Low-frequency Data Research Articles

Deep heat source detection using the magnetotelluric method and geothermal assessment of the Farafra Oasis, Western Desert, Egypt

Nonrenewable hydrocarbon energy sources provide the bulk of Egypt's energy. However, the nation needs to diversify its energy portfolio and use conventional and renewable energy in tandem to ensure its long-term economic growth; therefore, harvesting untapped geothermal resources might help Egypt meet its energy needs in a sustainable and effective manner. The Farafra Oasis is located in the midst of Egypt's Western Desert and has been selected as the focal point for sustainable long-term development in the Western Desert.Using a magnetotelluric (MT) survey, a deep exploration of the Farafra region has been conducted to explore the underlying geological state and, as a result, explain the origin of several thermal wells around the oasis. None of the prior studies reached the necessary depths to offer information on the surface of deep igneous rocks, which may be the source of heat in the study area. Two field excursions were conducted to measure 19 stations along a line crossing the Farafra Oasis and extending more than 130 km. The MT instrument measured periods greater than 1000 s to gather low-frequency data with great efficiency.On the basis of 2D inversion of MT data, a mathematical model of the Farafra region's geothermal system was built to visualize and characterize the heat sources underneath the Farafra Oasis. It was shown that the existence of a high-temperature pluton at considerable depth is the primary cause of the temperature rise in the aquifer under the Farafra Oasis, as the water mass builds up on top of plutonic rocks and flows upwards via fractures and faults.

(Micro)Biological Sediment Formation in a Non-Chlorinated Drinking Water Distribution System

Sediment formation in drinking water distribution systems can lead to brown water at customer taps. Previous studies have shown that sediment formation is closely linked with (micro)biological processes in the distribution system, however the mechanism is not fully understood. Most available studies on discoloration or sediment formation mechanism are based on modeling, pilot-scale experiments, or low frequency data collected during pipe flushing. In this study, long-term sediment development in a large-scale drinking water distribution system was studied at one location over 11 years and at several locations along a known water trajectory during one year. Particulate material was collected at several locations using built-in and mobile filters that were connected to transport and distribution pipes in a semi-continuous manner. The volume of the collected material varied seasonally and the highest volumes were collected in the summer season. The material followed similar variations as temperature, invertebrates biomass and concentration of Aeromonas. The results showed that particulate matter of the sediment at downstream distribution locations was not released by the treatment works but instead forms along the distribution network, with increasing particle/floc size, biomass and Fe and Mn content. The large crustacean, Asellus, contributed to material production through feces excretion and formation of detritus by degradation of exoskeletons of dead animals. Detailed chemical characterization of the collected material showed the presence of proteins, calcium carbonate and iron precipitates. A similar sediment composition in a reference distribution system where customer complaints about brown water are experienced less frequently suggests that the sediment formation mechanism is the same but that water quality of the treatment effluent impacts the extent of material formation and growth of invertebrates. Overall, the results indicate that sediment formation in the distribution system is the result of complex combinations of (micro)biological and bio-chemical processes, including aggregation of particles with organic and inorganic matter, microbial growth on particles and biofilm, biomineralization, and growth of invertebrates. The determining factors to limit sediment formation, however, could not be identified. Further research is required to focus on the impact of treatment on shaping the distribution system ecosystem.

Sea Ice Motion Vector Retrievals from AMSR2 89-GHz Data: Validation Algorithm with Simultaneous Multichannel Observations

Abstract In retrievals of sea ice motion vectors (SIMVs) based on passive microwave observations, the use of the high-resolution 89-GHz channel of the Advanced Microwave Scanning Radiometer 2 (AMSR2) has the advantage of enhancing the theoretical precision of correlation-based motion tracking. However, its higher sensitivity to atmospheric moisture than lower-frequency channels links maximum cross-correlation peaks to outlier vectors and obscures signals of valid vectors. This study develops an algorithm to select valid vectors from candidates detected by multiple cross-correlation peaks based on validations with large-scale sea ice displacements extracted from 19- and 37-GHz data after questionable vectors are prefiltered by comparing them with reanalysis surface wind and neighboring vectors. The algorithm selects a vector corresponding to large-scale motion as the optimal vector. The retrieved results from 2013 to 2020 show that by replacing outlier vectors with valid ones detected by second or third cross-correlation peaks, validation with simultaneous observations enables retrieval of more than 60% of the Arctic motion field from 89-GHz data in winter but only 10% in summer; therefore, lower-frequency data are employed for retrievals. The uncertainty assessment using in situ data from acoustic measurements from ocean moorings shows that the algorithm provides daily SIMVs with root-mean-square errors of only 1–2 cm s–1 in idealized winter conditions with the absence of diurnal brightness temperature (Tb) changes that make tracking of the similarity of Tb fields difficult. The analysis also illustrates the applicability limit of the algorithm for summer retrievals. Significance Statement An algorithm was developed to validate sea ice motion vectors retrieved from AMSR2 89-GHz data by those from lower-frequency data. The validation via simultaneous observations enabled that valid vectors are sorted from invalid ones resulting from weather effects.

