Source Location Research Articles

Magnetospheric Line Radiation: Temporal Modulation Corresponding to a Bouncing Wave

AbstractMagnetospheric line radiation (MLR) is a peculiar type of whistler‐mode emissions formed by several similarly spaced spectral lines, whose frequencies may vary with time. These emissions typically occur at frequencies between about 1 and 8 kHz and are observed in both ground‐based and satellite measurements. However, their origin and source locations are not yet understood, as their detection by spacecraft instruments is challenging due to the limited frequency resolution of onboard wave measurements. We use high‐resolution multicomponent wave data obtained by the Van Allen Probes to demonstrate that, in addition to the frequency modulation, MLR events possess a temporal modulation with periods on the order of seconds, which may be related to the wave bouncing between hemispheres. Observed modulation periods are used to estimate a tentative source L‐shell. The modulation periods are shorter for events with larger frequency spacing, and the estimated source L‐shells correlate with model plasmapause locations.

Comprehensive evaluation of rockburst risk by multiparameter characteristics based on microseismic signals: A case study

AbstractNowadays, the seismological monitoring system in China is a valuable tool in the rockburst risk evaluation for deep coal mines. In the past, only parameters, like source location and energy, are widely used to estimate the risk level of rockburst. Sometimes, it is effective; however, some other important physical parameters, such as apparent stress drop, static stress drop, P‐wave velocity, and moment tensor, should also be included in order to improve the accuracy of risk assessment. In this study, these parameters are calculated using mine tremor signals recorded in the LW35001 workface of Liangbaosi Coal Mine. These calculations provide an overall identification of periodical stress distribution and rock mass energy‐releasing type under high concentrated stress. Via linear moment tensor inversion procedure, the source mechanism of mine tremors and stress state of the rock mass is determined whether it is risk or not to underground roadway. This comprehensive analysis provides a specific guidance for rockburst prevention for coal mine management, that is, knowing when and where measures must be taken to decrease the risk level or induce the occurrence of rockburst under control.

Toggling near-field directionality via manipulation of matter's anisotropy.

Near-field directional excitation of dipolar sources is crucial for many practical applications, such as quantum optics, photonic integrated circuits, and on-chip information processing. Based on theoretical analyses and numerical simulations, here we find that the near-field directionality of circularly polarized dipoles can be flexibly toggled by engineering the anisotropy of the surrounding matter, in which the dipolar source locates. To be specific, if the circularly polarized dipole is placed close to the interface between a hyperbolic matter and air, the main propagation direction of excited surface waves would be reversed when the location of the dipolar source is changed from the air region to the hyperbolic-matter region. The underlying mechanism is that the spatial-frequency spectrum of evanescent waves carried by the dipolar source in a homogeneous surrounding matter could be flexibly reshaped by the matter's anisotropy, especially when the isofrequency contour of the surrounding matter changes from the circular shape to the hyperbolic one.

The impact of fire source location on hot gas layer temperature in multi-compartment pre-flashover fires: Analysis and semi-empirical model development

A numerical study of a two-room structure (multi-compartment) subjected to fire in the inner room was carried out, employing the CFD (Computational Fluid Dynamics) software called FDS (Fire Dynamics Simulator), to (i) analyze the influence of the fire source position on the hot gas layer temperature, Tu, and its behavior in a multi-compartment fire, and (ii) develop a semi-empirical engineering calculation model to predict Tu in the adjacent room to a pre-flashover well-ventilated fire room, taking into account the fire source position. The results showed that when the fire origin occurs close to walls or elevated above the floor level, a reduced air entrainment is observed in the fire plume and flame, causing an increase in the Tu and a reduction of the smoke production in the fire room. This reduction in the smoke production led to a lower increase in the Tu of the adjacent room in relation to the case when the fire source was at the center of the fire room. It was also observed that the Tu in both rooms was inversely proportional to the ventilation factor (A.H0.5), with a significant increase observed as the ventilation factor decreases, depending on the fire source position. Furthermore, a semi-empirical engineering calculation model was developed to predict the Tu in the adjacent room, considering different fire source locations at ground level and elevated. The model was validated against experimental data from the literature, showing a good agreement (deviation of ≈20 %), thus demonstrating its applicability to other cases.

