Free-surface multiple attenuation for blended data

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. V227-V233
Author(s):  
Jitao Ma ◽  
Xiaohong Chen ◽  
Mrinal K. Sen ◽  
Yaru Xue
Keyword(s):  
Free Surface ◽  
Seismic Data ◽  
Synthetic Data ◽  
Primary Energy ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Crosstalk Noise ◽  
Multiple Attenuation ◽  
Blended Data ◽  
Value Decomposition

Blended data sets are now being acquired because of improved efficiency and reduction in cost compared with conventional seismic data acquisition. We have developed two methods for blended data free-surface multiple attenuation. The first method is based on an extension of surface-related multiple elimination (SRME) theory, in which free-surface multiples of the blended data can be predicted by a multidimensional convolution of the seismic data with the inverse of the blending operator. A least-squares inversion method is used, which indicates that crosstalk noise existed in the prediction result due to the approximate inversion. An adaptive subtraction procedure similar to that used in conventional SRME is then applied to obtain the blended primary — this can damage the energy of primaries. The second method is based on inverse data processing (IDP) theory adapted to blended data. We derived a formula similar to that used in conventional IDP, and we attenuated free-surface multiples by simple muting of the focused points in the inverse data space (IDS). The location of the focused points in the IDS for blended data, which can be calculated, is also related to the blending operator. We chose a singular value decomposition-based inversion algorithm to stabilize the inversion in the IDP method. The advantage of IDP compared with SRME is that, it does not have crosstalk noise and is able to better preserve the primary energy. The outputs of our methods are all blended primaries, and they can be further processed using blended data-based algorithms. Synthetic data examples show that the SRME and IDP algorithms for blended data are successful in attenuating free-surface multiples.

Free-surface multiple attenuation using inverse data processing in the coupled plane-wave domain

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. V75-V81 ◽  
Author(s):  
Jitao Ma ◽  
Mrinal K. Sen ◽  
Xiaohong Chen
Keyword(s):  
Free Surface ◽  
Plane Wave ◽  
Data Processing ◽  
Synthetic Data ◽  
Primary Energy ◽  
Matrix Inversion ◽  
Data Matrix ◽  
Inversion Method ◽  
Multiple Attenuation ◽  
Invariant Embedding

Free-surface multiples contain a large amount of energy in the seismic data because of large reflectivity of the free surface. We propose a method for free-surface multiple attenuation by a simple muting in the inverse coupled plane-wave domain. Our method is based on inverse data processing and the well-known 2D invariant embedding technique. If the lateral variation in subsurface structure is smooth, the data are well compressed in the 2D coupled plane-wave domain, reducing computation costs and stabilizing the inversion procedure. Surface multiples and primaries are well separated in the inverse coupled plane-wave domain, and multiples can be eliminated by simple muting, which does not damage the primary energy. To reduce artifacts, wraparound, and noise introduced by the frequency-domain data matrix inversion, horizontal and vertical tapers are applied. A least-squares matrix inversion method is chosen to stabilize the inversion. Synthetic data examples show that plane-wave inverse data processing is stable and successful in attenuating free-surface multiples.

Using even terms of the scattering series for deghosting and multiple attenuation of ocean‐bottom cable data

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 579-592 ◽  
Author(s):  
Luc T. Ikelle
Keyword(s):  
Free Surface ◽  
Seismic Data ◽  
Primary Energy ◽  
Ocean Bottom ◽  
Receiver Side ◽  
Direct Wave ◽  
Sea Floor ◽  
Multiple Attenuation ◽  
Marine Surface ◽  
Streamer Data

Inverse scattering multiple attenuation (ISMA) is a method of removing free‐surface multiple energy while preserving primary energy. The other key feature of ISMA is that no knowledge of the subsurface is required in its application. I have adapted this method to multicomponent ocean‐bottom cable data (i.e., to arrays of sea‐floor geophones and hydrophones) by selecting a subseries made of even terms of the current scattering series used in the free‐surface multiple attenuation of conventional marine surface seismic data (streamer data). This subseries approach allows me to remove receiver ghosts (receiver‐side reverberations) and free‐surface multiples (source‐side reverberations) in multicomponent OBC data. I have processed each component separately. As for the streamer case, my OBC version of ISMA preserves primary energy and does not require any knowledge of the subsurface. Moreover, the preprocessing steps of muting for the direct wave and interpolating for missing near offsets are no longer needed. Knowledge of the source signature is still required. The existing ways of satisfying this requirement for streamer data can be used for OBC data without modification. This method differs from the present dual‐field deghosting method used in OBC data processing in that it does not assume a horizontally flat sea floor; nor does it require the knowledge of the acoustic impedance below the sea floor. Furthermore, it attenuates all free‐surface multiples, including receiver ghosts and source‐side reverberations.

