Considering the limitation of the Gabor transform due to the uncertainty principle, where time and frequency resolution cannot both be maximized simultaneously, we propose a post-processing strategy for the time-frequency spectrum to mitigate this limitation and improve time-frequency concentration. In the frequency band of seismic data, the time and frequency window sizes of the Gabor transform are fixed, implying that the Gabor transform’s time-frequency spectrum is formed by the 2D convolution of a high-resolution time-frequency spectrum with a Gaussian-shaped point spread function (PSF). Therefore, based on compressed sensing theory, we use sparse constraints to the time-frequency spectrum and solve the 2D deconvolution of the Gabor transform’s time-frequency spectrum using the alternating direction method of multipliers algorithm to eliminate the influence of the time-frequency window function. The PSF used for deconvolution is determined by the variances of the time and frequency windows, and by altering the size of the PSF, we can obtain frequency-sparse Gabor transform (FSGT) and time-sparse Gabor transform (TSGT). Simulation signals demonstrate the effectiveness of this post-processing strategy. For actual data, we prove that the sparse Gabor transform can enhance time-frequency concentration and improve the accuracy of seismic data analysis by integrating thin layer identification and frequency-dependent amplitude variation with offset attributes.

This research work deals with the establishment of a deconvolution approach with enhanced spatial resolution. The main focus of the developed algorithm is to remove the spatial smoothing effect of seismic data and to recover the seismic response of high-angle geological bodies, such as faults, thereby improving the accuracy of reservoir imaging. We assume that the data introduce smoothing of the spatial direction in the acquisition and processing, resulting in reduced resolution of geological body boundaries and fault response. We then employ the edge method and exhaustion method to estimate the spatial smoothing function and construct the 2-D (or 3-D) point spread function with a seismic wavelet. Additionally, low-resolution seismic data are generated by convolving unsmoothed reflection coefficients with a point spread function. Therefore, we proceed with constructing an optimized objective function and increasing the sparse constraint on the reflection coefficient. Utilizing the alternating direction of multipliers method, we can arrive at the reflection coefficient, which removes the effect of spatial smoothing. This reflection coefficient can be convolution with the high-resolution wavelet to obtain enhanced resolution data, and the subsurface impedance information can also be achieved with the low-frequency background.

In existing window time-frequency analysis methods, its resolution is commonly limited by the uncertainty principle. The time resolution and the frequency resolution restrict each other and cannot reach the maximum at the same time. For this reason, we employ the secondary processing technology of the time-frequency spectrum to enhance the time resolution of the S-transform (ST). By performing multiband filtering with a filter bank, the ST is capable of receiving the signal under different frequencies and its time-frequency coefficient. The window size is different, that is, the low-frequency time window is long, resulting in low time resolution and high-frequency resolution. The high-frequency time window is short, which makes the time resolution high and the frequency resolution low; however, the window size for the time component of each frequency is fixed. Based on the above idea, we perform “de-window” processing for each frequency component signal. We assume that the time-frequency spectrum is sparse without “windowing,” so the window effect can be removed by sparse inversion. Based on the alternating direction method of multipliers (ADMM), we use the nonnegative penalty terms and sparse terms for the joint constraints to solve the optimization problem, and obtain the sparse time-frequency spectrum to enhance the time resolution. This time sparse ST (TSST) obtained by the proposed method is suitable for improving the ability of thin-layer identification and also for the analysis of transient signals.

Linear noise attenuation is a troublesome problem in a variety of seismic exploration areas. Traditional methods often use differences in frequency or apparent velocity to separate the signals and linear noise. However, these applications are limited when the characteristics of the aforementioned differences between signals and linear noise are too small to be distinguished. For this reason, we proposed a rotate-time-shift FK (RTS-FK-CS) method based on compressed sensing (CS). Based on the deblending concept, the proposed method flattens the linear events using the rotating coordinate system and performs a lateral time shift for each time point. Because of the random time shift, events with different apparent velocities from the linear noise are disrupted as “a deblended data in common receiver domain,” and events with similar apparent velocities have a difference in frequency. To suppress linear noise, we apply the CS reconstruction algorithm in the frequency-wavenumber (FK) domain. The random time shift uniformly distributes the energy near the dominant frequency to each frequency in the FK domain, enhancing the sparsity in the transform domain. The proposed method can effectively suppress linear noise and reduce the loss of events whose apparent velocities and frequency are similar to linear noise. Synthetic and field data tests visually and quantitatively confirmed the superiority and robustness of the proposed method.