Low-Frequency Prediction Based on Multiscale and Cross-Scale Deep Networks in Full-Waveform Inversion

The well-known cycle-skipping problem in full-waveform inversion (FWI) can make the iterative solution fall into local minima and produce an undesired inverted result when reliable low-frequency components in seismic data and a good initial model are not available. The recovery of low-frequency data can effectively solve the cycle-skipping problem. However, hardware limitations have made it difficult to obtain reliable low-frequency components in seismic data. Thus, we adopt a multi-scale and cross-scale convolutional neural network (MCCNN) to build the nonlinear mapping between high-frequency and low-frequency data from synthetic training data sets. The major benefit of MCCNN is that it can fully use the multi-scale and cross-scale information in the high-frequency data to predict the low-frequency data. Several numerical experiments show the effectiveness and benefits of the low-frequency recovery of MCCNN. On the one hand, introducing the in-stage multi-scale and across-stage cross-scale information can accelerate the convergence rate in the training process and improve the low-frequency prediction accuracy. On the other hand, MCCNN has good generalization abilities in predicting the low-frequency data from the Marmousi and Overthrust models, different model sizes, and wavelet types and frequencies. The acoustic FWI results show that the predicted low-frequency data can effectively prevent the inversion from falling into a local minimum and help FWI obtain an accurate velocity model even if we start from a poor initial model.

Joint FWI of Active Source Data and Passive Virtual Source Data Reconstructed Using an Improved Multidimensional Deconvolution

Traditional full waveform inversion (FWI) highly depends on sufficient low-frequency data or a good initial model. Passive seismic data contain rich low-frequency components, and passive seismic FWI using virtual source data by seismic interferometry (SI) is a promising method. However, the distribution of passive sources in the subsurface is always inhomogeneous, which will lead to artifacts in the reconstruction results by SI using cross-correlation (CC). SI by multidimensional deconvolution (MDD) can counteract the source inhomogeneity but requires the separation of the reference wavefields, which is difficult to achieve for noise source data. To mitigate this problem, we propose an improved SI method by linear Radon transform based multidimensional deconvolution (LRTMDD). The interferometric point-spread function can be extracted more accurately and efficiently in the linear Radon domain, thus improving the reconstruction results. The passive virtual source full waveform inversion (PVSFWI) based on LRTMDD is further proposed, which can effectively use the low-frequency information in the virtual source data to invert the macroscopic velocity structures even in the case of inhomogeneous source distributions, and without the need to estimate the virtual source wavelets. A joint multi-source FWI strategy is proposed to solve the problem of missing low-frequency data suffered by active source FWI. Numerical experiments on the Marmousi model and the SEG/EAGE overthrust model show that the proposed methods can fully combine the respective advantages of multisource seismic data to stably achieve high-accuracy velocity models in the case of inhomogeneous passive source distributions and the lack of low-frequency data in active seismic data.

Image Fusion Based on NSCT and Sparse Representation for Remote Sensing Data

The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: first, the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing filter. Then comes the fusion process, which uses an approach that combines NSCT and SR to fuse low frequency components. Similarly, the Max-absolute fusion rule is used to merge high frequency components. Finally, the final image is obtained through the disintegration of fused low and high frequency data. In terms of correlation coefficient, Entropy, spatial frequency, and fusion mutual information, our method outperforms other methods in terms of image quality enhancement and visual evaluation.

Attention-Based Multitask Probabilistic Network for Nonintrusive Appliance Load Monitoring

Monitoring individual appliances’ operating state and energy consumption in a building enables significant energy-saving opportunities. These days, smart meters perform this task non-intrusively using sophisticated signal processing, machine learning, or deep learning approaches. To this end, this paper proposes a novel multi-task deep learning model that uses readily available low-frequency energy data from the smart meter for simultaneous appliance state detection and energy disaggregation. The model creatively adopts and customizes the famous transformer model from the field of language modeling for the above task. Further, the model output is produced as a mixture of probability density functions to handle uncertainties. The model performance is evaluated using the publicly available REFIT and UKDALE datasets. The test results indicate the proposed model’s superiority, generalizability, and transferability compared to other state-of-the-art models.

