Local Anomalies Research Articles

Surface Measure to Depth (SMeTD): a new low-budget system for 3D water temperature measurements for combining with UAV-based thermal infrared imagery

Characterising spatial patterns in water temperature is important for monitoring aquatic habitats and understanding physical and biogeochemical processes to support environmental management decisions. As freshwater bodies exhibit high spatial and temporal variability, high-resolution 3D temperature data are essential to understand local anomalies. The acquisition of simultaneously high spatial and temporal datasets in the field has so far been limited by costs and/or workload associated with commonly used monitoring systems.We present a new, low-cost, spatially and temporally flexible 3D water temperature monitoring system, Surface Measures to Depth (SMeTD). SMeTD can be used to provide information on the relation of water surface temperature to changes with depth, characterise water temperature in 3D and ground truth remotely sensed thermal infrared data. The systems performance was tested under laboratory conditions and under controlled conditions in the field. This revealed an accuracy comparable to established but more expensive monitoring systems. Field testing of SMeTD involved 1-min data collection of 3D water temperature for a full diurnal cycle in a lake. The 3D temperature patterns were supported by a thermal infrared image of the lakes surface. The field dataset demonstrated higher water temperatures and higher water temperature variation at the surface compared to deeper layers. SMeTD can be used to observe a broad range of hydrological processes in natural and artificial aquatic environments and help to understand processes involved with energy budgets, infiltration, limnology, or groundwater surface water exchange.

Symmetrically dispersion-engineered microcombs

Normal-dispersion microcombs have gained significant attention for their features, including high conversion efficiency, deterministic generation, and thermal management-free operation. However, most of the demonstrated microcombs in the normal-dispersion regime heavily rely on asymmetric local dispersion anomalies, which introduce odd-order dispersion components and originate asymmetric spectral characteristics. In this study, we present a scheme that employs two symmetrically positioned local dispersion alterations on either side of the pump mode. This configuration enables direct mode-locked microcombs, referred to as ‘dark pulses’ or ‘platicons’, while preserving spectral symmetry. The platicon microcombs exhibit efficient conversion, spectral symmetry, and can be generated with high repeatability. Furthermore, we demonstrate the deterministic generation of perfect platicon crystals with highly symmetric spectra by precisely controlling the position of the two symmetric dispersion alterations relative to the pump mode. Our proposed method offers a reliable approach for achieving power-efficient microcombs with highly symmetric spectra, and can be transferred to other integrated nonlinear platforms.

Anomaly detection and prediction evaluation for discrete nonlinear dynamical systems

Anomalies in dynamical systems mostly occur as deviations between measurement and prediction. Current anomaly detection methods in multivariate time series often require prior clustering, training data, or cannot distinguish local and global anomalies. Furthermore, no generalized metric exists to evaluate and compare different prediction functions regarding their amount of anomalous behavior. We propose a novel methodology to detect local and global anomalies in time series data of dynamical systems. For this purpose, a theoretical density distribution is derived assuming that only noise conceals the time series. If the theoretical and the empirical density distribution yield significantly different entropies, an anomaly is assumed. For a local anomaly detection, the Mahalanobis distance using the theoretical noise distribution’s covariance is applied to evaluate sequences of predictions and measurements. In addition, the Wasserstein metric enables a comparison of predictions using the distance between the noise and empirical distribution as a measure for selecting the best prediction function. The proposed method performs well on nonlinear time series such as logistic growth and enables a useful selection of a prediction model for satellite orbits. Thus, the proposed method improves anomaly detection in time series and model selection for nonlinear systems.

Extreme heat event over Northwest China driven by Silk Road Pattern teleconnection and its possible mechanism

Under the background of global warming, heat wave events have become significantly frequent and have received increasing attention. Northwest China is located in the drylands of central Eurasia, where the climate and ecological environment are more vulnerable to global warming than in other parts of the world. Over the past few decades, the frequency of heat waves in Northwest China has increased significantly, and its causes remain unclear. We found that the heat events in Northwest China were closely related to the Silk Road Pattern teleconnection (SRP), and the strongly positive phase of SRP frequently corresponded to the occurrence of heat waves. Furthermore, the wave source of SRP was determined by the Linear baroclinic model to be located in the North Atlantic Ocean. We then selected an extreme heat event over Northwest China in 2021 as a typical case to discuss the possible mechanism. The regressed circulation fields to daily SRP were highly consistent with the anomalies that occurred during July 9–22, 2021, indicating that the diurnal propagation of SRP modulated the circulation anomaly related to the heat wave event and that diabatic heating influenced by SRP was the primary factor to the thermal low over Northwest China. Diabatic heating was strengthened in the lower troposphere due to the enhancement of downward shortwave radiation and surface sensible heat flux accompanied by strong descending motions and cloud cover reduction by an SRP-guided anticyclone over Northwest China. This study enhances the understanding and certainty regarding large-scale circulation effects on local temperature anomalies in middle latitudes.

