A 15-year-Long catalog of seismicity in the Eastern Tennessee Seismic Zone(ETSZ)using matched filter detection

We present a detailed catalog of 13671 earthquakes in the Eastern Tennessee Seismic Zone(ETSZ)that spans January 1,2005 to July 31,2020.We apply a matched filter detection technique on over 15 years of continuous data,resulting in arguably the most complete catalog of seismicity in the ETSZ yet.The magnitudes of newly detected events are determined by computing the amplitude ratio between the detections and templates using a principal component fit.We also compute the b-value for the new catalog and comparatively relocate a subset of newly detected events using XCORLOC and hypoDD,which shows a more defined structure at depth.We find the greatest concentration along and to the east of the New York-Alabama Lineament,as defined by the magnetic anomaly,supporting the argument that this feature likely is related to the generation of seismicity in the ETSZ.We examine seismicity in the vicinity of the Watts Bar Reservoir,which is located about 5 km from the epicenter of the M_(W) 4.4 December 12,2018 Decatur,Tennessee earthquake,and find possible evidence for reservoir modulated seismicity in this region.We also examine seismicity in the entire ETSZ to search for a correlation between shallow earthquakes and seasonal hydrologic changes.Our results show limited evidence for hydrologicallydriven shallow seismicity due to seasonal groundwater levels in the ETSZ,which contradicts previous studies hypothesizing that most intraplate earthquakes are associated with the dynamics of hydrologic cycles.

A high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi earthquake sequence, Yunnan, China: Application of AI picker and matched filter

We present a high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi sequence.The catalog has a time range of 2021-05-01 to 2021-05-28,and contains~8,000 well located events.It captures the features of the whole foreshock sequence and the early aftershocks.We designed a detection strategy incorporating both an artificial intelligent(AI)picker and a matched filter algorithm.Here,we adopt a hybrid AI method incorporating convolutional and recurrent neural network(CNN&RNN)for event detection and phase picking respectively(i.e.CERP),a light-weight AI picker that can be trained with small volume of data.CERP is first trained with detections from a STA/LTA and Kurtosis-based method called PAL,and then construct a rather complete template set of~4,000 events.Finally,the matched filter algorithm MESS augments the initial detections and measures differential travel times with cross-correlation,which finally results in precise relocation.This process gives 9,026 detections,among which 7,943 events can be well relocated.The catalog shows as expected power-law distribution of frequency magnitude and reveals detailed pattern of seismicity evolution.The main features are:(1)the foreshock sequence images simple fault geometry with consistent strike,but also show a variable event depth along strike;(2)the mainshock ruptures the same fault of the foreshock sequence and activate conjugate faults further to the southeast;(3)complex seismicity are developed in the post-seismic period,indicating complex triggering mechanisms.Thus,our catalog provides a reliable basis for further investigations,such as b-value studies,rupture process,and triggering relations.

Enhanced spectrum sensing using a combination of energy detector,matched filter and cyclic prefix

In this paper,we propose a sensing scheme based on energy detection,matched filter and cyclic prefix.Both Equal Gain Combining(EGC)and optimal combination of the aforementioned detectors are investigated in cooperative and non-cooperative spectrum sensing scenarios.In packet transmission systems such as OFDM(Orthogonal Frequency Division Multiple access)systems,the proposed scheme takes advantage of utilizing more samples than individual detectors,i.e.,cyclic prefix,training or pilot samples,and data payload samples.The proposed combine-sensing scheme offers higher detection probability and lower false alarm probability,as compared with the performance of individual detectors over the same frame duration.Simulation results are congruent with the theoretical curves and confirm the validity of our derivations.

An extended processing scheme for coherent integration and parameter estimation based on matched filtering in passive radar

In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function(CAF) and the method based on matched filtering are the most common approaches. The method based on matched filtering is an approximation to CAF and the procedure is:(1) divide the signal into snapshots;(2) perform matched filtering on each snapshot;(3) perform fast Fourier transform(FFT) across the snapshots. The matched filtering method is computationally affordable and can offer savings of an order of 1000 times in execution speed over that of CAF. However, matched filtering suffers from severe energy loss for high speed targets. In this paper we concentrate mainly on the matched filtering method and we use keystone transform to rectify range migration. Several factors affecting the performance of coherent integration are discussed based on the matched filtering method and keystone transform. Modified methods are introduced to improve the performance by analyzing the impacts of mismatching, precision of the keystone transform, and discretization. The modified discrete chirp Fourier transform(MDCFT) is adopted to rectify the Doppler expansion in a multi-target scenario. A novel velocity estimation method is proposed, and an extended processing scheme presented. Simulations show that the proposed algorithms improve the performance of matched filtering for high speed targets.

