Arrival Time Detection Research Articles

Application of machine learning methods for earthquake detection from high-density temporary observation seismic records on a volcanic island

We applied two machine learning models to detect earthquakes from records observed with seismometers temporarily installed on a volcanic island. The two models are based on different principles: one regards seismic waveforms as images, using a convolutional neural network (CNN) to determine the first arrival times of P-waves, S-waves. The other model regards seismic waveforms as series data. The model processes seismic waveforms as data in a specific order of noise, P-wave, and S-wave, similar to natural language.The purpose of this study is to present the results of using machine learning first arrival times identification models with two principles for noisy seismic waveforms, caused by sea waves and strong winds in volcanic islands, and to evaluate the effectiveness of machine learning models for noisy observation records.We created a Confusion Matrix using first arrival times determined by an expert and evaluated the detection performance of these two models using some metrics of the matrix. Additionally, we assessed accuracy of the model-identified first arrival times by generating a frequency distribution of the difference from the expert's detecting time.The study discovered that the model treating data as series had superior detection ability for noisy data compared to the one treating data as images and the accuracy of the first arrival time detection was also better for the series data model too.We compared the results obtained on this island with those obtained at the permanent station, which is considered to have less noise interference, described in Mousavi et al., 2020. It was found that the difference in detection ability between the two models is slight for data obtained at permanent stations with low noise interference, but that the difference in detection ability between the algorithms of the two models is significant in noisy environments.

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone.

To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health. This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C-means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators. The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001). These findings suggest that fuzzy C-means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.

Retraction Note: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Retraction Note: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

On the use of fractional calculus to improve the pulse arrival time (PAT) detection when using photoplethysmography (PPG) and electrocardiography (ECG) signals.

The pulse arrival time (PAT) has been considered a surrogate measure for pulse wave velocity (PWV), although some studies have noted that this parameter is not accurate enough. Moreover, the inter-beat interval (IBI) time series obtained from successive pulse wave arrivals can be employed as a surrogate measure of the RR time series avoiding the use of electrocardiogram (ECG) signals. Pulse arrival detection is a procedure needed for both PAT and IBI measurements and depends on the proper fiducial points chosen. In this paper, a new set of fiducial points that can be tailored using several optimization criteria is proposed to improve the detection of successive pulse arrivals. This set is based on the location of local maxima and minima in the systolic rise of the pulse wave after fractional differintegration of the signal. Several optimization criteria have been proposed and applied to high-quality recordings of a database with subjects who were breathing at different rates while sitting or standing. When a proper fractional differintegration order is selected by using the RR time series as a reference, the agreement between the obtained IBI and RR is better than that for other state-of-the-art fiducial points. This work tested seven different traditional fiducial points. For the agreement analysis, the median standard deviation of the difference between the IBI and RR time series is 5.72 ms for the proposed fiducial point versus 6.20 ms for the best-performing traditional fiducial point, although it can reach as high as 9.93 ms for another traditional fiducial point. Other optimization criteria aim to reduce the standard deviation of the PAT (7.21 ms using the proposed fiducial point versus 8.22 ms to 15.4 ms for the best- and worst-performing traditional fiducial points) or to minimize the standard deviation of the PAT attributable to breathing (3.44 ms using the proposed fiducial point versus 4.40 ms to 5.12 ms for best- and worst-performing traditional fiducial points). The use of these fiducial points may help to better quantify the beat-to-beat PAT variability and IBI time series.

Tsunami Inundation Modelling in a Built-In Coastal Environment with Adaptive Mesh Refinement: The Onagawa Benchmark Test

In tsunami studies, understanding the intricate dynamics in the swash area, characterised by the shoaling effect, remains a challenge. In this study, we employed the adaptive mesh refinement (AMR) method to model tsunami inundation and propagation in the Onagawa town physical flume experiment. Using the open-source flow solver Basilisk, we implemented the Saint-Venant (SV) equations, Serre–Green–Naghdi (SGN) equations, and a nonhydrostatic multilayer (ML) extension of the SGN equations. A hydraulic bore tsunami-like wave was used as the input boundary condition. The objective was to assess the efficiency of the AMR method with nonhydrostatic tsunami models in overcoming limitations in 2D and quasi-3D models in flume experiments, particularly with respect to improving accuracy in arrival time and run-up detection. The results indicate improved performance of the SGN and SV models in determining tsunami arrival times. The ML model demonstrated enhanced wave run-up simulations on complex built-in terrain. The refined roughness coefficient determined using the ML solver captured the arrival time well in the northern section of the Onagawa model, albeit with a 1 s delay. The AMR method offered a computationally stable solution with an 86.3% reduction in computational time compared to a constant grid. While effective, the nonhydrostatic models entail the use of a great deal of computational resources.

