An Efficient Undersampled High-Resolution Radon Transform for Exploration Seismic Data Processing

Latif, Arbab; Mousa, Wail A.

doi:10.1109/tgrs.2016.2618848

Cited by 40 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The compressive sensing theory has an important use in data acquisition, that is, situations when it is intricate to obtain a lot of samples, for example in the case of seismic data [101]. The layers of the Earth can be estimated by measuring the reflections of a signal from different layers of the Earth.…”

Section: ) Seismologymentioning

confidence: 99%

A Review of Sparse Recovery Algorithms

et al. 2019

View full text Add to dashboard Cite

Nowadays, a large amount of information has to be transmitted or processed. This implies highpower processing, large memory density, and increased energy consumption. In several applications, such as imaging, radar, speech recognition, and data acquisition, the signals involved can be considered sparse or compressive in some domain. The compressive sensing theory could be a proper candidate to deal with these constraints. It can be used to recover sparse or compressive signals with fewer measurements than the traditional methods. Two problems must be addressed by compressive sensing theory: design of the measurement matrix and development of an efficient sparse recovery algorithm. These algorithms are usually classified into three categories: convex relaxation, non-convex optimization techniques, and greedy algorithms. This paper intends to supply a comprehensive study and a state-of-the-art review of these algorithms to researchers who wish to develop and use them. Moreover, a wide range of compressive sensing theory applications is summarized and some open research challenges are presented.

show abstract

Section: ) Seismologymentioning

confidence: 99%

A Review of Sparse Recovery Algorithms

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The inverse transform is then run to convert the corrected data back to the time-space domain. Particularly, the slant-stack (or ττ-p) transform integrates the data along planar surfaces where ττ is the time intercept and p is the moveout (Latif and Mousa, 2015). Ibrahim and Sacchi (2013) applied NMO-correction to common midpoint (CMP) data and performes a Radon transform along parabolic stacking curve to suppress multiples.…”

Section: Forward/inverse Radon Transformmentioning

confidence: 99%

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

Nainggolan¹,

Setiady²

2017

BoMG

View full text Add to dashboard Cite

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).

show abstract

“…Seismic denoising methods have been developed by many scholars. For random noise attenuation, prediction-based methods [2][3][4][5], sparse-transform-based methods [6,7], rankreduction-based methods [8][9][10], machine-learning-based methods [11][12][13][14] and orthogonalization [15] are most common used algorithms. For the removal of harmonic noise [16,17], there are also many different methods presented besides the analog notch filter.…”

Section: Introductionmentioning

confidence: 99%

Sinusoidal Seismic Noise Suppression Using Randomized Principal Component Analysis

2020

View full text Add to dashboard Cite

Sinusoidal interference is a common type of noise caused by power facilities. It usually has the fixed frequency and large amplitude which will have a negative impact on seismic data processing. We propose a new approach for suppressing sinusoidal noise, which is called randomized principal component analysis. It takes advantage of the characteristics of noisy record itself and does not need to calculate the amplitude or phase of the sinusoidal noise. The key algorithm steps of this method can be illustrated as follows: first, interpolation will be applied to get the improved time accuracy. Second, frequency search and narrow-band-pass filter are used to separate the noise-dominated component and determine its precise period. Then, for one of the periods, we use several periods around it to form a "section". The randomization is then implemented along the lateral direction to disturb the reflected signal in this "section". Besides, principal component analysis is applied to extract sinusoidal noise which still has strong correlation, and to filter the signal which has been converted into random noise. Last, the separated noise is subtracted from the record. Via the synthetic model and field examples, this method is compared to the notch filter to demonstrate its superiority.

show abstract

An Efficient Undersampled High-Resolution Radon Transform for Exploration Seismic Data Processing

Cited by 40 publications

References 48 publications

A Review of Sparse Recovery Algorithms

A Review of Sparse Recovery Algorithms

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

Sinusoidal Seismic Noise Suppression Using Randomized Principal Component Analysis

Contact Info

Product

Resources

About