Separation of simultaneous sources acquired with a high blending factor via coherence pass robust Radon operators

Deblending technology aims at separating simultaneous source seismic data between adjacent shots by allowing multiple sources to be shot simultaneously. Conventional deblending methods based on sparse inversion assume that the primary source is coherent, and the secondary source is randomized. The L2-norm minimization constraint can effectively minimize the Gaussian random noise while deblending in the transform domain. Nonetheless, the L2-norm misfit function is highly sensitive to outliers, negatively influencing the deblending performance. An effective optimization strategy is developed with deblending in pre-stack seismic data to eliminate outliers and enhance deblending accuracy. For this reason, we introduce the deblending algorithm in the morphological component analysis framework, modify the L2-norm misfit function to outlier-robust L1-norm and provide the corresponding derivation in detail via the alternating direction method of multipliers. Applications to synthetic and field data sets prove the improved robustness and efficiency of our deblending method.

Section: Introductionmentioning

confidence: 99%

Simultaneous random plus outlier attenuation and deblending based on L1‐norm misfit function

Sun

Cao

Chen

et al. 2023

“…The filtering‐based method is based on the assumption that the source with random excitation times is random noise after transforming the data into frequency–wavenumber (FK) domain or rearranging the data into common offset gathers. Median filtering (Chen et al., 2020b), radon transform (Lin & Sacchi, 2020) and FK filtering (Bahia et al., 2021) are common filtering‐based tools for deblending. The inversion‐based deblending method adopts an iterative way to attenuate the blending noise gradually (Ibrahim & Trad, 2020).…”

Section: Introductionmentioning

confidence: 99%

Deblending of seismic data in the wavelet domain via a convolutional neural network based on data augmentation

Wang

Song

Tan

et al. 2022

Blended acquisition, which allows multiple sources almost simultaneously fired, has become an effective way for accelerating seismic data acquisition. In order to use conventional processing methods for imaging, deblending is necessary for this special acquisition. Convolutional neural network‐based deblending methods provide a novel end‐to‐end framework for source separation. We proposed a field‐data‐based augmentation method that uses shuffled deblending noise as the features to be learned and take the inaccurate labels as the output of the network. Synthetic data experiments show that a network trained on data set with the proposed data augmentation method has higher accuracy for deblending even if the labelled data are noisy. Besides, 2D discrete wavelet transform, which has the advantage of multiscale decomposition and dimensionality reduction, is introduced to accelerate the computation of the network. The data augmentation method for data set generation and the computational speedup method for network training/predicting are also applied to field data. The results from synthetic and field data all confirm the performance of our methods.

“…Imposing a sparsity constraint, an inversion-based deblending algorithm utilizes sparse representations of signal components in auxiliary domains to retrieve desired signals, which is referred to as sparse inversion. A number of studies have shown high-quality deblending in various domains such as the Radon domain (Akerberg et al 2008;Moore 2010;Ibrahim and Sacchi 2013;Lin and Sacchi 2020), the Fourier domain (Abma et al 2015;Song et al 2019;Kumar et al 2020;Bahia et al 2020), the Curvelet domain (Zu et al, 2016) and the Seislet domain (Chen et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Deblending and merging of 3D multi‐sweep seismic blended data

Jeong

Tsingas

Almubarak

2021

Seismic blended source acquisition, also referred to as simultaneous source acquisition, is a cost-effective technology that achieves a significant reduction in acquisition cycle time and increases seismic crew field productivity. The dispersed source array is a blended acquisition field technique that simultaneously employs sources emitting different types of sweeps (i.e. multi-sweep), in terms of frequency bandwidth and length, which ultimately will result in a full broadband seismic data. In this paper, deblending of 3D multi-sweep seismic blended data and the subsequent merging of the data volumes having different frequency bandwidths will be discussed. In specific data domains where the signal component is coherent, interference shots (i.e. blending noise) are randomly distributed in the data space according to its own shot firing time. Therefore, the deblending process, which separates interference shots from a signal component, becomes a noise attenuation problem. A sparse inversion methodology is applied in the frequency-wavenumber-wavenumber (f-k x -k y ) domain to attenuate blending noise. By applying this deblending methodology to both dispersed source array's low-and mid-high-frequency bandwidths, we obtained high-quality deblending results. For both frequency bandwidths of the deblended dispersed source array data, additional effort was made to combine the two datasets to a single broadband data volume. Consequently, deblending and merging of the dispersed source array blended data generated a broadband, deblended and well-balanced seismic volume suitable for further processing and reservoir characterization applications.