Ground roll is a type of coherent noise with low velocity, low frequency, and high energy, which negatively affects the quality of the seismic data. The contamination of the ground roll is a persistent problem in the seismic processing field. For this reason, we considered the predictability of useful signals in thefxdomain, introduced the time-varying filtering based on the empirical mode decomposition (TVFEMD) into thefxdomain, and therefore proposed a novel algorithm (Ada-fx-TVFEMD). Similar to the variational mode decomposition (VMD), the TVFEMD algorithm consists in adaptive decomposition of multicomponent signals. The TVFEMD algorithm is more suitable for nonstationary signals because of its time-varying characteristic. The proposed Ada-fx-TVFEMD algorithm performs decomposition of each frequency slice and reconstruction of partial intrinsic mode functions (IMFs). Moreover, it allows to accurately select the IMFs that contain ground roll based on automatic ground roll identification. A presented synthetic example illustrates the superiority of the Ada-fx-TVFEMD algorithm in ground roll attenuation. Applying the proposed method on field data further demonstrates its potential in industrial applications, compared with frequency–wavenumber (fk) filtering andfx-VMD.

In seismic exploration, there are abandoned mines and bad traces, leading to missing data. In addition, unsuitable processing methods will introduce randomized amplitude anomalies. The traditional smoothing term with the$l_{2}$-norm assumes that the random noise is a Gaussian distribution. For the Gaussian distribution, the representation of the$l_{2}$-norm has a short tail compared with the$l_{1}$-norm, abnormal values in the data cannot be suppressed, and the stability of the solution is poor. For the Laplacian distribution, the representation of the$l_{1}$-norm has a longer tail compared with the$l_{2}$-norm, and it has a good tolerance for abnormal values. We propose a new method, based on the theory of compressed sensing, which uses the$l_{1}$-norm as the data fitting term and a sparse reconstruction equation for missing data with abnormal amplitude noise. To solve the equation for the complete data with no abnormal values, we apply the approximate projected subgradient method. Model and field data tests confirm the increased robustness of the proposed method.

In this article, we proposed a weighted stacking algorithm for obtaining high-resolution data and constructed an optimization objective function using the similarity of the stacked amplitude spectrum and constant (amplitude spectrum of impulse function). The optimization problem is solved to obtain the stacking weights involved in the common midpoint (CMP) gathers by using the alternating iterative method of gradient descent and subgradient descent. Then, the weighted stacking is performed to obtain resolution-enhanced data. A traditional poststack deconvolution algorithm decomposes the data and performs frequency-weighted recombination, which alters the original frequency composition. Furthermore, most existing methods require wavelet estimation, which may be inaccurate. The amplitude-spectrum shape of CMP gather controls the resolution-enhanced data using the proposed method, which is a stacking scheme that does not require wavelet estimation. On the other hand, our proposed stacking algorithm can handle data containing white noise, and we introduce a penalty term to avoid the mutual offset between the effective signals caused by negative weight, which improves the stacked signal-to-noise ratio. Applications to synthetic and field seismic datasets demonstrate that data stacked using the proposed method have higher resolution and can be more easily interpretated compared to the traditional equal-weight stacking.

Although existing surveillance cameras can identify people, their utility is limited by the unavailability of any direct camera-to-human communication. This paper proposes a real-time end-to-end system to solve the problem of digitally associating people in a camera view with their smartphones, without knowing the phones' IP/MAC addresses. The key idea is using a person's unique "context features", extracted from videos, as its sole address. The context address consists of: motion features, e.g. walking velocity; and ambience features, e.g. magnetic trend and Wi-Fi signal strengths. Once receiving a broadcast packet from the camera, a user's phone accepts it only if its context address matches the phone's sensor data. We highlight three novel components in our system: (1) definition of discriminative and noise-robust ambience features; (2) effortless ambient sensing map generation; (3) a context feature selection algorithm to dynamically choose lightweight yet effective features which are encoded into a fixed-length header. Real-world and simulated experiments are conducted for different applications. Our system achieves a sending ratio of 98.5%, an acceptance precision of 93.4%, and a recall of 98.3% with ten people. We believe this is a step towards direct camera-to-human communication and will become a generic underlay to various practical applications.