A framework for lossy compression and recovery of battery cell voltage

High-quality historical data of battery is important for state estimation and management. However, limited by bandwidth of 4G network and storage capacity, the cloud only receives low-frequency (LF) data and a few high-frequency (HF) signals. This paper proposes a model-based method to transfer adequate battery data between vehicle and cloud at a low cost. Firstly, a training dataset is built from real-world vehicle data. Then, a multi-task learning (MTL) model under semi-supervised learning (SSL) framework is proposed to learn HF voltage representation of each battery cell. SSL framework uses a large number of unlabeled low-frequency voltages to improve the voltage frequency recovery accuracy. MTL framework enables the model to focus on battery aging and solve the problem of target domain drifting over time. Finally, both real-world vehicle and experimental data are used to compare the results of different methods on the voltage recovering task. The results show that the proposed method can reduce the average voltage recovery error to less than 7mV at various driving conditions.

Ghost in the Shell: Evidence for Past Active Galactic Nucleus Activities in NGC 5195 from a Newly Discovered Large-scale Ionized Structure

The early-type galaxy NGC 5195 (alternatively known as M51b) possesses extended gas features detected in the multiwavelength, postulated to be associated with previous activities of the central supermassive black hole. Using integral field spectroscopic observations from the Canada–France–Hawaii Telescope/SITELLE, we report on the discovery of a new large-scale ionized gas structure traced by [O iii], [N ii], and Hα line emission, extending to ∼10 kpc from the nucleus of NGC 5195. Its bipolar morphology, emission-line ratio diagnostics, and comparison with the X-ray image from Chandra and low-frequency radio data from LOFAR all indicate that it is likely an outflow inflated by a past episode of elevated active galactic nucleus (AGN) activity. Assuming the ionized gas is outflowing from the central region of NGC 5195, the estimated mass and energy outflow rates are –27.9 M ☉ yr−1 and –7.9 × 1040 erg s−1, respectively, which cannot be provided by current star formation and the low-luminosity nucleus. Alternatively, considering the history of gravitational interaction between the M51 pair and the presence of the H i tidal tail, the northern large-scale ionized gas could very likely be associated with tidally stripped material illuminated by a luminous AGN in the past.

Ionospheric Phase Delay Correction for Time Series Multiple-Aperture InSAR Constrained by Polynomial Deformation Model

As a supplement to time-series interferometric synthetic aperture radar (TS-InSAR), time-series multiple-aperture InSAR (TS-MAI) can measure the spatiotemporal changes in SAR along-track surface deformation. TS-MAI is often applied with low-frequency SAR data (e.g., L-band data) due to its ability to retain high interferometric coherence. However, the low-frequency SAR signal is vulnerable to ionospheric delays, which can significantly degrade the measurement accuracy of TS-MAI. This letter presents an approach to correct the ionospheric errors in TS-MAI. A polynomial cubic model is employed to constrain the ground deformation, which is then incorporated into the observation model for effectively separating the deformation signal and the ionospheric delays. The proposed method is tested using the L-band ALOS-1 PALSAR-1 datasets covering the Tocopilla area in Chile between November 2007 and March 2011. The correction performance and accuracy of the proposed method are demonstrated by comparing the range split-spectrum interferometry (RSSI)-based method and the local GPS data, respectively. The root mean square error (RMSE) improvement rates between TS-MAI and GPS are 72.17% for the SRGD site and 84.51% for the VLZL site, and their correlation coefficients increase from 0.23 and 0.50 to 0.52 and 0.61 after the correction.

A clustering-based method for estimating pennation angle from B-mode ultrasound images.

B-mode ultrasound (US) is often used to noninvasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human-robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle. The clustering-based architecture assumes that muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: UltraTrack and ImageJ. The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20Hz), that is, similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream.

