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The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earth-quake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on 22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, we check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities.

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 signals and 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.

Nationalism is a fluid concept which needs contextual understanding. Construction of communal and caste identities played an important role in countering or qualifying nationalism in the United Provinces (UP) (1918–30). This article investigates the contents of some of the Brahman caste journals in order to study the evolution of identify consciousness, the construction of the ‘self’ vs. the ‘other’, where the self was always the Hindu upper caste, and the ‘other’ was invariably the ‘Musalman’. It is clear that identity consciousness had an overarching presence in middle-class UP in the 1920s—a presence which is clearly traceable in the caste journals published in that decade.

In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage. We employ ray tracing to simulate the propagation of seismic waves through the heterogeneous medium of a volcanic edifice, and an inverse modeling algorithm that uses an ANN to estimate the velocity structure from the “observed” travel-time data. The performance of the approach is evaluated through a 2-dimensional numerical study that simulates i) an active source seismic experiment with a few (explosive) sources placed on one side of the edifice and a dense line of receivers placed on the other side, and ii) earthquakes located inside the edifice with receivers placed on both sides of the edifice. The results are compared with those obtained from conventional damped linear inversion. The average Root Mean Square Error (RMSE) between the input and output models is approximately 0.03 km/s for the ANN inversions, whereas it is about 0.4 km/s for the linear inversions, demonstrating that the ANN-based approach outperforms the classical approach, particularly in situations with sparse ray coverage. Our study emphasizes the advantages of employing a relatively simple ANN architecture in conjunction with second-order optimizers to minimize the loss function. Compared to using first-order optimizers, our ANN architecture shows a ∼25% reduction in RMSE. The ANN-based approach is computationally efficient. We observed that even though the ANN is trained based on completely random velocity models, it is still capable of resolving previously unseen anomalous structures within the edifice with about 5% anomalous discrepancies, making it a potentially valuable tool for the detection of low velocity anomalies related to magmatic intrusions or mush.

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 Pwave 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 Pwave 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. 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 Pwave 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. 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 Pwave dataWe show that our model can outperform traditional methods like short‐term‐average/long‐term‐average (STA/LTA) and the existing deep learning models 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 magnitude The proposed model can perform reasonably well with 5 s windows containing only up to 2 s of Pwave data We show that our model can outperform traditional methods like short‐term‐average/long‐term‐average (STA/LTA) and the existing deep learning models

In this paper we discuss the effect of background pressure and synthesis temperature on the graphene crystal sizes in chemical vapor deposition (CVD) on copper catalyst. For the first time, we quantitatively demonstrate a fundamental role of the background pressure and provide the activation energy for graphene nucleation in atmospheric pressure CVD (9 eV), which is substantially higher than for the low pressure CVD (4 eV). We attribute the difference to a greater importance of copper sublimation in the low pressure CVD, where severe copper evaporation likely dictates the desorption rate of active carbon from the surface. At atmospheric pressure, where copper evaporation is suppressed, the activation energy is assigned to the desorption energy of carbon clusters instead. The highest possible temperature, close to the melting point of copper, should be used for large single crystal graphene synthesis. Using these conditions, we have synthesized graphene single crystals with sizes over 0.5 mm. Single crystal nature of synthesized graphene was confirmed by low-energy electron diffraction. We also demonstrate that CVD of graphene at temperatures below 1000 °C shows higher nucleation density on (111) than on (100) and (101) copper surfaces, but there is no identifiable preference at higher temperatures.

Electrochemical functionalization of treated epitaxial graphene samples on Si-face 6H-SiC are presented in this work. Three semi-insulating 6H-SiC substrates cut from different boules with varying off cut angle (on axis, 0.5° and 1.0° degrees off axis in the [112‾0] direction) were diced into 10mm x 10mm samples and quality EG grown on top. A home-build electrochemical cell was used with current applied though a 10% H2SO4 solution, with a Pt wire and exposed graphene as the anode and cathode respectively. Functionalization was determined using Raman spectroscopy and measured by an increase in D/G ratio, increase in fluorescence background and introduction of C-H bond peak at ~2930 cm-1. Components of the Raman spectra before and after functionalization of all samples used were analyzed to show a substrate dependent effect on functionalization with values such as D/G ratio and normalized fluorescence slope varying between the substrates.

The formation of epitaxial graphene on SiC(000-1) in a disilane environment is studied. The higher graphitization temperature required, compared to formation in vacuum, results in more homogeneous thin films of graphene. Some areas of the surface display unique electron reflectivity curves not seen in vacuum-prepared samples. Using selected area diffraction, these areas are found to have a graphene/SiC interface structure with a graphene-like buffer layer [analogous to what occurs on SiC(0001) surfaces].