Event‐consistent volume dip‐azimuth‐curvature estimation with complex‐trace beamforming

In order to accurately identify the distribution of the karst water conduction channel after water inrush, the ground penetrating radar (GPR) is used for fine detection of development of karst water conduction in front of the heading surface after water inrush, and the instantaneous amplitude, instantaneous phase and instantaneous frequency are extracted from the radar signal by Hilbert transform. Then, the three instantaneous properties are combined for multi-parameter comprehensive analysis of the karst water conduction channel. As shown in the study results, the instantaneous amplitude can better show the boundary characteristics and morphological size of the karst water conduction channel. The instantaneous phase can clearly show the heterogeneous characteristics inside the karst water conduction channel. The instantaneous frequency can reflect the water richness of the karst water conduction channel from the angle of energy decay. The combination of the three instantaneous attributes can avoid the explanatory bias due to the analysis of single waveform characteristics, and effectively improve the resolution and accuracy of the detection of the karst water conduction channel.

Borehole Radar (BHR) uses ultra-wideband electromagnetic (EM) waves to image discontinuities in formations. It has been a major bottleneck to extend BHR applications to obtain a clear and high-resolution radar profile in a complex and noisy environment, which increases ambiguity in the geology interpretation. To avoid this increased ambiguity in the geology interpretation, we proposed a scheme based on the empirical mode decomposition (EMD) and complex signal analysis theory to process the BHR data with low signal to noise ratio (SNR). The scheme includes four steps. First, the original radar profile is pre-processed to avoid mode confusion and noise interference to the radar echo. Next, the EMD method is used to process a single-channel radar dataset and to analyze the frequency components of the radar signal. Various intrinsic modes of the pre-processing radar profile are also obtained by using EMD. Finally, we reconstruct the intrinsic mode profile, which contains information about the formation, calculate the complex signals of the reconstructed radar profile using the Hilbert transform, extract the three instantaneous attributes (instantaneous amplitude, instantaneous phase, and instantaneous frequency), and draw the separate instantaneous attributes profiles. This processing scheme provides both the conventional time-distance profile also in addition to the three instantaneous attributes. The additional attributes reduce ambiguity when evaluating the original radar profile and avoid the deviation relying solely on a conventional time-distance profile. An actual radar profile, which was obtained by a BHR system in a limestone fracture zone, is used to verify the effectiveness of instantaneous attributes for improving interpretation accuracy. The results demonstrate that the EMD method is superior in processing the BHR signal under a low SNR and has the capability to separate the high-low components of the radar echo effectively.

Differential interferometric synthetic aperture radar has been shown to be effective for monitoring subsidence in coal mining areas. Phase unwrapping can have a dramatic influence on the monitoring result. In this paper, a filtering-based phase unwrapping algorithm in combination with path-following is introduced to unwrap differential interferograms with high noise in mining areas. It can perform simultaneous noise filtering and phase unwrapping so that the pre-filtering steps can be omitted, thus usually retaining more details and improving the detectable deformation. For the method, the nonlinear measurement model of phase unwrapping is processed using a simplified Cubature Kalman filtering, which is an effective and efficient tool used in many nonlinear fields. Three case studies are designed to evaluate the performance of the method. In Case 1, two tests are designed to evaluate the performance of the method under different factors including the number of multi-looks and path-guiding indexes. The result demonstrates that the unwrapped results are sensitive to the number of multi-looks and that the Fisher Distance is the most suitable path-guiding index for our study. Two case studies are then designed to evaluate the feasibility of the proposed phase unwrapping method based on Cubature Kalman filtering. The results indicate that, compared with the popular Minimum Cost Flow method, the Cubature Kalman filtering-based phase unwrapping can achieve promising results without pre-filtering and is an appropriate method for coal mining areas with high noise.

