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Seismic phase picking and magnitude estimation are fundamental aspects of earthquake monitoring and seismic event analysis. Accurate phase picking allows for precise characterization of seismic wave arrivals, contributing to a better understanding of earthquake events. Likewise, accurate magnitude estimation provides crucial information about an earthquake's size and potential impact. Together, these components enhance our ability to monitor seismic activity effectively. In this study, we explore the application of deep-learning techniques for earthquake detection and magnitude estimation using continuous seismic recordings. Our approach introduces DynaPicker, which leverages dynamic convolutional neural networks to detect seismic body-wave phases in continuous seismic data. We demonstrate the effectiveness of DynaPicker using various open-source seismic datasets, including both window-format and continuous recordings. We evaluate its performance in seismic phase identification and arrival-time picking, as well as its robustness in classifying seismic phases using low-magnitude seismic data in the presence of noise. Furthermore, we integrate the phase arrival-time information into a previously published deep-learning model for magnitude estimation. We apply this workflow to continuous recordings of aftershock sequences following the Turkey earthquake. The results of this case study showcase the reliability of our approach in earthquake detection, phase picking, and magnitude estimation, contributing valuable insights to seismic event analysis. [ABSTRACT FROM AUTHOR]

Aims The bacterial communities associated with the gastrointestinal (GI) tract are primarily involved in digestion, physiology, and the immune response against pathogenic bacteria for the overall development and health of the host. Hilsa shad (Tenualosa ilisha), a tropical anadromous fish, found predominantly in Bangladesh and India, has so far been poorly investigated for its gut bacterial communities. In this study, both culture-based and metagenomic approaches were used to detect intestinal isolates of hilsa, captured from both freshwater and seawater to investigate the community structure of intestinal microbiota. Methods and results Culture-dependent approach allowed to isolate a total of 23 distinct bacterial species comprising 16 Gram-negative, and 7 Gram-positive isolates, where Proteobacteria and Firmicutes were identified as the two most dominant phyla. While metagenomic approach explored a wide range of important GI bacteria, primarily dominated by Proteobacteria, Firmicutes, and Bacteroidetes, with Proteobacteria and Firmicutes, being the most abundant in freshwater and seawater samples, respectively. Conclusions A combination of these approaches provided the differential GI-associated bacterial diversity in freshwater and seawater hilsa with the prediction of overall functional potential. Impact Statement The study explored the diversity of gut microbiota in hilsa, one of the most preferred nutritious dietary fish, captured from freshwater and seawater habitats, which may encourage to comprehend the composition of the gut microbiome in relation to the migratory behavior and polyunsaturated fatty acid profile of anadromous fish in general. [ABSTRACT FROM AUTHOR]

Seismic phase picking and magnitude estimation are essential components of real-time earthquake monitoring and earthquake early warning systems. Reliable phase picking enables the timely detection of seismic wave arrivals, facilitating rapid earthquake characterization and early warning alerts. Accurate magnitude estimation provides crucial information about an earthquake’s size and potential impact. Together, these steps contribute to effective earthquake monitoring, enhancing our ability to implement appropriate response measures in seismically active regions and mitigate risks. In this study, we explore the potential of deep learning in real-time earthquake monitoring. To that aim, we begin by introducing DynaPicker which leverages dynamic convolutional neural networks to detect seismic body wave phases. Subsequently, DynaPicker is employed for seismic phase picking on continuous seismic recordings. To showcase the efficacy of Dynapicker, several open-source seismic datasets including window-format data and continuous seismic data are used to demonstrate it’s performance in seismic phase identification, and arrival-time picking. Additionally, DynaPicker’s robustness in classifying seismic phases was tested on the low-magnitude seismic data polluted by noise. Finally, the phase arrival time information is integrated into a previously published deep-learning model for magnitude estimation. This workflow is then applied and tested on the continuous recording of the aftershock sequences following the Turkey earthquake to detect the earthquakes, seismic phase picking and estimate the magnitude of the corresponding event. The results obtained in this case study exhibit a high level of reliability in detecting the earthquakes and estimating the magnitude of aftershocks following the Turkey earthquake. [ABSTRACT FROM AUTHOR]

COVID-19, which is caused by the novel coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2), is the deadliest outbreak of this millennium. Despite adopting several precautionary strategies and guidelines, COVID-19 has spread rapidly, and the number of cases is still in escalation across the world. The various immune-boosting drugs with severe side effects and the vaccines approved after negotiated clinical trials have been struggling to cope with the emergence of new variants of the virus. Nevertheless, given a large number of asymptomatic cases, a high magnitude of recovery rate, and a relatively higher prevalence of morbidity and mortality among immunologically compromised individuals, those affected by an illness, and the elderly, it appears that a healthy microbiome and the associated immune responses are the key factors for survival. Incidentally, the consumption of traditionally popular and nutritious fermented foods, which are composed of biologically functional ingredients and several health-promoting probiotics, offers promising health benefits through the improvement of the immune system in general. Given the progress in functional food research, it has become crucial to understand the impact of a healthy microbiome and the immunomodulatory roles of fermented foods on the battles to combat infectious diseases. Based on the evidence of the impact of probiotics-based fermented foods, the beneficial roles of a few frequently consumed fermented foods in the management of various infections have been resolutely discussed in the present study, with a focus on their antagonistic and immune-modulating effects in the context of the current COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The rapid characterisation of earthquake parameters such as its magnitude is at the heart of earthquake early warning (EEW). In traditional EEW methods, the robustness in the estimation of earthquake parameters has been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep-learning-based magnitude classifier based on data from a single seismic station and further investigate the effect of using five different durations of seismic waveform data after first P-wave arrival: 1, 3, 10, 20 and 30 s. This is accomplished by testing the performance of the proposed model that combines convolution and bidirectional long short-term memory units to classify waveforms based on their magnitude into three classes: "noise", "low-magnitude events" and "high-magnitude events". Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as "high-magnitude". We show that the variation in the results produced by changing the length of the data is no more than the inherent randomness in the trained models due to their initialisation. We further demonstrate that the model is able to successfully classify waveforms over wide ranges of both hypocentral distance and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

