Signal Anomalies Research Articles

Target magnetic moment orientation estimation method based on full magnetic gradient orthonormal basis function

Magnetic anomaly detection (MAD) is an effective method to detect the existence and localization of magnetic targets. Magnetic signal processing technology can extract target signals from complex background noise. However, traditional magnetic signal processing methods cannot greatly improve the signal-to-noise ratio (SNR) while also restoring information concerning the target. This is because the main existing method to calculate a target’s magnetic moment requires a pure target signal. Our research regarding the full magnetic gradient orthonormal basis function (FMG-OBF) addresses the problem of low SNRs for the target magnetic anomaly (TMA) signal. However, this method can only detect the presence of the target and cannot obtain the magnetic moment characteristics of the target. Benefiting from the FMG tensor, which contains large amounts of spatial magnetic field information, this paper is devoted to characterizing TMA in different orientations from the signal energy point of view. We analyze the influences of the target magnetic moment’s variation on the energy components of the TMA signal in various orientations, and further propose a target magnetic moment orientation estimation method. Compared with the traditional signal processing method, the proposed method can estimate the magnetic moment orientation of the target while greatly improving the SNR. Therefore, this method has significant application potential for the classification and identification of weak TMA signals in MAD.

Extension and application of an observation‐based local climate index aimed to anticipate the impact of El Niño–Southern Oscillation events on Colombia

AbstractThe Tumaco multivariate index (TMI) is a multidecadal monthly index constructed with unique time series of sea surface temperature, surface air temperature and rain measured at Tumaco bay, in the southern Pacific coast of Colombia, and available since 1961. In this work, this index is re‐evaluated after the addition of in situ sea level data, and its properties for different standardization periods are compared against oceanic El Niño–Southern Oscillation (ENSO) and other derived indices. In particular, we propose a modified TMI, hereinafter referred as TMI4, whose potential to identify the expected sign and the amount of future variations of rain induced by ENSO events in Colombia is analysed for selected extreme episodes. Results indicate that after the inclusion of sea level data, TMI4 can anticipate the development of El Niño events before the ENSO 3 and some other sea surface temperature‐based regional indices, although its predictability depends on the ENSO type (canonical or Modoki). The explanation is that sea level includes new information into TMI4 on the onset of El Niño events. In particular, the signal of intraseasonal sea level anomalies carried by downwelling Kelvin waves is detected at Tumaco tide‐gauge. Moreover, the analysis of the differences, both in magnitude and spatial distribution, of rainfall anomalies induced by positive (El Niño) and negative (La Niña) ENSO events characterized by TMI4 are regionally presented. As a result, we find that TMI4 is especially suited for extensive northern and western areas of mainland Colombia. For completeness, in the appendix we briefly introduce the semi‐automated implementation of TMI4, including a visual interface, which is currently being tested by personnel within the operational oceanography area at Centro de Investigaciones Oceanográficas e Hidrográficas del Pacífico (Dimar‐CCCP).

Detection of Geomagnetic Signals as Precursors to Some Earthquakes in China

Extracting weak seismic-magnetic signals from strong electromagnetic backgrounds has always been an important part of seismic-electromagnetic research. Based on the data of D/H/Z components measured through a three-axis ring-core-type fluxgate magnetometer in GLM (Geermu), JYG (Jiayuguan), and KSH (Kashi) observatories, we analyzed geomagnetic signals as precursors to some earthquakes in China using the polarization method. The most effective main frequency of seismic-electromagnetic emission was determined according to the skin effect. The results showed that only 5 out of 37 earthquakes were found to have probable signals of seismomagnetic anomalies before them. Further research revealed that the significant enhancements of polarization ratios of YZH and YZG (YZH, the spectral power ratio of Z to H and YZG, the spectral power ratio of Z to G) occurred five days prior to the Jinta earthquake (Ms 5.4) and continued for about two months. The polarization ratio of YZH reached an abnormally high value about one month before some earthquakes (Ms 7.4, Ms 6.7, Ms 6.7) near the KSH station and lasted for about one week to half a month, then returned to the normal value. Similarly, some enhancements of polarization ratios of YZH and YZG occurred two months before the Haixi earthquake (Ms 5.0) and lasted for about one month. Analysis results showed that the enhancements prior to earthquake events may be closely related to the Jinta earthquake and a series of earthquakes near the KSH station; however, the high value of YZH and YZG in GLM station had nothing to do with the Haixi earthquake.