Determination of the Rock Contact Lithology of the Nanggulan Formation with the Magnetic Method in the Hargorejo Area of Kulon Progo

The Nanggulan Formation is a formation unit consisting of sandstone, clay, and other sedimentary rocks. Lithological contact between clay and geologically old andesite could indicate an avalanche. The magnetic method in collecting data uses a natural magnetic source with a proton magnetometer. Acquisition data was determined according to the survey design, namely a grid with a distance of 250 meters per point. Data processing to remove noise data, namely noise from magnetic storms, position and altitude, and other natural disturbances. Later, Processing will produce a magnetic anomaly map, which must be separated between local and regional anomalies. This local anomaly will later form the basis for 2.5-dimensional modeling. This 2.5-dimensional modeling can get an overview of lithology, relationships between lithologies, and rock contacts according to the geological model in the field. The modeling results can describe the relationship between the claystone of the Nanggulan Formation and the igneous rocks of the Old Andesite Formation. Its relationship claystone is overlapping Andesite rocks in the Horgorejo Kulon Progo area. This can cause soil movement and landslides if the Andesite rocks experience massive weathering and erosion and if rainfall is high.

The causal impact of local weather anomalies on beliefs about the occurrence of climate change

Research has demonstrated that members of the public recognize anomalous weather patterns, and that subjective perceptions of the weather are related to beliefs about the occurrence of climate change. Yet despite two decades of scholarship and dozens of studies, inconsistent and insufficient data have made it difficult to credibly identify the causal impact of objective experiences on perceptions, and the impact of perceptions on beliefs regarding climate change occurrence. Here, we overcome these limitations by collecting and analyzing data from a 5-y panel survey of 2,500 individuals in Oklahoma, a US state that is highly divided on questions about climate change. Our findings indicate that the relationship between local weather anomalies and climate change beliefs is heavily dependent on baseline beliefs about whether climate change was occurring. For people who did not believe in climate change in the initial survey in our series, perceptions of anomalously hot and dry seasons shifted their beliefs towards the occurrence of anthropogenic climate change, whereas their perceptions of anomalously cool and wet seasons shifted their beliefs away from anthropogenic climate change. This relationship was not present among people who believed that climate change was occurring at the beginning of the study; their perceptions of seasonal temperature and precipitation anomalies had no effect on their beliefs about climate change. These patterns have substantial implications for the evolution of public beliefs about climate change.

The Bottom Relief and the Anomalous Magnetic Field of the Ninetyeast Ridge in Near 5° N

The geophysical survey in cruise# 42 of R/V Akademik Boris Petrov (2017) at the polygon near Site 758 ODP clarified significantly knowledge about the bottom topography of the northern segment of the Ninetyeast Ridge. It is crossed by E–W transversal linear troughs, apparently being young tectonic fractures. The morphology of the narrow elevated block in the central part of the deep depression between neighboring large volcanic edifices is characterized in detail. Along with linear tectonic structures, the main features of this block are two volcanic seamounts. The detail magnetic survey at the polygon identified for the first time the nature of the sources of magnetic anomalies on the Ninetyeast Ridge. Intense local magnetic anomalies are observed here, both confined to local structures of the bottom and basement relief, and clearly unrelated to them. These anomalies are of a complex interfering nature caused by the superposition of fields from sources separated by magnetization, spatial position and age. According to the results of paleomagnetic analysis of the anomalies, the age of the structures here is very wide: the youngest features were formed about 20 million years ago, the oldest ones–over 80 million years ago.