The detection and rapid characterization of earthquake parameters such as magnitude are important in real‐time seismological applications such as Earthquake Monitoring and Earthquake Early Warning (EEW). Traditional methods, aside from requiring extensive human involvement can be sensitive to signal‐to‐noise ratio leading to false/missed alarms depending on the threshold. We here propose a multitasking deep learning model—the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) that: (a) detects the earthquake signal from background seismic noise, (b) determines the first P wave arrival time, and (c) estimates the magnitude using the raw three‐component waveforms from a single station as model input. Considering, that speed is essential in EEW, we use up to 2 s of P wave information which, to the best of our knowledge, is a significantly smaller data window compared to the previous studies. To examine the robustness of CREIME, we test it on two independent data sets and find that it achieves an average accuracy of 98% for event versus noise discrimination and can estimate first P‐arrival time and local magnitude with average root mean squared errors of 0.13 s and 0.65 units, respectively. We compare CREIME with traditional methods such as short‐term‐average/long‐term‐average (STA/LTA) and show that CREIME has superior performance, for example, the accuracy for signal and noise discrimination is higher by 4.5% and 11.5%, respectively, for the two data sets. We also compare the architecture of CREIME with the architectures of other baseline models, trained on the same data, and show that CREIME outperforms the baseline models. Plain Language Summary: The detection of earthquakes and rapid determination of parameters such as magnitude is crucial in Earthquake Monitoring and Earthquake Early Warning (EEW). Existing methods used to make such estimations are empirical and require expert analysts to define involved parameters, which is quite challenging. They are also sensitive to noise, which could lead to erroneous results. In this paper, we propose the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) which is capable to detect an earthquake within 2 s of the first P wave arrival and provides a first estimate for its magnitude. We test the model on two independent data sets to demonstrate its generalizability. CREIME successfully discriminates between seismic events and noise with an average accuracy of 98% and can estimate first P‐arrival time and local magnitude with average root mean squared errors of 0.13 s and 0.65 units, respectively. We also show that CREIME can perform better than traditional methods like STA/LTA and previously published deep learning architectures in the context of rapid characterization. Key Points: We use a novel sequence‐to‐sequence mapping to train a deep learning model to detect an earthquake, pick the P‐wave arrival and estimate its magnitudeThe proposed model can perform reasonably well with 5 s windows containing only up to 2 s of P wave dataWe show that our model can outperform traditional methods like short‐term‐average/long‐term‐average (STA/LTA) and the existing deep learning models [ABSTRACT FROM AUTHOR]

The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Earthquake Early Warning (EEW). In traditional EEW methods the robustness in the estimation of earthquake parameters have been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep learning based magnitude classifier and, further we investigate the effect of using five different durations of seismic waveform data after first P-wave arrival-1s, 3s, 10s, 20s and 30s. This is accomplished by testing the performance of the proposed model that combines Convolution and Bidirectional Long-Short Term Memory units to classify waveforms based on their magnitude into three classes-"noise", "low-magnitude events" and "high-magnitude events". Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as high-magnitude. We show that the variation in the results produced by changing the length of the data, is no more than the inherent randomness in the trained models, due to their initialisation. [ABSTRACT FROM AUTHOR]

Structures hidden at depth can be assessed through drill cores recovered from them. A crucial part of petroleum geology study is to interpret structures from these cores collected from vertical and inclined drilling operations reaching different depths. We deduce analytically ideal structures in unrolled/unwrapped images of cylindrical drill cores. These structures are: (i) orthogonal and conjugate fractures, (ii) single generation periodic folds and type-1 and type-2 superposed folds, (iii) listric faults and (iv) different types of angular unconformities. The sinuous curves as found in unrolled images are not always characteristic of the structures. Therefore accurate identification of structures would require additional information, such as the drill core itself. • Unrolled images of ideal structures in unwrapped/unrolled images of drill cores are presented. • Correct identification of structures, however, would require other sources of geological information. • The work has direct bearing in petroleum geoscience. [ABSTRACT FROM AUTHOR]

Studying the kinematics of listric faults is of great importance in petroleum geology and seismicity. The deformation related to listric faults differ from the planar fault plane case. This study involves 3-D co-ordinate geometry with spherical co-ordinates to deduce the effective-slip (e s) and the net-slip (n s) of listric faults of three ideal geometries: spherical, paraboloid and ellipsoidal. We perform 3D slip analyses on two types of listric faults (Type-1 and 2). Previous authors have considered these shapes of fault planes in their modeling. Using these e s , one can track material points before and after faulting, similar to Mukherjee (2019) applied on translational faults with planar fault planes. The n s can be divided into a strike-slip ( s n s ) and a dip-slip component ( d n s ). This resolution being done on the listric fault plane itself, n s ≠ ( s n s 2 + d n s 2 ) 0.5 . This is unlike the planar fault plane case. The purpose of 3D-geometric analysis of listric faults could be, in long run, the restoration of rock blocks that underwent listric faulting. • Kinematics of listric faults with ideal geometries are analyzes using 3-D co-ordinate geometry. • "Effective slip" is defined and its expressions for listric faults are deduced. • This work will be important in fault block restoration and modeling. [ABSTRACT FROM AUTHOR]