analytic data was carried out, confirming that when a free-surface multiple interferes with a primary, applying SRME with adaptive subtraction can and will damage the primary, whereas ISS free-surface elimination will precisely remove the free-surface multiple without damaging the interfering primary. On the other hand, if the free-surface multiple is isolated, then SRME with adaptive subtraction can be a cost-effective toolbox choice. SRME and ISS FSME each have an important and distinct role to play in the seismic toolbox, and each method is the indicated choice under different circumstances.

Limited off-set consideration in de-multiple process of seismic Lake Towuti

Multiple attenuation effect study in Lake Towuti seismic data is based on limited offset recording. The goal of this study is to better understand the sediments structure of Lake Towuti that layered above East Sulawesi ophiolitic and metamorphic rocks for stratigraphic interpretation. Surface-related multiple and internal multiple are a common noise signal that recorded in this seismic data, alongside the primary signal from the sediments layer. In order to get a representative subsurface image of the lake, a combination of some de-multiple method to suppress this noise signal has been applied in previous study. However, the line seismic that used in this seismic recording is less than 100 m. From our perspective, this limited offset line fact have to be considered carefully. It’s because some application of de-multiple method in this case will not effectively suppress the noise. In this study, we give an alternative combination method in function to put out the surface-related multiple of the case. First, we enhance the S/N ratio and velocity analysis of the data by Common Reflection Surface (CRS) method. From this enhancement, CRS can give a direct advantage when applying some de-multiple method, especially in Predictive Deconvolution and SRME method. Moreover, we combine the application of de-multiple theory with the strict arrangement of Predictive Deconvolution, Surface-Related Multiple Elimination (SRME), and the last is F-K filter. Those combination methods precisely give a better imaging of Lake Towuti sediments layer in this seismic processing study.