Testing the Potential of Deep Learning in Earthquake Forecasting

Reliable earthquake forecasting methods have long been sought after, and so the rise of modern data science techniques raises a new question: does deep learning have the potential to learn this pattern? In this study, we leverage the large amount of earthquakes reported via good seismic station coverage in the subduction zone of Japan. We pose earthquake forecasting as a classification problem and train a Deep Learning Network to decide, whether a timeseries of length greater than 2 years will end in an earthquake on the following day with magnitude greater than 5 or not. Our method is based on spatiotemporal b value data, on which we train an autoencoder to learn the normal seismic behaviour. We then take the pixel by pixel reconstruction error as input for a Convolutional Dilated Network classifier, whose model output could serve for earthquake forecasting. We develop a special progressive training method for this model to mimic real life use. The trained network is then evaluated over the actual dataseries of Japan from 2002 to 2020 to simulate a real life application scenario. The overall accuracy of the model is 72.3 percent. The accuracy of this classification is significantly above the baseline and can likely be improved with more data in the futur

SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning

Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstrated their remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open source Python package specifically developed for fast data processing by implementing deep learning. SAIPy offers solutions for multiple seismological tasks, including earthquake detection, magnitude estimation, seismic phase picking, and polarity identification. We introduce upgraded versions of previously published models such as CREIMERT capable of identifying earthquakes with an accuracy above 99.8 percent and a root mean squared error of 0.38 unit in magnitude estimation. These upgraded models outperform state of the art approaches like the Vision Transformer network. SAIPy provides an API that simplifies the integration of these advanced models, including CREIMERT, DynaPickerv2, and PolarCAP, along with benchmark datasets. The package has the potential to be used for real time earthquake monitoring to enable timely actions to mitigate the impact of seismic events. Ongoing development efforts aim to enhance the performance of SAIPy and incorporate additional features that enhance exploration efforts, and it also would be interesting to approach the retraining of the whole package as a multi-task learning problem

11th EGU Galileo Conference: Solid Earth and Geohazards in the Exascale Era Consensual Document

The 11th Galileo Conference in Barcelona (May 23-26, 2023) addressed Exascale computing challenges in geosciences. With 78 participants from 15 countries, it focused on European-based research but welcomed contributions from worldwide institutions. The conference had four sessions covering HPC applications, data workflows, computational geosciences, and EuroHPC infrastructures. It featured keynote presentations, poster sessions, and breakout sessions, including Master Classes for 22 Early Career Scientists supported by EGU. This document represents the consensus among participants, capturing outcomes from breakout sessions and acknowledging diverse opinions and approaches.The 11th Galileo Conference of the European Geosciences Union (EGU) focused on "Solid Earth and Geohazards in the Exascale Era." This abstract presents the main outcomes and conclusions from the conference breakout sessions, which aimed to provide recommendations for the future of solid earth research. The discussions highlighted the challenges and opportunities associated with high-performance computing (HPC) in solid earth sciences. The key findings include the need for collaboration between computer scientists and solid earth domain-specific scientists, the importance of portability software layers for different hardware architectures, the adoption of programming models for easier development and deployment of applications, the necessity of HPC training at all career stages, the improvement of accessibility and authentication mechanisms for European machines, and the readiness of urgent computing services for natural catastrophes. The conference also emphasized the significance of sustainable funding, software engineering best practices, and the development of modular and interoperable codes and workflows. Overall, the conference provided insights into the current status of computational solid earth research and offered recommendations for future advancements in the field.European Geosciences Union (EGU), the EuroHPC Center of Excellence for Exascale in Solid Earth (ChEESE) under Grant Agreement No 101093038 (https://cheese2.eu), and the European Union's Next Generation/PRTR Program through grant PCI2022-134973-2.Peer reviewe