Dual-agent intelligent fire detection method for large commercial spaces based on numerical databases and artificial intelligence

Fires in commercial buildings can lead to significant casualties and property damage. Thus, rapidly and accurately identifying the source of a fire and its intensity is crucial for guiding rescue operations. This study aims to explore the use of distributed fiber optic temperature sensing systems, combined with deep learning algorithms, to predict key information about fires in commercial buildings, including the location of the fire source, the intensity of fire development, and the critical planar distribution of carbon monoxide. Based on the Fire Dynamic Simulation (FDS) data, a dual-agent model was developed, and with a sensor sampling interval of 3 s, 30 s of historical data achieved prediction accuracies of 94.23 % for fire intensity and 99.28 % for fire location. Upon reducing the intervals for fire location, the accuracy reached 93.56 %. Additionally, the Random Forest algorithm for mapping carbon monoxide distribution demonstrated an overall Mean Absolute Percentage Error (MAPE) of 5.87 %. Within the 0–150 ppm range, the MAPE was 2.46 %, far below the error levels of existing CO sensors. The database configurations and the sensors' spatiotemporal arrangement were also optimized to rapid and reliable fire prediction. This research demonstrates the feasibility of using artificial intelligence for critical fire scene information detection.

Effect of thermal radiation and inclined magnetic field on thermosolutal mixed convection in a partially active wavy trapezoidal enclosure filled with hybrid nanofluid

This work focuses on the examination of the magneto-thermosolutal convection in a lid-driven wavy trapezoidal enclosure in the presence of thermal radiation. The wavy cavity is filled with radiative Fe3O4-Cu-H2O hybrid nanoliquid. In this work, two cases are considered depending on the location of heat and concentration sources. A portion of the vertical walls are kept hot and in high concentration while the remaining parts are in adiabatic condition. The adiabatic flat upper lid is moving towards the right with equal speed. In addition, the lower wavy border is cold and low concentration. The governing Navier–Stokes equations are modeled to describe thermosolutal phenomena within the wavy enclosure. These equations are solved by reconstructing a recently developed compact scheme. Computed outcomes are presented in terms of streamlines, isotherms, iso-concentrations, average Nusselt and Sherwood numbers to evoke the thermosolutal phenomena for various physical parameters. Also, computing in-house code is validated with the published numerical and experimental and literatures. This investigation surveys the roles of several well-defined parameters such as Richardson number (Ri), Lewis number (Le), Buoyancy ratio number (N), Radiation parameter (Rd), Hartmann number (Ha), inclination of angle (γ), undulation of the wavy surface (d) and solid volume fraction (ϕhnp) of the hybrid nanofluid. An increase in the Lewis number (Le) enhances species diffusion within the system but diminishes thermal transport. This work reveal that a change in ϕhnp from 0% to 4%, heat transfer is upgraded up to 4.28% in Case I and 3.93% in Case II while mass transfer is declined by 2.24% in Case I and 1.60% in Case II. Results indicate that Case I performed better than Case II in the case of energy and solutal transfer. This work has many practical applications such as heat exchangers, electronic device cooling, food processing and porous industrial processes.

Bayesian Optimization Using Simulation-Based Multiple Information Sources over Combinatorial Structures

Bayesian optimization due to its flexibility and sample efficiency has become a standard approach for simulation optimization. To reduce this problem, one can resort to cheaper surrogates of the objective function. Examples are ubiquitous, from protein engineering or material science to tuning machine learning algorithms, where one could use a subset of the full training set or even a smaller related dataset. Cheap information sources in the optimization scheme have been studied in the literature as the multi-fidelity optimization problem. Of course, cheaper sources may hold some promise toward tractability, but cheaper models offer an incomplete model inducing unknown bias and epistemic uncertainty. In this manuscript, we are concerned with the discrete case, where fx,wi is the value of the performance measure associated with the environmental condition wi and p(wi) represents the relevance of the condition wi (i.e., the probability of occurrence or the fraction of time this condition occurs). The main contribution of this paper is the proposal of a Gaussian-based framework, called augmented Gaussian process (AGP), based on sparsification, originally proposed for continuous functions and its generalization in this paper to stochastic optimization using different risk profiles for combinatorial optimization. The AGP leverages sample and cost-efficient Bayesian optimization (BO) of multiple information sources and supports a new acquisition function to select the new source–location pair considering the cost of the source and the (location-dependent) model discrepancy. An extensive set of computational results supports risk-aware optimization based on CVaR (conditional value-at-risk). Computational experiments confirm the actual performance of the MISO-AGP method and the hyperparameter optimization on benchmark functions and real-world problems.