Unsupervised Spectrum Anomaly Detection Method for Unauthorized Bands

With the rapid development of wireless communication, spectrum plays increasingly important role in both military and civilian fields. Spectrum anomaly detection aims at detecting emerging anomaly signals and spectrum usage behavior in the environment, which is indispensable to secure safety and improve spectrum efficiency. However, spectrum anomaly detection faces many difficulties, especially for unauthorized frequency bands. In unauthorized bands, the composition of spectrum is complex and the anomaly usage patterns are unknown in prior. In this paper, a Variational Autoencoder- (VAE-) based method is proposed for spectrum anomaly detection in unauthorized bands. First of all, we theoretically prove that the anomalies in unauthorized bands will introduce Background Noise Enhancement (BNE) effect and Anomaly Signal Disappearance (ASD) effects after VAE reconstruction. Then, we introduce a novel anomaly metric termed as percentile (PER) score, which focuses on capturing the distribution variation of reconstruction error caused by ASD and BNE. In order to verify the effectiveness of our method, we developed an ISM Anomaly Detection (IAD) dataset. The proposed PER score achieves superior performance against different type of anomalies. For QPSK type anomaly, our method increases the recall rate from 80% to 93% while keeping a false alarm rate of 5%. The proposed method is beneficial to broadband spectrum sensing and massive spectrum data processing. The code will be released at git@github.com :QXSLAB/vae_ism_ano.git.

Investor Attention and Asset Pricing Anomalies

Abstract We investigate the relationship between investor attention and financial market anomalies. We find that anomaly returns tend to be higher following high-attention days. The result is robust after controlling for the effect of news and in a natural experiment setting in which a stock market regulation and rounding errors generate exogenous variations in attention. An analysis of order imbalances suggests that large traders trade on anomaly signals more aggressively upon observing higher attention. We discuss the extent to which the findings are driven by inattention-driven underreaction, bias amplification, or coordinated arbitrage mechanisms, thereby providing insight into the understanding of anomalies.

Lagrangian eddy tracking reveals the Eratosthenes anticyclonic attractor in the eastern Levantine Basin

Abstract. Statistics of anticyclonic eddy activity and eddy trajectories in the Levantine Basin over the 2000–2018 period are analyzed using the DYNED-Atlas database, which links automated mesoscale eddy detection by the Angular Momentum Eddy Detection and Tracking Algorithm (AMEDA) algorithm to in situ oceanographic observations. This easternmost region of the Mediterranean Sea, delimited by the Levantine coast and Cyprus, has a complex eddying activity, which has not yet been fully characterized. In this paper, we use Lagrangian tracking to investigate the eddy fluxes and interactions between different subregions in this area. The anticyclonic structure above the Eratosthenes Seamount is identified as hosting an anticyclone attractor, constituted by a succession of long-lived anticyclones. It has a larger radius and is more persistent (staying in the same position for up to 4 years with successive merging events) than other eddies in this region. Quantification of anticyclone flux shows that anticyclones that drift towards the Eratosthenes Seamount are mainly formed along the Israeli coast or in a neighboring area west of the seamount. The southeastern Levantine area is isolated, with no anticyclone transfers to or from the western part of the basin, defining the effective attraction basin for the Eratosthenes anticyclone attractor. Co-localized in situ profiles inside eddies provide quantitative information on their subsurface physical anomaly signature, whose intensity can vary greatly with respect to the dynamical surface signature intensity. Despite interannual variability, the so-called Eratosthenes anticyclone attractor stores a larger amount of heat and salt than neighboring anticyclones, in a deeper subsurface anomaly that usually extends down to 500 m. This suggests that this attractor could concentrate heat and salt from this subbasin, which will impact the properties of intermediate water masses created there.

Cognitive Radio Jamming Attack Detection Using an Autoencoder for CRIoT Network

IoT network-connected devices are increasing day by day. It is impossible to allocate a spectrum for all IoT devices. This spectrum scarcity can be solved by cognitive radio-based dynamic spectrum sharing, which is referred to as Cognitive Radio Internet of Things (CRIoT). The jamming physical layer attack in CRIoT resulting in a denial of cognitive radio services and make spectrum underutilization. Continuous jamming can be detected quickly based on time delay on spectrum access, but discrete random jamming detection is challenging. This article proposes an autoencoder-based jamming attack detection in cognitive radio. The jamming detection problem is modeled as anomaly detection. The autoencoder is used to detect the anomaly signal component and the jammer. The simulation of random jamming attack detecting with time instant of attack is carried out to mitigate it. The proposed mechanism able to detect the jammer with 89% of accuracy.

Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System.