Facial authentication mechanisms are gaining traction on smartphones because of their convenience and increasingly good performance of face recognition systems. However, mainstream systems use traditional 2D face recognition technologies, which are vulnerable to various spoofing attacks. Existing systems perform liveness detection via specialized hardware, such as infrared dot projectors and dedicated cameras. Although effective, such methods do not align well with the smartphone industry's desire to maximize screen space.

The time-frequency (TF) analysis always plays a major role in signal processing. In this letter, we propose a novel TF analysis method called the second-order adaptive synchrosqueezing S transform (ASST2) and apply it on seismic ground roll attenuation. ASST2 is a data-driven method so that it performs a highly sharp representation with a desirable resolution by the nature of the signal itself. Moreover, ASST2 is invertible that allows a reasonable reconstruction. By applying ASST2, the instantaneous features of ground rolls can be recognized exactly in the TF map and the desired signal can be recovered after suppressing the noise. The synthetic example proves the superiority of ASST2 in characterizing nonstationary signals. The application on field data also illustrates that the proposed ASST2 can attenuate ground rolls effectively and preserves most reflections.

This video presents a real-time end-to-end system which enables cameras to send personalized messages to people in a public area without knowing any addresses of their mobile phones. For facilitating this communication, we solve the problem of digitally associating people in the camera view with their smartphones without prior knowledge of the phones' IP/MAC addresses. The system doesn't need any dedicated devices and doesn't request people to wear digital tags. It utilizes users' motion patterns and leverages the diversity in motion features as the address for communication. The cameras broadcast a message to all the phones in the camera view using the target's motion features as the destination. Then a user's phone can locally compare the "motion address" of the packet against its own sensor data and will accept the packet if it's a "good" match. To protect the privacy of users' sensor data, we keep the users' personal sensing data on their phones instead of asking them to upload the data to server. Moreover, to prevent users' walking behavior from being revealed to public, we transform the raw motion features via principal component analysis (PCA) while maintaining their distinguishing power. On the whole, our system achieves 98%, 95%, 90%, 90%, 87% matching correctness for 2, 4, 6, 8 and 10 users respectively.

Time-frequency analysis always plays a central role in the field of seismic processing due to the advantage in characterizing nonstationary signals. In this letter, we present a novel technique for seismic time-frequency analysis based on the high-order synchrosqueezing transform, which obtains more accurate instantaneous frequencies by using the higher order approximations for both amplitude and phase in order to achieve a highly energy-concentrated time-frequency representation. A synthetic example is employed to demonstrate the validity of the proposed method in sharpening time-frequency representation. Application on field data example further proves its potential in enhancing time-frequency resolution and delineating stratigraphic characteristics with higher precision and renders that this technique is promising for seismic data analysis.

Amplitude-versus-offset (AVO) inversion always plays an important role in reservoir fluid identification, which allows the estimation of various rock and fluid properties from prestack seismic data. In this paper, we propose a new method for discrimination of hydrocarbon accumulation that combines frequency-dependent AVO inversion scheme and variational mode decomposition (VMD). VMD is a recently developed algorithm for adaptive signal decomposition that is able to nonrecursively decompose a multicomponent signal into a number of quasi-orthogonal intrinsic mode functions and avoid mode mixing effectively. VMD is superior to other state-of-the-art approaches in obtaining high-resolution and high-fidelity local time-frequency depiction performance. Two synthetic signals are employed to illustrate that VMD achieves higher temporal and frequency resolution than the conventional continuous wavelet transform (CWT) decomposition. Other synthetic examples, elastic and dispersive, are utilized to demonstrate that the proposed method is more reliable for the detection of hydrocarbon saturation and a comparison is made with the CWT-based inverted results. Application on field data has further shown that the proposed approach has the potential in identifying the reservoir related to hydrocarbon.

Instantiating a distributed application that involves extensive inter-node communication onto a network is a challenging task. In this work, we focus on the special case of mapping a network emulation experiment onto a cluster comprising several (possibly heterogeneous) physical machines. We automatically profile the available physical machine resources, and use this information, together with the characteristics of the experimental topology, to determine an efficient mapping that preserves performance fidelity. We design an algorithm, which we call the “Waterfall” algorithm, and integrate it into a complete framework for profiling and mapping. We demonstrate the effectiveness of our framework via simulations and two sets of Crossfire Distributed Denial of Service attack testbed experiments.

