Temporal Anomalies Research Articles

Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms

The research in anomaly detection lacks a unified definition of what represents an anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple paradigms of algorithms design and experimentation. Predictive maintenance is a special case, where the anomaly represents a failure that must be prevented. Related time series research as outlier and novelty detection or time series classification does not apply to the concept of an anomaly in this field, because they are not single points which have not been seen previously and may not be precisely annotated. Moreover, due to the lack of annotated anomalous data, many benchmarks are adapted from supervised scenarios.To address these issues, we generalise the concept of positive and negative instances to intervals to be able to evaluate unsupervised anomaly detection algorithms. We also preserve the imbalance scheme for evaluation through the proposal of the Preceding Window ROC, a generalisation for the calculation of ROC curves for time series scenarios. We also adapt the mechanism from a established time series anomaly detection benchmark to the proposed generalisations to reward early detection. Therefore, the proposal represents a flexible evaluation framework for the different scenarios. To show the usefulness of this definition, we include a case study of Big Data algorithms with a real-world time series problem provided by the company ArcelorMittal, and compare the proposal with an evaluation method.

Sinkhole Scanner: A New Method to Detect Sinkhole-Related Spatio-Temporal Patterns in InSAR Deformation Time Series

Sinkholes are sudden disasters that are usually small in size and occur at unexpected locations. They may cause serious damage to life and property. Sinkhole-prone areas can be monitored using Interferometric Synthetic Aperture Radar (InSAR) time series. Defining a pattern using InSAR-derived spatio-temporal deformations, this study presents a sinkhole pattern detector, called the Sinkhole Scanner. The Sinkhole Scanner includes a spatio-temporal mathematical model such as a 2-dimensional time evolving Gaussian function as a kernel, which moves over the study area using a sliding window approach. The scanner attempts to fit the model over deformation time series of Constantly Coherent Scatterers (CCS) intersected by the window and returns the posterior variance as a measure of goodness of fit. In this way, the scanner searches for subsiding regions resembling sinkhole shapes over a sinkhole prone area. It is designed to detect large sinkholes with a high efficiency, and small sinkholes with a lower efficiency. It is tested at four different spatial scales, and on a simulated and real set of deformation data. Real data were obtained from Sentinel-1A SLC data in IW mode, over Ireland where a large sinkhole occurred on 24 September 2018. The Sinkhole Scanner was able to identify a pattern of low posterior variance zones consistent with the simulated set. In case of the real data, it is able to identify significantly low posterior variance zones near the sinkhole area with the lowest value being 51.1% of the maximum value. The results from Sinkhole Scanner over the real sinkhole site were compared with Multiple Hypothesis Testing (MHT), which identifies Breakpoint and Heaviside temporal anomalies in the deformation time series of CCS. MHT was able to identify high likelihood for Heaviside anomalies in deformation time series of CCS near the sinkhole site about 10 epochs before the sinkhole occurrence. We show that the Sinkhole Scanner is efficient in monitoring a large area and search for sinkholes and that MHT can be used successively to identify temporal anomalies in the vicinity of areas detected by the Sinkhole Scanner. Future research may address other Sinkhole shapes whereas the underlying stochastic model may be adjusted. We conclude that the Sinkhole Scanner is important to be applied at different levels of scale to converge on potential sinkhole centers.

A comparative study between single and multi-frame anomaly detection and localization in recorded video streams

Video anomaly detection is usually studied by considering the spatial and temporal contexts. This paper focuses first on spatial context and shows that it can be a fast real-time solution. In the first part of this work there are two main contributions: employing a new deep network for reconstruction and introducing a new regularity scoring function. The new deep architecture is based on pyramid of input images and compared to UNet, the proposed architecture boosts AUC by 15% and the new regularity scoring function is based on SSIM. The second part employs a multiframe approach to distinguish temporal behavior anomalies. The second approach enhances the results by 7% compared to spatial anomaly detection. Comparing the two approaches, if computing power is limited and real time anomaly detection is looked for, single frame detection is preferred while multi frame analysis offers a much wider possibility of anomaly detection.

Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series

Multi-temporal consistency of space-borne observations is an essential requirement for studying inter-annual changes and trends of satellite-derived biophysical products. The Proba-V mission, launched in 2013, was designed to ensure the continuity of the SPOT-VEGETATION long-term data record of global daily observations for land applications. The suitability of Proba-V to provide a temporally consistent data record is, however, potentially jeopardized by the orbital drift effect, which is known to induce spurious trends in time series. The aim of this paper is therefore to evaluate, for the first time, the orbital drift effect on Proba-V surface reflectance time series at 1 km resolution. In order to reliably identify such an effect, a two-fold approach is adopted. A simulation study is first defined to predict the temporal anomalies induced by the drifting illumination conditions. The numerical simulations are used as a benchmark to predict the impact of the drift for a range of sun-viewing angles. Real observations are then analyzed over a large set of land sites, globally spread and spanning a wide range of surface and environmental conditions. The surface anisotropy is characterized using the Ross-Thick Li-Sparse Reciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model. Both the simulation and the analysis of real observations consistently show that the orbital drift induces distinct and opposite trends in the two sides of the sensor across-track swath. Particularly, a positive drift is estimated in backward and a negative one in the forward scattering direction. When observations from all angular conditions are retained, these opposite trends largely compensate, with no remaining statistically significant drifts in time series of surface reflectances or Normalized Difference Vegetation Index (NDVI). As such, the Proba-V archive at 1 km resolution can be reliably used for inter-annual vegetation studies.

Seasonal rainfall variability and its correlation with potential drivers over Western Oromia

Characterizing seasonal rainfall and identifying its association with the climate drivers are important to produce reliable seasonal climate forecasts of a given area. This study examined the rainfall variability and its relationships with SST, SLP and sunspot indices by statistical methods over Western Oromia. The regional is characterized by monomodal pattern of 60% seasonal rainfall contribution in Kiremt season. High temporal anomaly rainfall but no extremes were observed from 1981-2017. An insignificant decreasing Kiremt and Belg but, an increasing Bega trend was observed. ENSO and PDO/ DMI and AMO showed negative/ positive correlation with kiremt rainfall. ENSO, DMI and AMO showed positive correlation for belg rainfall whereas, PDO showed associated negatively for the most parts of the region. PSI to kiremt/Belg rainfall correlation showed significant positive/Negative spatial association over the western part (West Wolega and Illubabor) / the East part (Jimma & East wollega) of the region. The results showed that there is decreasing trends in rainfall anomalies for main rainy seasons of region and good correlations these various indices. Therefore, it is suggested that seasonal climate predictions may consider these large-scale determinants oscillation indices like AMO and PDO in predicting climatic events. Â

Anomaly Detection Based on Temporal Behavior Monitoring in Programmable Logic Controllers

As Programmable Logic Controllers (PLCs) are increasingly connected and integrated into the industrial Internet of things, cybersecurity threats to PLCs are also increasing. Adversaries can perform a denial of service (DoS) attack based on the transmission of a large number of network packets, and a control-logic injection attack through sophisticated packet transmission. We propose an approach to detecting and defending against attacks that exploit security vulnerabilities in a PLC system. In order to protect against indiscriminate packet transmission attacks that exploit uncontrolled resource consumption vulnerabilities, an abnormal temporal behavior detection method is proposed that monitors the CPU usage of tasks. If a temporal anomaly is detected, the proposed approach tries to detect control-flow anomalies by examining the sequences of function calls, then detects stack-based buffer overflow attacks. The proposed method is implemented in a water tank control system for evaluation purposes. The experimental results show that the proposed method can improve the security of the system by detecting anomalies in temporal behavior with little system overhead.

Soil moisture and streamflow deficit anomaly index: an approach to quantify drought hazards by combining deficit and anomaly

Abstract. Drought is understood as both a lack of water (i.e., a deficit compared to demand) and a temporal anomaly in one or more components of the hydrological cycle. Most drought indices, however, only consider the anomaly aspect, i.e., how unusual the condition is. In this paper, we present two drought hazard indices that reflect both the deficit and anomaly aspects. The soil moisture deficit anomaly index, SMDAI, is based on the drought severity index, DSI (Cammalleri et al., 2016), but is computed in a more straightforward way that does not require the definition of a mapping function. We propose a new indicator of drought hazard for water supply from rivers, the streamflow deficit anomaly index, QDAI, which takes into account the surface water demand of humans and freshwater biota. Both indices are computed and analyzed at the global scale, with a spatial resolution of roughly 50 km, for the period 1981–2010, using monthly time series of variables computed by the global water resources and the model WaterGAP 2.2d. We found that the SMDAI and QDAI values are broadly similar to values of purely anomaly-based indices. However, the deficit anomaly indices provide more differentiated spatial and temporal patterns that help to distinguish the degree and nature of the actual drought hazard to vegetation health or the water supply. QDAI can be made relevant for stakeholders with different perceptions about the importance of ecosystem protection, by adapting the approach for computing the amount of water that is required to remain in the river for the well-being of the river ecosystem. Both deficit anomaly indices are well suited for inclusion in local or global drought risk studies.