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Dynamical Downscaling in Seasonal Climate Forecasts: Comparison between RegCM- and WRF-Based Approaches

The purpose of the present study is to assess the large-scale signal modulation produced by two dynamically downscaled Seasonal Forecasting Systems (SFSs) and investigate if additional predictive skill can be achieved, compared to the driving global-scale Climate Forecast System (CFS). The two downscaled SFSs are evaluated and compared in terms of physical values and anomaly interannual variability. Downscaled SFSs consist of two two-step dynamical downscaled ensembles of NCEP-CFSv2 re-forecasts. In the first step, the CFS field is downscaled from 100 km to 60 km over Southern Europe (D01). The second downscaling, driven by the corresponding D01, is performed at 12 km over Central Italy (D02). Downscaling is performed using two different Regional Climate Models (RCMs): RegCM v.4 and WRF 3.9.1.1. SFS skills are assessed over a period of 21 winter seasons (1982–2002), by means of deterministic and probabilistic approach and with a metric specifically designed to isolate downscaling signal over different percentiles of distribution. Considering the temperature fields and both deterministic and probabilistic metrics, regional-scale SFSs consistently improve the original CFS Seasonal Anomaly Signal (SAS). For the precipitation, the added value of downscaled SFSs is mainly limited to the topography driven refinement of precipitation field, whereas the SAS is mainly “inherited” by the driving CFS. The regional-scale SFSs do not seem to benefit from the second downscaling (D01 to D02) in terms of SAS improvement. Finally, WRF and RegCM show substantial differences in both SAS and climatologically averaged fields, highlighting a different impact of the common SST driving field.

Study on Seismic Precursory Anomaly Signals Based on Critical Slowing Phenomena

This paper applied the principle of critical slowing down to the processing of radon concentartions observation data, calculated the autocorrelation coefficient and variance which can characterize critical slowing down by taking Wenchuan Ms8.0 earthquake on May 12, 2008, Lushan Ms7.0 earthquake on April 20, 2013, Minxian-Zhangxian Ms6.6 earthquake on July 22, 2013, Jiuzhaigou Ms7.0 earthquake on August 8, 2017 as examples. The result indicated that the water radon concentration at several stations has obvious critical slowing phenomenon before server earthquakes. The formation mechanism of radon concentration anomalies in seismic underground fluids was discussed in the context of the focal mechanism solution of several earthquakes. It is considered that the phenomenon of critical slowing down before server earthquakes is related to the earthquakes occurrenced combined with the hydrodynamic mechanism of radon anomaly and the structure of the abnormal points.

3D crustal-scale structure of the West Iberia margin: a novel approach to integrated structural characterization of passive margins

Hyperextended margins are very heterogeneous along the entire length of the margin, so the definition of tectonic domains made exclusively from 2D seismic sections presents serious limitations. In this work we present an approach of the 3D crustal-scale structure of the West Iberia margin (WIM) by modelling eight lithospheric sections, using seismic, wells and gravity data. The continuous nature of gravity data allowed us to propose a new map of tectonic domains within the WIM. Maps of total horizontal (THD) and vertical gradients (dZ) of Bouguer anomaly have been calculated and compared with other criteria such as the crustal structure and thinning factor. This comparative analysis has been carried out on a section proposed as a model for the Western Iberian Margin (Tugend et al. in Tectonics, 2014; Cadenas et al. in Tectonics 37:758–785, 2018), and on four 2 + 1/2D gravimetric models transversal to the margin. The results point out a significant variation in the absolute values of Bouguer anomaly, thinning factor and crustal structure along the margin and, therefore, of the position of the different domain boundaries. Clear patterns that correlating the Bouguer anomaly signal and its derivatives to the tectonic domain are evidenced. Most significantly, the necking-zone and its transition to the hyperextended domain are characterized by high values of the THD of the Bouguer anomaly. The observed patterns in Bouguer anomaly and its derivatives provide a solid constraint for mapping the boundaries between different tectonic domains along the margin, even in those areas where limited deep seismic information could lead to uncertain interpretations. The results of this work can also inform on the general kinematics of the WIM.

Magnetic anomaly detection based on fast convergence wavelet artificial neural network in the aeromagnetic field

The orthogonal basis functions (OBFs) detector is a detection method widely used in the aerial magnetic measurement. However, OBFs detector works ineffectively under non-Gaussian noise and colored noise. This paper proposes an OBFs detector based on fast convergence wavelet artificial neural network (FC-W-ANN), which can detect abnormal magnetic signals under low SNR. First, the magnetic anomaly signal is modelled. Then, the learning rate is corrected by the iterative error convergence rate under the stability of the network. Finally, the improved network is combined with the OBFs detector to detect magnetic abnormal signals. From the simulation and experimental results, the reconstructed signal of the new method has a higher SNR (SNR = 10.01) compared with OBFs (SNR = -0.21) and OBFs based on the wavelet artificial neural network (W-ANN; SNR = 9.59). Furthermore, the statistical methods are used to analyze FC-W-ANN and W-ANN, showing that FC-W-ANN has higher training accuracy and better stability.

