This work deals with a methodology applied to seismic early warning systems which are designed to provide real-time estimation of the magnitude of an event. We will reappraise the work of Simons et al. (2006), who on the basis of wavelet approach predicted a magnitude error of ±1. We will verify and improve upon the methodology of Simons et al. (2006) by applying an SVM statistical learning machine on the timescale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source-receiver distance of up to 150 km during the period 1998-2011. We applied a wavelet transform on the seismogram data and calculating scale-dependent threshold wavelet coefficients. These coefficients were then classified into low magnitude and high magnitude events by constructing a maximum margin hyperplane between the two classes, which forms the essence of SVMs. Further, the classified events from both the classes were picked up and linear regressions were plotted to determine the relationship between wavelet coefficient magnitude and earthquake magnitude, which in turn helped us to estimate the earthquake magnitude of an event given its threshold wavelet coefficient. At wavelet scale number 7, we predicted the earthquake magnitude of an event within 2.7 seconds. This means that a magnitude determination is available within 2.7 s after the initial onset of the P-wave. These results shed light on the application of SVM as a way to choose the optimal regression function to estimate the magnitude from a few seconds of an incoming seismogram. This would improve the approaches from Simons et al. (2006) which use an average of the two regression functions to estimate the magnitude.

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<p>This study attempts to investigate the patterns of correlation fractal dimension (Dc) prior to the occurrence of strong earthquakes by implementing modified Grassberger and Procaccia (1983) algorithm.  The primary input for current research is earthquake epicentre locations. Through this method, dispersed and clustered seismicity can be distinguished by analysing spatiotemporal distribution of earthquake clusters. The low Dc values suggest dense clusters while high Dc values imply a scattered distribution of occurrences. In other words, low Dc represents a highly stressed region. Therefore, by monitoring the variations in Dc, we get valuable insights regarding spatiotemporal clustering of events as well as state of stress. To confirm the high stress brought on by dense clusters prior to the mainshock, we make use of the coulomb failure criterion to measure the Coulomb stress. For testing this hypothesis we have done analysis in southern California (SC), Baja California (BaC), and Puerto Rico Island (PRI).</p> <p>Major plate movement between the North American plate and the Pacific plate is accommodated by the San Andreas Fault (SAF) and the remaining is by Eastern California Shear Zone (ECSZ). However, the ECSZ has experienced three strong earthquakes in the last thirty years. This indicates an anomalous pattern of seismicity developing in ECSZ. The recent rupture of 2019 Ridgecrest earthquake has caused stress perturbation along Garlock Fault (dormant fault, capable of producing M >7 earthquakes) throws light on the probable future event. We did fractal analysis on 30 years (1990-2020) of data considering 50 earthquakes per each window. Four strong earthquakes have chosen for studying; 2019 Ridgecrest (M<sub>w</sub>7.1), 2010 El-Mayor Cucapah (M<sub>w</sub>7.2), 1999 Hectormine (M<sub>w</sub>7.1), and 1992 Landers (M<sub>w</sub>7.3).In general, a relative decrease in Dc before each of the events is observed.</p> <p>The commencement of 2019 Puerto Rico sequence trailed by the incidence of Mw6.4, 07 January 2020, earthquake highlights the importance of studying seismicity patterns in the PRI. Tectonic setting of the PRI is highly complex; characterized by dynamic seismicity. We have analysed ~32 years of seismicity data (M≥ 2.8). The fractal study of the Puerto Rico earthquake suggests a relative decline in Dc during 2019. It should be noted that the emergence of spatially closed clusters occurred at the same time in the southwestern PRI. When the static stress is calculated, the southwestern clusters indicate a highly stressed crust. This validates the relationship between the stress and low Dc observed prior to the occurrence of Mw6.4 January 2020 event.</p> <p>Based on our study, it is possible to conclude that a significant drop in the Dc proceeds the mainshock. This pattern is explicit in the five major earthquakes in the study area. So we propose that our approach based on the patterns of correlation fractal dimension is a novel method to identify numerical precursors of strong earthquakes before the rupture.</p>

<p>Ambient noises are generated due to the interaction of atmosphere with the solid Earth and the noises which occur within the time period of 2-20s are known as microseisms. As the noise generation mechanism is not very well understood in the extreme climatic condition of Antarctic continent, in this study we target to understand the microseism generation in the South Pole station situated in the Antarctic continent. We have carried out our analysis using continuous data from IRIS data management center. Our main focus is to characterize the source direction of noise and their seasonal amplitude variations. We have employed the frequency dependent polarization analysis through the Eigen decomposition of the 3×3 spectral covariance matrix.</p> <p> The source of the noise have been analyzed using the backazimuth and time period for all the three bands of microseism, SPDF (short period double frequency), LPDF (long period double frequency), and PM (primary microseism). We observed that the noise is mainly due to the strong winds of Southern Ocean and some amounts of noise are also from the Ross Sea. In southern hemisphere, winter starts from May and it ends in August and also the number of polarized signals is lower in the winter season, and it is comparatively higher in the summer season. Additionally, when we plot Power spectral density against time period we see the splitting of the double frequency microseism into SPDF and LPDF which is only observed in the summer months and not in the winter months (only one single peak is observed).  Because, in the winter month’s sea ice concentration is extremely high in the South Pole; therefore, there is no significant wind interaction with sea waves of the coastal part which generates the SPDF. In winter, the continent is completely frozen; however, the amplitude of noise is high due to the strong winds. In summer, the noise is generated due to the low pressure systems develops in Southern Ocean which leads to cyclones in the Ross Sea. Antarctic circumpolar current also plays a significant role in the generation of noise. Therefore, we can conclude that the source of noise is from the Southern Ocean and Ross Sea. Also, we noticed the seasonal variation in the splitting of the double frequency microseism due to variation in the sea ice concentration.</p>

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