Arrival Time Error Research Articles

Identifying Elastic Wave Velocity Distribution with Observation Arrival Time Errors Using Weighted Potential Time in Acoustic Emission Tomography

Identifying Elastic Wave Velocity Distribution with Observation Arrival Time Errors Using Weighted Potential Time in Acoustic Emission Tomography

Updating Measures of CME Arrival Time Errors

AbstractCoronal mass ejections (CMEs) drive space weather effects at Earth and the heliosphere. Predicting their arrival is a major part of space weather forecasting. In 2013, the Community Coordinated Modeling Center started collecting predictions from the community, developing an Arrival Time Scoreboard (ATSB). Riley et al. (2018, https://doi.org/10.1029/2018sw001962) analyzed the first 5 years of the ATSB, finding a bias of a few hours and uncertainty of order 15 hr. These metrics have been routinely quoted since 2018, but have not been updated despite continued predictions. We revise analysis of the ATSB using a sample 3.5 times the size of that in the original study. We find generally the same overall metrics, a bias of −2.5 hr, mean absolute error of 13.2 hr, and standard deviation of 17.4 hr, with only a slight improvement comparing between the previously‐used and new sets. The most well‐established, frequently‐submitted model results tend to outperform those from seldomly‐contributed models. These “best” models show a slight improvement over the 11 year span, with more scatter between the models during early times and a convergence toward the same error metrics in recent years. We find little evidence of any correlations between the arrival time errors and any other properties. The one noticeable exception is a tendency for late predictions for short transit times and vice versa. We propose that any model‐driven systematic errors may be washed out by the uncertainties in CME reconstructions in characterization of the background solar wind, and suggest that improving these may be the key to better predictions.

Localization for surface microseismic monitoring based on arrival time correction and VFOM

Abstract Unconventional resources have emerged as the primary source to meet the escalating demand for energy consumption, with hydraulic fracturing standing out as an effective means of boosting production. The utilization of microseismic monitoring is crucial for acquiring real-time or semi-real-time extension information of the fracture network to guide the fracturing process. The precise positioning of microseismic events is a fundamental aspect of microseismic monitoring. Traditional methods relying on (relative) arrival time significantly impact positioning accuracy due to picking errors. While waveform-based methods offer high accuracy, they require precise velocity models and are time-consuming. To overcome challenges associated with arrival time pickup and velocity accuracy, we introduce a virtual field optimization method (VFOM) based on arrival time correction. Initially, an equivalent velocity model is established, and the arrival time difference resulting from the model transformation of the master event is calculated to correct the observed arrival time of the target event. Subsequently, we match detector pairs, establish hyperboloids based on the corrected arrival time difference, and employ the intersection point of all hyperboloids as the positioning result. After that, we use the location results of the master event to enhance the accuracy of the target event. Finally, we apply the proposed method to both synthetic test and field datasets, demonstrating a significant improvement in the positioning accuracy and stability provided by the novel method. The robustness against arrival time error renders it a suitable choice for surface monitoring applications where signal quality is compromised. Furthermore, the simplified velocity model significantly diminishes the computational requirements in the positioning process, enhancing its efficiency, and consequently holds vast potential for application in real-time monitoring.

Application of One-Dimensional Hydrodynamic Coupling Model in Complex River Channels: Taking the Yongding River as an Example

River conditions are complex and affected by human activities. Various hydraulic structures change the longitudinal slope and cross-sectional shape of the riverbed, which has a significant impact on the simulation of water-head evolution. With continuous population growth, the hydrological characteristics of the Yongding River Basin have undergone significant changes. Too little or too much water discharge may be insufficient to meet downstream ecological needs or lead to the wastage of water resources, respectively. It is necessary to consider whether the total flow in each key section can achieve the expected value under different discharge flows. Therefore, a reliable computer model is needed to simulate the evolution of the water head and changes in the water level and flow under different flow rates to achieve efficient water resource allocation. A one-dimensional hydrodynamic coupling model based on the Saint-Venant equations was established for the Yongding River Basin. Different coupling methods were employed to calibrate the coupling model parameters, using centralised water replenishment data for the autumn of 2022, and the simulation results were verified using centralised water replenishment data for the spring of 2023. The maximum error of the water-head arrival time between different river sections was 4 h, and the maximum error of the water-head arrival time from the Guanting Reservoir to each key cross-section was 6 h. The maximum flow error was less than 5 m3/s, and the changing trend of the flow over time was consistent with the measured data. The model effectively solved the problem of low accuracy of the water level and flow calculation results when using the traditional one-dimensional hydrodynamic model to simulate the flow movement of complex river channels in the Yongding River. The output results of the model include the time when the water head arrives at the key section, the change process of the water level and flow of each section, the change process of the water storage of lakes and gravel pits, and the change process of the total flow and water surface area of the key section. This paper reports data that support the development of an ecological water compensation scheme for the Yongding River.

