Anomalous Window Research Articles

The 2023 Mw 6.8 Morocco earthquake induced atmospheric and ionospheric anomalies

Earth observations through Global Navigation Satellite System (GNSS) and Remote Sensing (RS) technologies play a significant role in natural hazard surveillance, particularly in the context of earthquake prediction and detection. This study introduces a distinctive Deep Learning (DL) based approach to identify ionospheric and atmospheric precursors, utilizing data from multiple satellite sources and provides a comprehensive analysis of spatiotemporally varying precursors, contributing to the understanding and monitoring of seismic activity in earthquake-prone regions. In our investigation of the Morocco earthquake on September 08, 2023 (Mw 6.8), we analyzed various precursors including Total Electron Content (TEC), Air Pressure (AP), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Air Temperature (AT). Our study aims to identify a synchronized anomalous window of potential earthquake precursors using Standard Deviation (STDEV), Continuous Wavelet Transform (CWT), and Long Short-Term Memory Inputs (LSTM) network. Both statistical and deep learning methods revealed abnormal fluctuations as precursors occurring within 8–9 days before the earthquake near the epicenter. Additionally, we detected geomagnetic anomalies in the ionosphere 6 days prior to and 4 days after the earthquake, coinciding with active geomagnetic storm days. This research underlined the importance of combining multiple earthquake precursors using statistical and deep learning approaches to support the understanding of the Lithosphere-Atmosphere-Ionosphere-Coupling (LAIC) phenomena.

Possible seismo-ionospheric anomalies associated with Mw > 5.0 earthquakes during 2000–2020 from GNSS TEC

The recent advances in space-based ionosphere measurements provide more detailed information about transient ionospheric anomalies associated with earthquakes (EQs). In this paper, we study the possible relation of EQs and ionospheric anomalies in a statistical analysis by analyzing 534 EQs of Mw > 5.0 during 2000–2020 from Vertical Total Electron Content (VTEC) acquired from the Global Navigation Satellite System (GNSS) of International GNSS Services (IGS) network. The selection criteria of these EQs was mainly subjected to two conditions: (1) at least three or more GNSS stations around the Dobrovolsky region were present, (2) the EQs occurred during quiet geomagnetic activity (Kp < 3, Dst ≥ -20nT). Particularly, we performed a statistical analysis on the basis of the median and interquartile range (IQR) over VTEC for each EQ, and the positive/negative anomalies were analyzed in the form of cumulative count for 20 days prior and after the mainshock day. Moreover, this study demonstrates that EQ induced VTEC anomalies occurred more frequently with 3 or more GNSS stations around the epicenter in Dobrovolsky’s region within a 5–10-day window before the EQs. Furthermore, the anomalies with a low deviation also initiated after the mainshock day within 5–10-day window. On the other hand, the deviations are more clear for Mw > 6.0 EQs, where positive anomalies were observed after excluding false anomalies in the corresponding IGS stations, showing a higher percentage of positive than negative anomalies and also more positive (negative) anomalies occur in solar maximum (minimum) years of the solar cycles 23 and 24. The false anomalies were the ionospheric perturbations associated with geomagnetic storms (Kp > 3) and VTEC values beyond upper/lower bounds outside the 5–10 days anomalous window. This statistical analysis assists to support the lithosphere and ionosphere coupling for future EQ ionospheric precursors.

Density-Based Spatial Anomalous Window Discovery

The focus of this paper is to identify anomalous spatial windows using clustering-based methods. Spatial Anomalous windows are the contiguous groupings of spatial nodes which are unusual with respect to the rest of the data. Many scan statistics based approaches have been proposed for the identification of spatial anomalous windows. To identify similarly behaving groups of points, clustering techniques have been proposed. There are parallels between both types of approaches but these approaches have not been used interchangeably. Thus, the focus of our work is to bridge this gap and identify anomalous spatial windows using clustering based methods. Specifically, we use the circular scan statistic based approach and DBSCAN- Density based Spatial Clustering of Applications with Noise, to bridge the gap between clustering and scan statistics based approach. We present experimental results in US crime data Our results show that our approach is effective in identifying spatial anomalous windows and performs equal or better than existing techniques and does better than pure clustering.

Two symmetric four-wave mixing signals generated in a medium with anomalous refractive index

We report experimental and theoretical results of two symmetrical signals of degenerate four-wave mixing generated in rubidium vapor. Both nonlinear signals are induced by two almost copropagating laser beams, with and wave-vectors, and detected simultaneously in the and directions. In each direction, we observe a single peak when the two beams are tuned on the closed transition 85Rb 5S 1/2(F = 3) → 5P 3/2(F = 4). The excitation spectra reveal a small frequency separation between the two peaks, which is explained when propagation effects are taken into account. Furthermore, our theoretical analysis shows that a correct description of the frequency position of each peak is achieved with a variable refractive index for both lasers, in which the scanning laser experiences an anomalous window in the refractive index near resonance.