Hydrocarbons can cause anomalies in the energy density of seismic signals when seismic waves pass through them. Teager-Kaiser energy (TKE) is an important attribute that can be utilized to depict the energy density of a seismic signal and the energy distribution of a seismic wavefield. In this letter, a novel spectral decomposition-based approach for hydrocarbon detection is proposed that applies the variational mode decomposition (VMD) associated with TKE to seismic data, which is called the VMDTKE algorithm. The proposed method not only possesses the better performance of TKE in focusing instantaneous energy, but also inherits the merit of high time-frequency resolution from VMD. The Marmousi2 example is used to demonstrate that the VMDTKE approach is capable of depicting the location and extent of strong anomalies which correlate to hydrocarbons more clearly. We compare the spectral decomposition results with that from the conventional VMD-based method. Application on field data further confirms the potential of the VMDTKE algorithm in delineating strong amplitude anomalies that are associated with hydrocarbon reservoirs.

Observed seismic data are mostly irregularly sampled and seismic data interpolation is an essential procedure to provide accurate complete data for seismic data analysis, such as amplitude-versus-offset analysis, multiple suppression, and wave-equation migration. The well-known minimum weighted norm interpolation (MWNI) method could achieve a relatively good result. However, the algorithm needs many iterations and thus the total calculation is expensive. In this letter, we propose a fast interpolation algorithm. Instead of wavenumber spectrum, singular spectrum can give a more accurate description of the sampled data. We use shaping regularization to control the smoothness of the singular values matrix. Compared with the conventional MWNI method, we test the proposed method on both synthetic and field data sets. The results confirm that our proposed method is more effective.

Acquiring accurate location information of vehicles is of great importance. Global Positioning System (GPS) has been widely deployed and used to be the most convenient solution to outdoor localization. As more and more infrastructure such as elevated roads, tunnels and tall buildings is built, however, the ever-increasing complexity of urban environments makes vehicle localization especially in those urban canyons a new challenging problem. In this paper, we propose a novel scheme, called UPS, to tackle urban vehicle localization problem. Inspired by the observation from empirical study that the Received Signal Strength Indication (RSSI) values of cellular signals (e.g., GSM) perceived over a distance have ideal temporal-spatial characteristics for fingerprinting, UPS refines the location accuracy of a moving vehicle by matching its cellular-aware trajectory, which is an association between consecutive geographical positions and the corresponding wide-band GSM RSSI values, with a pre-constructed map. Moreover, UPS leverages large mobility of vehicles to construct large-scale maps. We implement a prototype system to validate the feasibility of the UPS design. We conduct extensive real-world experiments and results show that UPS can work stably in various urban settings and achieve an accuracy of 4.2 meters on average and 5.3 meters with a 90% precision.

Time-frequency analysis is able to reveal the useful information hidden in the seismic data. The high resolution of the time-frequency representation is of great importance to depict geological structures. In this letter, we propose a novel seismic time-frequency analysis approach using the newly developed empirical wavelet transform (EWT). It is the first time that EWT is applied in analyzing multichannel seismic data for the purpose of seismic exploration. EWT is a fully adaptive signal-analysis approach, which is similar to the empirical mode decomposition but has a consolidated mathematical background. EWT first estimates the frequency components presented in the seismic signal, then computes the boundaries, and extracts oscillatory components based on the boundaries computed. Synthetic, 2-D, and 3-D real seismic data are used to comprehensively demonstrate the effectiveness of the proposed seismic time-frequency analysis approach. Results show that the EWT can provide a much higher resolution than the traditional continuous wavelet transform and offers the potential in precisely highlighting geological and stratigraphic information.

The seislet transform can be used to interpolate regularly undersampled seismic data if an accurate local slope map can be obtained. The dealiasing capability of such method highly depends on the accuracy of the estimated local slope, which can be achieved by using the low-frequency components of the aliased seismic data in an iterative manner. Previous approaches to solving this problem have been limited to the unstable estimation of local slope via a large number of iterations. Here, we propose a new way to obtain the slope estimation. We first estimate the NMO velocity and then use a velocity-slope transformation to get the optimal local slope. The new method allows us to avoid the iterative slope estimation and can obtain an accurate slope field in one step. The one-step slope estimation can significantly accelerate the iterative seislet domain thresholding process and can also stabilize the iterative inversion. Both synthetic and field data examples are used to demonstrate the performance by using the proposed approach compared with alternative approaches.