Pervasive Micro Information Flow Tracking

Detection of advanced security attacks that exploit zero-day vulnerabilities or application-specific logic loopholes has been challenging due to the lack of attack signatures or substantial deviations in the overall system behavior. One has to zoom in to the affected code regions and look for local anomalies distinguishable from the benign workload to detect such attacks. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pervasive micro information flow tracking</i> (P <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</small> RMIT) that realizes variable-level online dynamic information flow tracking (DIFT) as a means to detect the attacks. The system uses hardware virtualization extension to monitor access to taint source variables and performs asynchronous code emulation to infer the local information flow. We demonstrate that the pervasive micro information flow can sufficiently capture the attacks and incurs only a small overhead. Given the program source code, the system can further enrich the semantics of micro information flow by embedding the variable names. We have integrated the system with machine learning algorithms to demonstrate the effectiveness of anomaly detection for zero-day attacks with pervasive micro information flow.

Recognizing Geochemical Anomalies Associated with Mineral Resources Using Singularity Analysis and Random Forest Models in the Torud-Chahshirin Belt, Northeast Iran

Identifying the local geochemical anomalies from stream sediment samples is challenging in regional-scale exploration programs. For this purpose, some robust and reliable techniques must be applied to distinguish the geochemical targets from the background values. In this research, a procedure of several tools, including singularity mapping (SM), random forests (RF), success-rate curves, and the t-Student method, were employed to analyze the geochemical anomalies within the intrusive-plutonic Torud-Chahshirin belt (TCB), northeast Iran. In this regard, the success-rate curves were initially applied to extract efficient geochemical signatures. Then, singularity analysis was used on the selected geochemical elements (Au, Cu, Pb, and Zn), which were transformed via centered log-ratio (clr) transformation. In the next step, due to the complexity of the ore-forming processes in the TCB, the structural factors (e.g., fault intersection and faults with different orientations) were determined. Based on the success-rate curves, NE-trending faults and fault density were distinguished as critical structural criteria. Afterward, the RF model as a robust machine learning algorithm was executed on the four efficient SM-based geochemical layers and two efficient structural factors. The anomaly map derived by the RF model (Accuracy = 98.85% and Error = 1.15%) illustrates a very high relationship with Cu ± Au mineral occurrences. Therefore, the RF algorithm assisted by the singularity method is more trustworthy for highlighting the weak geochemical prospectivity areas in the TCB.

The Comparison of Magpick Anomaly Contours toward Bouguer Anomaly Reduction

The subsurface structures model can be made by various methods, one of which is the gravity method. The basis of gravity modeling is the difference in density of materials. This can result in gravitational anomalies. Overall gravitational anomalies are known as bouguer anomalies. The gravity anomalies can be separated into local anomalies and regional anomalies. This research was in Mount Kelud, Kediri, Indonesia. The gravity data was processed using Surfer 9 software for the manufacture of two-dimensional contours. After that, researchers performed anomaly separation through MagPick software. In this study, researchers compared the two-dimensional contour results of MagPick gravitational anomalies toward the reduction of bouguer anomalies to regional anomalies. As a result, both show the same accurate contour.

A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics

Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amount of anomalous data, which can interfere with data analysis, affect the construction of spatial interpolation and prediction models, and then affect the accuracy of nutrient management decisions. The traditional method of identifying soil total nitrogen anomalies based on boxplots suffers from the problems of not being able to identify local anomalies, which can easily lead to misclassification of soil total nitrogen data anomalies, and the detection efficiency is not high. We propose a method to identify soil total nitrogen outliers by combining the Isolation Forest algorithm and local spatial autocorrelation analysis, which can simultaneously detect global and local outliers from large amounts of data and combine organic matter as an auxiliary indicator in the spatial analysis to help judge local outliers. Finally, the results of global and local anomalies were combined to provide a comprehensive assessment of the soil nitrogen data, avoiding the misjudgement or omission of judgement that can occur when using a single method. Using 25,930 soil test data from Yunnan Province in 2009 as an example, we compared and analysed the typical boxplot method and the unsupervised OneClassSVM method and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The results show that the proposed method has a correct detection rate (TR) of 99.97%, a false positive rate (FPR) of 8.06% and a false negative rate (FNR) of 0.01% on the data, which shows high validity and accuracy; it is also comparable to the independent isolated forests (FNR = 4.76%), boxplot (FNR = 3.90%) and OneClassSVM (FNR = 4.77%), and the false negative rate is reduced by 4.75%, 3.89% and 4.76%, respectively.

