N-th root slant stack for enhancing weak seismic signals

Seismic imaging of complicated underground structures with severe surface undulation(i.e.,double complex areas)is challenging owing to the difficulty of collecting the very weak reflected signal.Enhancing the weak signal is difficult even with state-of-the-art multi-domain and multidimensional prestack denoising techniques.This paper presents a time–space dip analysis of offset vector tile(OVT)domain data based on theτ-p transform.The proposed N-th root slant stack method enhances the signal in a three-dimensionalτ-p domain by establishing a zero-offset time-dip seismic attribute trace and calculating the coherence values of a given data sub-volume(i.e.,inline,crossline,time),which are then used to recalculate the data.After sorting,the new data provide a solid foundation for obtaining the optimal N value of the N-th root slant stack,which is used to enhance a weak signal.The proposed method was applied to denoising low signal-to-noise ratio(SNR)data from Western China.The optimal N value was determined for improving the SNR in deep strata,and the weak seismic signal was enhanced.The results showed that the proposed method effectively suppressed noise in low-SNR data.

Analysis of frequency shifting in seismic signals using Gabor-Wigner transform

A hybrid time-frequency method known as Gabor-Wigner transform (GWT) is introduced in this paper for examining the time-frequency patterns of earthquake damaged buildings. GWT is developed by combining the Gabor transform (GT) and Wigner-Ville distribution (WVD). GT and WVD have been used separately on synthetic and recorded earthquake data to identify frequency shifting due to earthquake damages, but GT is prone to windowing effect and WVD involves ambiguity function. Hence to obtain better clarity and to remove the cross terms (frequency interference), GT and WVD are judiciously combined and the resultant GWT used to identify frequency shifting. Synthetic seismic response of an instrumented building and real-time earthquake data recorded on the building were investigated using GWT. It is found that GWT offers good accuracy for even slow variations in frequency, good time-frequency resolution, and localized response. Presented results confirm the efficacy of GWT when compared with GT and WVD used separately. Simulation results were quantified by the Renyi entropy measures and GWT shown to be an adequate technique in identifying localized response for structural damage detection.

Joint Time-Frequency Analysis of Seismic Signals:A Critical Review

This paper presents an evaluation of time-frequency methods for the analysis of seismic signals.Background of the present work is to describe,how the frequency content of the signal is changing in time.The theoretical basis of short time Fourier transform,Gabor transform,wavelet transform,S-transform,Wigner distribution,Wigner-Ville distribution,Pseudo Wigner-Ville distribution,Smoothed Pseudo Wigner-Ville distribution,Choi-William distribution,Born-Jordan Distribution and cone shape distribution are presented.The strengths and weaknesses of each technique are verified by applying them to a particular synthetic seismic signal and recorded real time earthquake data.

Singularity detection of the thin bed seismic signals with wavelet transform

The location of singularities may be detected by local maxima of the wavelet transform modulus. The digital modeling and focusing process to wavelet transform of the reflecting seismic signals have been done. It has been found that the locations of singularities after wavelet transform are only affected by two factors, their original locations and the seismic wavelet length, which says it does not matter with what shape the wavelet will be. The wavelet length can be determined according to the wavelet transform results and be eliminated thereafter so that we are able to detect thin bed seismic signal with resolution of l/32 wavelength. The singularities have been recovered with improved resolution of the seismic section by real data processing.

Seismic electric signals in seismic prone areas

The Varotsos-Alexopoulos-Nomicos （VAN） method of short-term earthquake prediction was introduced in the 1980s. The VAN method enables estimation of the epicenter, magnitude and occurrence time of an impending earthquake by observing transient changes of the electric field of the Earth termed seismic electric signals （SES）. Here, we present a few examples of SES observed in various earthquake prone areas worldwide.

A reweighted damped singular spectrum analysis method for robust seismic noise suppression

(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.