Application of matched filters in time-scale domain for elastography

Elasticity imaging offers the possibility of detecting changes in elastic properties and assesses the biomechanical properties of soft tissue with increased sensitivity and spatial resolution compared with conventional palpation. However, the range of applied strains is limited by the concomitant increase of echo signal decorrelation, The decorrelation is mainly introduced by diffuse scattering, while the regular scattering is highly correlated. Because the regular scattering and diffuse scattering localize with different patterns in different ranges of time-scale plane, a new method is put forward to detect the regular scattering with matched filters based on wavelet transform using Generalized Likelihood Aatio Test (GLRT). The simulation results illustrate that the change in estimated mean interscatterer spacing introduced by a SNR of -10 dB is 1.1±2.8%. Thus, by tracking the highly correlated regular scattering, the internal strain can be estimated based on the change in interscatterer spacing under the condition of large surface deformation. The experiment studies show that the internal strain can be estimated up to 10% applied deformation in phantom and 5% strain in porcine liver.

Application of Newtonian approximate model to LIGO gravitational wave data processing

With the observation of a series of ground-based laser interferometer gravitational wave(GW)detectors such as LIGO and Virgo,nearly 100 GW events have been detected successively.At present,all detected GW events are generated by the mergers of compact binary systems and are identified through the data processing of matched filtering.Based on matched filtering,we use the GW waveform of the Newtonian approximate(NA)model constructed by linearized theory to match the events detected by LIGO and injections to determine the coalescence time and utilize the frequency curve for data fitting to estimate the parameters of the chirp masses of binary black holes(BBHs).The average chirp mass of our results is 22.05_(-6.31)^(+6.31)M_(⊙),which is very close to 23.80_(-3.52)^(+4.83)M_(⊙)provided by GWOSC.In the process,we can analyze LIGO GW events and estimate the chirp masses of the BBHs.This work presents the feasibility and accuracy of the low-order approximate model and data fitting in the application of GW data processing.It is beneficial for further data processing and has certain research value for the preliminary application of GW data.

Simultaneous denoising and resolution enhancement of seismic data based on elastic convolution dictionary learning

Enhancing seismic resolution is a key component in seismic data processing, which plays a valuable role in raising the prospecting accuracy of oil reservoirs. However, in noisy situations, existing resolution enhancement methods are difficult to yield satisfactory processing outcomes for reservoir characterization. To solve this problem, we develop a new approach for simultaneous denoising and resolution enhancement of seismic data based on convolution dictionary learning. First, an elastic convolution dictionary learning algorithm is presented to efficiently learn a convolution dictionary with stronger representation capability from the noisy data to be processed. Specifically, the algorithm introduces the elastic L1/2 norm as a sparsity constraint and employs a steepest gradient descent strategy to efficiently solve the frequency-domain linear system with substantial computational cost in a half-quadratic splitting framework. Then, based on the learned convolution dictionary, a weighted convolutional sparse representation paradigm is designed to encode the noisy data to acquire an optimal sparse approximation of the effective signal. Subsequently, a high-resolution dictionary with a broadband spectrum is constructed by the proposed parameter scaling strategy and matched filtering technique on the basis of atomic spectrum modeling. Finally, the optimal sparse approximation of the effective signal and the constructed high-resolution dictionary are used for data reconstruction to obtain the seismic signal with high resolution and high signal-to-noise ratio. Synthetic and field dataset examples are executed to check the effectiveness and reliability of the developed method. The results indicate that this method has a more competitive performance in seismic applications compared with the conventional deconvolution and spectral whitening methods.

Error Probability Analysis for Ultra-Massive MIMO System and Near-Optimal Signal Detection

In this paper,average bit error probability(ABEP)bound of optimal maximum likelihood(ML)detector is first derived for ultra massive(UM)multiple-input-multiple-output(MIMO)system with generalized amplitude phase modulation(APM),which is confirmed by simulation results.Furthermore,a minimum residual criterion(MRC)based lowcomplexity near-optimal ML detector is proposed for UM-MIMO system.Specifically,we first obtain an initial estimated signal by a conventional detector,i.e.,matched filter(MF),or minimum mean square error(MMSE)and so on.Furthermore,MRC based error correction mechanism(ECM)is proposed to correct the erroneous symbol encountered in the initial result.Simulation results are shown that the performance of the proposed MRC-ECM based detector is capable of approaching theoretical ABEP of ML,despite only imposing a slightly higher complexity than that of the initial detector.