Acoustic camera applied on sound transmission identification in closed spaces

In the ongoing context of office partitioning, users are increasingly paying attention to acoustic insulation between rooms. The issues commonly related are translated by confidentiality problems and annoying use of sound-enabled videoconferencing. In practice, when an insulation defect is perceived, standardized acoustic insulation measurements are carried out to characterize the overall insulation performance. However, it is often challenging to precisely identify by ear the various paths of sound transmission: through the partition, by poorly caulked junctions, through the ceiling, through the facade, etc. Acoustic antenna solutions employed to identify the location and frequency content of sources have existed for several decades. But, in addition of being expensive and bulky, until now their use was reserved for detection of sources on transportation vehicles (trains, cars, planes, etc.) or industrial equipment (wind turbines, boiler rooms, etc.). In recent years, more accessible and practical antenna solutions known as "acoustic camera" have become available. In this sense, we have been handling an acoustic camera to assist on the detection of the sound transmission paths between two rooms. This work address what has been experienced, outlining results obtained through the (evolving) methodology for sound transmission identification with acoustic camera in closed spaces.

Development of a gunshot location detection system based on acoustic data

Determination of sound source location is a crucial problem, especially for the assault threat detection in crowded areas or military applications. The chief objective of this study is to design a microphone array to detect the location of explosion type sound sources, such as gunshot, which have significant amplitudes when compared to the background noise levels. Furthermore, the time arrival difference algorithm is utilized for the determination of time arrival differences and sound source location. Evidently, for stationary microphones that are positioned close to each other, determination of time arrival differences with high accuracy is a significant problem. Thus, a significant effort is given to enhance the accuracy of time arrival difference predictions via proper sampling. The design process of the microphone array is conducted computationally, where the sound data received by the microphones are generated. The time arrival differences are then calculated numerically, which are used for the prediction of sound source location. The number of microphones, relative positions of the microphones are determined accordingly via digital experiments. Finally, the designed microphone array is built and experiments are conducted. In conclusion, it is observed that the numerical model successfully predicts the sound source location of the computational model.

Neural-network-based sound source localization from unmanned aerial vehicles

As a mobile platform, unmanned aerial vehicles (UAV) can carry optics and acoustics sensors recording visual and audio information from ground. Over the past decade, UAVs are widely used in different applications, e.g., search and rescue, pipe leakage detection, etc. Compared with visual information, audio information is challenging to capture from UAVs due to the strong ego-noise generated by the rotating motors, propellers on the UAV as well as the motion of the UAV, which result in a low signal to noise ratio (SNR) of the received acoustics signals. With the contaminated signals, the performance of sound source localization algorithms, e.g., conventional beamforming, is severely degraded. To improve the quality of sound source localization result, different sound source enhancement algorithms have been proposed by researchers. In this paper, simulation experiments were conducted to study the potential of apply convolutional neural networks (CNN) to improve the beamforming result. It was found that with CNN, the estimation of a single point sound source location and source strength can be improved. The model's localization accuracy and source strength estimation accuracy were evaluated respectively.