An automatic seismic signal detection method based on fourth-order statistics and applications

Real-time, automatic, and accurate determination of seismic signals is critical for rapid earthquake reporting and early warning. In this study, we present a correction trigger function（CTF） for automatically detecting regional seismic events and a fourth-order statistics algorithm with the Akaike information criterion（AIC） for determining the direct wave phase, based on the differences, or changes, in energy, frequency, and amplitude of the direct P- or S-waves signal and noise. Simulations suggest for that the proposed fourth-order statistics result in high resolution even for weak signal and noise variations at different amplitude, frequency, and polarization characteristics. To improve the precision of establishing the S-waves onset, first a specific segment of P-wave seismograms is selected and the polarization characteristics of the data are obtained. Second, the S-wave seismograms that contained the specific segment of P-wave seismograms are analyzed by S-wave polarization filtering. Finally, the S-wave phase onset times are estimated. The proposed algorithm was used to analyze regional earthquake data from the Shandong Seismic Network. The results suggest that compared with conventional methods, the proposed algorithm greatly decreased false and missed earthquake triggers, and improved the detection precision of direct P- and S-wave phases.

A Novel Approach for Estimating Debris Flow Velocities from Near-Field Broadband Seismic Observations

On August 7^(th),2010,Sanyanyu and Luojiayu debris flows triggered by a heavy rain have lashed Zhouqu City around midnight,leading to catastrophic destruction which killed 1765 people and resulted in enormous economic loss.The ZHQ Seismic Station is located approximately 170 m west of the outlet of the Sanyanyu Gully.The seismometer deployed at the seismic station started recording seismic signals of ever-enlarging amplitude around 10 minutes before the debris flow rushed out of the Sanyanyu Gully,showing ever approaching seismic source,i.e.the debris flow.In this study,we analyze this seismic event and propose an inversion algorithm to estimate the velocity of the debris flow by searching the best-fitting pairs of envelopes in the synthetic seismograms and the corresponding field seismic records in a least-square sense.Inversion results reveal that,before rushing out of the outlet,the average velocity of the debris flow gradually increased from 6.2 m/s to 7.1 m/s and finally reached 15 m/s at approximately 0.5 km above the outlet and kept this value since then.Obviously,the ever-increasing velocity of the debris flow is the key factor for the following disasters.Compared with other studies,our approach can provide the velocity distribution for the debris flow before its outbreak;Besides,it has the potential to provide technological support for a better understanding of the disaster process of a debris flow.

Characteristics and interpretation of the seismic signal of a field-scale landslide dam failure experiment

Outburst floods caused by breaches of landslide dams may cause serious damages and loss of lives in downstream areas; for this reason the study of the dynamic of the process is of particular interest for hazard and risk assessment. In this paper we report a field-scale landslide dam failure experiment conducted in Nantou County, in the central of Taiwan.The seismic signal generated during the dam failure was monitored using a broadband seismometer and the signal was used to study the dam failure process.We used the short-time Fourier transform(STFT) to obtain the time–frequency characteristics of the signal and analyzed the correlation between the power spectrum density(PSD) of the signal and the water level. The results indicate that the seismic signal generated during the process consisted of three components: a low-frequency band(0–1.5 Hz), an intermediate-frequency band(1.5–10 Hz) and a highfrequency band(10–45 Hz). We obtained the characteristics of each frequency band and the variations of the signal in various stages of the landslide dam failure process. We determined the cause for the signal changes in each frequency band and its relationship with the dam failure process. The PSD sediment flux estimation model was used to interpret the causes of variations in the signal energy before the dam failure and the clockwise hysteresis during the failure. Our results show that the seismic signal reflects the physical characteristics of the landslide dam failure process. The method and equipment used in this study may be used to monitor landslide dams and providing early warnings for dam failures.

Seismic signal recognition using improved BP neural network and combined feature extraction method

Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.