A New Inversion Method Using a Modified Bat Algorithm for Analysis of Seismic Refraction Data in Dam Site Investigation

2019 ◽  
Vol 24 (2) ◽  
pp. 201-214
Author(s):  
Rashed Poormirzaee ◽  
Siamak Sarmady ◽  
Yusuf Sharghi
Keyword(s):  
Seismic Data ◽  
Seismic Refraction ◽  
Bat Algorithm ◽  
Inversion Method ◽  
Model Parameters ◽  
Inversion Algorithm ◽  
Dam Site ◽  
Seismic Refraction Data ◽  
Modified Bat Algorithm ◽  
Refraction Data

Similar to any other geophysical method, seismic refraction method faces non-uniqueness in the estimation of model parameters. Recently, different nonlinear seismic processing techniques have been introduced, particularly for seismic inversion. One of the recently developed metaheuristic algorithms is bat optimization algorithm (BA). Standard BA is usually quick at the exploitation of the solution, while its exploration ability is relatively poor. In order to improve exploration ability of BA, in the current study, a hybrid metaheuristic algorithm by inclusion a mutation operator into BA, so-called mutation based bat algorithm (MBA), is introduced to inversion of seismic refraction data. The efficiency and stability of the proposed inversion algorithm were tested on different synthetic cases. Finally, the MBA inversion algorithm was applied to a real dataset acquired from Leylanchay dam site at East-Azerbaijan province, Iran, to determine alluvium depth. Then, the performance of MBA on both synthetic and real datasets was compared with standard BA. Moreover, the dataset was further processed following a tomographic approach and the results were compared to the results of the proposed MBA inversion method. In general, the MBA inversion results were superior to standard BA inversion and results of MBA were in good agreement with available boreholes data and geological sections at the dam site. The analysis of the seismic data showed that the studied site comprises three distinct layers: a saturated alluvial, an unsaturated alluvial, and a dolomite bedrock. The measured seismic velocity across the dam site has a range of 400 to 3,500 m/s, with alluvium thickness ranging from 5 to 19 m. Findings showed that the proposed metaheuristic inversion framework is a simple, fast, and powerful tool for seismic data processing.

scholarly journals Improving adaptive subtraction in seismic multiple attenuation

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. V59-V67 ◽  
Author(s):  
Shoudong Huo ◽  
Yanghua Wang
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Primary Energy ◽  
Multiple Models ◽  
Multiple Model ◽  
Data Set ◽  
Multiple Attenuation ◽  
Matching Filter ◽  
Theoretical Analyses ◽  
Different Order

In seismic multiple attenuation, once the multiple models have been built, the effectiveness of the processing depends on the subtraction step. Usually the primary energy is partially attenuated during the adaptive subtraction if an [Formula: see text]-norm matching filter is used to solve a least-squares problem. The expanded multichannel matching (EMCM) filter generally is effective, but conservative parameters adopted to preserve the primary could lead to some remaining multiples. We have managed to improve the multiple attenuation result through an iterative application of the EMCM filter to accumulate the effect of subtraction. A Butterworth-type masking filter based on the multiple model can be used to preserve most of the primary energy prior to subtraction, and then subtraction can be performed on the remaining part to better suppress the multiples without affecting the primaries. Meanwhile, subtraction can be performed according to the orders of the multiples, as a single subtraction window usually covers different-order multiples with different amplitudes. Theoretical analyses, and synthetic and real seismic data set demonstrations, proved that a combination of these three strategies is effective in improving the adaptive subtraction during seismic multiple attenuation.

An optimization of the inverse scattering multiple attenuation method for OBS and VC data

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1293-1303 ◽  
Author(s):  
Luc T. Ikelle ◽  
Lasse Amundsen ◽  
Seung Yoo
Keyword(s):  
Free Surface ◽  
Data Storage ◽  
Inverse Scattering ◽  
Water Depth ◽  
Seismic Data ◽  
Computation Time ◽  
Ocean Bottom ◽  
Multiple Attenuation ◽  
Obs Data ◽  
Streamer Data