Frequency Characteristics Analysis for Magnetic Anomaly Detection

This letter presents a frequency characteristics analysis for a magnetic anomaly detection (MAD) study. A magnetic dipole model is developed to study the energy distribution of the obtained MAD signal in the frequency domain, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{h}$ </tex-math></inline-formula> is proposed to define the signal high-frequency boundary. The relations between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{h}$ </tex-math></inline-formula> and other parameters, such as the velocity of the target (or platform), closest path approach (CPA), magnetic moment strength, magnetic moment orientation (MMO), and sensor orientation, are analyzed. We find that the frequency response is independent of the moment strength. The combined effects of the MMO and sensor orientation on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{h}$ </tex-math></inline-formula> are defined by the functio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n g(\alpha $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\phi$ </tex-math></inline-formula> ), which is studied by the Monte Carlo method, yielding a maximum value of max( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$g) \approx ~0.85$ </tex-math></inline-formula> . Thus, the function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{h} = 0.85v$ </tex-math></inline-formula> /CPA is advanced for frequency boundary estimation. The proposed theoretical analysis is examined in an experiment to aid in extracting the magnetic anomaly signal effectively.

Precursory Signals (SST and Soil Moisture) of Summer Surface Temperature Anomalies over the Tibetan Plateau

Understanding the variability of surface air temperature (SAT) over the Tibetan Plateau (TP) and its precursory signals is of great benefit to climate change adaptation and socioeconomic development. This study explores the precursory signals of summer SATs over the TP in oceanic and land boundary conditions. The results show that the summer eastern TP SAT is significantly correlated with three precursors in April: the high-latitude North Atlantic sea surface temperature (SST), the northern Indian Ocean SST, and the Indian soil moisture (SM). The April SST anomalies (SSTAs) in the high-latitude North Atlantic can exert a cross-season impact on the summer SAT over the TP through two processes. The SSTAs in the high-latitude North Atlantic maintain from April to summer and modulate atmospheric circulation over the eastern TP through exciting a downstream wave train during summer, and finally modulate the summer SAT over the eastern TP. In addition to the above process, the April SSTAs in the high-latitude North Atlantic may remotely regulate simultaneous SM in the Indian subcontinent through stimulating a downstream wave train pattern. Through a persistent SM–precipitation interaction, the April Indian SM anomaly can affect the local precipitation and associated condensation heating anomalies during the ensuing summer, which forces an anomalous cyclone–anticyclone pattern around the TP and accordingly affects the summer SAT over the eastern TP. Additionally, the SSTAs in the northern Indian Ocean can persist from April to summer and adjust the intensity and location of the western North Pacific subtropical high through the Kelvin-wave-induced Ekman divergence during summer, eventually affecting the summer eastern TP SAT. The three precursory signals, which synergistically contribute to the variability of the summer eastern TP SAT, can be applied in predicting the summer SAT over the eastern TP.

Comparisons of the different definitions of the western Pacific pattern and associated winter climate anomalies in Eurasia and North America

AbstractThe western Pacific pattern (WP), characterized by a meridional dipole structure in geopotential height anomalies centred at approximately 50°N over the North Pacific, is one of the most prominent teleconnection patterns over the Northern Hemisphere (NH) in boreal winter. Several methods to identify the WP exist in the literature. Based on eight WP indices defined by different methods, this study analyses their dominant frequencies, spatial structures, climatic impacts, and relations to the Arctic Oscillation (AO) and El Niño‐Southern Oscillation (ENSO). The correlation coefficients of the eight WP indices show a considerable spread, but most of them are statistically significant. Most of the WPIs capture a pronounced power peak at approximately 5 years. The meridional dipole structure of the WP can be captured by all the WPIs, but it shows a large spread in the location of the centre. Several WPIs are significantly correlated with the winter AO, with marked signals of atmospheric anomalies over the Arctic region. The connections of the WP with the simultaneous winter ENSO depend largely on their definitions. The impacts of the WP on the surface air temperature over many parts of Eurasia and North America are also sensitive to its definition. Differences in surface air temperature anomalies are related to differences in the spatial structure of the WP as captured by the range of WPIs.