Anomalous signals before 2011 Tohoku-oki Mw9.1 earthquake,detected by superconducting gravimeters and broadband seismometers

The 2011 Tohoku-oki earthquake,occurred on 11 March,2011,is a great earthquake with a seismic magnitude Mw9. 1,before which an Mw7. 5 earthquake occurred. Focusing on this great earthquake event,we applied Hilbert-Huang transform（ HHT） analysis method to the one-second interval records at seven superconducting gravimeter（ SG） stations and seven broadband seismic（ BS） stations to carry out spectrum analysis and compute the energy-frequency-time distribution. Tidal effects are removed from SG data by T-soft software before the data series are transformed by HHT method. Based on HHT spectra and the marginal spectra from the records at selected seven SG stations and seven BS stations we found anomalous signals in terms of energy. The dominant frequencies of the anomalous signals are respectively about 0. 13 Hz in SG records and 0. 2 Hz in seismic data,and the anomalous signals occurred one week or two to three days prior to the event. Taking into account that in this period no typhoon event occurred,we may conclude that these anomalous signals might be related to the great earthquake event.

Properties of an improved Gabor wavelet transform and its applications to seismic signal processing and interpretation

This paper presents an analytical study of the complete transform of improved Gabor wavelets （IGWs）, and discusses its application to the processing and interpretation of seismic signals. The complete Gabor wavelet transform has the following properties. First, unlike the conventional transform, the improved Gabor wavelet transform （IGWT） maps time domain signals to the time-frequency domain instead of the time-scale domain. Second, the IGW＇s dominant frequency is fixed, so the transform can perform signal frequency division, where the dominant frequency components of the extracted sub-band signal carry essentially the same information as the corresponding components of the original signal, and the sub- band signal bandwidth can be regulated effectively by the transform＇s resolution factor. Third, a time-frequency filter consisting of an IGWT and its inverse transform can accurately locate target areas in the time-frequency field and perform filtering in a given time-frequency range. The complete IGW transform＇s properties are investigated using simulation experiments and test cases, showing positive results for seismic signal processing and interpretation, such as enhancing seismic signal resolution, permitting signal frequency division, and allowing small faults to be identified.

Study on automatic recognition of the first motion in a seismic event

In this paper, we have studied the waveforms of background noise in a seismograph and set up an AR model to characterize them. We then complete the modeling and the automatic recognition program. Finally, we provide the results from automatic recognition and the manual recognition of the first motion for 25 underground explosions.

Local Magnitude Study of the Seismic Activity on Itacarambi,MG

The importance of studying the local magnitude related to seismic activity occurred recently in the region of Itacarambi, state of Minas Gerais, is due to the fact that these were earthquakes of intraplate origin. From the study of [1] and the relation between local magnitude and seismic signal duration, was performed a data analysis obtained in the same region, on the period between October/2007 and June/2008, in which we can estimate the equation MD = 2.153 (±0.072) LogD – 1.925 (±0.132) to calculate the magnitude of local duration. We can also estimate one value for the b parameter using the equation LogN = a – bMD from a frequency-magnitude study. It was found the value of b = 0.826 (±0.020) for the general activity of Itacarambi, MG, that is within the universal range proposed by [2].

EQGraphNet:Advancing single-station earthquake magnitude estimation via deep graph networks with residual connections

Magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution.In this context,we propose the Earthquake Graph Network(EQGraphNet)to enhance the performance of single-station magnitude estimation.The backbone of the proposed model consists of eleven convolutional neural network layers and ten RCGL modules,where a RCGL combines a Residual Connection and a Graph convolutional Layer capable of mitigating the over-smoothing problem and simultaneously extracting temporal features of seismic signals.Our work uses the STanford EArthquake Dataset for model training and performance testing.Compared with three existing deep learning models,EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales.To evaluate the robustness,we add natural background noise to the model input and find that EQGraphNet achieves the best results,particularly for signals with lower signal-to-noise ratios.Additionally,by replacing various network components and comparing their estimation performances,we illustrate the contribution of each part of EQGraphNet,validating the rationality of our approach.We also demonstrate the generalization capability of our model across different earthquakes occurring environments,achieving mean errors of±0.1 units.Furthermore,by demonstrating the effectiveness of deeper architectures,this work encourages further exploration of deeper GNN models for both multi-station and single-station magnitude estimation.

