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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]

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]

Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data has been achieved. This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods. In this study, we develop 1D deep Residual Neural Network (ResNet), for tackling the problem of seismic signal detection and phase identification. This method is trained and tested on the dataset recorded by the Southern California Seismic Network. Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signal sand the identification of seismic phases. Compared to previously proposed deep learning methods, the introduced framework achieves around 4% improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center. The model generalizability is also tested further on the STanford EArthquake Dataset. In addition, the experimental result on the same subset of the STanford EArthquake Dataset, when masked by different noise levels, demonstrates the model's robustness in identifying the seismic phases of small magnitude. [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]

Background: Traditional therapy using natural products, especially flavonoids and alkaloids have been in practice for a long time. Among flavonoids, curcumin, quercetin, berberine, and epigallocatechin have been studied in greater detail in terms of their anticancer and anti-inflammatory activities. Although many studies focused on the PI3K, MAP kinase and NF-κB pathways, a thorough investigation of modulation of players in the apoptotic and Wnt/β-catenin signaling pathway by curcumin and quercetin has not been done. Also, only few studies have been carried out on curcumin and quercetin co-treatment studies.Hypothesis/purpose: We hypothesized that the combination of natural products will have synergistic effects and the antiproliferative effect will be attenuated via apoptotic as well as Wnt/β-catenin signaling pathways.Study Design and Methods: To test our hypothesis, we compared potency of natural anticancer agents in four cancer cell lines, A549, HCT116, MCF7, and A375 by MTT and colony proliferation assays and investigated mechanism of anticancer activities by analyzing players in apoptotic and Wnt/β-catenin signaling pathways in A375 cells treated with test agents individually or in combination.Results: Epicatechins, up to 100 μM concentration, did not inhibit cancer cell proliferation, while curcumin inhibited proliferation in A549 and HCT116 cancer cell lines with an IC50 of 3 to 8.5 μM. Quercetin showed stronger inhibition of cell proliferation than berberine. Combination study with two most potent agents, curcumin and quercetin, in 4 cancer cell lines, suggested synergistic effect on cell proliferation with several fold decreases in IC50. Further investigation of the mechanism of action of curcumin and quercetin in melanoma cells, A375, suggested that inhibition of cell proliferation occurred through down-regulation of Wnt/β-catenin signaling pathway proteins, DVL2, β-catenin, cyclin D1, Cox2, and Axin2. In addition, both curcumin and quercetin induced apoptosis by down-regulating BCL2 and inducing caspase 3/7 through PARP cleavage.Conclusion: These results demonstrate that curcumin and quercetin inhibit cancer cell proliferation synergistically and Wnt/β-catenin signaling and apoptotic pathways are partly responsible for antiproliferative activities. [ABSTRACT FROM AUTHOR]