How Accurately Are MERMAID Seismograms Located?

Abstract Floating seismographs (Mobile Earthquake Recorder in Marine Areas by Independent Divers project “MERMAIDs”) record the data at depth at a location that is determined by linearly interpolating between the Global Positioning System positions when surfacing, assuming a constant drift velocity at depth. We study the influence of a changing drift velocity between surfacings and of a curvature of the drift trajectory. We separate localizations that directly follow a triggered ascent from those that are interpolated later. The first ones have on average a mislocation of 99 m due to curvature of the drift, against 685 m for interpolated localizations. Mislocations due to nonconstant velocity are somewhat smaller. Equivalent time errors have a distribution with heavier tails than Gaussian. The halfwidth of the 95% interval for equivalent arrival-time errors is smaller than 27 ms if the seismogram recording triggers an immediate ascent. If the recording is transmitted at a later surfacing, the interpolation is less precise with a 95% confidence interval halfwidth of 222 ms, but 67% of the errors are below 44 ms. We conclude that the localization errors have no significant impact on the accuracy of picked arrival times.

Prediction of the Transit Time of Coronal Mass Ejections with an Ensemble Machine-learning Method

Coronal mass ejections (CMEs), a kind of violent solar eruptive activity, can exert a significant impact on space weather. When arriving at the Earth, they interact with the geomagnetic field, which can boost the energy supply to the geomagnetic field and may further result in geomagnetic storms, thus having potentially catastrophic effects on human activities. Therefore, accurate forecasting of the transit time of CMEs from the Sun to the Earth is vital for mitigating the relevant losses brought by them. XGBoost, an ensemble model that has better performance in some other fields, is applied to the space weather forecast for the first time. During multiple tests with random data splits, the best mean absolute error (MAE) of ∼5.72 hr was obtained, and in this test, 62% of the test CMEs had absolute arrival time error of less than 5.72 hr. The average MAE over all random tests was ∼10 hr. It indicates that our method has a better predictive potential and baseline. Moreover, we introduce two effective feature importance ranking methods. One is the information gain method, a built-in method of ensemble models. The other is the permutation method. These two methods combine the learning process of the model and its performance to rank the CME features, respectively. Compared with the direct correlation analysis on the sample data set, they can help select the important features that closely match the model. These two methods can assist researchers to process large sample data sets, which often require feature selection in advance.

Hybrid TOA-AOA WLS Estimator for Aircraft Network Decentralized Cooperative Localization

Cooperative localization is widely used to solve the network localization problem in global navigation satellite system (GNSS) denied environments. In particular, gathering the measurement results of all nodes on a specific node requires substantial time for aircraft networks. Therefore, methods simultaneously calculating all the target node positions are challenging to apply due to information delay. This study uses only two hops measurements information relying on the time of arrival (TOA) and angle of arrival (AOA) to propose a weighted least squares (WLS) method that decentralizes the calculation of a specific single target node position in a 3D environment. Moreover, the effect of TOA and AOA measurement errors on localization accuracy is analyzed based on the geometric relationship. Consequently, a novel weight-factor calculation method is designed. The simulation results show that the proposed cooperative localization method can improve localization accuracy compared with the decentralized non-cooperative method. Furthermore, the proposed hybrid approach provides superior localization accuracy than the weight-factor calculation method which only considers the distance.

Aberration correction in 2D echocardiography.