Separation of pressure signals caused by waves traveling in opposite directions

Abstract Hydraulic transient analysis allows the condition assessment of pipeline systems by the measurement of a system's transient pressure response subject to input pressure excitations. The detection of a pressure wave's arrival time and amplitude at one or more sections can be used to detect unexpected anomalies, such as leaks, blockages, or corroded sections. Wave separation approaches, based on signal processing techniques involving two sensors, enable a directional attribution to any measured pressure perturbations. Being able to determine the direction of origin of a perturbation through a signal-splitting approach greatly facilitates anomaly detection through the resolution of this ambiguity. The signal-splitting procedure can be sensitive to the analysis conditions (i.e. the signal processing procedure used, the presence of noise within the signal, and the spacing of the sensors) and, as a result, produce spurious results. This paper explores this issue and proposes, and analyses, a range of strategies to improve the signal-splitting results. The strategies explored involve the consideration of alternative time- and frequency-domain formulations; the use of filters and wavelet to condition the signal; and processing the time-shifted differenced signal as opposed to the original raw signal. Results are presented for a range of numerical and laboratory systems.

Arrival-Time Detection With Multiscale Wavelet Analysis and Source Location of Acoustic Emission in Rock

In order to overcome the difficulties of traditional arrival time-picking techniques applied to Acoustic Emission (AE) events with low signal-to-noise ratio (SNR) and complex waveforms, this work proposed a new time-picking method. It combines the advantage of the wavelet transform technique, the ratio of Short-Term Average to Long Term Average (STA/LTA) method, and the Akaike Information Criterion (AIC), which is called the Wavelet-STA/LTA-AIC (WSA) picker. This work assessed the performance of picking arrival times of the WSA picker by locating the AE events from pencil-lead breaking on the marble panel (artificial sources) and tensile cracking sources on the granite. For all experiment results, the proposed WSA picker shows less location error compared with the AIC picker, regardless of geometry and source type. For location results of the pencil-lead breaking events on the marble panel, the average location errors are 1.4cm (AIC picker) and 0.6cm (WSA picker). For location results of the tensile cracking events on granite, the WSA picker is more concentrated in the region of the fracture surface and has fewer wrong-locating points at the specimen edge when compared with the AIC picker. It demonstrates that the proposed WSA picker can improve the accuracy and capability of AE location and be a viable method for arrival-time picking.

Arrival-time detection with histogram distance for acoustic emission signals

Arrival-time picking is a fundamental step in time-of-arrival (TOA)-based localisation in current and future acoustic emission (AE), microseismic and seismic localisation systems. The accurate detection of TOA is of great importance for high localisation accuracy. This work presents a histogram distance-based method for TOA detection of elastic wave signals. The authors treat an original elastic waveform that includes the arrival as two different locally stationary segments, namely the intervals following and preceding arrival. To determine the optimal separation of these two stationary segments, ie the arrival time, histograms of these intervals are calculated and a measure of the distance between them modified from the Bhattacharyya coefficient is proposed. The performance of the proposed method is evaluated using TOA picking for AE. The method is shown to provide accurate and robust picks on AE signals under various signal-to-noise ratios. To evaluate the adaptability of the method to other TOA picking scenarios, it is applied to detect the seismic P-phase. The detection accuracy is adequate and errors are constrained to within a few seconds. Factors that influence the detection accuracy are discussed. The results suggest that the proposed method has the potential to detect TOA in various fields.

