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The Chepaizi Exploration Area, Junggar Basin (NW China) holds substantial importance for seismic exploration endeavors, yet it poses notable challenges due to the intricate nature of its subsurface and near-surface conditions. To address these challenges, we introduce a novel and comprehensive workflow tailored to evaluate and optimize seismic acquisition geometries while considering the impacts of near-surface viscosity. By integrating geological knowledge, historical seismic data, and subsurface modeling, we conduct simulations employing the visco-acoustic wave equation and reverse-time migration to produce detailed subsurface images. The quality of these images is quantitatively evaluated using a local similarity metric, a pivotal tool for evaluating the accuracy of seismic imaging. The culmination of this workflow results in an automated optimization strategy for acquisition geometries that enhances subsurface exploration. Our proposed methodology underscores the importance of incorporating near-surface viscosity effects in seismic imaging, offering a robust framework for improving the accuracy of subsurface imaging. Herein, we aim to contribute to the advancement of seismic imaging methodologies by providing valuable insights for achieving high-quality seismic exploration outcomes in regions characterized by complex subsurface and near-surface conditions. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have broad application prospects in various industries, and node localization technology is the foundation of WSN applications. Recently, many range-free node localization algorithms have been proposed, but most of them suffer from low accuracy. In order to improve the localization accuracy, in this paper we proposed the node localization algorithm based on multi-hop distance vector and error correction (MDV-EC). In terms of distance estimation, firstly the MDV-EC algorithm calculates the neighbor distance according to node neighbor relationship, then estimates the distance between unknown node and anchor node in multi-hop manner, and finally calibrates the distance refer to distance correction coefficient. In view of similarity of localization errors of nodes in similar regions, an error correction scheme is also investigated, which corrects the node initial estimated locations of nodes refer to the localization error vector of nearby anchor node. Simulation results show that our proposed MDV-EC has better performance than the other two algorithms in terms of node localization accuracy, and the error correction scheme can effectively reduce the localization errors. [ABSTRACT FROM AUTHOR]

The detection and removal methods of impulse noise often need to estimate the noise level of the damaged image in advance to obtain a better detection rate. An effective method of random-value impulse noise level estimation based on local similarity is proposed in this paper. Firstly, quantify the similarity between the center pixel x and any pixel y in the neighborhood of x based on their Euclidean distance and gray difference, then the similarity of pixel x and each pixel in its neighborhood is accumulated and summed to obtain the local similarity (LS) of pixel x. The value of LS represents the local consistency of a given pixel with respect to its neighboring pixels in which can also determine if a pixel is an impulse noise or a clean pixel. Hence, the LS value of a pixel could be regarded as an effective index to measure whether it is a clean pixel. Then the noise pixels in multiple flat regions of the noise image are detected to obtain the noise level of each region, and the noise level of these flat regions are processed with average operation to estimate the impulse noise level of the entire image finally. Extensive experiments were conducted to verify the effectiveness of the method and the experimental results show that the method is effective in scenarios with various noise levels, and the estimation error of the noise level of most images is within 1%. By comparing the RMSE and Std of different noise level estimation algorithms, it can be found that the algorithm proposed in this paper has higher robustness and accuracy, which can be well applied to practical applications with impulse noise level as the key parameter. [ABSTRACT FROM AUTHOR]

Considering the 3D propagation characteristics of seismic waves, theoretically, 3D surface-related multiples elimination (3D SRME) can suppress multiples with high accuracy. However, 3D SRME has strict requirements for acquisition geometry, which makes it difficult to be implemented in practice. In the process of 3D SRME, the multiple contribution gather (MCG) is a collection of wavefields with different propagation paths. The accuracy of the multiple propagation paths in the MCGs can be directly characterized by the inclination of the wavefields, which can achieve the weighted superposition of the wavefields. The direct summation of the sparse MCGs in the crossline direction produces serious spatial aliasing, which can easily cause the contamination of primaries. Based on the kinematic characteristics of multiple propagation, MCGs can be considered as a set of hyperbolas with temporal and spatial characteristics. Then, the direct summation of the sparse MCGs can be transformed into a process of superposition along the hyperbolic integration paths. However, as the stable phase points of the events, the apexes of the hyperbola have different spatial distributions in complex geological structures. Such hyperbolic stacking paths are difficult to be controlled by conventional Radon transform or constrained inversion. In this paper, we modify the apex-shifted hyperbolic Radon transform (ASHRT) to implement the summation of crossline MCGs with variable stable phase points along the hyperbolic integration paths. Improved ASHRT uses local similarity to locate the position of stable phase points, which can improve the stability of the algorithm and the efficiency of the computation. The proposed method is demonstrated on a 3D synthetic data set, as well as on a 3D marine data set, effectively avoiding the spatial aliasing caused by sparse crossline MCGs and improving the accuracy of multiple suppression. [ABSTRACT FROM AUTHOR]