An aberration correction algorithm has been implemented and demonstrated in an echocardiographic clinical trial using two-dimensional (2D) imaging. The method estimates and compensates arrival time errors between different sub-aperture processor (SAP) signals in a matrix array probe. Five standard views of channel data cineloops were recorded from 22 patients (11 male and 11 female) resulting in a total of 116 cineloops. The channel data were processed with and without the aberration correction algorithm, allowing for side-by-side comparison of images processed from the same channel data cineloops. The aberration correction algorithm improved image quality, as quantified by a coherence metric, in all 7,380 processed frames. In a blinded and left-right-randomized side-by-side evaluation, four cardiologists (two experienced and two in training) preferred the aberration corrected cineloops in 97% of the cases. The clinicians reported that the corrected cineloops appeared sharper with better contrast and less noise. Many structures like valve leaflets, chordae, endocardium, and endocardial borders appeared narrower and more clearly defined in the aberration corrected images. An important finding is that aberration correction improves contrast between the endocardium and ventricle cavities for every processed image. The gain difference was confirmed by the cardiologists in their feedback and quantified with a median global gain difference estimate between the aberration-corrected and non-corrected images of 1.2 dB. The study shows the potential value of aberration correction in clinical echocardiography. Systematic improvement of images acquired with state-of-art equipment was observed both with quantitative metrics of image quality and clinician preference.

Analysis of reliability and accuracy of lunar core detection based on Apollo moonquake observation

Introduction:In the 1960s, the Apollo missions deployed seismometers on the near side of the Moon, and the obtained moonquake data enabled the seismic study of internal structure of the Moon. The lunar seismic waveforms are dominated by strong coda waves caused by scattering in the near surface, which masks the reflected and converted waves from the discontinuities inside the Moon. The Double Array Stacking method is an important technique to enhance the reflected and converted waves. However, its performance would be affected by many factors including signal to noise ratio, arrival time errors, differences in amplitude and polarity of waveforms caused by different station azimuth, and source mechanisms, thus needs to be further analyzed.Method:In this study, we use the pseudo-spectral and finite-difference hybrid method on staggered grid to simulate wave propagation of deep moonquakes, considering near surface scattering, to analyze the influence of the above factors on the stacking results of reflected and converted phases from outer core of the Moon.Results and discussion:Our results indicate that coda and noises would greatly reduce the stacking energy but have no significant impact on the peak position of the stacked waveforms. The arrival time errors have a significant influence on the vertical component of ScP phase and the radial component of the ScS phase, but have little influence on the transverse component of ScS phase. The difference in amplitude also leads to differences in stacking energy, but the stacking image shows good consistency in the shape. When the polarities of seismic phases are relatively consistent, the peak of the stacking energy comes from reflected phases from the core mantle boundary of the Moon. This research may be helpful to improve the detection accuracy of the internal structure of the Moon and provide a solid reference for the deployment of future seismic stations, which is of great significance for understanding the formation and evolution process of the Moon.

A Novel Polarization Estimation Method for Seismic Recordings

Abstract The polarization attributes of seismic recordings are usually used to determine the seismic phase and to design a filter. However, different time window lengths lead to different time-domain polarization estimation results; the time window length is difficult to determine and has no uniform criterion. We propose a time-domain polarization estimation method employing an optimal time window based on the instantaneous frequency of the seismic signals. First, the time–frequency spectrum of the seismic signal is obtained using the continuous wavelet transform, and an optimal time window is constructed using the corresponding instantaneous dominant frequency. Then, a complex polarization analysis algorithm is used to estimate the polarization parameters. Compared with the conventional constant and center frequency time window, the polarization properties estimated using an optimal time window can achieve more accurate seismic phase identification. The picked arrival time error decreases from 82.4 to 32.0 s, about 60%. Furthermore, the corresponding polarization filter can effectively denoise noisy seismic recordings and increase the signal-to-noise ratio (SNR) increased by 0.49–2.65 times for original signals with different SNRs. The cross-correlation coefficient is increased by 0.26 on average, further preservation of signal information.

Can One Predict Coronal Mass Ejection Arrival Times With Thirty‐Minute Accuracy?