Classification of Elastic Wave for Non-Destructive Inspections Based on Self-Organizing Map

An arrival time of an elastic wave is the important parameter to visualize the locations of the failures and/or elastic wave velocity distributions in the field of non-destructive testing (NDT). The arrival time detection is conducted generally using automatic picking algorithms in a measured time-history waveform. According to automatic picking algorithms, it is expected that the detected arrival time from low S/N signals has low accuracy if low S/N signals are measured in elastic wave measurements. Thus, in order to accurately detect the arrival time for NDT, the classification of measured elastic waves is required. However, the classification of elastic waves based on algorithms has not been extensively conducted. In this study, a classification method based on self-organizing maps (SOMs) is applied to classify the measured waves. SOMs visualize relation of measured data wherein the number of classes is unknown. Therefore, using SOM selects high and low S/N signals adequately from the measured waves. SOM is validated on model tests using the pencil lead breaks (PLBs), and it was confirmed that SOM successfully visualize the classes consisted of high S/N signal. Moreover, classified high S/N signals were applied to the source localization and it was noteworthy that localized sources were more accurate in comparison with using all of the measured waves.

Acoustic thermometer operating up to 11 m: uncertainty assessment and new values for Cramer coefficients around 40 kHz

The present article describes an acoustic thermometer to measure the average air temperature integrated along a path ranging from 1 m to 11 m. It is based on time-of-flight measurement of ultrasound pulses at frequencies close to 40 kHz. Several methods for the detection of arrival times were investigated, notably cross-correlation and cross-spectrum. The uncertainty of the instrument itself, independent of that of the Cramer equation has been estimated at between 0.13 K to 0.09 K for distances ranging from 3 m to 11 m respectively. In practice, an experimental comparison with Pt100 probes (uncertainty of 0.1 K) has shown that the estimated uncertainty levels are relatively compatible, although the linearity of the system does not appear to be very good. To solve this problem, appropriate values for the Cramer coefficients a0 and a1 for an acoustic frequency of about 40 kHz have been determined, which contributes to improved knowledge of this equation as a function of acoustic frequency.

Timing estimation of multiple hyperbolic frequency‐modulated signals based on multicarrier underwater acoustic communication

AbstractUnderwater acoustic channel is much more complex than radio channel. Background noise, multipath extension, and Doppler frequency shift are all factors that affect high‐speed and stability for underwater communication. Orthogonal frequency division multiplexing (OFDM) is a modulation technology with high spectrum efficiency and strong anti‐multipath ability, usually used in high‐speed underwater acoustic communication. Signal arrival time detection and timing estimation before receiving data is an essential module in an underwater communication system. Timing estimation error will seriously affect the demodulation of the signal. In order to solve the problem of a complex channel environment in underwater acoustic communication system, a timing estimation method is proposed based on multiple hyperbolic frequency‐modulated (MHFM) signals. In order to improve the performance of underwater acoustic communication systems, this article proposes an iterative method of correlation‐sum‐mean absolute error (MAE) based on hyperbolic frequency‐modulated (HFM). The simulation results show that the proposed method can provide better performance and higher timing estimation accuracy under the conditions of low signal‐to‐noise ratio (SNR) and Doppler effect under oceanic noise channel and Doppler spread channel.

Bi-Long Short-Term Memory Networks for Radio Frequency Based Arrival Time Detection of Partial Discharge Signals

Partial discharge (PD) monitoring of electrical substations could provide early warning of insulation failures. Among the various technologies, Radio Frequency (RF) based PD monitoring system could be a promising solution. The RF-based monitoring system detects PD sources in the substation and can also localise the PD sources. The time difference of arrival (TDOA) based PD localisation system primarily require arrival time (AT) of the impulsive RF signal. Though many localisation algorithms have been proposed in the recent past to overcome the TDOA estimation errors, less attention has been given to the accurate estimation of RF PD signal arrival time. This paper presents the AT's automatic labelling in the RF PD signal using Bi-Long Short-Term Memory (Bi-LSTM) network applied on the continuous wavelet transformed (CWT) signal. Further, it also shows PD signal augmentation to overcome the problem of limited representative training data set. The behavior of the radiated RF signals is influenced by many factors and has almost stochastic characteristics. The proposed system has been validated with laboratory-based experimental signals and the data set obtained from different electrical substations. The results show that the improved performance is obtained from the combination of a multilayer Bi-LSTM model and an augmented training set.

