An Efficient Seismic Data Acquisition Based on Compressed Sensing Architecture With Generative Adversarial Networks

Zhang, Xiaopu; Zhang, Shuai; Lin, Jun; Sun, Fang Hong; Zhu, Xiaojin; Yang, Yang; Tong, Xunqian; Yang, Hao-Jan

doi:10.1109/access.2019.2932476

Cited by 20 publications

(10 citation statements)

References 36 publications

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“…CS and its applications are reviewed in detail in [2]. CS is commonly used in such areas as super-resolution imaging [3][4][5], MRI fast image acquisition [6][7][8], and seismic data compression [9][10][11]. A signal possesses in these fields natural s-sparse representation corresponding to a specific domain, and the resultant regularized solutions of (1) with the minimal 1 norm leads to sufficiently correct outcomes.…”

Section: Many Kinds Of Modeling Problems Can Be Represented Asmentioning

confidence: 99%

Design of ℓ1 New Suboptimal Fractional Delays Controller for Discrete Non-Minimum Phase System under Unknown-but-Bounded Disturbance

et al. 2021

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ℓ1-regularization methodologies have appeared recently in many pattern recognition and image processing tasks frequently connected to ℓ1-optimization in the control theory. We consider the problem of optimal stabilizing controller synthesis for a discrete non-minimum phase dynamic plant described by a linear difference equation with an additive unknown-but-bounded noise. Under considering the “worst” case of noise, the solving of these optimization problem has a combinatorial complexity. The choosing of an appropriate sufficiently high sampling rate allows to achieve an arbitrarily small level of suboptimality using a noncombinatorial algorithm. In this paper, we suggest to use fractional delays to achieve a small level of suboptimality without increasing the sampling rate so much. We propose an approximation of the fractional lag with a combination of rounding and a first-order fractional lag filter. The suggested approximation of the fractional delay is illustrated via a simulation example with a non-minimum phase second-order plant. The proposed methodology appears to be suitable to be used in various pattern recognition approaches.

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Section: Many Kinds Of Modeling Problems Can Be Represented Asmentioning

confidence: 99%

Design of ℓ1 New Suboptimal Fractional Delays Controller for Discrete Non-Minimum Phase System under Unknown-but-Bounded Disturbance

et al. 2021

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“…, where E s is the transmitted energy per symbol and Es N0 = Cs P N T s B, where B is the channel bandwidth (Hz), T s is the symbol duration and C s /P N is the transmitted signal power to noise power ratio (power is in watt). Since, the symbol duration is related to bit duration as T s = log 2 (M )T b , where T b = 1/R and R is data rate, therefore, γ b = γ s /K to be used in (8). The noise power P N can be classified as, environmental noise (noise This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: N0mentioning

confidence: 99%

“…The convergence is achieved when the mean square error between the values obtained from two successive iterations becomes equal to a predefined value, e.g., 10 −6 in this work. Physical channel generation using model in Table 1 Compute γ s and γ b as in Section III-B Compute P b using (8) Compute P data using (5)- 7Compute P phy using (4) s converge? This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: Energy Consumption Of a Wgmentioning

confidence: 99%

“…4-QAM modulation scheme with data rate of 3.6 Mbits/s is used for the throughput analysis in all the previous figures. In equation (8), probability of bit error is given for various modulation schemes. In reality, standards use adaptive modulation schemes, i.e., a modulation scheme is selected based on the channel condition.…”

Section: G Modulation Schemementioning

confidence: 99%

“…Typically, data are collected by a large number of geophones deployed over an area of more than 20 km 2 [3]. Recently, there has been a growing interest to deploy wireless geophone networks for seismic acquisition especially in large land surveys [2]- [8]. In [2], future potential of wireless geophone structure is discussed for highdensity land seismic acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Layer Design and Analysis of Wireless Geophone Networks Utilizing TV White Space

et al. 2020

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Traditional seismic data acquisition systems used for surveying during the exploration of oil and gas rely on cables between geophones and the data collection center. Despite the fact that cable-based systems provide reliable seismic data transfer, their deployment and maintenance costs increase substantially as the survey area increases in scale. Therefore, a three layer wireless network architecture is proposed in this work, which consists of wireless geophones (WG) and a data center with an intermediate wireless gateway node (WGN). This paper investigates the aggregate data throughput, transmission time, and energy consumption from WGs to the WGN in a wireless geophone network architecture based on the IEEE 802.11af standard. This standard is considered in order to have the maximum possible range and low power consumption due to operating in TV bands. Analytical expressions of the aforementioned quantities are derived using Markov chain models. Two Markov models are considered for this purpose: one for modeling the access method that allows multiple WGs to connect to a WGN and the other for representing a buffer in a WG. Since seismic data is recorded at regular intervals, arrivals of data packets in the buffer of the WG is deterministic. On the other hand, departure is random due to the multiple access method. Hence, in this work D/M/1/B queue is used for the first time to model the buffer in a wireless geophone. Furthermore, the physical layer constraints are also taken into account together with proper wireless path-loss channel models. The results obtained are useful for designing such wireless seismic networks without extensive simulations. In particular, the proposed joint medium access control, physical layer, and D/M/1/B model enables us to optimize the required number of WGNs. Finally, sectoring is also introduced in order to minimize the total number of WGNs needed to cover the whole surveying area.

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Seismic data compression: an overview

Sebai,

Zouaoui,

Ghorbel

2024

Multimedia Systems

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An Efficient Seismic Data Acquisition Based on Compressed Sensing Architecture With Generative Adversarial Networks

Cited by 20 publications

References 36 publications

Design of ℓ1 New Suboptimal Fractional Delays Controller for Discrete Non-Minimum Phase System under Unknown-but-Bounded Disturbance

Design of ℓ1 New Suboptimal Fractional Delays Controller for Discrete Non-Minimum Phase System under Unknown-but-Bounded Disturbance

Cross-Layer Design and Analysis of Wireless Geophone Networks Utilizing TV White Space

Seismic data compression: an overview

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