Source Estimation Research Articles

Dynamic Characteristics of Aerosol Optical Properties over Dibrugarh City in the North-Eastern Indian Region during 2018–2021

Aerosols play an important role in the earth's environment across the globe through their involvement in various earth system cycles. The change in the aerosol properties may cause short and long-term impacts, the knowledge of such changes is useful in the estimation of the pollution sources of any region. We have carried out the analysis of the aerosols' optical and radiative properties using AERONET station data from 2018 to 2021 in Dibrugarh City. The higher Aerosol Optical Depth (AOD) values during winter and pre-monsoon months indicate high anthropogenic activities, and biomass burning in Dibrugarh. The impact of various sources and daily meteorological parameters help in understanding the diurnal variations of the AOD, Ångström Exponent (AE), and column water (CW). Fine aerosol fractions dominate the aerosol volume, but sometimes the long-range transport of dust affects aerosol properties during pre-monsoon months (MAM). MODIS-derived AOD and AERONET AOD values show a good correlation, with R2 = 0.68. The highest volume of the aerosols reaches up to 0.11 µm3 µm-2 during pre-monsoon months, whereas it lies below 0.05 µm3 µm-2 in other seasons. SSA values indicate the presence of scattering aerosols but in 2020, a sudden decline in the SSA values shows a strong rise in the absorbing aerosols. Throughout the study period (2018–2021), the positive radiative forcing indicates a rise in atmospheric heating.

Weighted Frequency Smoothing for Enhanced Speaker Localization

The coherent signal subspace method may be used in order to apply subspace localization methods (e.g. MUSIC) to coherent sources. This method involves a focusing process followed by frequency smoothing, which is intended to decorrelate source signals from coherent sources. In practice, however, only moderate decorrelation is obtained, which may lead to performance degradation. Although decorrelation can be improved by widening the smoothing bandwidth, a wider bandwidth may increase focusing error and the smoothing bandwidth is limited by the bandwidth of the actual signal. In this paper, a weighted frequency smoothing that improves decorrelation for a given bandwidth is proposed. It is shown that better decorrelation is obtained by selecting the weights to be inversely proportional to the source signal power at the given frequency. However, since the power of the source is not known, it is estimated by the trace of the array spatial covariance matrix. An experimental study is presented that investigates the effect of the proposed weighting on DOA estimation of speech sources in a reverberant environment.

Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs

In this paper, we present a new model for Direction of Arrival (DOA) estimation of sound sources based on an Icosahedral Convolutional Neural Network (CNN) applied over SRP-PHAT power maps computed from the signals received by a microphone array. This icosahedral CNN is equivariant to the 60 rotational symmetries of the icosahedron, which represent a good approximation of the continuous space of spherical rotations, and can be implemented using standard 2D convolutional layers, having a lower computational cost than most of the spherical CNNs. In addition, instead of using fully connected layers after the icosahedral convolutions, we propose a new soft-argmax function that can be seen as a differentiable version of the argmax function and allows us to solve the DOA estimation as a regression problem interpreting the output of the convolutional layers as a probability distribution. We prove that using models that fit the equivariances of the problem allows us to outperform other state-of-the-art models with a lower computational cost and more robustness, obtaining root mean square localization errors lower than 10{\deg} even in scenarios with a reverberation time $T_{60}$ of 1.5 s.