Passive seismic source imaging can be utilized to recover geophysical information from subsurface ambient noise. Compared with conventional active seismic exploration, passive seismic source imaging is cost-effective and environmentally friendly. However, passive data acquisition cannot easily satisfy the theoretical condition, leading to noised virtual-shot gathers. Furthermore, coherent noise limits the application of passive source data. Although image quality improvement techniques for passive source data have recently attracted considerable interest, the denoising problem for virtual-shot gathers is seldom considered. In this study, we propose an iterative denoising approach for passive seismic data. The criterion used to extract useful signals is the difference between the wavefield similarity of useful events and the coherent noise in various gathers, i.e., the common shot gather and common receiver gather. We adopted local similarity to measure the similarity level and extract major useful events. However, the close local similarity between weak events and coherent noise may cause signal leakages and singular noise residuals. We incorporated an iterative two-dimensional model shrinkage algorithm into the denoising process to suppress the singular noise residual and highlight useful events. The proposed approach can overcome the limits of strong coherent noise in virtual-shot gathers, which can extend the choice range for data processing. Synthetic and field examples demonstrate a promising coherent noise attenuation performance, illustrating the effectiveness and feasibility of the proposed method. The denoised migrated section exhibits a smaller depth error and higher quality. [ABSTRACT FROM AUTHOR]

Passive seismic source imaging can extract geophysical information from underground noise and has been widely utilized in geophysical research. Compared with conventional active seismic exploration, it is low-cost and eco-friendly; however, the application of passive seismic data is limited by coherent noise in the virtual-shot gathers. An approach involving direct denoising in the virtual-shot gathers has not previously been discussed; therefore, we present an iterative denoising strategy for passive seismic data. The reflection-preserving characteristic of focal transformation is adopted in the virtual-shot gathers to eliminate the coherent noise, and L1-norm sparse inversion is utilized to obtain a more accurate solution during focal transformation. A key aspect of this strategy is clean focal operator building at high noise levels. We apply local similarity as the criterion for extracting the majority of reflection energy for the focal operator. Because of strong coherent noise, a clean focal operator cannot be obtained in one iteration. We therefore obtain both denoised passive seismic data and a clean focal operator by denoising using a cleaner focal operator and operator building using updated denoising results. The presented approach can overcome the limits of coherent noise in virtual-shot gathers, which is significant for subsequent data processing and wider application. Synthetic examples achieve excellent performance in coherent noise attenuation and reflection energy reconstruction, especially in far-offset sections. [ABSTRACT FROM AUTHOR]

Few-shot learning is one of the most challenging problems in computer vision due to the difficulty of sample collection in many real-world applications. It aims at classifying a sample when the number of training samples for each identity is limited. Most of the existing few-shot learning models learn a distance metric with pairwise or triplet constraints. In this paper, we make initial attempts on learning local and global similarities simultaneously to improve the few-shot classification performance in terms of accuracy. In particular, our system differs in two aspects. Firstly, we develop a neural network to learn the pairwise local relationship between each pair of samples in the union set that is composed of support set and query set, which fully utilize the supervision. Secondly, we design a global similarity function from the manifold perspective. The latent assumption is that if the neighbors of one sample are similar to those of another sample, the global similarity between them will be high. Otherwise, the global similarity of the two samples will become very low even if the local similarity between them is high. Meanwhile, we propose a new loss according to the pairwise local loss and task-specific global loss, encouraging the model toward better generalization. Extensive experiments on three popular benchmarks (Omniglot, miniImageNet and tieredImageNet) demonstrate that our simple, yet effective approach can achieve competitive accuracy compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Singular value decomposition (SVD) is a useful method for random noise suppression in seismic data processing. A structure-oriented SVD (SOSVD) approach which incorporates structure prediction to the SVD filter is effcient in attenuating noise except distorting seismic events at faults and crossing points. A modified SOSVD approach using a weighted stack, called structure-oriented weighted SVD (SOWSVD), is proposed. In this approach, the SVD filter is used to attenuate noise for prediction traces of a primitive trace which are produced via the plane-wave prediction. A weighting function related to local similarity and distance between each prediction trace and the primitive trace is applied to the denoised prediction traces stacking. Both synthetic and field data examples suggest the SOWSVD performs better than the SOSVD in both suppressing random noise and preserving the information of the discontinuities for seismic data with crossing events and faults. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tri-directional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities. [ABSTRACT FROM AUTHOR]