Validation of elastic wave arrival detection method based on use of sparse matrix computation

Abstract In the field of non-destructive investigations, the arrival time of the elastic waves is the important parameters to visualize the locations of the failures and/or elastic wave velocity distributions that are generally used for the soundness evaluation. The accuracy of the visualization depends on the accuracy of the arrival times, and it is demanded that the arrival times are detected automatically with required accuracy from huge amount of the elastic wave measurement data for the practical applications. In this paper, a method of the arrival time detection is proposed on the basis of the sparse matrix computation. The root mean square voltages in the noise of the wave form, is used as an index to separate the elastic waves between the noise and the signal, and the boundary of the index between the noise and the signal in the wave forms is determined in the sparse matrix. Therefore, the arrival times located in the boundary of the noise and it is detected by sparse matrix computations. The proposed method is verified on the model tests and compared with the conventional method, and it was confirmed that the proposed method detects more arrival time accurately in comparison with the conventional method.

Travelling wave fault location for LCC–MMC–MTDC transmission lines based on frequency-dependent characteristic

The paper studies the fault location principles of multi-terminal direct-current (MTDC) hybrid transmission systems based on line-commutated converter (LCC) and modular multi-level converter (MMC). Two issues should be addressed: i) the travelling wave (TW) fault location principle adaptable for the hybrid multi-terminal transmission system; and ii) the algorithm of obtaining frequency-dependent characteristic of the TW signal. In the paper, a novel TW fault location principle adaptable for the LCC–MMC–MTDC hybrid transmission lines is developed based on the frequency-dependent characteristic of TW signal. Firstly, the frequency-dependent characteristic of HVDC transmission line is studied. Secondly, a novel TW fault location principle is proposed, comprised of TW arrival time detection, TW propagation velocity calculation, fault section identification and fault location method. A typical ±400kV LCC–MMC–MTDC hybrid system is constructed using PSCAD/EMTDC for case study to validate the accuracy of the proposed principle. Its robustness against fault location, fault resistance and fault type is well demonstrated.

Traveling wave arrival time detection for two-terminal non-contact measurement based on short time matrix pencil scheme

This paper presents an accurate/efficient scheme to distinguish the first and successive time of arrival signals from the fault points of the transmission lines based on non-contact measurement of traveling wave dataset and short time matrix pencil method. In multiple reflection and refraction of traveling waves at characteristic impedance, the first and successive waves overlap at the local and remote terminals, introducing some difficulties in detecting the reflected wave accurately. In such a situation, the detection of the first reflected wave from the second successive wave becomes a challenge, which leads to difficulties in the detection process. Therefore, with the use of a non-contact measurement signal of the overhead transmission line, and with the short-time matrix pencil method of the non-contact dataset, the waves corresponding to the first and successive time reflected signals are distinguished. The simulation results are discussed, and the validity measurement results from the traveling wave generator have been evaluated and analyzed effectively. This indicates that the proposed scheme is robust and accurate for the time of arrival detection for fault location in overhead transmission lines using a non-contact traveling wave dataset with a short time matrix pencil scheme.

Reliable detection of pulse arrival time for partial discharge location in HV cable based on improved Allen-Bear algorithm

In multi-sensor data based partial discharge (PD) source location in high voltage (HV) cable, poor reliability of detection of pulse arrival time against noises, waveform distortion, amplitude attenuation is the major barriers to practical application. In this paper, an improved Allen-Bear PD pulse detection algorithm, which combines the advantage of the characteristic functions of the two time-window energy ratio based detection algorithms, is proposed for robust PD location. The most critical contribution of proposed method is the extension of frequency domain component compared to the characteristic functions of the original algorithm. Simulation and physical experiment results demonstrate that the proposed method not only ensures the detection accuracy of the PD pulse arrival time, but also improves the reliability under harsh practical scenarios. It performs significantly better than the existing detection algorithm in terms of anti-noise, anti-waveform distortion, time window length variation, etc. It has significant value for practical applications.

A novel traveling wave arrival time detection method in power system

This paper proposes a high-speed and accurate method for extracting the arrival times (ATs) of traveling waves (TWs) in the power system that can be used for fault location applications and transmission line protection. The proposed method is based on the expression of the traveling wave as the exponentially damped component superimposed on the sinusoidal wave in a small time window. Also the sine component is removed by using approximation and consecutive differences of the input signal samples (the modal components of three-phase voltages or currents) from the resulting wave. Due to the fact that the estimation of traveling wave arrival times (TWATs) is done applying basic mathematical operations in the time domain, the proposed method, in addition to the simplicity of implementation, has the appropriate accuracy and speed for applications such as online fault location and protection purposes. In order to evaluate the performance of the proposed approach in fault locating, a 230 kV transmission line is modelled in EMTP/ATP. Then, the fault location under various conditions of faults such as changes in location and type of fault, fault conditions as well as variations in the measurement noise level and sampling frequency are studied using MATLAB.

