A hybrid approach to seismic deblending: when physics meets self-supervision

• URL: http://arxiv.org/abs/2205.15395v1
• Date: Mon, 30 May 2022 19:24:21 GMT
• Title: A hybrid approach to seismic deblending: when physics meets self-supervision
• Authors: Nick Luiken and Matteo Ravasi and Claire E. Birnie
• Abstract summary: We introduce a new concept that consists of embedding a self-supervised denoising network into the Plug-and-Play framework. A novel network is introduced whose design extends the blind-spot network architecture of [28 ] for partially correlated noise. The network is then trained directly on the noisy input data at each step of the supervised time algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To limit the time, cost, and environmental impact associated with the acquisition of seismic data, in recent decades considerable effort has been put into so-called simultaneous shooting acquisitions, where seismic sources are fired at short time intervals between each other. As a consequence, waves originating from consecutive shots are entangled within the seismic recordings, yielding so-called blended data. For processing and imaging purposes, the data generated by each individual shot must be retrieved. This process, called deblending, is achieved by solving an inverse problem which is heavily underdetermined. Conventional approaches rely on transformations that render the blending noise into burst-like noise, whilst preserving the signal of interest. Compressed sensing type regularization is then applied, where sparsity in some domain is assumed for the signal of interest. The domain of choice depends on the geometry of the acquisition and the properties of seismic data within the chosen domain. In this work, we introduce a new concept that consists of embedding a self-supervised denoising network into the Plug-and-Play (PnP) framework. A novel network is introduced whose design extends the blind-spot network architecture of [28 ] for partially coherent noise (i.e., correlated in time). The network is then trained directly on the noisy input data at each step of the PnP algorithm. By leveraging both the underlying physics of the problem and the great denoising capabilities of our blind-spot network, the proposed algorithm is shown to outperform an industry-standard method whilst being comparable in terms of computational cost. Moreover, being independent on the acquisition geometry, our method can be easily applied to both marine and land data without any significant modification.

Related papers

• A convolutional neural network approach to deblending seismic data [1.5488464287814563]
  We present a data-driven deep learning-based method for fast and efficient seismic deblending. A convolutional neural network (CNN) is designed according to the special character of seismic data. After training and validation of the network, seismic deblending can be performed in near real time.
  arXiv  Detail & Related papers  (2024-09-12T10:54:35Z)
• Ground-roll Separation From Land Seismic Records Based on Convolutional Neural Network [9.579207147600247]
  Ground-roll wave is a common coherent noise in land field seismic data. This paper proposes a novel way to separate ground-roll from reflections using convolutional neural network (CNN) model based method.
  arXiv  Detail & Related papers  (2024-09-05T19:34:21Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Mesh Denoising Transformer [104.5404564075393]
  Mesh denoising is aimed at removing noise from input meshes while preserving their feature structures. SurfaceFormer is a pioneering Transformer-based mesh denoising framework. New representation known as Local Surface Descriptor captures local geometric intricacies. Denoising Transformer module receives the multimodal information and achieves efficient global feature aggregation.
  arXiv  Detail & Related papers  (2024-05-10T15:27:43Z)
• Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling [7.755439545030289]
  Deep learning models such as U-Net often underperform when the training and test missing patterns do not match. We propose a novel framework that is built upon the multi-modal diffusion models. Inference phase, we introduce the denoising diffusion implicit model to reduce the number of sampling steps. To enhance the coherence and continuity between the revealed traces and the missing traces, we propose two strategies.
  arXiv  Detail & Related papers  (2023-07-09T16:37:47Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• Transfer learning for self-supervised, blind-spot seismic denoising [0.0]
  We propose an initial, supervised training of the network on a frugally-generated synthetic dataset prior to fine-tuning in a self-supervised manner on the field dataset of interest. Considering the change in peak signal-to-noise ratio, as well as the volume of noise reduced and signal leakage observed, we illustrate the clear benefit in initialising the self-supervised network with the weights from a supervised base-training.
  arXiv  Detail & Related papers  (2022-09-25T12:58:10Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• The potential of self-supervised networks for random noise suppression in seismic data [0.0]
  Blind-spot networks are shown to be an efficient suppressor of random noise in seismic data. Results are compared with two commonly used random denoising techniques: FX-deconvolution and Curvelet transform. We believe this is just the beginning of utilising self-supervised learning in seismic applications.
  arXiv  Detail & Related papers  (2021-09-15T14:57:43Z)
• Forecasting Sequential Data using Consistent Koopman Autoencoders [52.209416711500005]
  A new class of physics-based methods related to Koopman theory has been introduced, offering an alternative for processing nonlinear dynamical systems. We propose a novel Consistent Koopman Autoencoder model which, unlike the majority of existing work, leverages the forward and backward dynamics. Key to our approach is a new analysis which explores the interplay between consistent dynamics and their associated Koopman operators.
  arXiv  Detail & Related papers  (2020-03-04T18:24:30Z)
• Temporal-Spatial Neural Filter: Direction Informed End-to-End Multi-channel Target Speech Separation [66.46123655365113]
  Target speech separation refers to extracting the target speaker's speech from mixed signals. Two main challenges are the complex acoustic environment and the real-time processing requirement. We propose a temporal-spatial neural filter, which directly estimates the target speech waveform from multi-speaker mixture.
  arXiv  Detail & Related papers  (2020-01-02T11:12:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.