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Register a new userProperties of three seismic events in September 2017 in the northern Korean Peninsula from moment tensor inversion
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Moment tensor inversion is conducted to characterize the source properties of the September 3, M6.3, the September 3, M4.6, and the September 23, M3.4 seismic events occurred in 2017 in the nuclear test site of DPRK. To overcome the difficulties in the comparison, the inversion uses the same stations, the same structural model, the same algorithm, and nearly the same filters in the processing of waveforms. It is shown that the M6.3 event is with predominant explosion component, the M4.6 event is with predominant implosion component, while the M3.4 event is with a predominant double couple component (~74%) and a secondary explosion component (~25%). The three seismic events are with a similar centroid depth. The double couple component of the M3.4 event shows a normal fault striking northeastward.
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An interval of exceptional solar activity was registered in early September 2017, late in the decay phase of solar cycle 24, involving the complex Active Region 12673 as it rotated across the western hemisphere with respect to Earth. A large number of eruptions occurred between 4-10 September, including four associated with X-class flares. The X9.3 flare on 6 September and the X8.2 flare on 10 September are currently the two largest during cycle 24. Both were accompanied by fast coronal mass ejections and gave rise to solar energetic particle (SEP) events measured by near-Earth spacecraft. In particular, the partially-occulted solar event on 10 September triggered a ground level enhancement (GLE), the second GLE of cycle 24. A further, much less energetic SEP event was recorded on 4 September. In this work we analyze observations by the Advanced Composition Explorer (ACE) and the Geostationary Operational Environmental Satellites (GOES), estimating the SEP event-integrated spectra above 300 keV and carrying out a detailed study of the spectral shape temporal evolution. Derived spectra are characterized by a low-energy break at few/tens of MeV; the 10 September event spectrum, extending up to ~1 GeV, exhibits an additional rollover at several hundred MeV. We discuss the spectral interpretation in the scenario of shock acceleration and in terms of other important external influences related to interplanetary transport and magnetic connectivity, taking advantage of multi-point observations from the Solar Terrestrial Relations Observatory (STEREO). Spectral results are also compared with those obtained for the 17 May 2012 GLE event.
Most of the seismic inversion techniques currently proposed focus on robustness with respect to the background model choice or inaccurate physical modeling assumptions, but are not apt to large-scale 3D applications. On the other hand, methods that are computationally feasible for industrial problems, such as full waveform inversion, are notoriously bogged down by local minima and require adequate starting models. We propose a novel solution that is both scalable and less sensitive to starting model or inaccurate physics when compared to full waveform inversion. The method is based on a dual (Lagrangian) reformulation of the classical wavefield reconstruction inversion, whose robustness with respect to local minima is well documented in the literature. However, it is not suited to 3D, as it leverages expensive frequency-domain solvers for the wave equation. The proposed reformulation allows the deployment of state-of-the-art time-domain finite-difference methods, and is computationally mature for industrial scale problems.
Seismic wave propagation forms the basis for most aspects of seismological research, yet solving the wave equation is a major computational burden that inhibits the progress of research. This is exaspirated by the fact that new simulations must be performed when the velocity structure or source location is perturbed. Here, we explore a prototype framework for learning general solutions using a recently developed machine learning paradigm called Neural Operator. A trained Neural Operator can compute a solution in negligible time for any velocity structure or source location. We develop a scheme to train Neural Operators on an ensemble of simulations performed with random velocity models and source locations. As Neural Operators are grid-free, it is possible to evaluate solutions on higher resolution velocity models than trained on, providing additional computational efficiency. We illustrate the method with the 2D acoustic wave equation and demonstrate the methods applicability to seismic tomography, using reverse mode automatic differentiation to compute gradients of the wavefield with respect to the velocity structure. The developed procedure is nearly an order of magnitude faster than using conventional numerical methods for full waveform inversion.
Inspired by recent work on extended image volumes that lays the ground for randomized probing of extremely large seismic wavefield matrices, we present a memory frugal and computationally efficient inversion methodology that uses techniques from randomized linear algebra. By means of a carefully selected realistic synthetic example, we demonstrate that we are capable of achieving competitive inversion results at a fraction of the memory cost of conventional full-waveform inversion with limited computational overhead. By exchanging memory for negligible computational overhead, we open with the presented technology the door towards the use of low-memory accelerators such as GPUs.
Forest fire models may be interpreted as a simple model for earthquake occurrence by translating trees and fire into stressed segments of a fault and their rupture, respectively. Here we adopt a twodimensional forest-fire model in continuous time, and focus on the temporal changes of seismicity and the b-value. We find the b-value change and seismic quiescence prior to large earthquakes by stacking many sequences towards large earthquakes. As the magnitude-frequency relation in this model is directly related to the cluster-size distribution, decrease of the b-value can be explained in terms of the change in the cluster-size distribution. Decrease of the b-value means that small clusters of stressed sites aggregate into a larger cluster. Seismic quiescence may be attributed to the decrease of stressed sites that do not belong to percolated clusters.
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