Aerobic methane emissions from the shoots of Scots pine

Plants are recognized sources of methane (CH4) but plant-mediated CH4 emissions have mostly been studied on herbaceous species, although also trees are known to emit CH4. Emissions from tree canopies likely mostly derive from an aerobic, abiotic process. Aerobic CH4 production from trees has not been thoroughly studied, leaving uncertainties to the global source strength estimates, which vary from 0 to 240 Tg yr-1. Even less is known about how aerobic emissions affect the CH4 cycles in boreal forest ecosystems, as the environmental and physiological drivers are not fully understood. In this study, shoot-level CH4 fluxes of boreal conifer trees were measured outdoors and in the greenhouse, to investigate the environmental and physiological drivers and regulators of shoot-level CH4 fluxes. Most of the measurements were done from saplings of Pinus sylvestris L. (Scots pine), one of the most important tree species of the boreal region of the Eurasian continent, by using chamber enclosure methods with spectral, online greenhouse gas analysers. The measurements were conducted either manually, or with an automated measurement system, developed to overcome issues related to manual measurement techniques. The shoots of Scots pine showed small but significant emissions of CH4 in all experimental setups, the emissions were driven by light, enhanced by elevated temperature, and occurred independently from drought and photosynthesis. Solar radiation was a more significant driver of these CH4 emissions than artificial light with UV-A. These results show that Scots pine canopies have the potential to produce CH4 in a similar process that has been described before for the foliage of herbaceous plants, but these emissions are smaller than the initial estimates of the aerobic CH4 source from vegetation. The boreal forest canopies are sources of CH4 and have the capacity to decrease the CH4 sink strength of boreal upland forests by ~ 5 %.

Advances in UAV-Assisted Localization: Joint Source and UAV Parameter Estimation

This paper presents efficient estimators to address the problem of source localization aided by unmanned aerial vehicles (UAVs) with unknown location parameters. Leveraging semidefinite relaxation technique and successive weighted least squares estimation, the source and UAV parameters are simultaneously estimated in both mixed line-of-sight/non-line-of-sight (NLOS) and NLOS scenarios. The proposed estimators are shown to approximately achieve the Cramer-Rao bound (CRB) under mild Gaussian noise conditions when the measurement noises are small compared to the associated ranges. Numerical simulations demonstrate that localization without prior UAV location information is still possible with a meter-level positioning and sub-meter per seconds velocity estimation accuracy.

Measuring photometric redshifts for high-redshift radio source surveys

Abstract With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to $z = 3$ before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify $\approx$ 76% of the highest redshift sources—sources at redshift $z > 2.51$ —as being in either the highest bin ( $z > 2.51$ ) or second highest ( $z = 2.25$ ).

Robust min-norm algorithms for coherent sources DOA estimation based on Toeplitz matrix reconstruction methods

Most of the classical high-resolution algorithms such as ESPRIT, MUSIC or Min-Norm, demonstrate their ability to estimate the directions by which non-coherent signals are arrived on a sensor array. However, the need to enhance this kind of algorithm is becoming increasingly important in order to obtain good estimation also in coherent environments. In this paper, two different algorithms for Direction-of-Arrival (DOA) estimation are devised. These two new algorithms improve the performance of the Min-Norm algorithm by incorporating decorrelation techniques as a tool to overcome coherent source estimation problems. Simulation examples are conducted to validate the robustness and the effectiveness of the new proposed algorithms compared to the conventional Min-Norm high resolution algorithm.

A practical guide of the 2D acoustic full waveform inversion on synthetic land seismic data

Full Waveform Inversion (FWI) has been successfully applied in seismic hydrocarbon exploration as it results in the highest resolution inverted velocity and density models. However, the application of FWI on land datasets is a much more challenging process than its application on marine datasets. This paper suggests a step-by-step guide for the 2D acoustic FWI on a synthetic dataset, in particular, in which we used the SEAM Phase II-Foothills dataset that exhibits many of the characteristics of real land data. The methodology includes tools for the processing of seismic data based on the features of acquisition geometry, source estimation, first arrival tomography, plus the software strategies to meet the requirements of the FWI (memory and computation requirements). The document also includes a discussion of the results of velocity models obtained for a low-resolution Dip 2D line of the SEAM data, using a workstation with low-to-intermediate hardware requirements.