A large aortopulmonary window found in the setting of a right aortic arch with discontinuous pulmonary arteries.

Aortopulmonary window (APW) is a rare but serious congenital cardiac malformation, most patients with APW will die from congestive heart failure within one year after birth. In fact, patients with large APW is rarely seen in childhood or adult life. However, we report an older child with a large anomalous "window" on the ascending aorta, with discontinuous pulmonary arteries, and the left pulmonary artery (LPA) arising via a left-sided arterial duct in the presence of a right aortic arch. Preoperative diagnosis made by echocardiography and chest computerized tomography revealed anatomical futures clearly. Cardiac catheterization indicated that the pulmonary resistances indices were 2.92 Wood unit/m2 in LPA and 3.35 Wood unit/m2 in RPA, was 3.26. This patient underwent surgical correction at the age of 10 and successfully survived.

An algorithmic framework for investigating the temporal relationship of magnetic field pulses and earthquakes applied to California

An end-to-end algorithm is described wherein field-collected magnetometer time series data were processed and analyzed for potential statistical correlation with pre-seismic activity. The process included windowing the data, extraction of statistically-determined anomalies via a short term average - long term average (STA-LTA) signal processing technique, collating and ranking the anomalous windows as precursory behavior, and testing the results via a Receiver Operating Characteristic (ROC) formulation. The algorithm was employed on a large dataset of over 100 magnetic observatories in California totaling hundreds of thousands of station-days. Using the ROC curve to evaluate its performance, this implementation of the algorithm obtained a 2.20 z-score. This number improved with the preliminary attempt at removing a severe cultural noise source. This work emphasizes an analytic framework more than parametric exploration or optimization, nevertheless there appears to be some suggestion of predictive power in the magnetic field time series.

Discovery of Anomalous Windows through a Robust Nonparametric Multivariate Scan Statistic (RMSS)

This paper studies unusual phenomena by discovering anomalous windows in multivariate spatial data. Such an anomalous window is a group of contiguous spatial objects indicating the occurrence of unusual phenomenon in terms of multiple variables. The paper presents a novel Robust non-parametric Multivariate Scan Statistic (RMSS). In contrast to the existing work, the authors’ approach is designed to deal with anomalous window discovery in multivariate data. They propose their multivariate scan statistic that employs the robust Mahalanobis distance which enables taking into account multiple behavioral attributes at the same time and their correlations for the discovery of significant anomalous windows. The proposed multivariate scan statistic is non-parametric such that it does not rely on any prior assumption about the data distribution. It is robust such that it can handle data with large amount of outliers, up to 50% of the overall data size. It is also affine equivariant such that affine transformation such as stretch or rotation of the data would not affect the results. The authors evaluate their approach with (a) real-world multivariate climate data for discovering natural disasters and climate changes, (b) real-world multivariate traffic accident data for identifying accident hubs, which are route segments with underlying accident-prone issues, and (c) synthetic data of both continuous and discrete multivariate distribution for identifying clusters of known outliers under different outlier percentage in data. They compare their results to state of the art multivariate scan statistic method (Kulldorff et al., 2007). The evaluation shows the detection power of the authors’ method, and the significant improvement over the existing methods.

Multi-domain anomaly detection in spatial datasets

A spatial anomaly captures a phenomenon occurring in a region which is vastly deviant in behavior with respect to the other normal observations. However, in reality this anomaly may impact other phenomena in the region across multiple domains, for example, crime is often linked to other sociopolitical factors or phenomenon such as poverty and education. Similarly, accidents in the region may be linked to other environmental factors such as weather and surface condition. So, finding anomalies across multiple domains is important in various applications. In this paper, we propose an approach for finding such a tangible anomalous window across multiple domains where window refers to the set of contiguous points in space, and since the window is multi-domain, there are several overlapping windows in the same space across domains. Our approach for finding anomalous window across the domains comprises the following steps: (1) single-domain anomaly detection: discovering anomalous window in each domain; (2) association rule mining: discovering relationship between the anomalous windows across domains using association rule mining; and (3) validation: validating the result using (a) Monte Carlo simulation, (b) correlation using lift and (c) ground truth evaluation. In addition, we also provide a probabilistic framework to evaluate the relationships between the spatial nodes as a postprocessing step. Finally, we provide a visualization technique for viewing the multi-domain anomalous window and the probabilistic relationships between the nodes. We provide detailed experimental results and comparisons with other approaches using real-world health ranking [51] and transportation datasets [50] with known ground truth windows. The results show that our approach is effective in finding the anomalies in multiple domains as compared to other approaches.