Online learning techniques have been widely used in many fields where instances come one by one. However, in early stage of a data stream, online learning models cannot exhibit good classification accuracy for it cannot collect sufficient instances to learn. For example, a well-known online learning algorithm named as very fast decision tree (VFDT) needs to wait for Hoeffding bound satisfied to split, which leads to poor classification accuracy at the beginning of data stream. Thus, VFDT may not be appropriate for some real applications which demand us a fast and accurate online detection. This situation will become more serious in the scenario of data stream classification with concept drift. This paper attempts to take transfer learning algorithm to make up this shortcoming of VFDT. To achieve this goal, a new decision tree method named as VFDT-D is first proposed to cache instances in its leaf nodes to handle numerical attributes and adapt to a framework of online transfer learning (OTL), and then a measure which considers tree path, classification accuracy and classification confidence is proposed to evaluate the local similarity between source and target domain classifiers. At last, a multiple-source online transfer learning algorithm named as DMOTL is proposed to take VFDT-D as base classifier and use the proposed measure of local similarity to select the optimal source domain classifier to help transfer learning. The extensive experiments on several synthetic and real-world datasets demonstrate the advantage of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The scaled standard deviations of temperature and humidity are investigated in complex terrain. The study area is a steep Alpine valley, with six measurement sites of different slope, orientation and roughness (i-Box experimental site, Inn Valley, Austria). Examined here are several assumptions forming the basis of Monin-Obukhov similarity theory (MOST), including constant turbulence fluxes with height and the degree of self-correlation between the involved turbulence variables. Since the basic assumptions for the applicability of the MOST approach—horizontally homogeneous and flat conditions—are violated, the analysis is performed based on a local similarity hypothesis. The scaled standard deviations as a function of local stability are compared with previous studies from horizontally homogeneous and flat terrain, horizontally inhomogeneous and flat terrain, weakly inhomogeneous and flat terrain, as well as complex terrain. As a reference, similarity relations for unstable and stable conditions are evaluated using turbulence data from the weakly inhomogeneous and flat terrain of the Cabauw experimental site in the Netherlands, and assessed with the same post-processing method as the i-Box data. Significant differences from the reference curve and also among the i-Box sites are noted, especially for data derived from the i-Box sites with steep slopes. These differences concern the slope and the magnitude of the best-fit curves, illustrating the site dependence of any similarity theory. [ABSTRACT FROM AUTHOR]

The simulation of horizontally homogeneous boundary layers that have characteristics of weakly and moderately stable atmospheric flow is investigated, where the well-established wind engineering practice of using ‘flow generators’ to provide a deep boundary layer is employed. Primary attention is given to the flow above the surface layer, in the absence of an overlying inversion, as assessed from first- and second-order moments of velocity and temperature. A uniform inlet temperature profile ahead of a deep layer, allowing initially neutral flow, results in the upper part of the boundary layer remaining neutral. A non-uniform inlet temperature profile is required but needs careful specification if odd characteristics are to be avoided, attributed to long-lasting effects inherent of stability, and to a reduced level of turbulent mixing. The first part of the wind-tunnel floor must not be cooled if turbulence quantities are to vary smoothly with height. Closely horizontally homogeneous flow is demonstrated, where profiles are comparable or closely comparable with atmospheric data in terms of local similarity and functions of normalized height. The ratio of boundary-layer height to surface Obukhov length, and the surface heat flux, are functions of the bulk Richardson number, independent of horizontal homogeneity. Surface heat flux rises to a maximum and then decreases. [ABSTRACT FROM AUTHOR]