Climate and Socioeconomic Factors Drive Irrigated Agriculture Dynamics in the Lower Colorado River Basin

The Colorado River Basin (CRB) includes seven states and provides municipal and industrial water to millions of people across all major southwestern cities both inside and outside the basin. Agriculture is the largest part of the CRB economy and crop production depends on irrigation, which accounts for about 74% of the total water demand cross the region. A better understanding of irrigation water demands is critically needed as temperatures continue to rise and drought intensifies, potentially leading to water shortages across the region. Yet, past research on irrigation dynamics has generally utilized relatively low spatiotemporal resolution datasets and has often overlooked the relationship between climate and management decisions such as land fallowing, i.e., the practice of leaving cultivated land idle for a growing season. Here, we produced annual estimates of fallow and active cropland extent at high spatial resolution (30 m) from 2001 to 2017 by applying the fallow-land algorithm based on neighborhood and temporal anomalies (FANTA). We specifically focused on diverse CRB agricultural regions: the lower Colorado River planning (LCRP) area and the Pinal and Phoenix active management areas (PPAMA). Utilizing ground observations collected in 2014 and 2017, we found an overall classification accuracy of 88.9% and 87.2% for LCRP and PPAMA, respectively. We then quantified how factors such as climate, district water rights, and market value influenced: (1) annual fallow and active cropland extent and (2) annual cropland productivity, approximated by integrated growing season NDVI (iNDVI). We found that for the LCRP, a region of winter cropping and senior (i.e., preferential) water rights, active cropland productivity was positively correlated with cool-season average vapor pressure deficit (R = 0.72; p < 0.01). By contrast, for the PPAMA, a region of summer cropping and junior water rights, annual fallow and active cropland extent was positively correlated with cool-season aridity (precipitation/potential evapotranspiration) (R = 0.46; p < 0.05), and active cropland productivity was positively correlated with warm-season aridity (precipitation/potential evapotranspiration) (R = 0.42; p < 0.01). We also found that PPAMA cropland productivity was more sensitive to aridity when crop prices were low, potentially due to the influence of market value on management decisions. Our analysis highlights how biophysical (e.g., temperature and precipitation) and socioeconomic (e.g., water rights and crop market value) factors interact to explain seasonal patterns of cropland extent, water use and productivity. These findings indicate that increasing aridity across the region may result in reduced cropland productivity and increased land fallowing for some regions, particularly those with junior water rights.

Temporal anomaly detection on IIoT-enabled manufacturing

Along with the coming of industry 4.0 era, industrial internet of things (IIoT) plays a vital role in advanced manufacturing. It can not only connect all equipment and applications in manufacturing processes closely, but also provide oceans of sensor data for real-time work-in-process monitoring. Considering the corresponding abnormalities existing in these sensor data sequences, how to effectively implement temporal anomaly detection is of great significance for smart manufacturing. Therefore, in this paper, we proposed a novel time series anomaly detection method, which can effectively recognize corresponding abnormalities within the given time series sequences by standing on the hierarchical temporal representation. Extensive comparison experiments on the benchmark datasets have been conducted to demonstrate the superiority of our method in term of detection accuracy and efficiency on IIOT-enabled manufacturing.

Temporal variations in stable isotopes and synchronous earthquake-related changes in hot springs

Earthquake-related hydrogeochemical changes are considered a possible means of identifying earthquake precursors. This work investigated the time series of hydrogen and oxygen stable isotopes at three hot springs in Kangding with different temperatures, namely, the Erdaoqiao, Longtougou, and Guanding hot springs, located in the Xianshuihe fault zone, one of the most active seismic fault zones in China. The hot springs were observed with a sampling frequency of 3 or 4 days for one year. The results showed that the three hot springs were recharged by meteoric water, and there was a slight water–rock interaction at the Longtougou hot spring. The different isotopic compositions of the three hot springs were related to the recharge and groundwater circulation environment. Temporal anomalies in the stable isotope values at the three hot springs occurred synchronously; these anomalies were closely related to the 2019 Mw 5.8 Changning earthquake, indicating possible earthquake-related changes. Moreover, the amplitudes of stable isotope variations at the three hot springs were closely related to the seismic energy density, and the background isotopic changes were related to the water temperature and flow. These findings reflect the hydrological cycle processes of geothermal water in the Kangding geothermal area on the eastern margin of the Tibetan Plateau. Simultaneously observed temporal variations in hydrogen and oxygen stable isotopes among multiple hot springs may have relevance for distinguishing earthquake precursors.

Identification of Novel Candidate Genes and Variants for Hearing Loss and Temporal Bone Anomalies.