A New Two-Stage Tunable Space-Time Adaptive Detector

In order to improve the rejection capability of mismatched interferer signals,a new two-stage detector is proposed under homogeneous scenarios with unknown covariance matrix,which is obtained by cascading the adaptive matched filter(AMF)detector and the enhanced RAO(EnRAO)detector.The new detector has constant false alarm performance,and the closed-form expression of probability of false alarm and probability of detection is derived.The performance of the new detector is assessed,and analyzed in comparison with other detectors.The results show that,the proposed detector can provide enhanced rejection capability in the case of mismatch,but the performance of the detector is slightly lost under the condition of matching.

JOINT MF COMBINING AND TURBO EQUALIZATION FOR WIRELESS COMMUNICATIONS OVER ISI CHANNELS WITH DIVERSITY RECEPTION

A new method called joint Matched Filter (MF) combining and turbo equalization is proposed for wireless communications over Inter-Symbol Interference (ISI) channels with diversity reception. This method takes diversity combining and equalization as integrity and need just one turbo equalizer for all diversity branches. Computer simulations prove that our method can take advantage of turbo equalization and diversity reception to combat fading of wireless channels.

Circadian Rhythm and Intersexual Differences in Auditory Frequency Sensitivity in Emei Music Frogs

In anurans, calling behaviour is strongly seasonal and circadian. Previous studies have revealed that a uditory sensitivity in frogs exhibits seasonal plasticity, and electroencephalographic signals exhibit highly correlated circadian patterns;of which, the circadian rhythm remains unknown. In this study,the circadian rhythm and intersexual differences of auditory sensitivity were tested in the Emei music frog(Nidirana daunchina). This was achieved by comparing thresholds and latencies of auditory brainstem responses(ABRs) evoked by tones and clicks stimuli between male and female frogs during the day and at night,respectively. Our results revealed that both auditory thresholds and latencies had no differences between day and night except the la tencies in 3.5–4.0 kHz frequencies. However, the thresholds of tone pip evoked ABRs differed significantly between male and female frogs from 2.5 to 5.0 kHz. This demonstrated that the auditory sensitivity of Emei music frogs exhibits sexual dimorphism at high frequencies, with female frogs exhibiting greater auditory sensitivity than that of male frogs. Simultaneously, the power spectra of male advertisement calls are matched well with the frequency range of auditory sensitivity in male and female frogs,which supports the matched filter hypothesis. Our study enhances the understanding of circadian plasticity and sexual dimorphism of auditory sensitivity in frogs.

Study on Azimuth Variation of Airgun Signal Propagation

Based on the data recorded by the observation network during the intensive excitation period from November to December 2015 at Binchuan Earthquake Signal Transmitting Seismic Station(BESTSS)in Yunnan Province,the noise in waveform recording is removed by S-transform template filtering method,and the azimuth of airgun signal propagation is calculated and analyzed from the horizontal waveform recordings.The results show that:①the azimuth angle of airgun signal after propagation is sensitive to stress change,and can clearly reflect the diurnal variation of tidal stress,which can be used to monitor the change of stress state in crustal medium;②the azimuth angle of airgun signal in some stations has changed abruptly after propagation on December 4,which may be related to the change of airgun source;③five-shot superposition or fivepoint smoothing of azimuth angle of single shot are carried out for airgun signals in stations far away from epicenter,and results show that azimuth angle from superposition or smoothing is more stable and has clear diurnal variation characteristics after propagation.

Radar Imaging of Sidelobe Suppression Based on Sparse Regularization

Synthetic aperture radar based on the matched filter theory has the ability of obtaining two-di- mensional image of the scattering areas. Nevertheless, the resolution and sidelobe level of SAR imaging is limited by the antenna length and bandwidth of transmitted signal. However, for sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then analyze several common sparse imaging algorithms: the greed algorithm, the convex optimization algorithm. We apply some of these algorithms into SAR imaging using RadBasedata. The results show the presented method based on sparse construction theory outperforms the conventional SAR method based on MF theory.

A group velocity of seismic love surface wave and lithosphere structure in Antarctic Peninsula

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A Modified Multiple Matching Method Based on Equipoise Pseudomulti-Channel Filter and Huber Nor

This study is aimed at improving multiple adaptive subtraction.We propose a modified pseudomulti-channel matching method based on the Huber norm,to adjust the matching differences on frequency and phase between the predicted multi-ples and original data.The second-order derivative of the predicted multiples is utilized to replace the derivative of its Hilbert transform.Due to the additional frequency term,this method can enhance the high-frequency component.We introduce 180◦phase rotation of the multiple channels,which can decrease phase differences.The Huber norm interpolates between smooth L2 norm treatment of small residuals and robust L1 norm treatment of large residuals.This method can eliminate the restriction of large value conditions from the L2 norm and weaken the condition of orthogonality from the L1 norm.The applications of the Pluto and Delft models shows that compared with pseudomulti-channel matching filter,the main frequency is increased from 36 Hz to 38 Hz,and the primary reflection wave is more concentrated.The practical application of field data verifies the effectiveness of the proposed method.