Precision local anomaly positioning technology for large complex electromechanical systems

In recent years, Prognostics Health Management (PHM) technology has become an important reference technology in fields such as avionics and electromechanical systems due to its ability to reduce costs and achieve state based maintenance and autonomous support. However, with the operation of large and complex electromechanical systems (ES), the data generated gradually ages the status of components, and traditional PHM technology is difficult to solve the problem of electromechanical system components becoming more complex. Based on this, this study takes the hydraulic actuator cylinder as an example to construct a local component fault detection model. Firstly, fault data features are extracted using wavelet packet energy spectrum, and then a fault detection model is constructed based on support vector machine (SVM). In response to the shortcomings of SVM, a smooth support vector machine (SSVM) is proposed to replace SVM, and an improved crow search algorithm (ICSA) is used to improve SVM. Finally, an intelligent detection model for hydraulic actuator cylinder faults based on ICSA-SSVM was constructed based on the above algorithms. The experimental results show that the ICSA-SSVM model has the fastest Rate of convergence, among which, the positioning accuracy is 0.96, the fitting degree is 0.984, the fault detection accuracy is 99.16 %, the recall value is 94.52 %, and the AUC value is 0.986, all of which are better than the existing fault detection models. From this, it can be seen that the precise local anomaly localization technology for large-scale complex electromechanical systems based on the ICSA-SSVM algorithm proposed in this study can improve the efficiency and accuracy of fault detection, achieve accurate and intelligent detection of ES local anomalies, and have certain positive significance for the development of China’s industry.

Coupled-mode separation of multiply scattered wavefield components in two-dimensional waveguides.

For a waveguide that is invariant in one of the horizontal directions, this paper presents a mathematically exact partial-wave decomposition of the wavefield for assessment of multiple scattering by horizontally displaced medium anomalies. The decomposition is based on discrete coupled-mode theory and combination of reflection/transmission matrices. In particular, there is no high-frequency ray approximation. Full details are presented for the scalar case with plane-wave incidence from below. An application of interest concerns ground motion induced by seismic waves, which may be severely amplified by local medium anomalies such as alluvial valleys. Global optimization techniques are used to design an artificial medium termination at depth for a normal-mode representation of the field. A derivation of a horizontal source array to produce an incident plane wave gives, as a by-product, an extension of a previous Fourier-transform relation involving Bessel functions. Like purely numerical methods, such as finite differences and finite elements, the method can handle all kinds of (two-dimensional) anomaly shapes.

WiFi-Based Lightweight Gesture Recognition for Coal Miners

With the advancement of smart mines, the need for gesture recognition for remote interaction between underground workers and machines has become crucial. However, traditional gesture recognition techniques require complex models that are very difficult to be deployed to the edge. To address this challenge, a gesture recognition method based on knowledge distillation is proposed in this study. First, the CSI ratio model is used to eliminate phase error and environmental noise, followed by the application of discrete wavelet transform to eliminate hardware noise interference. Then, the processed data is adaptively segmented using the principal component analysis and local anomaly factor algorithm to eliminate redundant static components. After that the processed CSI data is transformed into images using the relative position matrix method. Finally, knowledge distillation is employed to migrate knowledge from a teacher model to a student model, reducing the number of model parameters. Experiments conducted on the proposed method showed that it can achieve a recognition accuracy of 94.2% for hand gesture detection, which meets the requirement for gesture recognition in the mining industry.

Accumulation and Cross-Shelf Transport of Coastal Waters by Submesoscale Cyclones in the Black Sea

High- and medium-resolution satellite optical imagery show that submesoscale cyclonic eddies (SCEs) trap coastal waters and induce their rapid cross-shelf transport. Due to the presence of a rigid boundary, the convergence is observed in the coastal part of SCEs. It causes accumulation of suspended matter, which spins inward in a spiral motion toward the SCE core. Small SCEs with a radius of 1–10 km transport waters with local anomalies in the concentration of chlorophyll, total suspended matter and temperature to a distance of up to 150 km and are observed for more than 10 days. Lagrangian calculations based on realistic NEMO numerical model are used to estimate the fate of the coastal waters in such SCEs. The eddy entrains the largest number of particles during its separation from the coast when its vorticity reaches the maximum. Then, the SCE weakens, which is accompanied by the flattening of initially risen isopycnals and deepening of the trapped coastal waters. The described mechanism shows that coastal SCEs may cause intense short-period cross-shelf transport of the biological and chemical characteristics, and is another process affecting the functioning of the marine ecosystems.