Anomalous Window Discovery for Linear Intersecting Paths

The focus of this paper is to discover anomalous windows in linear intersecting paths. Anomalous windows are the contiguous groupings of data points. A linear path refers to a path represented by a line with a single dimensional spatial coordinate marking an observation point. In this paper, we propose an approach for discovering anomalous windows using a class of algorithms based on scan statistics, specifically 1) an Order invariant algorithm using Scan Statistics for Linear Intersecting Paths (SSLIP), 2) Brute force-SSLIP (BF-SSLIP), and 3) Central Brute Force-SSLIP (CBF-SSLIP). We further present two efficient variants of SSLIP: SSLIP* which employs a upper bound on the scan window size, and SSLIP-Acc, which adopts an accelerator function to speed up the scan process. The proposed approach for discovering anomalous windows along linear paths comprises the following distinct steps: 1) Cross Path Discovery: where we identify a subset of intersecting paths to be considered, 2) Anomalous Window Discovery: where we outline the various algorithms for the traversal of the cross paths to identify varying size directional windows along the paths. For identifying an anomalous window, an unusualness metric is computed, in the form of a likelihood ratio to indicate the degree of unusualness of this window with respect to the rest of the data. We identify the window with the highest likelihood ratio as our anomalous window, and 3) Monte Carlo Simulations: to ascertain whether this window is truly anomalous and not merely random occurrence, we perform hypothesis testing by computing a p-value using Monte Carlo Simulations. We present extensive experimental results in real world accident data sets for various highways with known issues (code and data available from [32], [27]). Additionally, we also perform comparisons with current approaches [18], [34] to show the efficacy of our approach. Our results show that our approach indeed is effective in identifying anomalous traffic accident windows along multiple intersecting highways.

Discovery of anomalous spatio‐temporal windows using discretized spatio‐temporal scan statistics

AbstractIn this paper, we address the discovery of anomalous spatio‐temporal windows using discretized spatio‐temporal scan (DSTS) Statistics. Anomalous spatio‐temporal window discovery is required in several key applications such as disease outbreaks in a region over a period of time, monitoring drinking water quality over time, identifying health risks to the population in a polluted region and urbanization patterns in a city, to name a few. In this paper, we address the issues arising out of the simultaneous effects of the properties of space and time in the discovery of anomalous windows. In such a framework, we identify (i) at what point in time the window changes, (ii) the spatial patterns of change over time, and (iii) a spatial extent in time which is completely or partially deviant with respect to the rest of the anomalous spatio‐temporal windows. None of the current approaches address all these issues in combination. We identify this knowledge keeping in mind the spatial and temporal autocorrelation, morphing shape of the window, and possible spatial or temporal discontinuities of the window. Subsequently, we perform experiments on several real‐world datasets, to validate our approach, while comparing with the established approaches. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011

Random Walks to Identify Anomalous Free-Form Spatial Scan Windows

Often, it is required to identify anomalous windows over a spatial region that reflect unusual rate of occurrence of a specific event of interest. A spatial scan statistic-based approach essentially considers a scan window and computes the statistic of a parameter(s) of interest, and identifies anomalous windows by moving the scan window in the region. While this approach has been successfully employed in identifying anomalous windows, earlier proposals adopting spatial scan statistic suffer from two limitations: (1) In general, the scan window should be of a regular shape (e.g., circle, rectangle, cylinder). Thus, most approaches are capable of identifying anomalous windows of fixed shapes only. However, the region of anomaly, in general, is not necessarily of a regular shape. Recent proposals to identify windows of irregular shapes identify windows much larger than the true anomalies or penalize large-sized windows. (2) These techniques take into account autocorrelation among spatial data but not spatial heterogeneity. As a result, they often result in inaccurate anomalous windows. To address these limitations, in this paper, we propose a random-walk-based Free-Form Spatial Scan Statistic (FS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). We construct a weighted Delaunay nearest neighbor (WDNN) graph to capture both spatial autocorrelation and heterogeneity. We then use random walks to identify natural free-form scan windows that are not restricted to a predefined shape. We use spatial scan statistics to identify anomalous windows and prove that they are not random but indeed are formed as a result of an anomaly. Application of FS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> on real data sets has shown that it can identify more refined anomalous windows with better likelihood ratio of it being an anomaly than those identified by earlier spatial scan statistic approaches.

The Ag/As2S3 photodoping mechanism: Photon absorption

The sensitivity of the Ag-photodoping process has been measured as a function of photon energy between 1.9 and 6.4 eV. The sensitivity increases with increasing photon energy except at 3.8 eV (325 nm) where Ag exhibits an anomalous window. At this energy there is an abrupt and large decrease of sensitivity. In a second measurement, the transmission as a function of wavelength between 200 and 900 nm of a 2000-Å-thick film of As2S3 doped with an imaging dose of Ag and a second sample doped with a larger than imaging dose are compared with an undoped sample. Band-gap shrinkage is different for the two doses. Both measurements suggest that under certain conditions, photon absorption in the Ag-photodoping process occurs in the Ag.