AbstractJ. Schmidt and Cairns (2019, https://doi.org/10.48550/arXiv.1905.08961) have recently claimed that they can predict Coronal Mass Ejection (CME) arrival times with an accuracy of 0.9 ± 1.9 hr for four separate events. They also stated that the accuracy gets better with increased grid resolution. Here, we show that combining their results with the Richardson extrapolation (Richardson & Gaunt, 1927, https://doi.org/10.1098/rsta.1927.0008), which is a standard technique in computational fluid dynamics, could predict the CME arrival time with 0.2 ± 0.26 hr accuracy. The CME arrival time errors of this model would lie in a 95% confidence interval [−0.21, 0.61] hr. We also show that the probability of getting these accurate arrival time predictions with a model with a standard deviation exceeding 2 hr is less than 0.1%, indicating that these results cannot be due to random chance. This unprecedented accuracy is about 20 times better than the current state‐of‐the‐art prediction of CME arrival times with an average error of about ±10 hr. Based on our analysis there are only two possibilities: the results shown by J. Schmidt and Cairns (2019, https://doi.org/10.48550/arXiv.1905.08961) were not obtained from reproducible numerical simulations, or their method combined by the Richardson extrapolation is in fact providing CME arrival times with half an hour accuracy. We believe that this latter interpretation is very unlikely to hold true. We also discuss how the peer‐review process apparently failed to even question the validity of the results presented by J. Schmidt and Cairns (2019, https://doi.org/10.48550/arXiv.1905.08961).

Sensitivity of Model Estimates of CME Propagation and Arrival Time to Inner Boundary Conditions

AbstractAccurately forecasting the arrival of coronal mass ejections (CMEs) at Earth is important to enabling mitigation of the associated space weather risks to society. This is only possible with accurate modeling of the event. To do so, we must understand the propagation of a CME through the heliosphere and quantify the performance of models through comparison with spacecraft observations. For the 12 December 2008 Earth‐directed CME event, we compute ensembles using the HUXt solar wind model to analyze CME distortion with a structured solar wind and explore hindcast arrival time error (ATE). By highlighting the impact CME shape has on Root‐Mean‐Square‐Error (RMSE) values, we show that time‐elongation profiles of fronts captured by the Heliospheric Imager (HI) instruments onboard NASA's STEREO mission match those of the modeled CME nose and flank and can therefore be used to infer details of the longitudinal extent of the CME. We then show that accounting for CME distortion is important to enable accurate estimates of the CME arrival at Earth. This can be achieved by either using observations of multiple features in HI data to infer CME evolution or mapping the solar wind back to a lower inner boundary to allow CMEs to be distorted close to the Sun. For the event studied we show that these approaches resulted in reduced RMSEs of 0.73° and 0.64° with an ATE of 1 hour and 3 hours respectively.

String Stable Heterogeneous Aircraft in a Windy Environment

This research develops and analyzes an approach to solve the continuous monitoring problem. The analysis focuses on the string stability of a system of airspeed heterogeneous nonlinear aircraft that are controlled to overfly an endpoint at specified time intervals. The system is modeled as a cascaded set of subsystems where the position of each subsystem, an individual aircraft, is based off the estimated overfly time of the preceding vehicle. The model also includes the effects of steady-state wind and turbulence. The system is made to be string stable by nondimensionalizing a portion of the dynamics, selecting variable command limits, and solving for exponentially stable controller gains. The simulation results confirm the stability guarantees of the theoretical analysis and show that a randomized group of 20 airspeed heterogeneous aircraft experiencing realistic wind and turbulence results in an estimated arrival time error that converges toward a relatively small steady-state range.