Graphene on Carbon-face SiC{0001} Surfaces Formed in a Disilane Environment

<p>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].</p

Formation of Graphene on SiC( 0001 ) Surfaces in Disilane and Neon Environments

<p>The formation of graphene on the SiC(000) surface (the <em>C-face</em> 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 × √43-<em>R</em>  ± 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 × √43-R ± 7.6° structure [i.e., analogous to the 6√3 × 6√3-<em>R</em>30° “buffer layer” that forms on the SiC(0001) surface (the <em>Si-face</em>)]. Oxidation of the surface is found to modify (eliminate) the √43 × √43-<em>R</em> ± 7.6° structure, which is interpreted in the same manner as the known “decoupling” that occurs for the Si-face buffer layer.</p

Comparison of Graphene Formation on C-face and Si-face SiC {0001} Surfaces

The morphology of graphene formed on the (0001̅ ) surface (the C-face) and the (0001) surface (the Si-face) of SiC, by annealing in ultrahigh vacuum or in an argon environment, is studied by atomic force microscopy and low-energy electron microscopy. The graphene forms due to preferential sublimation of Si from the surface. In vacuum, this sublimation occurs much more rapidly for the C face than the Si face so that 150 °C lower annealing temperatures are required for the C face to obtain films of comparable thickness. The evolution of the morphology as a function of graphene thickness is examined, revealing significant differences between the C face and the Si face. For annealing near 1320 °C, graphene films of about 2 monolayers (MLs) thickness are formed on the Si face but 16 ML is found for the C face. In both cases, step bunches are formed on the surface and the films grow continuously (carpetlike) over the step bunches. For the Si face, in particular, layer-by-layer growth of the graphene is observed in areas between the step bunches. At 1170 °C, for the C face, a more three-dimensional type of growth is found. The average thickness is then about 4 ML but with a wide variation in local thickness (2–7 ML) over the surface. The spatial arrangement of constant-thickness domains are found to be correlated with step bunches on the surface, which form in a more restricted manner than at 1320 °C. It is argued that these domains are somewhat disconnected so that no strong driving force for planarization of the film exists. In a 1 atm argon environment, permitting higher growth temperatures, the graphene morphology for the Si face is found to become more layer by layerlike even for graphene thickness as low as 1 ML. However, for the C face the morphology becomes much worse, with the surface displaying markedly inhomogeneous nucleation of the graphene. It is demonstrated that these surface are unintentionally oxidized, which accounts for the inhomogeneous growth.</p

Exploring a CNN model for earthquake magnitude estimation using HR-GNSS data

Highlights • We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. • The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. • The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33. Abstract High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements

Formation of Epitaxial Graphene on SiC(0001) using Vacuum or Argon Environments

The formation of graphene on the (0001) surface of SiC (the Si-face) is studied by atomic force microscopy, low-energy electron microscopy, and scanning tunneling microscopy/spectroscopy. The graphene forms due to preferential sublimation of Si from the surface at high temperature, and the formation has been studied in both high-vacuum and 1-atm-argon environments. In vacuum, a few monolayers of graphene forms at temperatures around 1400°C, whereas in argon a temperature of about 1600°C is required in order to obtain a single graphene monolayer. In both cases considerable step motion on the surface is observed, with the resulting formation of step bunches separated laterally by >10 µm. Between the step bunches, layer-by-layer growth of the graphene is found. The presence of a disordered, secondary graphitic phase on the surface of the graphene is also identified.</p

Interface structure of graphene on SiC(0001̅ )

<p>Graphene films prepared by heating the SiC(0001̅ ) surface (the C-face of the {0001} surfaces) in a vacuum or in a Si-rich environment are compared. It is found that different interface structures occur for the two situations. The former yields a well known 3 × 3 reconstructed interface, whereas the latter produces an interface with √43 × √43-R ± 7.6° symmetry. This structure is shown to contain a graphene-like layer with properties similar to the 6√3 × 6√3-R30° “buffer layer” that forms on the Si(0001) surface (the Si-face).</p

PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms

Highlights • We present PolarCAP, a deep learning model that can classify the polarity of a waveform with a 98% accuracy. • The first-motion polarity of seismograms is a useful parameter, but its manual determination can be laborious and imprecise. • We demonstrate that in several cases the model can assign trace polar-ity more accurately than a human analyst. Abstract 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