Monitoring of seismic signals generated by slow deformation at convergent and transform plate boundaries worldwide, known as tectonic tremor, might provide insights into deformation processes in the source regions of megathrust earthquakes. Tremor signals occur dominantly in the 2–8 Hz frequency band and can last for tens of seconds to several minutes, in contrast to typical earthquakes that produce seismic signals at frequencies up to several tens of hertz and last less than a minute. Because tremor is caused by stochastic processes, the resultant waveforms are represented by a stochastic function and construction of deterministic measures to discriminate tremor signals from earthquakes is very difficult. In this study, we used a convolutional neural network (CNN) to discriminate the signals of tectonic tremor from those of local earthquakes in running spectral images of these signals. We developed a method (seismic running spectra‐CNN [SRSpec‐CNN]) that is sensitive to the absolute frequency of signal appearance, which reflects the physical properties of the signal source, but is insensitive to the time of signal onset. SRSpec‐CNN has 130,211 parameters that were trained by 17,213 images of $64×64pixels$⁠. Based on simultaneous analyses of the frequency contents and durations of the signals, we achieved 99.5% accuracy for our identifications of signals from tectonic tremor, local earthquakes, and noise. Because running spectra clearly differentiate the characteristic features of these signals, we were able to achieve this high accuracy by using a CNN of simple architecture.

The International Federation of Digital Seismographic Networks (FDSN) is a non-governmental organization formed by institutions dedicated to seismological research and seismic monitoring. The FDSN is a successful complement to the International Seismological Centre (ISC) in pursuing a more than a century old tradition of global seismic data exchange. The main goal of the FDSN is the production and dissemination of seismic waveform data from high fidelity seismic observatories. The federation is formed by 65 organizations from 52 countries that contribute data to three main data centers in the United States, Europe, and Japan. A subset of the stations that conform the FDSN send real-time to the data management center (DMC) of the Incorporated Research Institutions of Seismology (IRIS) in the United States. Data from this real-time network is crucial to the determination of the seismic parameters of large earthquakes in a very short time after their occurrence and to support the efforts of institutions that are responsible for disaster relief or prevention. Most notably, tsunami warning centers use this information as a fundamental underpinning to issue warnings and alerts. The FDSN is an early participant of the global earth observation system of systems (GEOSS), contributing high-quality, timely and freely accessible seismic data. The purpose of this paper is to provide an overview of the FDSN from the viewpoint of an integrated system of observatories and to share with other GEOSS networks the successes, challenges and lessons learned by the FDSN in promoting the open and free access of seismological data for the benefit of scientific research and disaster prevention and mitigation.

The mantle beneath the Japanese islands is complex because of subduction of the Pacific and Philippine Sea plates and the deformation associated with it. Detection of seismic anisotropy should be useful for understanding the processes occurring in such mantle. Here we resolve seismic anisotropy of the mantle wedge from that of the underlying subducted Pacific slab using a large number of measurements of shear wave splitting for a family of core‐reflected shear phases: ScS, sScS, ScS2, and sScS2. The anisotropy of the mantle wedge changes sharply across the volcanic front. On the Pacific side of the volcanic front a vertically propagating shear wave is polarized with the fast direction approximately parallel to the trench, whereas on the back‐arc side it is polarized with the fast direction approximately parallel to the plate convergence direction. The Pacific slab is uniformly anisotropic with the NNW fast direction, consistent with the paleospreading direction of the northwestern Pacific seafloor. The preferential alignment of anisotropic crystals frozen in the plate at the time of its formation appears to be preserved down to depths beyond 400 km within the slab.

We present high-performance simulations of global seismic wave propagation with an unprecedented accuracy of 1.2 s seismic period for a realistic three-dimensional Earth model using the spectral element method on the K computer. Our seismic simulations use a total of 665.2 billion grid points and resolve 1.8 trillion degrees of freedom. To realize these large-scale computations, we optimize a widely used community software code to efficiently address all hardware parallelization, especially thread-level parallelization to solve the bottleneck of memory usage for coarse-grained parallelization. The new code exhibits excellent strong scaling for the time stepping loop, that is, parallel efficiency on 82,134 nodes relative to 36,504 nodes is 99.54%. Sustained performance of these computations on the K computer is 1.24 petaflops, which is 11.84% of its peak performance. The obtained seismograms with an accuracy of 1.2 s for the entire globe should help us to better understand rupture mechanisms of devastating earthquakes.

In the present study, we propose a new approach for determining earthquake hypocentral parameters. This approach integrates computed theoretical seismograms and deep machine learning. The theoretical seismograms are generated through a realistic three-dimensional Earth model, and are then used to create spatial images of seismic wave propagation at the Earth’s surface. These snapshots are subsequently utilized as a training data set for a convolutional neural network. Neural networks for determining hypocentral parameters such as the epicenter, depth, occurrence time, and magnitude are established using the temporal evolution of the snapshots. These networks are applied to seismograms from the seismic observation network in the Hakone volcanic region in Japan to demonstrate the suitability of the proposed approach for locating earthquakes. We demonstrate that the determination accuracy of hypocentral parameters can be improved by including theoretical seismograms for different earthquake locations and sizes, in the learning data set for the deep machine learning. Using the proposed method, the hypocentral parameters are automatically determined within seconds after detecting an event. This method can potentially serve in monitoring earthquake activity in active volcanic areas such as the Hakone region.