The inverse scattering multiple attenuation (ISMA) algorithm for ocean‐bottom seismic (OBS) data can be formulated in the form of a series expansion for each of the four components of OBS data. Besides the actual data, which constitute the first term of the series, each of the other terms is computed as a multidimensional convolution of OBS data with streamer data, and aims at removing one specific order of multiples. If the streamer data do not contain free‐surface multiples, we found that the computation of only the second term of the series is needed to predict and remove all orders of multiples, whatever the water depth. As the computation of the various terms of the series is the most expensive part of ISMA, this result can produce significant savings in computation time, even in data storage, as we no longer need to store the various terms of the series. For example, if the streamer data contained free‐surface multiples, OBS seismic data of 6‐s duration, corresponding to a geological model of the subsurface with 250‐m water depth, require the computation of five terms of the series for each of the four components of OBS data. With the new implementation, in which the streamer data do not contain free‐surface multiples, we need the computation of only one term of the series for each component of the OBS data. The saving in CPU time for this particular case is at least fourfold. The estimation of the inverse source signature, which is an essential part of ISMA, also benefits from the reduction of the number of terms needed for the demultiple to two because it becomes a linear inverse problem instead of a nonlinear one. Assuming that the removal of multiple events produces a significant reduction in the energy of the data, the optimization of this problem leads to a stable, noniterative analytic solution. We have also adapted these results to the implementation of ISMA for vertical‐cable (VC) data. This implementation is similar to that for OBS data. The key difference is that the basic model in VC imaging assumes that data consist of receiver ghosts of primaries instead of the primaries themselves. We have used the following property to achieve this goal. The combination of VC data with surface seismic data, which do not contain free‐surface multiples, allows us to predict free‐surface multiples and receiver ghosts as well as the receiver ghosts of primary reflections. However, if the direct wave arrivals are removed from the VC data, this combination will not predict the receiver ghosts of primary reflections. The difference between these two predictions produces data containing only receiver ghosts of primaries.

Simultaneous multifrequency inversion of full-waveform seismic data

Geophysics ◽  
2009 ◽  
Vol 74 (2) ◽  
pp. R1-R14 ◽  
Author(s):  
Wenyi Hu ◽  
Aria Abubakar ◽  
Tarek M. Habashy
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Regularization Parameter ◽  
Synthetic Data ◽  
Frequency Data ◽  
Perfectly Matched Layers ◽  
Inversion Algorithm ◽  
Inversion Process ◽  
High Convergence Rate ◽  
Velocity Image

We present a simultaneous multifrequency inversion approach for seismic data interpretation. This algorithm inverts all frequency data components simultaneously. A data-weighting scheme balances the contributions from different frequency data components so the inversion process does not become dominated by high-frequency data components, which produce a velocity image with many artifacts. A Gauss-Newton minimization approach achieves a high convergence rate and an accurate reconstructed velocity image. By introducing a modified adjoint formulation, we can calculate the Jacobian matrix efficiently, allowing the material properties in the perfectly matched layers (PMLs) to be updated automatically during the inversion process. This feature ensures the correct behavior of the inversion and implies that the algorithm is appropriate for realistic applications where a priori information of the background medium is unavailable. Two different regularization schemes, an [Formula: see text]-norm and a weighted [Formula: see text]-norm function, are used in this algorithm for smooth profiles and profiles with sharp boundaries, respectively. The regularization parameter is determined automatically and adaptively by the so-called multiplicative regularization technique. To test the algorithm, we implement the inversion to reconstruct the Marmousi velocity model using synthetic data generated by the finite-difference time-domain code. These numerical simulation results indicate that this inversion algorithm is robust in terms of starting model and noise suppression. Under some circumstances, it is more robust than a traditional sequential inversion approach.

Simultaneous inversion of prestack seismic data for rock properties using simulated annealing

Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 1877-1885 ◽  
Author(s):  
Xin‐Quan Ma
Keyword(s):  
Simulated Annealing ◽  
Seismic Data ◽  
Low Frequency ◽  
Optimization Procedure ◽  
P Wave ◽  
Rock Properties ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Simultaneous Inversion ◽  
Reflection Seismic

A new prestack inversion algorithm has been developed to simultaneously estimate acoustic and shear impedances from P‐wave reflection seismic data. The algorithm uses a global optimization procedure in the form of simulated annealing. The goal of optimization is to find a global minimum of the objective function, which includes the misfit between synthetic and observed prestack seismic data. During the iterative inversion process, the acoustic and shear impedance models are randomly perturbed, and the synthetic seismic data are calculated and compared with the observed seismic data. To increase stability, constraints have been built into the inversion algorithm, using the low‐frequency impedance and background Vs/Vp models. The inversion method has been successfully applied to synthetic and field data examples to produce acoustic and shear impedances comparable to log data of similar bandwidth. The estimated acoustic and shear impedances can be combined to derive other elastic parameters, which may be used for identifying of lithology and fluid content of reservoirs.

scholarly journals Practical Implementation of Multiple Attenuation Methods on 2D Deepwater Seismic Data : Seram Sea Case Study

2017 ◽  
Vol 32 (1) ◽  
Author(s):  
Tumpal Bernhard Nainggolan ◽  
Deny Setiady
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Complex Structure ◽  
Least Square ◽  
Inversion Method ◽  
Practical Implementation ◽  
Proper Solution ◽  
Multiple Attenuation ◽  
Multiple Elimination ◽  
Water Bottom