Estimators for instantaneous phase

Summary Geological subtle difference identification poses great challenge to seismic interpretation. Although some effective seismic attributes (e.g. instantaneous amplitude, instantaneous phase, instantaneous frequency, coherence etc.) have been developed to map changes in seismic data, subtle changes in detail are still difficult to be distinguished by these methods. Therefore, it is necessary to explore the better attribute with far greater detail than these conventional ones. Unlike most seismic attributes based on linear theory, logistic nonlinear convergence rate attribute we proposed in this paper generated from a nonlinear system - logistic equation. Logistic equation is a famous polynomial nonlinear iterative dynamical system which is often cited as a chaos producer. However, few works pay attention to nonlinear convergence of the equation. This paper just focused on this part and discussed application of logistic nonlinear convergence. Method experiments demonstrated that logistic nonlinear convergence rate can be conducive to magnify subtle differences. And the case study in Tarim illustrated that our method did be sensitive to subtle differences between reservoirs and its geological background in seismic responses, and can well delineate the distribution of reservoirs

Cognitive Radio (CR) networks has been presented in the last decade as a solution to the problem of increasing congestion in frequency spectrum, through opportunistic spectrum access techniques. One of the main components of Cognitive Radio receivers is the Automatic Modulation Classification (AMC), in which the CR can blindly identify the modulation scheme of a detected signal. AMC has several applications, including military, spectrum surveillance and management, and commercial applications. In this paper we propose an AMC based on two different Neural Networks (NN) classifiers: Feed-Forward with Resilient Back-Propagation NN and Probabilistic NN. NN classifiers take their inputs as a feature vector from a Features extraction phase. Features selected for classification are the statistical features of the received signal's instantaneous amplitude, frequency and phase. Simulations show that both NN classifiers can achieve over 80% correct classification up to Signal-to-Noise Ratio (SNR) of 5 dB, and the probability of correct classification increases up to 99% at a SNR of 15 dB.

In general, a vibration signal consists of several frequency modulation (FM) components. Every component contains different information, and can be characterized by its instantaneous amplitude (IA) and instantaneous phase (IP). In engineering applications, conventional time-frequency analysis methods and signal decomposition methods have shown their power in investigating features of the vibration signal. However, they are limited in resolution and it is hard to analyze these FM components individually. To overcome these deficiencies, a novel signal decomposition algorithm, named time-frequency distribution decomposition (TFDD), is proposed in this paper, which reconstructs one FM component of the signal at a time by estimating its IP and IA. The IA and IP are approximated by two polynomial functions respectively. One important advantage of TFDD is that it can directly extract the component we are interested in. Therefore, we can analyze the key component of the signal with little influence from other components. This will help us to characterize the vibration signal more deeply. Furthermore, it is very stable to noise. This is conductive to protecting the information of the vibration signal. The effectiveness of the TFDD is validated by a numerical simulation and the study of the vibration response signal collected from a viscoelastic sandwich structure (VSS). From the value of permutation entropy of the component extracted by TFDD, the looseness state of the VSS is recognized.

Earthquake is one of the main factors causing structural disasters in current buildings. Under earthquake action, adjacent building structures are generally in different vibration stages, and collisions may occur in the structures, which may cause serious damage to the structure. In order to prevent certain earthquakes from damaging the designed buildings, this article mainly introduced the design and analysis of a numerical algorithm for seismic nonlinear structural dynamic response based on the HVSR algorithm. This article evenly divided the acceleration response time series into 15 time periods and then selected the position corresponding to the peak point of instantaneous amplitude within each period as the selected data point position. The same seismic load can be applied at the bottom of the established nonlinear model to extract the dynamic response data of the top layer of the structure, and then, the instantaneous amplitude and corresponding instantaneous phases and frequencies of the main components of the structural dynamic response can be extracted through the time-varying filter and Hilbert transform based on the discrete analytic mode decomposition. Under the influence of these four ground motions, the collision force within the range of 0-50 kN accounted for over 87% of the total number of collisions. In the comparison results of collision response to peak displacement, the four ground motions all led to structural collision, and the collision inhibited the positive peak displacement response of node 1512. Compared with noncollision, the peak displacement was reduced by 27.273, 33.675, 27.727, and 37.248%, respectively. The peak displacement of 1512 nodes was suppressed and reduced by 18.856%. The results indicate that the HVSR algorithm can obtain the instantaneous characteristic parameters of nonlinear structural dynamic response and achieve model correction.

Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject-dependent variations. Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data. In this article, a GSP-based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. A graph spectral representation of the Hilbert-transformed EEG signals is obtained, in which simultaneous diagonalization of covariance matrices provides the basis of a subspace that differentiates two classes of right hand and right foot MI tasks. To determine the most discriminative subspace, an exploratory analysis was conducted in the spectral domain of the graphs by ranking the graph frequency components using a feature selection method. The selected features are fed into a binary support vector machine that predicts the label of the test trials. The performance of the proposed approach was evaluated on brain-computer interface competition III (IVa) dataset. Experimental results reflect that brain functional connectivity graphs derived using the instantaneous amplitude and phase of the EEG signals show comparable performance with the best results reported on these data in the literature, indicating the efficiency of the proposed method compared to the state-of-the-art methods.

Beam damage detection using synchronisation of peaks in instantaneous frequency and amplitude of vibration data

On an ambiguity in the definition of the amplitude and phase of a signal

The instantaneous amplitude, phase, and frequency (APF) characterize a nonstationary signal on a fundamental level. In this paper, we seek to obtain a consistent multidimensional APF where the estimated phase and frequency agree and where the frequency can be obtained as the gradient of an analytical phase model. Currently existing multidimensional phase unwrapping algorithms are incapable of providing this because they erroneously assume bandlimits on the trajectory of the instantaneous frequency and thus suffer from the "phase aliasing" phenomenon. We present a new multidimensional phase unwrapping algorithm based on tensor product splines that does not make this assumption and thereby largely eliminates the deleterious effects of phase aliasing.

ABSTRACTSeismic detection of faults, dykes, potholes and iron‐rich ultramafic pegmatitic bodies is of great importance to the platinum mining industry, as these structures affect safety and efficiency. The application of conventional seismic attributes (such as instantaneous amplitude, phase and frequency) in the hard‐rock environment is more challenging than in soft‐rock settings because the geology is often complex, reflections disrupted and the seismic energy strongly scattered. We have developed new seismic attributes that sharpen seismic reflections, enabling additional structural information to be extracted from hard‐rock seismic data. The symmetry attribute is based on the invariance of an object with respect to transformations such as rotation and reflection; it is independent of the trace reflection amplitude, and hence a better indicator of the lateral continuity of thin and weak reflections. The reflection‐continuity detector attribute is based on the Hilbert transform; it enhances the visibility of the peaks and troughs of the seismic traces, and hence the continuity of weak reflections. We demonstrate the effectiveness of these new seismic attributes by applying them to a legacy 3D seismic data set from the Bushveld Complex in South Africa. These seismic attributes show good detection of deep‐seated thin (∼1.5 m thick) platinum ore bodies and their associated complex geological structures (faults, dykes, potholes and iron‐rich ultramafic pegmatites). They provide a fast, cost‐effective and efficient interpretation tool that, when coupled with horizon‐based seismic attributes, can reveal structures not seen in conventional interpretations.

Simultaneous acquisition of brain activity signals from the sensorimotor area using NIRS combined with EEG, imagined hand clenching force and speed modulation of brain activity, as well as 6-class classification of these imagined motor parameters by NIRS-EEG were explored. Near infrared probes were aligned with C3 and C4, and EEG electrodes were placed midway between the NIRS probes. NIRS and EEG signals were acquired from six healthy subjects during six imagined hand clenching force and speed tasks involving the right hand. The results showed that NIRS combined with EEG is effective for simultaneously measuring brain activity of the sensorimotor area. The study also showed that in the duration of (0, 10) s for imagined force and speed of hand clenching, HbO first exhibited a negative variation trend, which was followed by a negative peak. After the negative peak, it exhibited a positive variation trend with a positive peak about 6-8 s after termination of imagined movement. During (-2, 1) s, the EEG may have indicated neural processing during the preparation, execution, and monitoring of a given imagined force and speed of hand clenching. The instantaneous phase, frequency, and amplitude feature of the EEG were calculated by Hilbert transform; HbO and the difference between HbO and Hb concentrations were extracted. The features of NIRS and EEG were combined to classify three levels of imagined force [at 20/50/80% MVGF (maximum voluntary grip force)] and speed (at 0.5/1/2 Hz) of hand clenching by SVM. The average classification accuracy of the NIRS-EEG fusion feature was 0.74 ± 0.02. These results may provide increased control commands of force and speed for a brain-controlled robot based on NIRS-EEG.

Instantaneous amplitude and phase signal modeling for harmonic removal in wind turbines