Advancements in railway noise simulation: assessment of tools for virtual validation

In pursuit of more efficient and cost-effective measurement campaigns, the railway industry is embracing the prospect of virtual validation based on simulations. The primary obstacle lies in the need for noise simulation tools capable of accurately replicating complex sources, installation effects, noise propagation, ground reflection and, for some cases, the diffraction in the different train surfaces. This paper presents insights derived from European project Shift2Rail S2R-CFM-CCA-01-2019 Energy and Noise & Vibration WP6, concentrating on the validation and enhancement of two exterior noise simulation tools, tailored for railway applications. The paper covers four reference cases, simulating exterior noise at standstill with diverse noise sources and varying source locations and noise emission (i.e. electrical noise, cooling, etc.). A correlation with previously conducted measurements at typical receiver locations (7.5 meters from the track centerline) is carried out. The measurements were conducted in a "controlled" configuration, isolating as much as possible the analyzed noise source and gathering all the needed information about the ground and train geometry. The evaluation between measurements and simulations is done against a global and a 1/3rd octave band criteria with successful results. Finally, key features essential for achieving more accurate simulations are listed.

Studying infrasound propagation in the middle atmosphere with ICON and UA-ICON: comparison with the IFS and ground-based remote sensing

Infrasound signals are used to monitor various anthropogenic and natural sources. In particular, infrasound is one of the four verification technologies used by the International Monitoring System (IMS) of the Comprehensive Test-Ban Treaty organisation (CTBTO). To determine accurate source locations and energies, an accurate model of wind and temperature up to the lower thermosphere is necessary, hence operational NWP products are of great importance for routine infrasound monitoring activities. However, many of these models focus on tropospheric conditions, and the middle atmosphere (MA) is not well represented. UA-ICON is an upper atmosphere version of the ICON model that provides modelled atmospheric parameters up to 150 km. First, to assess ICON and IFS operational analysis products, comparisons to lidar observations are made. The main differences between both products were analysed with respect to winds and temperatures in the MA. Second, UA-ICON simulations outputs were compared to ICON and IFS fields to demonstrate the increased wave activity above ~30 km with UA-ICON. The added value of UA-ICON with respect to ICON and IFS for infrasound propagation simulations is discussed. The comparisons between the instrumental observations and the models will be presented, as well as comparisons between modelled and measured infrasound propagation.

Investigation and comparison of heat pump noise in the lab and field

The presentation covers a contribution from the publicly funded HP-QUEEN MENESA project on the characterisation of a heat pump in the laboratory with comparisons in the field at comparable operation conditions. The data in the lab are obtained from a hemi-anechoic chamber that is equipped with temperature control to allow standardized measurements at -7 °C and 7 °C, for example. In this environment spherical sound radiation pattern are determined on a movable hemi-circle microphone arrangement allowing the assessment of source location and radiation contribution as well as sound power determination at the different operation points of the heat pump. These data are compared and analysed to measurements with the same heat pump in the field at model houses at the Fraunhofer-IBP location in Holzkirchen. Furthermore, we are also investigating the transmission path from the noise of the heat pump outdoors into the building taking sound reduction index of i.e., windows into account. It is planned to connect a psychoacoustic analysis together with a questionnaire to these investigations that allows assessment of the final sound characteristics inside the building.

Machine learning-Based method for gas leakage source term estimation in highway tunnels

Incidents involving the leakage of flammable and explosive chemicals in highway tunnels can lead to disastrous events such as fires and explosions. The distinctive features of highway tunnels, characterized by their confined and elongated structure, intensify the complexity of managing such accidents, potentially resulting in significant casualties and property damage. Rapid detection and acquisition of leakage source information during a flammable gas leak in a highway tunnel are of utmost importance for effective accident management. This study introduces a predictive model developed using a machine learning approach. The model uses time series data from gas concentration sensors and anemometers installed within the tunnel to predict parameters such as gas leakage velocity, source location, and initiation time. A computational fluid dynamics simulation of gas leakage within the tunnel was constructed, and a dataset was generated using sample generation and preprocessing methods. The results demonstrate that this method exhibits robust predictive performance. Furthermore, a prediction strategy is proposed to enhance the model’s predictive accuracy and resilience to data noise. This model offers valuable insights for improving accident management strategies for gas-related incidents in highway tunnels.

Loss of C1q alters the auditory brainstem response.