Some deepwater multiple attenuation processing methods have been developed in the past with partial success. The success of surface multiple attenuation relies on good water bottom reflections for most deepwater marine situations. It brings the bigger ability to build an accurate water bottom multiple prediction model. Major challenges on 2D deepwater seismic data processing especially such a geologically complex structure of Seram Sea, West Papua – Indonesia are to attenuate surface related multiple and to preserve the primary data. Many multiple attenuation methods have been developed to remove surface multiple on these seismic data including most common least-squares, prediction-error filtering and more advanced Radon transform.Predictive Deconvolution and Surface Related Multiple Elimination (SRME) method appears to be a proper solution, especially in complex structure where the above methods fail to distinguish interval velocity difference between primaries and multiples. It does not require any subsurface info as long as source signature and surface reflectivity are provided. SRME method consists of 3 major steps: SRME regularization, multiple modeling and least-square adaptive subtraction. Near offset regularization is needed to fill the gaps on near offset due to unrecorded near traces during the acquisition process. Then, isolating primaries from multiples using forward modeling. Inversion method by subtraction of input data with multiple models to a more attenuated multiple seismic section.Results on real 2D deepwater seismic data show that SRME method as the proper solution should be considered as one of the practical implementation steps in geologically complex structure and to give more accurate seismic imaging for the interpretation.Keywords : multiple attenuation, 2D deepwater seismic, Radon transform, Surface Related Multiple Elimination (SRME). Banyak metode atenuasi pengulangan ganda dikembangkan pada pengolahan data seismik dengan tingkat keberhasilan yang rendah pada masa lalu. Keberhasilan dalam atenuasi pengulangan ganda permukaan salah satunya bergantung pada hasil gelombang pantul pada batas dasar laut dan permukaan pada hampir seluruh survei seismik laut. Hal tersebut menentukan keakuratan dalam membuat model prediksi pengulangan ganda dasar laut dan permukaan air. Tantangan utama dalam pemrosesan data seismik 2D laut dalam khususnya struktur geologi kompleks seperti Laut Seram, Papua Barat – Indonesia adalah pada kegiatan menekan pengulangan ganda permukaan sekaligus mempertahankan data primer. Beberapa metode yang dikembangkan untuk menghilangkan pengulangan ganda permukaan pada data seismik seperti least-square, filter prediksi kesalahan dan transformasi Radon.Dekonvolusi Prediktif dan Metode Surface Related Multiple Elimination (SRME) digunakan sebagai solusi yang baik pada struktur kompleks dimana metode-metode lain gagal untuk memisahkan perbedaan kecepatan interval data primer dan pengulangan ganda. Metode tersebut tidak membutuhkan informasi bawah permukaan selain parameter sumber dan reflektivitas permukaan. Metode SRME terdiri dari 3 tahapan utama : regularisasi SRME, pemodelan pengulangan ganda dan pengurangan adaktif least-square. Regularisasi near offset diperlukan untuk mengisi kekosongan pada near offset yang disebabkan oleh adanya sejumlah tras terdekat yang tidak terekam selama akuisisi. Pemodelan maju digunakan untuk memisahkan data primer dan pengulangan ganda kemudian inversi dengan pengurangan input data dengan model multiple.Hasil pada data seismik 2D laut dalam menunjukkan bahwa metode SRME layak diterapkan sebagai salah satu pengembangan metode atenuasi multiple permukaan serta untuk meningkatkan akurasi data seismik terutama untuk struktur geologi kompleks.Kata kunci : peredaman pengulangan ganda (multiple), seismik 2D laut dalam, transformasi Radon, Surface Related Multiple Attenuation (SRME).

Depth-domain seismic reflectivity inversion with compressed sensing technique

2017 ◽  
Vol 5 (1) ◽  
pp. T1-T9 ◽  
Author(s):  
Rui Zhang ◽  
Kui Zhang ◽  
Jude E. Alekhue
Keyword(s):  
Compressed Sensing ◽  
Time Domain ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Synthetic Data ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Depth Migration ◽  
Band Limited ◽  
The Time Domain

More and more seismic surveys produce 3D seismic images in the depth domain by using prestack depth migration methods, which can present a direct subsurface structure in the depth domain rather than in the time domain. This leads to the increasing need for applications of seismic inversion on the depth-imaged seismic data for reservoir characterization. To address this issue, we have developed a depth-domain seismic inversion method by using the compressed sensing technique with output of reflectivity and band-limited impedance without conversion to the time domain. The formulations of the seismic inversion in the depth domain are similar to time-domain methods, but they implement all the elements in depth domain, for example, a depth-domain seismic well tie. The developed method was first tested on synthetic data, showing great improvement of the resolution on inverted reflectivity. We later applied the method on a depth-migrated field data with well-log data validated, showing a great fit between them and also improved resolution on the inversion results, which demonstrates the feasibility and reliability of the proposed method on depth-domain seismic data.

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