The optimal fractional Gabor transform based on the adaptive window function and its application

We designed the window function of the optimal Gabor transform based on the time-frequency rotation property of the fractional Fourier transform. Thus, we obtained the adaptive optimal Gabor transform in the fractional domain and improved the time-frequency concentration of the Gabor transform. The algorithm first searches for the optimal rotation factor, then performs the p-th FrFT of the signal and, finally, performs time and frequency analysis of the FrFT result. Finally, the algorithm rotates the plane in the fractional domain back to the normal time-frequency plane. This promotes the application of FrFT in the field of high-resolution reservoir prediction. Additionally, we proposed an adaptive search method for the optimal rotation factor using the Parseval principle in the fractional domain, which simplifies the algorithm. We carried out spectrum decomposition of the seismic signal, which showed that the instantaneous frequency slices obtained by the proposed algorithm are superior to the ones obtained by the traditional Gabor transform. The adaptive time frequency analysis is of great significance to seismic signal processing.

Wavelet analysis and its application to signal processing

The construction of basic wavelet was discussed and many basic analyzing wavelets was compared. Acomplex analyzing wavelet which is continuous, smoothing, orthogonal and exponential decreasing was presented, andit was used to decompose two blasting seismic signals with the continuous wavelet transforms (CWT). The resultshows that wavelet analysis is the better method to help us determine the essential factors which create damage effectsthan Fourier analysis.

Frequency domain polarization weighted ESPRIT method for bearing angle

The signal to noise ratio （SNR） of seismic waves is usually very low after long distance transmission. For this condition, to improve the bearing estimation capability in the low SNR, a frequency domain polarization weighted ESPRIT method using a single vector device is proposed. The frequency domain polari- zation parameters extracted from the signals are used to design the weighted function which is applied to the received signals. The bearing angle and the target frequency are estimated through ESPRIT using the weighted signals. The simulation and experiment results show that the presented method can obtain accurate estimation values under the low SNR with little prior information.

Identifying the occurrence time of an impending major earthquake:a review

The procedure through which the occurrence time of an impending major earthquake can be determined is reviewed in the light of the recent advances. This can be achieved by analyzing in natural time the seismicity in the candidate area. To apply this general procedure, we need two important elements： first, to know when we should start the analysis, i.e., set the natural time equal to zero. This is the time at which the system enters the critical stage. Second a reliable estimation of the candidate epicentral area. If geoelectrical measurements are taken and the VAN method （after the initials of the three researchers Varotsos, Alexopoulos and Nomicos）is applied, both these elements become available upon the recording of a precursory Seismic Electric Signals （SES） activity, because its initiation marks the time when the system enters the critical stage, and in addition the SES data enable the determination of the epicentral area of the impending mainshock. On the other hand, if geoelectrical data are lacking, we make use of the following two recent findings by means of natural time analysis： First, the fluctuations of the order parameter of seismicity in a large area exhibit a minimum a few months before a major earthquake almost simultaneously with the initiation of an SES activity. Second, a spatiotemporal study of this minimum unveils an estimate of the epicentral area of the impending major earthquake. Two examples are given that refer to the strongest earthquakes that occurred in Greece and Japan during the last 3 decades, i.e., the Mw6.9 earthquake in southwestern Greece on 14 February 2008 and the Mw9.0 Tohoku earthquake in Japan on 11 March 2011.

Mw9 Tohoku earthquake in 2011 in Japan:precursors uncovered by natural time analysis

This paper reviews the precursory phenomena of the 2011 Mw9 Tohoku earthquake in Japan that emerge solely when we analyze the seismicity data in a new time domain termed natural time. If we do not consider this analysis, important precursory changes cannot be identified and hence are missed. Natural time analysis has the privilege that enables the introduction of an order parameter of seismicity. In this frame, we find that the fluctuations of this parameter exhibit an unprecedented characteristic change, i.e., an evident minimum, approximately two months before Tohoku earthquake, which strikingly is almost simultaneous with unique anomalous geomagnetic field variations recorded mainly on the z component. This is consistent with our finding that such a characteristic change in seismicity appears when a seismic electric signal （SES） activity of the VAN method （from the initials of Varotsos, Alexopoulos, Nomicos） initiates, and provides a direct confirmation of the physical interconnection between SES and seismicity.