Compressive Seismic Imaging

Mosher, Charles C.; Keskula, Erik; Kaplan, Sam T.; Keys, Robert; Li, Chengbo; Ata, Elias; Morley, Larry; Brewer, Joel; Janiszewski, Frank; Eick, Peter M.; Olson, Robert; Sood, Swati

doi:10.1190/segam2012-1460.1

Cited by 31 publications

(2 citation statements)

References 2 publications

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“…The SRF we propose reflects the resolution of a spectral peak in the Fourier domain. The SRF is not entirely new by itself and is related to the point spread function used in the compressive sensing (Lustig et al, 2007(Lustig et al, , 2008 and seismic data acquisition (Herrmann, 2010;Mosher et al, 2012;Zhang, 2021) literature. In summary, we characterize a sampling pattern by four properties that are histogram of intervals, rose diagram of angles, sensor density and SRF, respectively.…”

Section: Properties Of Sampling Pattern and Information Sampling Abilitymentioning

confidence: 99%

Ergodic sampling: Acquisition design to maximize information from limited samples

Zhang,

2023

Geophysical Prospecting

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Data acquisition using equal spacing has been a standard practice in geophysics. The dense uniform sampling derived from Nyquist‐Shannon sampling includes redundant samples and it is sufficient but not necessary to adequately record target signals. We propose an ergodic sampling, which avoids the redundant samples in the dense uniform sampling and possesses the ability to capture the sufficiently similar information content as does Nyquist sampling. Ergodicity means that a key part of system can represent the average performance of the entire system. Our ergodic samples are a critical subset of dense uniform samples, and can represent the full uniform Nyquist samples. To find such a critical subset, we first examine the properties of different sampling patterns, including the sampling interval distribution, sampling angle distribution, areal sample density, and resolution in the spectral domain. We define the information sampling ability (ISA) of sampling patterns based on these properties. The concept of ISA which we have proposed serves as the criterion to compare different sampling patterns and assess their sampling performances. The sampling patterns with the same ISA have the same capability to gather information, even though the appearance of sampling patterns may be different. We formulate an optimization problem to find this critical subset of sample locations, which has the fewest number of samples but has a similar ISA as that of the desired dense uniform samples. This critical subset is irregularly located, has the optimized properties, and forms the ergodic sampling pattern. We define this process of sampling design and associated understanding as the ergodic sampling. Ergodic sampling can be applied to gain two major benefits in practice. First, this approach can save significant number of samples. We demonstrate ergodic sampling using 1D synthetic data and a 2D field geophysical data set. The simulations confirm that, compared with other sampling strategies, ergodic sampling can use fewer samples to acquire the same amount of information, so that we can save cost. Alternatively, with the same budget, we can use the same number of samples through ergodic sampling to acquire more information. The new ergodic sampling can lead to a new generation of economic and efficient geophysical data acquisition, which could assist in increasing the discovery rate in resource exploration, tackling more earth science problems with limited budget, and can also benefit the environment in the process by reducing the invasiveness in potentially sensitive regions.This article is protected by copyright. All rights reserved

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Section: Properties Of Sampling Pattern and Information Sampling Abilitymentioning

confidence: 99%

Ergodic sampling: Acquisition design to maximize information from limited samples

Zhang,

2023

Geophysical Prospecting

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“…We remark the versatility of the proposed regularizations for different applications aiming different purposes, such as the design of binary or real-valued codifications, or capturing different fields of the light. Furthermore, the proposed E2E scheme can be extended to other optical coding architectures and other sensing systems that include a CA element such as seismic data [80] and radar signals [81]; it can also be extended to further applications such as, estimation of depth maps, object detection, or video processing in which the temporal correlations can be exploited, or to consider the relative position between the CA and the sensor; as well as it can be extended to consider previously formulated regularizers as the uniform sensing presented in [7], [19] and the KL-divergence in [20]. Finally, the proposed method is limited to work on real-valued domain, in such a manner that further analysis have to be done to use it on applications such as phase-retrieval in which the complex nature of the CA ensembles conduct to critical differences.…”

Section: Quality Improvement Via Regularizers Experimentsmentioning

confidence: 99%

Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks

Bacca¹,

Gelvez²,

Argüello³

2021

Preprint

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Covering from photography to depth and spectral estimation, diverse computational imaging (CI) applications benefit from the versatile modulation of coded apertures (CAs). The light wave fields as space, time, or spectral can be modulated to obtain projected encoded information at the sensor that is then decoded by efficient methods, such as the modern deep learning decoders. Despite the CA can be fabricated to produce an analog modulation, a binary CA is mostly preferred since easier calibration, higher speed, and lower storage are achieved. As the performance of the decoder mainly depends on the structure of the CA, several works optimize the CA ensembles by customizing regularizers for a particular application without considering critical physical constraints of the CAs. This work presents an end-to-end (E2E) deep learning-based optimization of CAs for CI tasks. The CA design method aims to cover a wide range of CI problems easily changing the loss function of the deep approach. The designed loss function includes regularizers to fulfill the widely used sensing requirements of the CI applications. Mainly, the regularizers can be selected to optimize the transmittance, the compression ratio and the correlation between measurements, while a binary CA solution is encouraged, and the performance of the CI task is maximized in applications such as restoration, classification, and semantic segmentation.

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Compressive Sensing Acquisition with Application to Marchenko Imaging

Zhang

2022

Pure Appl. Geophys.

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Compressive Seismic Imaging

Cited by 31 publications

References 2 publications

Ergodic sampling: Acquisition design to maximize information from limited samples

Ergodic sampling: Acquisition design to maximize information from limited samples

Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks

Compressive Sensing Acquisition with Application to Marchenko Imaging

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