Improved image denoising via RAISR with fewer filters

In recent years,accurate Gaussian noise removal has attracted considerable attention for mobile applications,as in smart phones.Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time.Therefore,we propose a rapid post-processing method for Gaussian noise removal in this paper.Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise.Although these nonlocal image denoising methods have quantitatively high performance,some fine image details are lacking due to the loss of high frequency information.To tackle this problem,an improvement to the pioneering RAISR approach(rapid and accurate image super-resolution),is applied to rapidly post-process the denoised image.It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost,preserving important image structures well.Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements:geometric conversion and reduction of the strength classes.In addition,following RAISR,the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results.Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods,efficiently and with low memory requirements.

Indoor massive multiple-input multiple-output channel characterization and performance evaluation

We present a measurement campaign to characterize an indoor massive multiple-input multiple-output (MIMO) channel system, using a 64-element virtual linear array, a 64-element virtual planar array, and a 128-element virtual planar array. The array topologies are generated using a 3D mechanical turntable. The measurements are conducted at 2, 4, 6, 11, 15, and 22 GHz, with a large bandwidth of 200 MHz. Both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios are considered. The typical channel parameters are extracted, including path loss,shadow fading, power delay profile, and root mean square (RMS) delay spread. The frequency dependence of these channel parameters is analyzed. The correlation between shadow fading and RMS delay spread is discussed. In addition, the performance of the standard linear precoder—the matched filter, which can be used for intersymbol interference (ISI) mitigation by shortening the RMS delay spread, is investigated. Other performance measures,such as entropy capacity, Demmel condition number, and channel ellipticity, are analyzed. The measured channels,which are in a rich-scattering indoor environment, are found to achieve a performance close to that in independent and identically distributed Rayleigh channels even in an LOS scenario.

Mapping alteration minerals using sub-pixel unmixing of ASTER data in the Sarduiyeh area,SE Kerman,Iran

This paper is an attempt to introduce the role of earth observation technology and a type of digital earth processing in mineral resources exploration and assessment.The sub-pixel distribution and quantity of alteration minerals were mapped using linear spectral unmixing(LSU)and mixture tuned matched filtering(MTMF)algorithms in the Sarduiyeh area,SE Kerman,Iran,using the visible-near infrared(VNIR)and short wave infrared(SWIR)bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)instrument and the results were compared to evaluate the efficiency of methods.Three groups of alteration minerals were identified:(1)pyrophylite-alunite(2)sericite-kaolinite,and(3)chlorite-calcite-epidote.Results showed that high abundances within pixels were successfully corresponded to the alteration zones.In addition,a number of unreported altered areas were identified.Field observations and X-ray diffraction(XRD)analysis of field samples confirmed the dominant mineral phases identified remotely.Results of LSU and MTMF were generally similar with overall accuracy of 82.9 and 90.24%,respectively.It is concluded that LSU and MTMF are suitable for sub-pixel mapping of alteration minerals and when the purpose is identification of particular targets,rather than all the elements in the scene,the MTMF algorithm could be proposed.

Signal Processing for Fast Fault Detection in HVDC Grids

For multiterminal or meshed Voltage Source Converter(VSC)High-voltage Direct Current(HVDC)systems,high speed protection against DC faults is essential,as power electronic components cannot withstand the rapidly increasing fault currents which would otherwise result.Recently proposed DC fault detection methods were developed based on time domain simulations in EMT-type software,which requires considerable modeling and computational efforts and results in methods specifically designed for the HVDC grid under study.To simplify the initial design of DC fault detection methods,this paper proposes general guidelines based on fundamental theory and offers a reduced modeling approach.Furthermore,the impact of non-ideal measurements is investigated and a method to choose the filters that optimally discriminate these fault signals from noise,is proposed.The approach was evaluated in a case study on fault detection in a realistically dimensioned HVDC grid.The paper shows that the initial design of fast fault detection methods can be based on the relatively simple proposed guidelines and reduced models.The paper furthermore shows that a sufficiently high sampling frequency and a filter matched to the fault signal enable fault detection within hundreds of microseconds and discrimination of DC faults from transients not related to DC faults.