Land-Streamer vs. Conventional Seismic Data for High-Resolution Near-Surface Surveys

Near-surface high-resolution seismic mapping is very important in many applications such as engineering and environmental. However, the conventional setup of the seismic technique requires planting geophones, connecting cables, and then collecting all equipment after completing the survey, which is time-consuming. In this study, we suggest using a land-streamer setup rather than the conventional setup for fast, accurate, and high-resolution near-surface seismic surveys. Only one field data set is recorded using both the conventional and the land-streamer setups. The recorded data is then compared in terms of time, frequency, wavenumber domains, and acquisition time. Following this, we compared the accuracy of the subsurface mapping of both setups using a synthetic example. The results show that the conventional setup can reach deeper depths but with lower accuracy, where the errors in imaging the local anomalies’ widths and thicknesses are 77% to 145% and 35% to 50%, respectively. The land-streamer setup provides accurate near-surface results but shallower penetration depth, here the errors in the anomalies’ widths and thicknesses are 5% to 12% and 10% to 20%, respectively.

A Modified DBSCAN Algorithm for Anomaly Detection in Time-series Data with Seasonality

Anomaly detection concerns identifying anomalous observations or patterns that are a deviation from the dataset's expected behaviour. The detection of anomalies has significant and practical applications in several industrial domains such as public health, finance, Information Technology (IT), security, medical, energy, and climate studies. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm is a density-based clustering algorithm with the capability of identifying anomalous data. In this paper, a modified DBSCAN algorithm is proposed for anomaly detection in time-series data with seasonality. For experimental evaluation, a monthly temperature dataset was employed and the analysis set forth the advantages of the modified DBSCAN over the standard DBSCAN algorithm for the seasonal datasets. From the result analysis, we may conclude that DBSCAN is used for finding the anomalies in a dataset but fails to find local anomalies in seasonal data. The proposed Modified DBSCAN approach helps to find both the global and local anomalies from the seasonal data. Using normal DBSCAN we are able to get 19 (2.16%) anomaly points. While using the modified approach for DBSCAN we are able to get 42 (4.79%) anomaly points. In comparison we can say that we are able to get 2.11% more anomalies using the modified DBSCAN approach. Hence, the proposed Modified DBSCAN algorithm outperforms in comparison with the DBSCAN algorithm to find local anomalies.

Analysis of Temperature Anomalies during Thermal Monitoring of Frozen Wall Formation

The paper describes a distributed temperature sensing system that was used to monitor the artificial freezing of soils during the construction of a potash mine shaft. The technique of reconstructing the temperature field by solving the inverse problem in the entire volume of frozen soils using the measured temperatures in four thermal monitoring (TM) wells is described. Two local anomalies in temperature distributions in TM wells are described and analyzed theoretically using thermo-hydraulic modeling. The first anomaly concerns the asymmetric temperature distribution in one of the soil layers and is associated with the influence of natural groundwater flow in the horizontal direction. The second anomaly consists of a sharp decrease in water temperature in the section of the TM well located inside the freezing contour. Calculations showed that it is most likely associated with the entry of cold groundwater from the overlying layers of soils through a well filter at a depth of 160 m and the subsequent movement of the water up the well.

A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics

With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by surveillance systems are often incomplete, creating a need for the data gaps to be filled using techniques such as interpolation methods. However, such approaches do not decrease the uncertainty of ship activities. Depending on the frequency of the data generated, they may even confuse operators, inducing errors when evaluating ship activities and tagging them as unusual. Using domain knowledge to classify activities as anomalous is essential in the maritime navigation environment since there is a well-known lack of labeled data in this domain. In an area where identifying anomalous trips is a challenging task using solely automatic approaches, we use visual analytics to bridge this gap by utilizing users’ reasoning and perception abilities. In this work, we propose a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and score them. These scores are displayed in a tabular visualization where users can rank trips by segment to find local anomalies. The amount of interpolation in subtrajectories is displayed together with scores so that users can use both their insight and the trip displayed on the map to determine if the score is reliable.

Omega vs. pi, and 6d anomaly cancellation

In this note we review the role of homotopy groups in determining non-perturbative (henceforth ‘global’) gauge anomalies, in light of recent progress understanding global anomalies using bordism. We explain why non-vanishing of πd(G) is neither a necessary nor a sufficient condition for there being a possible global anomaly in a d-dimensional chiral gauge theory with gauge group G. To showcase the failure of sufficiency, we revisit ‘global anomalies’ that have been previously studied in 6d gauge theories with G = SU(2), SU(3), or G2. Even though π6(G) ≠ 0, the bordism groups $$ {\Omega}_7^{\mathrm{Spin}}(BG) $$ Ω 7 Spin BG vanish in all three cases, implying there are no global anomalies. In the case of G = SU(2) we carefully scrutinize the role of homotopy, and explain why any 7-dimensional mapping torus must be trivial from the bordism perspective. In all these 6d examples, the conditions previously thought to be necessary for global anomaly cancellation are in fact necessary conditions for the local anomalies to vanish.