Elastic Reflection Waveform Inversion With a Nonlinear Born Scattering Operator for Multiparameter Reconstruction

Elastic full waveform inversion (FWI) is one of the most popular tools in recovering the subsurface P- and S-velocity models with high resolution. However, it requires either a good starting model or low-frequency data to avoid the cycle-skipping issue. Elastic reflection waveform inversion (ERWI) uses the migration/demigration process to compute the low-wavenumber gradient. One drawback of conventional ERWI is that it mainly relies on the first-order Born scattering and only utilizes primary reflection wave paths, providing limited wavenumber illumination in the update. In this letter, a nonlinear Born scattering operator is incorporated with ERWI for multiparameter reconstruction to mitigate the drawback of the conventional linear Born approximation. Finally, we use numerical examples to show that the nonlinear Born scattering operator allows us to take advantage of the multiples and enlarge the wavenumber illumination in ERWI results.

Consumption per effective labour in Brazil: testing for the optimising behaviour

We contributed to the literature on household consumption in specific ways: firstly, we adopted a long-run cointegration analysis instead of a first-difference one, which was conventional in previous works; second, by building on monthly data, we enlarged the number of observations of our sample, thus overcoming a common problem of low-frequency data in studies based on quarterly or annual time series. We thus confirmed the hypothesis of an optimising behaviour in consumption for the Brazilian economy, although jointly with a relevant role of income. We also inferred the effect of risk aversion in lowering the optimality of consumption after the Subprime crisis period, based on a Markov-switching approach.

Spatial Resolution Enhancement of HY-2B Scanning Microwave Radiometer Low-Frequency Data

Spatial Resolution Enhancement of HY-2B Scanning Microwave Radiometer Low-Frequency Data

Chinese Economic Growth Projections Based on Mixed Data of Carbon Emissions under the COVID-19 Pandemic

Current research on carbon emissions and economic development has tended to apply more homogeneous low-frequency data to construct VAR models with impulse responses, ignoring some of the sample information in high-frequency data. This study constructs a MIDAS model to forecast GDP growth rate based on monthly carbon emission data and quarterly GDP data in the context of the COVID-19 pandemic. The results show that: (1) The MIDAS model has smaller RMSE than the VAR model in short-term forecasting, and provides more stable real-time forecasts and short-term forecasts of quarterly GDP growth rates, which can provide more accurate reference intervals; (2) China’s future macroeconomic growth rate has recently declined due to the impact of the sudden epidemic, but the trend is generally optimistic. By improving urban planning and other methods, the authorities can achieve the two-carbon goal of carbon capping and carbon neutrality at an early date. In the context of the impact of COVID-19 on China’s economic development, we need to strike a balance between ensuring stable economic growth and ecological protection, and build environmentally friendly cities, so as to achieve sustainable economic and ecological development and enhance human well-being.

Disentangling scatter in long-term concentration–discharge relationships: the role of event types

Abstract. Relationships between nitrate concentrations and discharge rates (C–Q) at the catchment outlet can provide insights into sources, mobilization and biogeochemical transformations of nitrate within the catchment. Nitrate C–Q relationships often exhibit considerable scatter that might be related to variable hydrologic conditions during runoff events at sampling time, corresponding to variable sources and flow paths despite similar discharge (Q) rates. Although previous studies investigated the origins of this scatter in individual or in a few catchments, the role of different runoff event types across a large set of catchments is not yet fully understood. This study combines a hydrological runoff event classification framework with low-frequency nitrate samples in 184 catchments to explore the role of different runoff events in shaping long-term C–Q relationships and their variability across contrasting catchments. In most of the catchments, snow-impacted events produce positive deviations of concentrations, indicating an increased nitrate mobilization compared to the long-term pattern. In contrast, negative deviations occur mostly for rainfall-induced events with dry antecedent conditions, indicating the occurrence of lower nitrate concentrations (C) in river flows than their long-term pattern values during this type of event. Pronounced differences in event runoff coefficients among different event types indicate their contrasting levels of hydrologic connectivity that in turn might play a key role in controlling nitrate transport due to the activation of faster flow paths between sources and streams. Using long-term, low-frequency nitrate data, we demonstrate that runoff event types shape observed scatter in long-term C–Q relationships according to their level of hydrologic connectivity. In addition, we hypothesize that the level of biogeochemical attenuation of catchments can partially explain the spatial variability of the scatter during different event types.