Nonlinear Stochastic Seismic Response Analysis of Three-Dimensional Reinforced Concrete Piles

A reliable assessment and design of engineering structures requires appropriate estimation and consideration of different sources of uncertainty. The randomness of seismic ground motion is one major uncertainty that needs to be considered from the perspective of performance-based earthquake engineering. To properly account for this uncertainty and its corresponding effect on pile foundations, a stochastic seismic response analytical framework based on a probability density evolution method and a stochastic ground motion model is proposed for the nonlinear stochastic seismic response analysis of pile foundations. A three-dimensional finite element model of a reinforced concrete pile with a large diameter embedded in the soil foundation is firstly established and calibrated using the full-scale lateral load test results from the literature, in which the nonlinear behavior of soil, the soil–pile interaction, the concrete damaged plasticity, and the steel yielding of the pile are properly modeled. Then, the calibrated three-dimensional numerical model is employed as an illustrative example for the stochastic seismic response analysis using the proposed framework. The mean, standard deviation, and probability density function of pile settlement and the dynamic reliabilities of the pile concerning various performance requirements are obtained in the present study. The settlements of the pile show great variability under the excitation of various stochastic ground motions, and the maximum mean value of the pile settlements is about 8 mm. The changing shape of the settlement probability density functions in every moment demonstrates that it is unreasonable to use assumed probabilistic distribution models to characterize the seismic responses of pile foundations during a seismic reliability analysis. Based on the proposed method, it is found that the dynamic reliabilities of the selected reinforced concrete pile concerning four different performance levels are 0, 0.1293, 0.8247, and 1, respectively. The proposed stochastic seismic response analytical method in the present study can provide a proper and comprehensive way to quantify various uncertainties and their corresponding effects on the seismic performance of pile foundations. It can also be used to estimate the actual reliability level of pile foundations that are designed by the current codes when they are subjected to seismic loads.

Estimation of nutrient sources and fate in groundwater near a large weir-regulated river using multiple isotopes and microbial signatures

The excessive input of nutrients into groundwater can accelerate eutrophication in associated surface water systems. This study combined hydrogeochemistry, multi isotope tracers, and microbiological data to estimate nutrient sources and the effects of groundwater-surface water interactions on the spatiotemporal variation of nutrients in groundwater connected to a large weir-regulated river in South Korea. δ11B and δ15N-NO3- values, in combination with a Bayesian mixing model, revealed that manure and sewage contributed 40 % and 25 % respectively to groundwater nitrate, and 42 % and 27 % to nitrate in surface water during the wet season. In the dry season, the source apportionment was similar for groundwater while the sewage contribution increased to 52 % of nitrate in river water. River water displayed a high correlation between NO3- concentration and cyanobacteria (Microcystis and Prochlorococcus) in the wet season. The mixing model using multiple isotopes indicated that manure-derived nutrients delivered with increased contributions of groundwater to the river during the wet season governed the occurrence of cyanobacterial blooms in the river. We postulate that the integrated approach using multi-isotopic and microbiological data is highly effective for evaluating nutrient sources and for delineating hydrological interactions between groundwater and surface water, as well as for investigating surface water quality including eutrophication in riverine and other surface water systems.

Coarray Polarization Smoothing for DOA Estimation with Coprime Vector Sensor Arrays

In this paper, the problem of direction-of-arrival (DOA) estimation of multiple sources using a coprime electromagnetic vector sensor (EMVS) array is addressed. Each EMVS consists of three mutually orthogonal electric dipoles and three mutually orthogonal magnetic loops, all collocated at a single point in space. By exploiting the polarization diversities embedded in the vector sensors, the coarray polarization smoothing (COPS) is presented to increase the degrees of freedom (DOFs) from the polarization coarray domain. In contrast to the widely used spatial smoothing technique, the COPS offers the following insights: (1) it is source polarization-dependent and is proved to offer 20 DOFs at most, (2) it does not reduce the efficient spatial coarray aperture of the coprime array so that it enables the use of all virtual coarray sensors without resorting to the nontrivial sparse recovery or array interpolation operations, and (3) it can be synthesized with the spatial smoothing to get the DOFs multiplied. Finally, the efficacy of the COPS scheme is verified by numerical examples.