Background: Hearing loss remains an important global health problem that is potentially addressed through early identification of a genetic etiology, which helps to predict outcomes of hearing rehabilitation such as cochlear implantation and also to mitigate the long-term effects of comorbidities. The identification of variants for hearing loss and detailed descriptions of clinical phenotypes in patients from various populations are needed to improve the utility of clinical genetic screening for hearing loss. Methods: Clinical and exome data from 15 children with hearing loss were reviewed. Standard tools for annotating variants were used and rare, putatively deleterious variants were selected from the exome data. Results: In 15 children, 21 rare damaging variants in 17 genes were identified, including: 14 known hearing loss or neurodevelopmental genes, 11 of which had novel variants; and three candidate genes IST1, CBLN3 and GDPD5, two of which were identified in children with both hearing loss and enlarged vestibular aqueducts. Patients with variants within IST1 and MYO18B had poorer outcomes after cochlear implantation. Conclusion: Our findings highlight the importance of identifying novel variants and genes in ethnic groups that are understudied for hearing loss.

An effective downscaling model for operational prediction of summer precipitation over China

A hybrid downscaling model for operational prediction of summer precipitation at 160 stations over China is proposed in this paper. The prediction model is conducted in February, and six predictors extending from the tropics to high latitudes with specific mechanisms are involved. Simultaneous sea level pressure over pan-East Asia and spring sea surface temperature (SST) over the tropical Atlantic Ocean are from the Climate Forecast System version 2 (CFSv2). The others are observational predictors including winter SST over the tropical Indian Ocean, sea ice concentration (SIC) over the Hudson Bay–Baffin Bay–Davis Strait in winter and the Barents–Kara Sea in autumn, and winter outgoing longwave radiation over the tropics. Different predictors impact the summer precipitation of different regions in China. Validations for schemes based on individual predictors (IP-schemes) and all predictors (HD-scheme) indicate that downscaling schemes outperform CFSv2 in predicting summer precipitation over China, especially the HD-scheme. The temporal and spatial anomaly correlation coefficient, root-mean-square errors, and ratio of the same sign (RSS) are estimated. For RSS (Anomalous RSS), the results are improved from 27.8% (23.8%) for CFSv2 to 77.8% (61.9%) for HD-scheme. Notably, the RSSs of IP-schemes based on SIC are equivalent to that of HD-scheme. This partly emphasizes the importance of preceding Arctic sea ice to summer rainfall in China. Moreover, two cases from hindcasts and two from the operational application are chosen for further validation of the prediction skill of HD-scheme. The case studies confirm that the hybrid downscaling model proposed in this paper not only outperforms CFSv2 but can also be applied well to real-time forecasting of summer precipitation over China.

Multi-object Spatial–Temporal Anomaly Detection Using an LSTM-Based Framework

Spatial–temporal anomaly detection methods are mostly used for single object, but rarely for multiple objects with changing positions. This problem is often encountered in multi-player online battle arena (MOBA) games, train control systems and modern battlefield command systems, and so on. However, due to the time dependence, object correlation and Display Constraint, there are few methods for solving such problem properly. In this paper, we defined the problem of multi-object spatial–temporal anomaly detection with Display Constraint in detail. To address this problem, we proposed a long short-term memory (LSTM)-based framework. First, we proposed a Display Constraint Graph to represent location relationship and designed an LSTM framework to calculate the reconstruction error. Then we used the DCG based anomaly score to discriminate abnormal subsequences and objects. We applied this method to 18 MOBA game data streams, and achieved better results than traditional methods.

Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique

Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.

Assessment and Improvement of Two Low-Cost Particulate Matter Sensor Systems by Using Spatial Interpolation Data from Air Quality Monitoring Stations

Two low-cost fine particulate matter (PM2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events.

Phenological corrections to a field-scale, ET-based crop stress indicator: An application to yield forecasting across the U.S. Corn Belt

