Improved Low-Rank Tensor Approximation for Seismic Random Plus Footprint Noise Suppression

Abstract

Seismic acquisition footprints appear as stably faint and dim structures and emerge fully spatially coherent, causing inevitable damage to useful signals during the suppression process. Various footprint removal methods, including filtering and sparse representation (SR), have been reported to attain promising results for surmounting this challenge. However, these methods, e.g., SR, rely solely on the handcrafted image priors of useful signals, which is sometimes an unreasonable demand if complex geological structures are contained in the given seismic data. As an alternative, this article proposes a footprint removal network (dubbed FR-Net) for the unsupervised suppression of acquired footprints without any assumptions regarding valuable signals. The key to the FR-Net is to design a unidirectional total variation (UTV) model for footprint acquisition according to the intrinsically directional property of noise. By strongly regularizing a deep convolutional autoencoder (DCAE) using the UTV model, our FR-Net transforms the DCAE from an entirely data-driven model to a prior-augmented approach, inheriting the superiority of the DCAE and our footprint model. Subsequently, the complete separation of the footprint noise and useful signals is projected in an unsupervised manner, specifically by optimizing the FR-Net via the backpropagation (BP) algorithm. We provide qualitative and quantitative evaluations conducted on three synthetic and field datasets, demonstrating that our FR-Net surpasses the previous state-of-the-art (SOTA) methods.

STGNet: Short‐term residential load forecasting with spatial–temporal gated fusion network

“…In recent years, how to effectively estimate and remove noise errors is a research focus of various fields. [56][57][58] To solve the problems of environmental noise interference and insufficient input information, Ma et al 59 proposed measurement error prediction using improved local outlier factor and kernel support vector regression. Kong et al 60 proposed a remote prediction method for smart meter errors.…”

“…For these errors, we can reduce them by increasing sampling frequency, using high‐quality measuring equipment, improving the reliability of data transmission and storage system, continuously monitoring and regularly evaluating the data collection process. In recent years, how to effectively estimate and remove noise errors is a research focus of various fields 56–58 . To solve the problems of environmental noise interference and insufficient input information, Ma et al 59 proposed measurement error prediction using improved local outlier factor and kernel support vector regression.…”

A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

“…Meanwhile, deep neural networks (DNNs) have proven to be particularly effective in processing the complex data obtained from SAR images [3,4]. Their advanced representation capabilities facilitate accurate analysis and have been instrumental in the development of SAR automatic target recognition (SAR-ATR) models [5][6][7][8]. By leveraging the computational prowess of DNNs, these models have rapidly gained popularity due to their efficiency and reliability in identifying and classifying targets in diverse environments.…”

Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder

“…For feature extraction, both modeldriven methods and data-driven methods are employed to obtain a more insightful representation of seismic data. In model-driven methods, time-series transforms are generally implemented to provide nonredundant insights into the underlying prior properties of seismic data [7], [8], including time-domain [1], [9]- [12], time-frequency-domain [13]- [17], and frequency-domain [1], [18]- [20]. In contrast, data-driven methods are aimed at automatically learning local patterns in seismic data without prior knowledge or assumptions, which builds upon the learning capability of the autoencoder [21]- [26], or recurrent network [27]- [29].…”