Inverse estimation of multiple contaminant sources in three-dimensional heterogeneous aquifers with variable-density flows

Groundwater contamination is an exacerbating global issue that severely threatens human health. Subsurface remediation thus has gained increased interest in recent years. To effectively remediate subsurface contaminated sites, one needs to identify the locations of contaminant sources and characterize contaminant spreading. Groundwater contamination is often driven by multiple contaminant sources, making source identification problems challenging. Further, when the densities of dissolved contaminants and ambient groundwater differ, the resultant variable-density flows add complexity to the task of source identification. However, most previous studies are limited to a single source identification in a two-dimensional aquifer. This study presents a novel inversion method based on ensemble smoothing that identifies the locations of multiple contaminant sources in three-dimensional heterogeneous aquifer systems. A new covariance localization algorithm based on a clustering method is integrated into the inversion method, which improves the accuracy of multi-source identification. Using the proposed inversion framework, we successfully estimate the locations of multiple contaminant sources and three-dimensional permeability fields utilizing pressure and concentration data from monitoring wells. Further, we investigate and elucidate the effects of aquifer heterogeneity and variable-density flows on multiple source identification. We find that variable-density flow increases the data information contents and thus improves the inversion accuracy. This is the first-time demonstration of the effects of variable-density flow on the inversion accuracy of multi-source identification in three-dimensional heterogeneous aquifer systems.

Detection of Einstein telescope gravitational wave signals from binary black holes using deep learning

ABSTRACT The expected volume of data from the third-generation gravitational waves (GWs) Einstein Telescope (ET) detector would make traditional GWs search methods such as match filtering impractical. This is due to the large template bank required and the difficulties in waveforms modelling. In contrast, machine learning (ML) algorithms have shown a promising alternative for GWs data analysis, where ML can be used in developing semi-automatic and automatic tools for the detection and parameter estimation of GWs sources. Compared to second generation detectors, ET will have a wider accessible frequency band but also a lower noise. The ET will have a detection rate for Binary Black Holes (BBHs) and Binary Neutron Stars (BNSs) of the order of 105–106 and 7 × 104 yr−1, respectively. We explored the efficiency of using convolutional neural networks (CNNs) for the detection of BBHs’ mergers in synthetic noisy data that was generated according to ET’s parameters. Without performing data whitening or applying bandpass filtering, we trained four CNN networks with the state-of-the-art performance in computer vision, namely VGG, ResNet, and DenseNet. ResNet has significantly better performance, and was able to detect BBHs sources with SNR of 8 or higher with 98.5 per cent accuracy, and with 92.5 per cent, 85 per cent, 60 per cent, and 62 per cent accuracy for sources with SNR range of 7–8, 6–7, 5–6, and 4–5, respectively. ResNet, in qualitative evaluation, was able to detect a BBH’s merger at 60 Gpc with 4.3 SNR. It was also shown that CNN can be used efficiently for near-real time detection of BBHs.

Numerical estimation of a tsunami source at the flexural area of Kuril and Japan Trenches in the fifteenth to seventeenth century based on paleotsunami deposit distributions in northern Japan