Soil moisture deficiency is a major factor in determining crop yields in water-limited agricultural production regions. Evapotranspiration (ET), which consists of crop water use through transpiration and water loss through direct soil evaporation, is a good indicator of soil moisture availability and vegetation health. ET therefore has been an integral part of many yield estimation efforts. The Evaporative Stress Index (ESI) is an ET-based crop stress indicator that describes temporal anomalies in a normalized evapotranspiration metric as derived from satellite remote sensing. ESI has demonstrated the capacity to explain regional yield variability in water-limited regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to interannual phenological variability. This investigation selected three study sites across the U.S. Corn Belt – Mead, NE, Ames, IA and Champaign, IL – to investigate the potential operational value of 30-m resolution, phenologically corrected ESI datasets for yield prediction. The analysis was conducted over an 8-year period from 2010 to 2017, which included both drought and pluvial conditions as well as a broad range in yield values. Detrended yield anomalies for corn and soybean were correlated with ESI computed using annual ET curves temporally aligned based on (1) calendar date, (2) crop emergence date, and (3) a growing degree day (GDD) scaled time axis. Results showed that ESI has good correlations with yield anomalies at the county scale and that phenological corrections to the annual temporal alignment of the ET timeseries improve the correlation, especially when the time axis is defined by GDD rather than the calendar date. Peak correlations occur in the silking stage for corn and the reproductive stage for soybean – phases when these crops are particularly sensitive to soil moisture deficiencies. Regression equations derived at the time of peak correlation were used to estimate yields at county scale using a leave-one-out cross-validation strategy. The ESI-based yield estimates agree well with the USDA National Agricultural Statistics Service (NASS) county-level crop yield data, with correlation coefficients ranging from 0.79 to 0.93 and percent root-mean-square errors of 5–8%. These results demonstrate that remotely sensed ET at high spatiotemporal resolution can convey valuable water stress information for forecasting crop yields across the Corn Belt if interannual phenological variability is considered.

A Hybrid Ensemble Canonical Correlation Prediction Model of the Winter Siberian High

AbstractAs one of the main components of the East Asian winter monsoon, the winter Siberian High (SH) plays an important role in the variability of East Asian climate. However, the Climate Forecast System, version 2 (CFSv2), shows limited prediction skill for the winter SH. To improve the prediction skill, a hybrid ensemble canonical correlation (ECC) prediction model is established for the winter SH and SH intensity index (SHI) basing the year‐to‐year increment method and an efficient downscaling approach. Hence, three preceding predictors from observation/reanalysis [sea‐ice concentration (SIC), snow‐cover extent (SCE), and sea surface temperature (SST)] and one integrated current predictor from CFSv2 [surface air temperature and sea level pressure (SAT&SLP)] are selected based on their fundamental physical roles. Considering these individual predictors, four separate downscaling schemes are constructed. The regional‐mean temporal anomaly correlation coefficient (ACC) of the winter SH for each scheme is 0.54 (SIC‐scheme‐SH), 0.50 (SCE‐scheme‐SH), 0.72 (SST‐scheme‐SH) and 0.42 (SAT&SLP‐scheme‐SH) [four schemes exceed significant at the 1% level]. However, the skill of each scheme differs in regional and temporal distribution. Thus, a hybrid ECC prediction model is proposed by employing multiple linear regression. The regional‐mean temporal ACC of the winter SH between the observed and predicted results increases from −0.12 (CFSv2; not significant at the 10% level) to 0.85 (significant at the 1% level). Besides, the correlation coefficient between the observation and hybrid ECC scheme for winter SHI is 0.90 (significant at the 1% level). Furthermore, the strongest winter SH in 2012 is reproduced well by ECC‐scheme‐SH.

Estimating Stable Measured Values and Detecting Anomalies in Groundwater Geochemistry Time Series Data Across the Athabasca Oil Sands Area, Canada

Regional groundwater monitoring in the Athabasca region of Alberta, Canada, provides information on groundwater quality and geochemical changes over time, including data useful for evaluating potential impacts of industrial activity such as oil sands mining and in situ operations. Data collected from over 5000 wells from the 1950s to 2014, including 161 wells from government’s monitoring network, were used to develop and apply bootstrap techniques for the detection of changes in groundwater geochemistry over time and at specific points in time. Increasing temporal anomalies were identified in Cl, TDS, B, and naphthenic acids in the McMurray formation across 2003 and 2008, while decreasing anomalies were found for SO4. Temporal variance for 15 indicators was quantified for a smooth bootstrap approach to arrive at stable values representative of the most recent samples taken from wells in the study area. Stable values revealed sampling bias in the Devonian, Grand Rapids, Empress, Channel Beverly, and Muriel Lake formations suggesting expansion of sampling may be necessary. Although temporal anomalies were found in the McMurray formation, sampling bias was not identified. The entropy and relative magnitude of time series were evaluated to identify candidate wells for continued observations, which consist of wells with low measurements and low entropy that are near active industry lease boundaries. Temporal anomalies, stable values, and entropy were combined into type-well information to provide plots for visual inspection and interpretation. Stable values are useful for regional mapping, for detecting future changes and trends, and for identifying areas of interest warranting further investigation.

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.

Identification of novel candidate genes and variants for hearing loss and temporal bone anomalies

Identification of novel candidate genes and variants for hearing loss and temporal bone anomalies