Anomalies, Management Expectations, and Stock Returns

We investigate the extent to which managers incorporate public information in their earnings expectations and its implications for capital market efficiency. We find that management earnings forecasts are biased upward (downward) for stocks that are overvalued (undervalued) based on anomaly signals. On average, these biases are more severe than those in consensus analyst forecasts. Anomaly returns are higher (lower) when managers issue forecasts that update the market in the direction that is consistent (inconsistent) with the anomaly information. This return difference is about two percent in the month after anomaly portfolio formation and persists for over 12 months. Consistent with managers' biases exacerbating anomaly-related mispricing, the return difference is more salient during high sentiment periods and when there are firm news events.

Capability of TEC correlation Analysis and Deceleration at Propagation Velocities of Medium-Scale Traveling Ionospheric Disturbances: Preseismic Anomalies before the Large Earthquakes

Data analysis method (CRA, hereafter) to correlate multiple TEC anomaly signals has detected pre-seismic anomalies before the 2011 Tohoku-Oki earthquake (Iwata & Umeno 2016), the 2016 Kumamoto earthquake (Iwata & Umeno 2017) and the 2016 Tainan earthquake (Goto et al. 2019). However, a critical argument said that those anomalies detected by CRA would not be pre-seismic anomalies published by Journal of Geophysical Research-Space Physics (126), 2021 (JGR-SP (126), hereafter). In this paper, we would point out its incorrect use of statistical anomalies in evaluating CRA as the following points: CRA is shown to increase the signal-to-noise ratio (SNR) to amplify pre-seismic TEC’s small anomaly signals with synchronizing and correlating multiple GNSS receivers’ data. We proved again that pre-seismic anomalies certainly exist before the 2011 Tohoku-Oki earthquake and the 2016 Kumamoto earthquake with additional data analysis. In particular, as a temporal anomaly, deceleration at propagation velocities of medium-scale traveling ionospheric disturbances (MSTID, hereafter) before the 2016 Kumamoto earthquake captured by CRA (Iwata & Umeno 2017) is elucidated as pre-seismic anomalies. Furthermore, we proposed a physical model to predict that 35 m/s change at MSTID propagation velocities estimated by TEC’s CRA requires 0.58 × 10-3 V/m electric field in the F Layer ionosphere. Contrary to the claim with the incorrect use of statistical anomalies in JGR-SP (126), TEC’s correlation anomalies detected by CRA (Iwata & Umeno 2016 and Iwata & Umeno 2017) clearly provided supporting evidence that physical pre-seismic anomalies really exist.

Dielectric switching, SHG response and Pd(ii) adsorption of a multifunctional phase-transition complex

One-dimensional coordination polymer [(C2H4OH)C4H9NS]CdCl3 has the performance of dielectric switching and SHG response. And the compound can adsorb precious Pd(ii) and the adsorption could be monitored by dielectric anomaly and SHG signal.

Underwater magnetic target signal denoising based on modified wavelet decomposition and reconstruction algorithm

Noise reduction is crucial for magnetic anomaly signal detection of underwater ferromagnetic target. Modified wavelet decomposition and reconstruction algorithm is proposed to suppress the colored noise and improve the signal to noise ratio. Hamming window is employed to make the preprocessed signal continuous. Evaluation index based on signal-to-noise ratio function is selected, wavelet decomposition and reconstruction algorithm iterates adaptively to select the optimal order of decomposition and reconstruction. The experiment result emphasizes that the signal-to-noise ratio of novel algorithm is 71.6dB. In this particle, we provide a new method to improve signal-to-noise ratio and enhance real-time signal processing for underwater target magnetic anomaly signal detection.

Weak magnetic anomaly signal detection based on the entropy of mixed differential signal

Magnetic anomaly detection is of great significance in geological exploration and underwater target detection. However, the magnetic anomaly signal is not easy to be detected because of its weakness relative to the magnetic environmental noise, and the detection probability of the magnetic anomaly signal is affected by signal frequency. We propose a mixed differential minimum entropy method which can detect the weak magnetic anomaly signal. The proposed method combines the coherence difference method with the synchronous reference subtraction method in preprocess, which can suppress common-mode noise in the signal and relax the limitation of the frequency of the magnetic anomaly signal on the detection ability. Using the concept of entropy to describe the preprocessed signal can eliminate the residual high-frequency noise and further improve the signal-to-noise ratio of the target signal. Results show that the proposed method may effectively improve the signal-to-noise ratio and increase the frequency range of the magnetic anomaly signal, which has better detection performance than that of traditional method.