Liquefaction-induced ground failure is one amongst the leading causes of infrastructure damage due to the impact of large earthquakes in unconsolidated, non-cohesive, water saturated alluvial terrains. The city of Kolkata is located on the potentially liquefiable alluvial fan deposits of Ganga-Bramhaputra-Meghna Delta system with subsurface litho-stratigraphic sequence comprising of varying percentages of clay, cohesionless silt, sand, and gravel interbedded with decomposed wood and peat. Additionally, the region has moderately shallow groundwater condition especially in the post-monsoon seasons. In view of burgeoning population, there had been unplanned expansion of settlements in the hazardous geological, geomorphological, and hydrological conditions exposing the city to severe liquefaction hazard. The 1897 Shillong and 1934 Bihar-Nepal earthquakes both of M 8.1 reportedly induced Modified Mercalli Intensity of IV-V and VI-VII respectively in the city reportedly triggering widespread to sporadic liquefaction condition with surface manifestation of sand boils, lateral spreading, ground subsidence, etc., thus posing a strong case for liquefaction potential analysis in the terrain. With the motivation of assessing seismic hazard, vulnerability, and risk of the city of Kolkata through a consorted federal funding stipulated for all the metros and upstart urban centers in India located in BIS seismic zones III, IV, and V with population more than one million, an attempt has been made here to understand the liquefaction susceptibility condition of Kolkata under the impact of earthquake loading employing modern multivariate techniques and also to predict deterministic liquefaction scenario of the city in the event of a probabilistic seismic hazard condition with 10% probability of exceedance in 50 years and a return period of 475 years. We conducted in-depth geophysical and geotechnical investigations in the city encompassing 435 km area. The stochastically synthesized bedrock ground motion for both the 1897 and 1934 earthquakes on non-linear analysis of local site conditions through DEEPSOIL Geotechnical analysis package present surface level peak ground acceleration of the order of 0.05-0.14 g for the 1934 Bihar-Nepal earthquake while for the 1897 Shillong earthquake it is found to be in the range of 0.03-0.11 g. The factor of safety (FOS) against liquefaction, the probability of liquefaction ( P ), the liquefaction potential index (LPI), and the liquefaction risk index are estimated under the influence of these two earthquakes wherein the city is classified into severe (LPI > 15), high (5 < LPI ≤ 15), moderate (0 < LPI ≤ 5), and non-liquefiable (LPI = 0) susceptibility zones. While the 1934 Bihar-Nepal earthquake induced moderate to severe liquefaction hazard condition in the city in mostly the deltaic plain and interdistributary marsh geomorphologic units with 13.5% sites exhibiting moderate hazard with a median LPI of 1.8, 8.5% sites depicting high with a median LPI of 9.1 and 4% sites with a median LPI of 18.9 exhibiting severe hazard condition, 1897 Shillong earthquake induced mostly non-liquefaction condition with very few sites depicting moderate and high liquefaction hazard. A conservative liquefaction hazard scenario of the city on the other hand estimated through deterministic approach for 10% probability of exceedance in 50 years predicts a high hazard zone in the 3.5-19 m depth region with FOS < 1 and P > 65% comprising of coarse-grained sediments of sand, silty sand, and clayey silty sand in mostly the deltaic plain geomorphologic unit with 39.1% sites depicting severe liquefaction hazard with a median LPI of 28.3. A non-linear regression analysis on both the historical and deterministic liquefaction scenarios in P versus LPI domain with ± 1 standard deviation confidence bound generated a cubic polynomial relationship between the two liquefaction hazard proxies. This study considered a bench mark for other cities in the country and elsewhere forms an integral part of the mega-seismic microzonation endeavors undertaken in all the earthquake-prone counties in the world. [ABSTRACT FROM AUTHOR]

We deliver a next-generation Probabilistic Seismic Hazard model of West Bengal based on improved seismogenic source characterization considering both the Layered Polygonal sources & Tectonic sources in the hypocentral depth range of 0–25 km & 25–70 km, seismic local-specific site condition, and adaption of appropriate region specific ground motion prediction equations in a logic tree framework. The surface consistent Probabilistic seismic hazard distribution in terms of Peak Ground Acceleration (PGA) & 5% damped Pseudo Spectral Acceleration (PSA) at different time periods for 10% probability of exceedance in 50 years have been generated and the design response spectra computed. [ABSTRACT FROM PUBLISHER]

The formation of graphene on the SiC(0001) surface (the C-face of the {0001} surfaces) has been studied, utilizing both disilane and neon environments. In both cases, the interface between the graphene and the SiC is found to be different than for graphene formation in vacuum. A complex low-energy electron diffraction pattern with √43 x √43-R ± 7.6° symmetry is found to form at the interface. An interface layer consisting essentially of graphene is observed, and it is argued that the manner in which this layer covalently bonds to the underlying SiC produces the √43 x √43-R ± 7.6 structure [i.e., analogous to the 6√3 x 6√3-R30° "buffer layer" that forms on the SiC(0001) surface (the Si-face)]. Oxidation of the surface is found to modify (eliminate) the √43 x √43-R ± 7.6° structure, which is interpreted in the same manner as the known "decoupling" that occurs for the Si-face buffer layer. [ABSTRACT FROM AUTHOR]

Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications. [ABSTRACT FROM AUTHOR]