Neural circuits in the auditory brainstem compute interaural time and intensity differences used to determine the locations of sound sources. These circuits display features that are specialized for these functions. The projection from the ventral cochlear nucleus (VCN) to the medial nucleus of the trapezoid (MNTB) body travels along highly myelinated fibers and terminates in the calyx of Held. This monoinnervating synapse emerges during development as multiple inputs are eliminated. We previously demonstrated that elimination of microglia with a colony stimulating factor-1 inhibitor results in impaired synaptic pruning so that multiple calyceal terminals reside on principal cells of MNTB. This inhibitor also resulted in impaired auditory brainstem responses (ABRs), with elevated thresholds and increased peak latencies. Loss of the microglial fractalkine receptor, CX3CR1, decreased peak latencies in the ABR. The mechanisms underlying these effects are not known. One prominent microglial signaling pathway involved in synaptic pruning and plasticity during development and aging is the C1q-initiated compliment cascade. Here we investigated the classical complement pathway initiator, C1q, in auditory brainstem maturation. We found that C1q expression is detected in the MNTB by the first postnatal week. C1q levels increased with age and were detected within microglia and surrounding the soma of MNTB principal neurons. Loss of C1q did not affect microglia-dependent calyceal pruning. Excitatory and inhibitory synaptic markers in the MNTB and LSO were not altered with C1q deletion. ABRs showed that C1q KO mice had normal hearing thresholds but shortened peak latencies. Altogether this study uncovers the developmental time frame of C1q expression in the sound localization pathway and shows a subtle functional consequence of C1q knockdown.

An Approach to Calculate Source Locations for Thicker Plates when Acoustic Emission Signals are Dominated by Trailing Waves

In many acoustic emission (AE) applications, signal arrival times from an array of sensors are coupled with an appropriate velocity to calculate the source location of AE events. To date, the AE signals present in these applications are Lamb and/or Rayleigh waves. In a previous publication [1], finite element modeled (FEM) AE signals in thicker plates were dominated by trailing wave (TW) packets, when no Rayleigh wave was present. The typical character of the TW dominated signals was described as a spaced-in-time train of short-in-time wave packets. Further, it was pointed out, that when TW dominate the signals, it is not clear how arrival times for source location calculations might be obtained along with an appropriate propagation velocity. The goal of this work is to develop a source location approach for cases of dominant TWs in the AE signals in thicker plates. The FEM-signal database from the previous publication was used for this study. The database consisted of in-plane dipole sources at five different depths and four different steel plate thicknesses (50, 75, 100 and 125 mm). Bottom-surface out-of-plane displacement signals versus time for propagation distances from 250 to 1500 mm were examined. The FEM signals were frequency filtered to examine a broadband case of 80 to 500 kHz and a narrowband case of 100 to 300 kHz. To study the potential source location accuracy, a total of seven four-sensor arrays were employed. The study results describe a process to select appropriate arrival times and obtain a propagation “velocity.” This information was used to calculate source locations for the sensor arrays. The location errors were shown to be less than the plate thickness.

Volcanic tremor associated with successive gas emission activity at a boiling pool: Analyses of seismic array and visible image data recorded at Iwo-Yama in Kirishima Volcanic complex, Japan

Volcanic tremors are often observed during volcanic activity and volcanic eruptions, and their generation processes provide clues for understanding volcanic fluid activity underground and eruption dynamics. However, tremors are characterized by continuous oscillations that mask P- and S-waves; hence few studies have precisely located the source, which is the most fundamental information for understanding the generation mechanism. In this study, we focus on volcanic tremors excited by continuous gas emissions occurring at a vent called Y2a in Iwo-Yama, the Kirishima Volcanic Complex, Japan, to clarify the source process of the tremor as well as gas emission activity. We simultaneously observed the volcanic tremor by deploying a small aperture array consisting of six seismometers and the gas emission activity by using a newly developed visual IoT system that can be operated without commercial electricity. MUSIC analysis locates the tremor at depths ranging from the ground surface to approximately 200 m beneath the Y2a and Y2b vents, which are approximately 30 m apart, for approximately four months from November 2021 to February 2022. The source locations of the tremors in the 2 Hz (1.2–2.6 Hz), 4 Hz (3–4 Hz), and 5 Hz (4–5.5 Hz) ranges show some differences and changes with time. The source location tends to become deeper when the 2 Hz amplitude is large. The infrasound generated by gas emission activity is dominant in the tremor signals, which are recognized in the wave propagation velocity with an acoustic velocity of 330 m/s when the 2 Hz amplitude is small. The visual IoT system succeeded in detecting long-term changes in the gas emission activity, and we found that the 2 Hz amplitude of tremor was well correlated with the amount of hot water in the boiling pool of Y2a, which was controlled by precipitation and evaporation during non-rainy days. From these observations, we infer that the volcanic tremor is generated by resonance of volcanic gas and hot water in a crack-like structure beneath Y2a. The resonance was triggered by the counterforces of the gas emissions in the boiling pool, and the infrasound was dominant during periods of hot water depletion in the boiling pool. Temporal changes in the source depths may be caused by changes in the fluid properties, configuration of the resonator and/or the strengths of the underground sources and infrasound. Our simultaneous observations of seismic array and visual IoT system clarify that even the continuous gas emission activity that looks stable is controlled by external sources such as precipitation.