Paleotsunami deposit investigations and numerical tsunami computations have been performed to elucidate the source and size of large tsunamis along the Kuril to Japan Trenches, particularly for unusual tsunamis that occurred in the seventeenth century, the 1611 CE Keicho tsunami (M 8.1) along the Japan Trench and seventeenth-century tsunami (> Mw 8.8) along the Kuril Trench, which caused serious damages on the coastal residents and environments. Moreover, several paleotsunami deposits dating from the thirteenth to eighteenth centuries have been reported along the area between the Kuril and Japan subduction zones, but their sources have not been clarified. In this study, we estimated the tsunami sources from numerical simulations using the distribution of fifteenth- to seventeenth-century tsunami deposits at Sekinehama along the coast of the Shimokita Peninsula. Based on numerical simulations with previously proposed fault models, the tsunami deposits showing similar ages at Sekinehama and another site on the coast of Shimokita Peninsula, which are within 50 km apart, could not be explained except with the huge earthquake models (> Mw 9.1), whose rupture zones extend to not only the Kuril or Japan Trenches but also their flexural area. Thus, we modified or newly proposed twelve fault models located in the flexural area between the two trenches to explain tsunami deposits possibly around the seventeenth century at the above-mentioned two sites on the coast of Shimokita Peninsula. Simulations using these models elucidated that the rupture in the shallow or deep plate boundaries with > 14–32 m slip (> Mw 8.55–8.76) is necessary. If the tsunami deposits around the seventeenth century along the Iburi–Hidaka coast in Hokkaido and those at the two sites mentioned above might be left by an identical event, an interplate earthquake with > 18–40 m slip (> Mw 8.62–9.2) in the flexural area is needed. Moreover, this interplate earthquake might have occurred in the deep plate boundary than in the shallower plate boundary based on slip deficit and slow earthquake distribution data. Our results offer significant insights into a large earthquake (> M 8) along the Kuril and Japan Trenches in the fifteenth to seventeenth century.

Performance analysis of deep neural networks for direction of arrival estimation of multiple sources

AbstractRecently, popular machine learning algorithms have successfully been applied to the direction of arrival (DOA) estimation. An implementation of determination of DOA estimation is presented based on deep neural networks (DNNs) to reduce the computational complexity of traditional superresolution DOA estimation methods. The classical DOA estimation algorithms have limitations due to unforeseen effects, such as array perturbations. Instead of computing an inverse mapping based on the incomplete forward mapping that relates the signal directions to the array outputs, the DOA problem is approached as a mapping, which can be approximated using a suitable DNN trained with input output pairs. The neural network architecture is based on a multilayer perception and a group of parallel DNNs to perform detection and DOA estimation, respectively. Simulation results are performed to investigate the effect of network parameters on estimation accuracy so that they can be roughly determined in the case of one signal scenario. Based on a set of simulations and experimental measurements, the performance of the optimum network is also assessed and compared to that of the classical DOA estimation methods for multiple signals. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also dramatically reduce the computational complexity and the memory space.

A Robust Algorithm for DOA Estimation of Coherent Sources with UCA

Direction of arrival (DOA) estimation of coherent sources with a uniform circular array (UCA) is an intractable problem. The method-of-direction-estimation (MODE) algorithm has strong superiority in handling coherent sources compared with the classical MUSIC, and ESPRIT algorithms. However, MODE is sensitive to source numbers and does not work well in the UCA scenario. In order to improve the performance of MODE, a robust DOA estimation method named UCA-PUMA (principal-eigenvector-utilization-for-modal-analysis) is proposed. The complicated non-Vandermonde structured steering vector of UCA is transformed into a virtual Vandermonde structured steering vector in mode space. The proposed method gives a closed-form solution compared with the original UCA-MODE algorithm. The performance of the UCA-PUMA method is evaluated by simulations. Simulation results demonstrate that the UCA-PUMA is more robust to source numbers than the UCA-MODE, and coherent sources can be handled without spatial smoothing. In addition, the UCA-PUMA fully takes advantage of the UCA, which is able to discriminate sources coming from a 360o azimuthal field of view.

First Principles Calibration of 40Ar Abundances in 40Ar/39Ar Mineral Neutron Fluence Monitors: Methodology and Preliminary Results

