Deep prior-based seismic data interpolation via multi-res U-net

Kong, Fantong; Picetti, Francesco; Lipari, Vincenzo; Bestagini, Paolo; Tubaro, Stefano

doi:10.1190/segam2020-3426173.1

Cited by 17 publications

(12 citation statements)

References 17 publications

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“…In [124], a UNNP based algorithm named DSPRecon was proposed for the reconstruction of seismic data. The authors demonstrated that the proposed method performs comparatively better than the spectrum analysis (SSA) and Cadzow based reconstruction methods.…”

Section: Seismic Data Reconstructionmentioning

confidence: 99%

See 1 more Smart Citation

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum¹,

Ilahi²,

Shamshad³

et al. 2021

Preprint

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Section: Seismic Data Reconstructionmentioning

confidence: 99%

“…Similarly, Park et al proposed an approach integrates projection onto convex sets (POCS) based regularization with UNNP for the reconstruction of seismic data [125]. In a similar study, UNNP with multiple U-nets was used for the interpolation of seismic data to reconstruct 3D shot gathers [124].…”

Section: Seismic Data Reconstructionmentioning

confidence: 99%

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum¹,

Ilahi²,

Shamshad³

et al. 2021

Preprint

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show abstract

“…The U-Net is a convolutional autoencoder (i.e., a CNN aiming at reconstructing a processed version of its input) characterized by the so called skip-connections and originally introduced for medical image processing [16]. If properly trained according to the standard deep learning paradigm, it proves very effective for multidimensional signal processing tasks such as denoising [30], [31], interpolation [32], [33], segmentation [34], [35], inpainting [36], and domain-specific post-processing operators [37], [38].…”

Section: Cnn Architecturementioning

confidence: 99%

DIPPAS: A Deep Image Prior PRNU Anonymization Scheme

Picetti¹,

Mandelli²,

Bestagini³

et al. 2020

Preprint

Self Cite

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Source device identification is an important topic in image forensics since it allows to trace back the origin of an image. Its forensics counter-part is source device anonymization, that is, to mask any trace on the image that can be useful for identifying the source device. A typical trace exploited for source device identification is the Photo Response Non-Uniformity (PRNU), a noise pattern left by the device on the acquired images. In this paper, we devise a methodology for suppressing such a trace from natural images without significant impact on image quality. Specifically, we turn PRNU anonymization into an optimization problem in a Deep Image Prior (DIP) framework. In a nutshell, a Convolutional Neural Network (CNN) acts as generator and returns an image that is anonymized with respect to the source PRNU, still maintaining high visual quality. With respect to widely-adopted deep learning paradigms, our proposed CNN is not trained on a set of input-target pairs of images. Instead, it is optimized to reconstruct the PRNUfree image from the original image under analysis itself. This makes the approach particularly suitable in scenarios where large heterogeneous databases are analyzed and prevents any problem due to lack of generalization. Through numerical examples on publicly available datasets, we prove our methodology to be effective compared to state-of-the-art techniques.

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“…Here, the number of epochs is considered as the number of iterations to reach the best network parameters (Ulyanov et al ., 2017). Besides, the idea of the deep image prior network has been proposed for seismic data denoising and interpolating (Kong et al ., 2020; Liu et al ., 2020b; Park et al ., 2020; Shi et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

A fully unsupervised and highly generalized deep learning approach for random noise suppression

Saad

Chen

2021

Geophysical Prospecting

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In this study, we proposed a deep learning algorithm (PATCHUNET) to suppress random noise and preserve the coherent seismic signal. The input data are divided into several patches, and each patch is encoded to extract the meaningful features. Following this, the extracted features are decompressed to retrieve the seismic signal. Skip connections are used between the encoder and decoder parts, allowing the proposed algorithm to extract high‐order features without losing important information. Besides, dropout layers are used as regularization layers. The dropout layers preserve the most meaningful features belonging to the seismic signal and discard the remaining features. The proposed algorithm is an unsupervised approach that does not require prior information about the clean signal. The input patches are divided into 80% for training and 20% for testing. However, it is interesting to find that the proposed algorithm can be trained with only 30% of the input patches with an effective denoising performance. Four synthetic and four field examples are used to evaluate the proposed algorithm performance, and compared to the f−x deconvolution and the f−x singular spectrum analysis. The results indicate the ability of the proposed algorithm in attenuating the random noise and preserving the seismic signal effectively despite the existence of a large amount of random noise, for example, when the input signal‐to‐noise ratio is as low as −14.2 dB.

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Deep prior-based seismic data interpolation via multi-res U-net

Cited by 17 publications

References 17 publications

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

DIPPAS: A Deep Image Prior PRNU Anonymization Scheme

A fully unsupervised and highly generalized deep learning approach for random noise suppression

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