Novel travelling wave fault location principle for VSC-HVDC transmission line

Currently, voltage source converter (VSC) based high voltage direct current (HVDC) technology is widely utilized in power transmission system. It has high transmission efficiency, no commutation failure issue and ability to supply power to passive system. In order to enhance the reliability of VSC-HVDC transmission system and accelerate the maintenance progress, travelling wave (TW) fault location principle is adopted to discriminate the exact location of transmission line (TL) fault, and its fault location accuracy is highly related to TW arrival time detection and TW propagation velocity. Therefore, aiming to enhance the fault location accuracy, the paper proposes a novel TW fault location principle for VSC-HVDC transmission line. Firstly, the detailed calculation method of propagation velocity of TW along HVDC-TL is described. Secondly, a novel TW fault location principle is proposed including TW arrival time detection method and TW’s accurate frequency calculation method. Eventually, based on PSCAD/EMTDC, a typical ±400kV VSC-HVDC simulation model is established to validate the robustness of novel TW fault location principle against fault type issue, fault resistance issue and fault location issue.

Further investigations of a radiation detector based on ionization-induced modulation of optical polarization

Optical property modulation induced by ionizing radiation is a promising approach for ultra-fast, lower time jitter detection of photon arrival time. If successful, this method can be utilized in time-of-flight positron emission tomography to achieve a coincidence time resolution approaching 10 ps. In this work, the optical property modulation based method is further developed with focus on a detection setup based on two crossed polarizers. Previous work demonstrated that such an optical setup could be utilized in radiation detection, though its detection sensitivity needed improvement. This work investigates the angle between polarizers and electric field distribution within the detection crystal to understand and improve the detection sensitivity of an optical polarization modulation based method. For this work, cadmium telluride (CdTe) was studied as the detector crystal . The ‘magic’ angle (i.e. optimal working angle) of the two crossed polarizers based optical setup with CdTe were explored theoretically and experimentally. The experimental results show that the detection sensitivity could be improved by around 10% by determining the appropriate ‘magic’ angle. We then studied the dependence of detection sensitivity on electric field distribution as well as on the bias voltage across the detector crystal using CdTe crystals. The experimental results show that a smaller electrode on the detector crystal, or a more concentrated electric field distribution could improve detection sensitivity. For CdTe, a detector crystal sample with 2.5 mm × 2.5 mm square electrode has twice the detection sensitivity of a detector crystal with 5 mm × 5 mm square electrode. Increasing the bias voltage before saturation for CdTe could further enhance the modulation strength and thus, the sensitivity. Our investigations demonstrated that by determining the proper working angle of polarizers and bias electrical distribution to the detector, we could improve the sensitivity of the proposed optical setup.

Relationship between precursory signals and corresponding earthquakes using different spectral analysis techniques

The origin of precursory signals that precede the P-waves is controversial, as it may be caused by ground effect (seismic nucleation phase) or instrumental effect due to a digitizer. Such signals cause errors in the automatic detection of P-wave arrival times and consequently decrease the accuracy of earthquake parameter estimations. In this paper, we proposed spectral analysis techniques to distinguish between different types of precursory segments, and to find if there is a relation between existence of these precursors and their corresponding earthquakes. These algorithms are applied to 1015 local events recorded by eleven permanent seismic stations belonging to the Egyptian National Seismic Network. Manual inspection of these events reveals the existence of these precursors. The investigations provided evidence that one type of these precursors originated because of an instrumental effect. This study shows the underlying causes of the precursory segment’s appearance prior to P-wave impulses. Many events had similar parameters; however, some of them had such precursors while others did not, in this inspection, multi resolution analysis details of the Discrete Wavelet Transform have been used. When large amplitudes for high frequency P-wave detail exceed 104 counts (1 count = 1 nm/s), the appearance percentage for the precursory segment is 100%.