Conventional f− x empirical mode decomposition (EMD) is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events. However, when a seismic event is not horizontal, the use of f− x EMD is harmful to most useful signals. Based on the framework of f− x EMD, this study proposes an improved denoising approach that retrieves lost useful signals by detecting effective signal points in a noise section using local similarity and then designing a weighting operator for retrieving signals. Compared with conventional f− x EMD, f− x predictive filtering, and f− x empirical mode decomposition predictive filtering, the new approach can preserve more useful signals and obtain a relatively cleaner denoised image. Synthetic and field data examples are shown as test performances of the proposed approach, thereby verifying the effectiveness of this method. [ABSTRACT FROM AUTHOR]

In this paper, a new method for the identification and removal of random-valued impulse noise (RVIN) from images is proposed. We propose to identify the central pixel of the current sliding window as a noisy or noise free pixel based on the similar local statistics of the current window. Our proposed RVIN identifier works in an iterative way. Pixel identified as a noisy pixel is replaced by proposed minimum difference similar value in an optimal directions. The performance of the proposed method is evaluated on different test images and compared with state-of-the-art methods. Experimental results show that the proposed method cannot only identify the impulse noise efficiently, but can also preserve the detailed information of an image. [ABSTRACT FROM AUTHOR]

In this study, we investigate the validity of similarity scaling over a steep mountain slope (30-41 $$^\circ $$). The results are based on eddy-covariance data collected during the Slope Experiment near La Fouly (SELF-2010); a field campaign conducted in a narrow valley of the Swiss Alps during summer 2010. The turbulent fluxes of heat and momentum are found to vary significantly with height in the first few metres above the inclined surface. These variations exceed by an order of magnitude the well-accepted maximum 10 % required for the applicability of Monin-Obukhov similarity theory in the surface layer. This could be due to a surface layer that is too thin to be detected or to the presence of advective fluxes. It is shown that local scaling can be a useful tool in these cases when surface-layer theory breaks down. Under convective conditions and after removing the effects of self-correlation, the normalized standard deviations of slope-normal wind velocity, temperature and humidity scale relatively well with $$z/\varLambda $$, where $$z$$ is the measurement height and $$\varLambda (z)$$ the local Obukhov length. However, the horizontal velocity fluctuations are not correlated with $$z/\varLambda $$ under all stability regimes. The non-dimensional gradients of wind velocity and temperature are also investigated. For those, the local scaling appears inappropriate, particularly at night when shallow drainage flows prevail and lead to negative wind-speed gradients close to the surface. [ABSTRACT FROM AUTHOR]

In stably stratified flows vertical movement of eddies is limited by the fact that kinetic energy is converted into potential energy, leading to a buoyancy displacement scale z B . Our new mixing-length concept for turbulent transport in the stable boundary layer follows a rigid-wall analogy, in the sense that we assume that the buoyancy length scale is similar to neutral length scaling. This implies that the buoyancy length scale is: ℓ B = κ B z B , with κ B ≈ κ, the von Karman constant. With this concept it is shown that the physical relevance of the local scaling parameter z/Λ naturally appears, and that the α coefficient of the log-linear similarity functions $${\phi_{m,h}=1+\alpha z/\Lambda}$$ is equal to c/ κ 2, where c is a constant close to unity. The predicted value α ≈ 1/ κ 2 = 6.25 lies within the range found in observational studies. Finally, it is shown that the traditionally used inverse linear interpolation between the mixing length in the neutral and buoyancy limits is inconsistent with the classical log-linear stability functions. As an alternative, a log-linear consistent interpolation method is proposed. [ABSTRACT FROM AUTHOR]

