Ultrasonic and seismic constraints on crystallographic preferred orientations of the Priestley Glacier shear margin, Antarctica

Crystallographic preferred orientations (CPOs) are particularly important in controlling the mechanical properties of glacial shear margins. Logistical and safety considerations often make direct sampling of shear margins difficult and geophysical measurements are commonly used to constrain the CPOs. We present here the first direct comparison of seismic and ultrasonic data with measured CPOs in a polar shear margin. The measured CPO from ice samples from a 58 m deep borehole in the left lateral shear margin of the Priestley Glacier, Antarctica, is dominated by horizontal c-axes aligned sub-perpendicular to flow. A vertical seismic profile experiment with hammer shots up to 50 m away from the borehole, in four different azimuthal directions, shows velocity anisotropy of both P-waves and S-waves. Matching P-wave data to the anisotropy corresponding to CPO models defined by horizontally aligned c-axes gives two possible solutions for c-axis azimuth, one of which matches the c-axis measurements. If both P-wave and S-wave data are used, there is one best fit for azimuth and intensity of c-axis alignment that matches well the measurements. Azimuthal P-wave and S-wave ultrasonic data recorded in the laboratory on the ice core show clear anisotropy that matches that predicted from the CPO of the samples. With good quality data, azimuthal increments of 30° or less will constrain well the orientation and intensity of c-axis alignment. Our experiments provide a good framework for planning seismic surveys aimed at constraining the anisotropy of shear margins.

Direct shear-wave seismic survey in Sanhu area, Qaidam Basin, west China

The formation containing shallow gas clouds poses a major challenge for conventional P-wave seismic surveys in the Sanhu area, Qaidam Basin, west China, as it dramatically attenuates seismic P-waves, resulting in high uncertainty in the subsurface structure and complexity in reservoir characterization. To address this issue, we proposed a workflow of direct shear-wave seismic (S-S) surveys. This is because the shear wave is not significantly affected by the pore fluid. Our workflow includes acquisition, processing, and interpretation in calibration with conventional P-wave seismic data to obtain improved subsurface structure images and reservoir characterization. To procure a good S-wave seismic image, several key techniques were applied: (1) a newly developed S-wave vibrator, one of the most powerful such vibrators in the world, was used to send a strong S-wave into the subsurface; (2) the acquired 9C S-S data sets initially were rotated into SH-SH and SV-SV components and subsequently were rotated into fast and slow S-wave components; and (3) a surface-wave inversion technique was applied to obtain the near-surface shear-wave velocity, used for static correction. As expected, the S-wave data were not affected by the gas clouds. This allowed us to map the subsurface structures with stronger confidence than with the P-wave data. Such S-wave data materialize into similar frequency spectra as P-wave data with a better signal-to-noise ratio. Seismic attributes were also applied to the S-wave data sets. This resulted in clearly visible geologic features that were invisible in the P-wave data.

Wave Prediction in a Harbour using Deep Learning with Offshore Data

In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.

Genomic Epidemiology of SARS-CoV-2 Divulge B.1, B.1.36, and B.1.1.7 as the Most Dominant Lineages in First, Second, and Third Wave of SARS-CoV-2 Infections in Pakistan

The present study aims to investigate the genomic variability and epidemiology of SARS-CoV-2 in Pakistan along with its role in the spread and severity of infection during the three waves of COVID-19. A total of 453 genomic sequences of Pakistani SARS-CoV-2 were retrieved from GISAID and subjected to MAFFT-based alignment and QC check which resulted in removal of 53 samples. The remaining 400 samples were subjected to Pangolin-based genomic lineage identification. And to infer our SARS-CoV-2 time-scaled and divergence phylogenetic trees, 3804 selected global reference sequences plus 400 Pakistani samples were used for the Nextstrain analysis with Wuhan/Hu-1/2019, as reference genome. Finally, maximum likelihood based phylogenetic tree was built by using the Nextstrain and coverage map was created by employing Nextclade. By using the amino acid substitutions, the maximum likelihood phylogenetic trees were developed for each wave, separately. Our results reveal the circulation of 29 lineages, belonging to following seven clades G, GH, GR, GRY, L, O, and S in the three waves. From first wave, 16 genomic lineages of SARS-CoV-2 were identified with B.1(24.7%), B.1.36(18.8%), and B.1.471(18.8%) as the most prevalent lineages respectively. The second wave data showed 18 lineages, 10 of which were overlapping with the first wave suggesting that those variants could not be contained during the first wave. In this wave, a new lineage, AE.4, was reported from Pakistan for the very first time in the world. However, B.1.36 (17.8%), B.1.36.31 (11.9%), B.1.1.7 (8.5%), and B.1.1.1 (5.9%) were the major lineages in second wave. Third wave data showed the presence of nine lineages with Alpha/B.1.1.7 (72.7%), Beta/B.1.351 (12.99%), and Delta/B.1.617.2 (10.39%) as the most predominant variants. It is suggested that these VOCs should be contained at the earliest in order to prevent any devastating outbreak of SARS-CoV-2 in the country.