Modeling geomagnetic spikes: the Levantine Iron Age anomaly

The Levantine Iron Age anomaly (LIAA) is a regional short-decadal geomagnetic strength field variation located at the Levantine region characterized by high intensities with maximum virtual axial dipole moments around 190 ZAm2. It has been constrained by archeomagnetic data coming from Eastern Europe and Western Asia between 1050 and 700 BC. The LIAA can be related to a fast and spatially localized geomagnetic positive anomaly (spike) at the Earth’s surface. In this study, we model the LIAA by using a Fisher–von Mises function that fits the most recent archeomagnetic intensity database in the region. A spherical harmonic analysis is implemented for this spike function to perturb a base model in order to build a global reconstruction (perturbed-model) that reproduces the spatial and temporal characteristics of the LIAA. Our results show the importance of harmonic degrees from n = 3–4 to n = 20 to reconstruct the anomaly extension suggested by the database. Two maxima linked with the LIAA are reproduced by our global perturbed-model at the Levantine region at 950 BC and 750 BC. A third maxima in intensity around 500 BC is also observed, affecting the whole Europe.Graphical

ENSO and NAO linkages to interannual salinity variability in north central Gulf of Mexico estuaries through teleconnections with precipitation

Though the importance of Earth's internal climate modes such as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) to regional-scale climate variability is well recognized, the degree to which these oscillations are reflected by spatio-temporal salinity variability over interannual timescales in estuaries is less understood. Here an 11-year continuous salinity monitoring dataset spanning 223 stations across Louisiana's coastal wetlands along the northern Gulf of Mexico is examined with empirical orthogonal function (EOF) analysis to identify dominant modes of interannual variability in the salinity field. The first EOF mode accounts for 72% of the variance in the salinity field and captures a domain-wide pattern where salinities vary in-phase through space in response to local precipitation anomalies occurring in the vicinity of the study area. This local precipitation anomaly is positively correlated with ENSO (Nino3.4 index), consistent with the El Niño – wet (La Niña – dry) precipitation teleconnection that is prevalent throughout the northern Gulf of Mexico coast. The second EOF mode, which accounts for 13% of the variance in the salinity field, is expressed primarily in the marshes across the lower reaches of the Mississippi River deltaic plain (MRDP). EOF2 is anticorrelated with annual Mississippi River discharge anomaly such that salinities in the lower MRDP decrease as discharge increases, pointing to enhanced advection of fresh river plume waters over the shelf into the estuary via estuary-ocean exchange during years of anomalously high river discharge. Mississippi River discharge anomaly is positively correlated with the NAO at a one-year time lag, through a teleconnection with precipitation throughout much of the central region of the Mississippi River drainage basin. Together, these findings indicate that most of the interannual salinity variability across Louisiana's coastal wetlands can be linked to climate variability through teleconnections with precipitation. Incorporating these dynamics into restoration planning, monitoring, and adaptive management efforts may help constrain background environmental variation and better isolate restoration effects.

Meteorological impact on gravimeter and seismometer readings

Background. The density of atmospheric air varies with temperature. Therefore, variations in the atmospheric layer temperature leads the emergence of local gravity anomalies. The amplitude of such anomalies can exceed ±0.1 mGal. The results obtained by the Arti Geophysical Observatory(Yekaterinburg, Russia) on the non-tidal variations of gravity confirm the influence of temperature variations on gravimeter readings, which may significantly exceed the calculated data. Aim. To assess the impact of weather changes on seismic and gravity fields.Materials and methods. At the RAS Bishkek Geodynamic Test Area (Bishkek, Kyrgyzstan), RAS Institute of Geophysics (Yekaterinburg, Russia) and Zapolye Observatory (Vladimir, Russia), gravity was measured by CG-5 AUTOGRAV gravimeters. At the Shults Cape (Vladivostok, Russia), Arti (Yekaterinburg, Russia) and BFO (Black Forest, Germany [10]) observatories, gravimetric measurements were conducted by La Coste &amp; Romberg tidal gravimeters. Results. Variations in the atmospheric layer temperature were found to lead to the emergence of local gravity anomalies. The amplitude of such anomalies can exceed ±0.1 mGal. The results obtained by the Arti Geophysical Observatory (Yekaterinburg, Russia) on the non-tidal variations of gravity confirm the influence of temperature on gravimeter readings, which may significantly exceed the calculated data. Conclusion. Meteorological processes, such as liquid-water content and the Earth’s atmospheric temperature, affect the readings of gravimeters and seismometers. The atmospheric air density varies with temperature. Along with temperature, gravimeters and seismometers are affected by pressure, humidity and water content of the atmosphere. This is related to atmospheric deformations under the influence of the Moon and the Sun. Meteor streams and their impact on the Earth’s atmosphere have the greatest influence on the readings of gravimeters and seismometers.