A method for identification of fragments with high local similarity to human proteins within potentially immunopathogenic regions of HIV-1 proteins was developed. The method is based on the use of an original matrix of antigenic similarity of amino acids. The regions whose fragments are frequent in human proteins, and regions exhibiting high similarity to the proteins responsible for important physiological functions, were identified in HIV-1 proteins. A possibility of participation of such regions in immunopathogenesis of HIV infection either through induction of cross-reacting effectors of the immune system or through molecular mimicry of physiologically important human proteins, leading to alteration of homeostasis of the organism, is discussed. Most of the regions identified in HIV-1 proteins contain either T-cell (CD8+ CTL or CD4+ Th) or B-cell epitopes, or both of them simultaneously. The criteria for the design of safe polyepitopic antiviral vaccines that would allow exclusion of epitopes with (immuno)pathogenic potential are discussed. According to these criteria, polyepitopic immunogens should be free of the virus protein regions whose fragments are frequent in human proteins, as well as of regions exhibiting pronounced local similarity to proteins that provide for important physiological functions. [ABSTRACT FROM AUTHOR]

Local similarity, referred to as type II similarity, in the interfacial, stably-stratified layer at the top of the atmospheric (or oceanic) mixed layer is discussed. Type II scales for scalars are based on the local values of scalar gradients. Similarity predictions are derived from the second-order closure model of Yamada and Mellor, and also from similarity arguments. The obtained formulation is verified for active and passive scalars based on the large-eddy simulation model. [ABSTRACT FROM AUTHOR]

Micrometeorological data for wind and temperature from a 325 m high lower in Beijing City are analyzed by use of local similarty theory Non–dimentional wind and temperature gradients Φ and Φ are determined by three techniques called respectively, eddy-correlation, mean profiles and inertia subrange cospectra (ISC) method for a wide range of atmospheric stratification from unstable to stable condition Average dissipation rate Φof turbulent kinetic energy (TKE) is evaluated from u-spectrum as a quanlity required in the last technique. Ratio of the eddy transfer coefficients. α(= K / K) is calculated from Φ and Φ estimations. The results from various techniques are compared with each other and with some available empirical results in the lwor -layer. it is shown that the empirical relationship detefrmined by mean profiles and ISC methods in the lower-layer turbulence are in agreement with each other and with some other results. [ABSTRACT FROM AUTHOR]

Stable internal boundary layers form when warm air is advected over a cooler surface, a common occurrence in coastal areas. The internal boundary layer deepens with distance along-wind, eventually reaching equilibrium with the surface and becoming a fully developed marine boundary layer. We present observations of the late stages of internal boundary-layer evolution made by the U.K. Meteorological Office's C-130 Hercules research aircraft over the Persian Gulf in April 1996. Northwesterly winds brought warm dry air from the surrounding desert landmass across the cooler waters of the Gulf. Loss of heat to the surface resulted in the formation of a shallow, stable internal boundary layer downwind of the coast. The aircraft measurements were made several hundred kilometres downwind, by which point the original deep convective boundary layer had been eroded away and the internal boundary layer was well developed, effectively a new marine atmospheric boundary layer. Throughout most of its depth the boundary layer was statically stable and a downward heat flux of approximately 15 W m-2 was observed; however, an exceptionally strong latent heat flux, in excess of 250 W m-2 near the surface, was sufficient to overcome the downwards heat flux and maintain weak buoyant convection in the lower 30–50% of the boundary layer. Scaling of boundary-layer turbulence statistics using local similarity theory produces results in agreement with previous studies. Because of the strong humidity contribution to the buoyancy flux, however, care is required with the definition of the similarity scales. It is usual for either the sensible heat or buoyancy flux to be used in the definitions of both the temperature and length scales; the latter being used over water where humidity plays a significant role in determining stability. In the present case we find that while the buoyancy flux is appropriate in the definition of the length scale, the temperature scale must be defined in terms of the sensible heat flux. [ABSTRACT FROM AUTHOR]

An approximate method for the efficient calculation of stagnation-streamline quantities in hypersonic flows about spheres or cylinders is suggested. Based on the local similarity of the flow field the two-dimensional Navier-Stokes equations are simplified to a one-dimensional approximation for the stagnation streamline. These equations are solved with an implicit finite-volume scheme. Comparisons with fully two–dimensional Euler and Navier–Stokes calculations for flows about spheres are presented, that include perfect gas flows and flows in chemical non-equilibrium. Comparisons with a number of experiments conclude this report. [ABSTRACT FROM AUTHOR]