The algebra of anomaly interplay

We give a general description of the interplay that can occur between local and global anomalies, in terms of (co)bordism. Mathematically, such an interplay is encoded in the non-canonical splitting of short exact sequences known to classify invertible field theories. We study various examples of the phenomenon in 2, 4, and 6 dimensions. We also describe how this understanding of anomaly interplay provides a rigorous bordism-based version of an old method for calculating global anomalies (starting from local anomalies in a related theory) due to Elitzur and Nair.

Pre-seismic geomagnetic anomalous signature related to the Mw7.0 earthquake generated in the northern coastal zone of Samos island – Greece, on October 30, 2020

<p>A strong earthquake of magnitude Mw7.0 struck the northern coastal zone of Samos Island, Aegean See, Greece, on October 30, 2020, at 11:51 UTC. This earthquake was felt at a wide area including Athens (at 270km) and city of Heraklion, Crete (at 320km), causing over 120 deaths and a lot of damages on houses, buildings and infrastructures mainly in Samos Island and Izmir (Turkey). With the aim to identify an anomalous geomagnetic signature before the onset of this earthquake, we have retrospectively analyzed the data collected, on the interval September 16 - October 31, 2020, at the two geomagnetic observatories, Pedeli (PEG)-Greece and Panagjurishte (PAG)-Bulgaria, by using the polarization parameter (BPOL) and the strain effect–related to geomagnetic signal identification. Thus, for the both observation sites (PEG and PAG), the daily mean distribution of the BPOL and its standard deviation (SD) are carried out using a FFT band-pass filtering in the ULF range (0.001-0.0083Hz). Further on, a statistical analysis based on a standardized random variable equation was applied for the following two particular cases: a) to assess on the both time series BPOL*(PEG) and BPOL*(PAG) the anomalous signature related to Mw7.0 earthquake; b) to differentiate transient local anomalies associated with Mw7.0 earthquake from the internal and external parts of the geomagnetic field, taking the PAG Observatory as reference. Finally, on the BPOL*(PEG-PAG) time series, carried out on the interval 1-31 October, 2020, a very clear anomaly of maximum, greater than 1.2SD, was detected on October 27, with 3days before the onset of Mw7.0 earthquake.</p>

Morphologic abnormalities in Amblyomma mixtum, Amblyomma naponense, Amblyomma cf. oblongoguttatum and Amblyomma tapirellum (Acari: Ixodidae) of Costa Rica

In this work, we describe abnormalities in adults of Amblyomma mixtum, Amblyomma naponense, Amblyomma cf. oblongoguttatum and Amblyomma tapirellum of Costa Rica. General anomalies manifested as body asymmetry, nanism, and gigantism, while local anomalies occurred in the legs, festoons, anal pore and spiracle. These are the firsts mention of abnormalities in ticks from Costa Rica.

Possible Correlations between the ULF Geomagnetic Signature and Mw6.4 Coastal Earthquake, Albania, on 26 November 2019

An earthquake of Mw6.4 hit the coastal zone of Albania on 26 November 2019, at 02:54:11 UTC. It was intensively felt at about 34 km away, in Tirana City, where damages and lives lost occurred. To emphasize a pre-seismic geomagnetic signature before the onset of this earthquake, the data collected on the interval 15 October–30 November 2019, at the Panagjurishte (PAG)-Bulgaria and Surlari (SUA)-Romania observatories were analyzed. Further on, for geomagnetic signal identification we used the polarization parameter (BPOL) which is time invariant in non-seismic conditions and it becomes unstable due to the strain effect related to the Mw6.4earthquake. Consequently, BPOL time series and its standard deviations are performed for the both sites using ultra low frequency (ULF)-fast Fourier transform (FFT) band-pass filtering. A statistical analysis, based on a standardized random variable equation, was applied to emphasize on the BPOL* (PAG) and ABS BPOL* (PAG) time series the anomalous signal’s singularity and, to differentiate the transient local anomalies due to the Mw6.4 earthquake, from the internal and external parts of the geomagnetic field, taken PAG observatory as reference. Finally, the ABS BPOL* (PAG-SUA) time series were obtained on the interval 1–30 November 2019, where a geomagnetic signature greater than 2.0, was detected on 23 November and the lead time was 3 days before the onset of the Mw6.4earthquake.