VIRTUAL HULL MONITORING: CONTINUOUS FATIGUE ASSESSMENT WITHOUT ADDITIONAL INSTRUMENTATION

A novel technique to monitor hull stresses using data currently collected on most ships is explored. This technique, referred to herein as virtual hull monitoring, uses global position signals, measured or numerically-modelled wave data, and a database of calculated stress transfer functions. This enables monitoring of short-term stress states and corresponding fatigue damage accumulation for many structural locations, either onboard or at a central location, for an entire fleet. The components, benefits, and limitations of this proposed technique are discussed. Wave buoy and strain gauge measurements from a full-scale naval vessel trial are used in comparisons with hindcast wave data and the calculated stress spectra for one structural location. Close agreement between the wave data sources and corresponding stress spectra warrants further examination of virtual hull monitoring. 

SHIP OPERABILITY PREDICTED FROM LONG TERM DIRECTIONAL WAVE RECORDS

Ship operability assessments have traditionally been made using wind and wave data derived from wave atlases, however there are several drawbacks, including the fact that they are usually based on observation rather than measurement, and that spreading or directional effects are lost – such as the separation of sea and swell directions. An alternative approach is demonstrated here, instead of the data summarised in the wave atlas scatter diagram, long term hourly historical wave buoy data may be used. Detailed data sets, including directional wave spectra, are available for a number of specific locations. Direct use of many years’ hourly wave data involves significant computational effort, but results may be achieved within a reasonable time. The technique is demonstrated with the examples of four naval ships and two sites. Analysis considered two main themes, the differences in the ship performance calculated when (a) using wave buoy data rather than wave atlas data for the same sea area and (b) using the most complex available model of the ocean waves compared with the simplified wave descriptions in common use. For (a) the wave buoy data both looked rather different than the wave buoy data for the same nominal area, and produced rather different ship performance results. For (b) it was shown that there were also significant differences between the operability calculated for the four different ships at one of the sites. The implications for operability assessment in the ship procurement process are briefly discussed.

De-noising Receiver function data using the unsupervised deep learning approach

Summary The converted wave data (P-to-s or S-to-p), traditionally termed as receiver functions, are often contaminated with noise of different origin that may lead to the erroneous identification of phases and thus influence the interpretations. Here we utilize an unsupervised deep learning approach called Patchunet to de-noise the converted wave data. We divide the input data into several patches, which are input to the encoder and decoder network to extract some meaningful features. The method de-noises an image patch-by-patch and utilizes the redundant information on similar patches to obtain the final de-noised results. The method is first tested on a suite of synthetic data contaminated with various amount of Gaussian and realistic noise and then on the observed data from three permanent seismic stations: HYB (Hyderabad, India), LBTB (Lobatse, Botswana, South Africa), COR (Corvallis, Oregon, USA). The method works very well even when the signal-to-noise ratio is poor or with the presence of spike noise and deconvolution artifacts. The field data demonstrate the effectiveness of the method for attenuating the random noise especially for the mantle phases, which show significant improvements over conventional receiver function based images.

A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring

Purpose: The aim of this research is to develop an accurate and interpretable aggregated score not only for hospitalization outcome prediction (death/discharge) but also for the daily assessment of the COVID-19 patient's condition.Patients and Methods: In this single-center cohort study, real-world data collected within the first two waves of the COVID-19 pandemic was used (27.04.2020–03.08.2020 and 01.11.2020–19.01.2021, respectively). The first wave data (1,349 cases) was used as a training set for the score development, while the second wave data (1,453 cases) was used as a validation set. No overlapping cases were presented in the study. For all the available patients' features, we tested their association with an outcome. Significant features were taken for further analysis, and their partial sensitivity, specificity, and promptness were estimated. Sensitivity and specificity were further combined into a feature informativeness index. The developed score was derived as a weighted sum of nine features that showed the best trade-off between informativeness and promptness.Results: Based on the training cohort (median age ± median absolute deviation 58 ± 13.3, females 55.7%), the following resulting score was derived: APTT (4 points), CRP (3 points), D-dimer (4 points), glucose (4 points), hemoglobin (3 points), lymphocytes (3 points), total protein (6 points), urea (5 points), and WBC (4 points). Internal and temporal validation based on the second wave cohort (age 60 ± 14.8, females 51.8%) showed that a sensitivity and a specificity over 90% may be achieved with an expected prediction range of more than 7 days. Moreover, we demonstrated high robustness of the score to the varying peculiarities of the pandemic.Conclusions: An extensive application of the score during the pandemic showed its potential for optimization of patient management as well as improvement of medical staff attentiveness in a high workload stress. The transparent structure of the score, as well as tractable cutoff bounds, simplified its implementation into clinical practice. High cumulative informativeness of the nine score components suggests that these are the indicators that need to be monitored regularly during the follow-up of a patient with COVID-19.