An adaptive tracking measurement method for inner surface of laser fusion targets

We propose an adaptive tracking measurement method for accurate, rapid, and non-invasive measurement of the topography of the inner surface of fusion targets. During the first phase of the measurement, we measure the low-frequency components of the inner surface of the target using a laser differential confocal axial point-by-point scanning measurement method with a large dynamic range. During to the second phase of the measurement, based on the low-frequency components data, the objective lens is driven by a scanner for adaptive large-interval movement, simultaneously, we measure the high-frequency components in the large-interval using a laser differential confocal linear sensing measurement method. Combined with the mutually perpendicular slewing shafts system, the three-dimensional topography of the inner surface of the target can be measured.

Temporal Disaggregation of Gross Domestic Product Data with Night-Time Lights Data for South Africa

The absence of high-frequency time series data is a real constraint for many researchers worldwide. Generating high-frequency economic data, especially at the sub-national level, generates significant advantages and opportunities. Research otherwise impossible now becomes possible and indeed desirable. Therefore, this study aims to investigate the usefulness of several temporal disaggregation methods and the reliability of the derived results. Temporal disaggregation is one such method that can be used to generate high-frequency time series data from low-frequency time series data. The methods employed will specifically refer to methods that use related series as the high-frequency time series data related to the South African economy. To this effect, monthly night-time lights sensed by satellite were assessed. The results derived through the study seem to generate robust estimates suggesting that it is indeed possible to use night-time lights as a related indicator and the various temporal disaggregation methods contained within the Ecotrim computer software program and ‘tempdisagg’ R package to disaggregate annual national and provincial gross domestic product (GDP). The derived quarterly and monthly national and provincial GDP time series/data will subsequently assist in addressing the constraints researchers face, especially at a sub-national level. From a policy perspective, policymakers should note that remote sensing technologies offer unique opportunities to conduct sub-national economic policy and research.

Extended radio halo of the supernova remnant CTB87 (G74.9+1.2)

Context. Breaks in the radio spectra of supernova remnants (SNRs) reflect the maximum energy of either shock-accelerated electrons or – in the case of pulsar wind nebulae – of electrons injected by the central pulsar. Otherwise, the break may result from energy losses due to synchrotron ageing or it is caused by energy-dependent diffusion. A spectral steepening of the plerionic SNR CTB87 at around 11 GHz was observed in the 1980s, but a recent analysis of CTB87’s energetic properties based on new radio data raised doubt on it. CTB87 consists of a central compact component surrounded by a diffuse, centrally peaked, almost circular halo. Missing faint halo emission due to insufficient sensitivity of early high-frequency observations may be the reason for the reported spectral break. Aims. We intend to clarify the high-frequency spectrum of CTB87 by new sensitive observations. Methods. We used the broad-band λ2 cm receiver at the Effelsberg 100-m telescope for sensitive continuum observations of CTB87 and its halo in two frequency bands. Results. The new λ2 cm maps of CTB87 show halo emission with a diameter of about 17′ or 30 pc for a distance of 6.1 kpc in agreement with lower-frequency data. The measured flux densities are significantly higher than those reported earlier. Conclusions. The new λ2 cm data establish the high-frequency continuation of CTB87’s low-frequency spectrum. Any significant high-frequency spectral bend or break is constrained to frequencies well above about 18 GHz. The extended halo of CTB87 has a faint counterpart in γ rays (VER J2016+37) and thus indicates a common origin of the emitting electrons.

Real-time macroeconomic monitoring using mixed frequency data: Evidence from China

Timely and effective monitoring of macroeconomic dynamics is crucial for the management and stabilization of the macroeconomy. However, existing studies tend to rely on low-frequency macro-level data to monitor economic fluctuations and fail to capture changes that take place in real time. This study therefore develops a novel real-time monitoring system of China's macroeconomic prosperity by incorporating monthly and quarterly macroeconomic data with high-frequency daily Internet search data from January 1, 2012 to March 31, 2019. We find that our daily prosperity indexes can provide real-time monitoring signals with daily updates, significantly improving the accuracy of key macroeconomic indicators for nowcasting. Moreover, our monitoring system incorporates leading, coincident, and lagging indexes, and enables simultaneous monitoring of the synergistic and asymmetric characteristics of cyclical fluctuations, depicting a comprehensive picture of the business cycle. Our findings have practical implications for enhancing the quality and timeliness of macroeconomic monitoring and early intervention. • We develop a novel real-time monitoring system of macroeconomic prosperity. • Our model incorporates macroeconomic data with daily Internet search data. • Leading, coincident, and lagging indexes are integrated into one monitoring system. • The constructed daily indexes improve the accuracy of macroeconomic monitoring. • Our findings provide practical references for timely macroeconomic management.