Parametric Investigation of Vibratory Responses for Reduced-Order Models of Rotating Bladed Disks

Attempting to predict the physical response of a complex system under realistic conditions is extremely challenging. In this work, a parametric reduced-order model (PROM) of a bladed disk is briefly presented that allows for the quick and efficient modeling of rotational speed effects and mistuning effects in bladed disks. This computational model is tuned to match experimental data. The tuned PROM is then investigated through a parametric study. Three internal parameters investigated are i) forcing magnitude, ii) mistuning value magnitude, and iii) damping magnitude, and four external parameters are iv) forcing location, v) forcing radius, vi) probe placement error, and vii) arrival time error. These parameters are common in the structural analysis of turbomachines. Each internal parameter is increased and decreased by 10, 20, and 50%, while external parameters are changed by varying amounts due to geometric and practical limitations. The model is time integrated to simulate the conditions present in the experimental test rig. The maximum blade amplitudes are then compared to the baseline case and analyzed. The damping, followed by the forcing, is shown to cause the greatest impact on the final system response. This indicates a greater need to accurately measure the damping compared to other parameters.

Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks

It is difficult to accurately and efficiently detect seismic wave signals at the time of arrival for automatic positioning from microseismic waves. A U-net model to detect the arrival time of seismic waves is constructed based on the convolutional neural network (CNN) theory. The original data for 1555 segments and synthetic data of 7764 segments were detected using Akaike’s information criterion (AIC) algorithm, the time window energy eigenvalue algorithm, and the U-net model. During uniaxial compression of the test block, acoustic emission equipment is used to collect the vibration wave generated by the rupture of the test block. Source imaging images are drawn using the Origin software, the arrival time error is counted, and the advantages and disadvantages of the three arrival time methods are discussed. Similarities between the source image and the actual fracture image are observed. There is a high similarity between the source imaging map and the physical trajectory map when the U-net model is used. Thus, it is feasible to use the U-net model to detect the arrival time of seismic waves. Its accuracy is greater than that of the time window energy eigenvalue algorithm but lower than that of the AIC algorithm for high signal-to-noise ratios. After reducing the signal-to-noise ratio, the stability and accuracy of the U-net model to detect the arrival time have improved over the other two algorithms.

Ensemble Simulations of the 2012 July 12 Coronal Mass Ejection with the Constant-turn Flux Rope Model

Flux-rope-based magnetohydrodynamic modeling of coronal mass ejections (CMEs) is a promising tool for prediction of the CME arrival time and magnetic field at Earth. In this work, we introduce a constant-turn flux rope model and use it to simulate the 2012 July 12 16:48 CME in the inner heliosphere. We constrain the initial parameters of this CME using the graduated cylindrical shell (GCS) model and the reconnected flux in post-eruption arcades. We correctly reproduce all the magnetic field components of the CME at Earth, with an arrival time error of approximately 1 hr. We further estimate the average subjective uncertainties in the GCS fittings by comparing the GCS parameters of 56 CMEs reported in multiple studies and catalogs. We determined that the GCS estimates of the CME latitude, longitude, tilt, and speed have average uncertainties of 5.°74, 11.°23, 24.°71, and 11.4%, respectively. Using these, we have created 77 ensemble members for the 2012 July 12 CME. We found that 55% of our ensemble members correctly reproduce the sign of the magnetic field components at Earth. We also determined that the uncertainties in GCS fitting can widen the CME arrival time prediction window to about 12 hr for the 2012 July 12 CME. On investigating the forecast accuracy introduced by the uncertainties in individual GCS parameters, we conclude that the half-angle and aspect ratio have little impact on the predicted magnetic field of the 2012 July 12 CME, whereas the uncertainties in longitude and tilt can introduce relatively large spread in the magnetic field predicted at Earth.

An edge intelligence empowered flooding process prediction using Internet of things in smart city

Floods result in substantial damage throughout the world every year. Accurate predictions of floods can significantly alleviate casualties and property losses. However, due to the complexity of hydrology process especially in a city with complicated pipe network, the accuracy of traditional flood forecasting models suffer from the performance degradation with the increasing of required prediction period. In the work, based on the collected historical data of Xixian City, Henan Province, China, using the Internet of Things system (IoT) in 2011-2018, a Bidirectional Gated Recurrent Unit (BiGRU) multi-step flood prediction model with attention mechanism is proposed. In our model, the attention mechanism is used to automatically adjust the matching degree between the input features and output. Besides, we use a bidirectional GRU model, which can process the input sequence from two directions of time series (chronologically and antichronologically), then merge their representations together. Compared with the prediction model using Long Short Term Memory (LSTM), our method can generate better prediction result, as can be seen from the arrival time error and peak error of floods during multi-step predictions.