The geomagnetic field of the Ukrainian Carpathians and a 3D magnetic model of the Transcarpathian Depression

The article presents the results of a qualitative and quantitative analysis of the geomagnetic field of the Ukrainian Carpathians and 3D magnetic modeling along the PANCAKE geotransect and Transcarpathian Depression, as well as a comparison of the obtained results with fault-block tectonics, deep structure, and distribution of a number of types of endogenous ore deposits and hydrocarbon accumulations.&#x0D; It is shown that the overthrust northeastern part of the Carpathian arc lies on the magnetic crust, and the southwestern one — on its non-magnetic lower part, and between the Rava-Rus’ka and Krakovets and Pre-Carpathian faults, the Earth’s crust is magnetized throughout.&#x0D; For the territory of the Transcarpathian Depression, the regional and local components of the geomagnetic field were identified for the first time, geological and geophysical characteristics were provided, and their tectonic interpretation was proposed.&#x0D; A three-dimensional magnetic model of the Transcarpathian Depression was created taking into account the magnetization of rocks according to measurements. A detailed magnetic model of the upper part of the crustal section of the Transcarpathian Depression was developed. The connection of magnetic sources with fault zones was analyzed; the results were compared with the deep structure and distribution of ore deposits and hydrocarbon accumulations.&#x0D; The regional source with magnetism І=1.0 A/m is located within the Mukachevo Depression and is located at a depth of 6.0 km to 13 km. The local component of the geomagnetic field reflects the magnetic inhomogeneity of the Earth’s crust in the upper 3—4 km of the section and mainly reflects the volcano-tectonic structures and dike formations of the Vyhorlat-Gutyn Ridge, the Chop-Berehove Uplift, and the Velika Dobron’ Uplift. The maximum depths of magnetic sources (up to 3.5 km) and their magnetization (1.22 A/m) are characteristic of the structures of the Vyhorlat-Gutynsky Ridge, intermediate values of depths (2.0—3.0 km) and magnetization (up to 0.93 A/m) belong to the Chop-Berehove Uplift, and the minimum depths (up to 1.1 km) and magnetization (up to 0.7 A/m) are characteristic of the Velika Dobron’ Uplift.&#x0D; It is shown that the gas fields of Transcarpathia correspond to local positive magnetic anomalies and are localized above a deep magnetic source. Within the Berehove Uplift, positive anomalies and magnetic sources indicate andesiteporphyrite shafts and andesite domes of Sarmatian age associated with gold, gold-polymetallic and silver mineralization. A zone of antimony mineralization is associated with the Pannonian-Pontic volcanic structures along the southern foot of the Vyhorlat-Gutyn Ridge, and deposits and occurrences of bismuth and mercury correlate well with intrusive formations of the Dacian-Romanian age.

Quantification of Statistical Error in the Estimate of Strain Power Spectral Density Transmissibility for Operational Strain Modal Analysis

The use of strain modes in structural health monitoring has been constantly increasing because of their superior sensitivity to local structural anomalies. This study aims to investigate the applicability and robustness of power spectral density transmissibility (PSDT) in operational strain modal analysis (OSMA). By noting that OSMA in the frequency domain is vulnerable to the error of spectral estimates, uncertainty quantification stemming from strain spectral estimates and the error propagation analysis in OSMA are conducted from an analytical perspective. The main contributions include the following: (i) the mean and variance of strain PSDT estimates are asymptotically derived based on statistical moment theory and the statistics of PSD estimate error, (ii) the coefficients of variation (c.o.v.) of the strain PSDT estimate and strain spectral estimates are compared with each other through asymptotic analysis to elaborate the robustness of strain PSDT, and (iii) the variability of the strain mode shape is quantified based on the asymptotic formula of strain PSDT estimates tending to local minima of asymptotic zero variance at the resonances. The accuracy and efficiency of the quantification and propagation analysis are validated through numerical and experimental test data accompanied by various parametric studies.