The accuracy and traceability of geochronometers are of vital importance to questions asked by many Earth scientists. The widely applied 40Ar/39Ar geochronometer relies on the co‐irradiation of samples with neutron fluence monitors (reference materials) of known ages; the ages and uncertainties of these monitors are critical to our ability to apply this chronometer. Previously, first principles, astronomical and optimisation calibrations have been made. The first principles method for determining the age of monitor minerals is the K‐Ar method, which involves measurement of their 40K and 40Ar* abundances. The AQuA (Absolute Quantities of Argon) pipette system, which emits calibrated quantities of 40Ar* via the ideal gas law, was used to calibrate the sensitivity of the system across a range of source pressures and estimate 40Ar* abundances in neutron fluence monitors. These 40Ar abundances were combined with existing 40K abundance data for these monitors. Ages for HD‐B1 and MD2 (GA1550) biotite fluence monitors were calculated and combined with intercalibration data for HD‐B1 and Fish Canyon sanidine (FCs) to determine ages for FCs. Current results do not have the targeted accuracy when compared with previous calibrations; however, we show how the extensive methodology development presented here can be used towards making reliable future measurements.

Contamination Status of Pet Cats in Thailand with Organohalogen Compounds (OHCs) and Their Hydroxylated and Methoxylated Derivatives and Estimation of Sources of Exposure to These Contaminants.

In this study, we analyzed serum samples of pet cats from Thailand and estimated the contribution to organohalogen compounds (OHCs) exposure through cat food and house dust intake. BDE-209 was predominant in cat sera and accounted for 76% of all polybrominated diphenyl ethers (PBDEs). Decabromodiphenyl ether (BDE-209) is a major contaminant in dry cat food and house dust, which has been estimated to be a source of exposure for Thai pet cats. BDE-209 is a major contaminant of OHCs in dry cat food and house dust, which was estimated to be a source of exposure for Thai pet cats. On the other hand, the level of contamination by PCBs was lower than in other countries. Analysis of pet foods suggested that BDE-209 in pet cat serum was attributable to the consumption of dry cat food. On the other hand, house dust also contained high concentrations of BDE-209. Thus, high levels of BDE-209 in pet cat sera can be attributed to the consumption of dry cat food and house dust. These results suggest that pet cats are routinely exposed to non-negligible levels of OHCs.

Abstract representations in temporal cortex support generative linguistic processing

ABSTRACT Generativity, the ability to create and evaluate novel constructions, is a fundamental property of human language and cognition. The productivity of generative processes is determined by the scope of the representations they engage. Here we examine the neural representation of reduplication, a productive phonological process that can create novel forms through patterned syllable copying (e.g. ba-mih → ba-ba-mih, ba-mih-mih, or ba-mih-ba). Using MRI-constrained source estimates of combined MEG/EEG data collected during an auditory artificial grammar task, we identified localised cortical activity associated with syllable reduplication pattern contrasts in novel trisyllabic nonwords. Neural decoding analyses identified a set of predominantly right hemisphere temporal lobe regions whose activity reliably discriminated reduplication patterns evoked by untrained, novel stimuli. Effective connectivity analyses suggested that sensitivity to abstracted reduplication patterns was propagated between these temporal regions. These results suggest that localised temporal lobe activity patterns function as abstract representations that support linguistic generativity.

Joint DOA tracking and spectral signature estimation of wideband sources using a multi-spectral TBD-CPHD filter

In this paper, a novel method for direction of arrival (DOA) tracking of multiple unknown time-varying number of wideband emitters is presented using a random finite set (RFS) framework. This method combines multiple narrowband covariance-based measurements to form a wideband cardinalized probability hypothesis density (CPHD) filter using the parallel combination (PC) Lemma generally used in multisensor fusion. By doing this, each narrowband frequency bin acts as a separate virtual spectral sensor. The covariance-based measurements, represented by complex Wishart random matrices, also allow the method to have a track-before-detect (TBD) property, which has shown to be advantageous in very low signal-to-noise ratios (SNR). Furthermore, the proposed method can simultaneously monitor the spectrum of the environment and extract the spectral characteristics of the active sources, i.e. can work as some sort of cognitive radio (CR). The suggested algorithm is implemented using an auxiliary particle filter. The simulation results and the comparison with the known state-of-the-art approaches confirm the superiority of the proposed method in negative SNR scenarios and low number of snapshots.