A short-term flood prediction based on spatial deep learning network: A case study for Xi County, China

Floods cause substantial damage across the world every year. Accurate and timely prediction of floods can significantly minimize the loss of life and property. Recently, numerous machine learning models have been used for flood prediction, showing that their performance is preferable to traditional statistical models. However, the existing models neglect the spatial features of floods, which drive flood generation and concentration. In this paper, the area of interest is divided into grids based on longitude and latitude, and the rainfall and discharge collected by stations are combined into tensors according to station coordinates. Different from one-dimensional time series, our input feature is a two-dimensional time series with spatial information. Hence, combining a Convolutional Neural Network (CNN) with a Long Short Term Memory Network (LSTM), we propose the convolution LSTM (ConvLSTM) to extract spatiotemporal features of hydrological information. The methodology is demonstrated using the hydrological data collected at the Xi County stations, located on the Huai River in Henan Province, China. Numerical results indicate that the relative error of arrival time is within 30%, and the relative error of peak discharge is within 20%, satisfying the 2005 Chinese Water Resource Standard on flood prediction permit error. The experiments also show that the ConvLSTM outperforms the recent models in terms of flood arrival time and peak discharge, thereby proving a promising alternative.

Comparison of hypocentre locations from the reprocessing of a downhole microseismic dataset

ABSTRACTFocusing on a single vertical‐well microseismic monitoring dataset, the aim here is to objectively look at the hypocentre location determination workflow and compare results from another independent processing effort of the same dataset. To that end, we use the Akaike information criterion–based picker followed by the cross‐correlation‐based refinement for P‐ and S‐wave arrival times. We apply a particle swarm optimization algorithm to calibrate a one‐dimensional velocity model using a single ball‐drop event. Due to the absence of any a priori information, we obtain three different local solutions for the velocity model using the particle swarm optimization algorithm with different upper and lower bounds on the search space. We also use the particle swarm optimization algorithm to determine hypocentre locations for microseismic events. In addition, we perform a waveform‐similarity‐based analysis to identify clusters of closely located microseismic events. Our results show that the hypocentre locations from all three local solutions exhibit similar fracture orientations and dimensions, as compared to the results from another independent processing effort. However, random differences with mean and standard deviation values in x, y, z as (53.9, −23.2, −6.2 m) and (35.5, 42.6, 17.6 m) exist between the two processing versions. These differences can be explained by the combined effect of errors in arrival times, back‐azimuths and velocity models, and the use of different algorithms in the two processing efforts. We also find that the comparison of event clusters other than full event distributions is an effective way of identifying any systematic differences between the two processing results.

Quantifying the Uncertainty in CME Kinematics Derived From Geometric Modeling of Heliospheric Imager Data

AbstractGeometric modeling of Coronal Mass Ejections (CMEs) is a widely used tool for assessing their kinematic evolution. Furthermore, techniques based on geometric modeling, such as ELEvoHI, are being developed into forecast tools for space weather prediction. These models assume that solar wind structure does not affect the evolution of the CME, which is an unquantified source of uncertainty. We use a large number of Cone CME simulations with the HUXt solar wind model to quantify the scale of uncertainty introduced into geometric modeling and the ELEvoHI CME arrival times by solar wind structure. We produce a database of simulations, representing an average, a fast, and an extreme CME scenario, each independently propagating through 100 different ambient solar wind environments. Synthetic heliospheric imager observations of these simulations are then used with a range of geometric models to estimate the CME kinematics. The errors of geometric modeling depend on the location of the observer, but do not seem to depend on the CME scenario. In general, geometric models are biased towards predicting CME apex distances that are larger than the true value. For these CME scenarios, geometric modeling errors are minimised for an observer in the L5 region. Furthermore, geometric modeling errors increase with the level of solar wind structure in the path of the CME. The ELEvoHI arrival time errors are minimised for an observer in the L5 region, with mean absolute arrival time errors of 8.2 ± 1.2 h, 8.3 ± 1.0 h, and 5.8 ± 0.9 h for the average, fast, and extreme CME scenarios.