Research of Acoustic Emission Characteristics and Applicability of Artificial Intelligence for Source Localization

Within this research, the deployment of artificial intelligence for the localization of acoustic emissions (AE) is being investigated. In detail, series of experiments were conducted on a plate of granite with homogeneous and isotropic material behavior. Acoustic emissions could be generated by the breaking of pencil leads, as well as the impact of rice grains and steel balls on 81 specific positions with a distance of 50 mm in both horizontal axes to each other. The response of the system was recorded using 16 acoustic emission sensors, of which 8 sensors were positioned on each surface side of the plate. In further steps the data could be processed, so that the resulting characteristic signal features could be translated into spec-trograms and furthermore being used as input for the artificial intelligence. Additionally, the corresponding source locations appeared as labels. Both, the signal features as well as the labels, could be implemented in the process of supervised learning using a convolutional neural network (CNN). The results of this network indicated a validation accuracy of over 99% as well as a low loss value by classifying the inserted features to the right labels. Thus demonstrates the excellent applicability of artificial intelligence for the source localization of acoustic emission (AE).

Differential Phase Analysis for Volcanic Tremor Detection and Source Location

AbstractWe present observations showing that during episodes of volcanic tremors, the phase of inter‐station cross‐correlations becomes stable. We propose a new quantity, the phase coherence, to identify the differential phase stability in recordings obtained from a single pair of stations, which is extrapolated to the seismic network. Then, we present a new approach based on the estimation of differential travel times through the differential phase measurements, to locate the sources of tremors occurring at the end of 2015 at the Klyuchevskoy Volcanic Group in Kamchatka, Russia. We present evidence supporting the existence of two types of activity happening simultaneously during the tremor episode: the main tremor source, originating from a region located between 7 and 9 km depth under the main volcanoes, and the widespread occurrence of weak low‐frequency earthquakes occurring at random locations. We show how the phase coherence and the differential phases can be used to provide information on the stability of the tremor source position and to estimate its location.

Range-dynamical low-rank split-step Fourier method for the parabolic wave equation.

Numerical solutions to the parabolic wave equation are plagued by the curse of dimensionality coupled with the Nyquist criterion. As a remedy, a new range-dynamical low-rank split-step Fourier method is developed. The integration scheme scales sub-linearly with the number of classical degrees of freedom in the transverse directions. It is orders of magnitude faster than the classic full-rank split-step Fourier algorithm and saves copious amounts of storage space. This enables numerical solutions of the parabolic wave equation at higher frequencies and on larger domains, and simulations may be performed on laptops rather than high-performance computing clusters. Using a rank-adaptive scheme to optimize the low-rank equations further ensures the approximate solution is highly accurate and efficient. The methodology and algorithms are demonstrated on realistic high-resolution data-assimilative ocean fields in Massachusetts Bay for two three-dimensional acoustic configurations with different source locations and frequencies. The acoustic pressure, transmission loss, and phase solutions are analyzed in the two geometries with seamounts and canyons across and along Stellwagen Bank. The convergence with the rank of the subspace and the properties of the rank-adaptive scheme are demonstrated, and all results are successfully compared with those of the full-rank method when feasible.