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Sunda-Arc Seismicity: Continuing Increase of High- Magnitude Earthquakes Since 2004

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Sunda-Arc Seismicity: Continuing Increase of High- Magnitude Earthquakes Since 2004 Sunda-arc seismicity: continuing increase of high- magnitude earthquakes since 2004 Nishtha Srivastava1, Omar El Sayed1,2, Megha Chakraborty1,3, Jonas Köhler1, Jan Steinheimer1,2, Johannes Faber1,2, Alexander Kies1, Kiran Kumar Thingbaijam5, Kai Zhou1,2, Georg Rümpker1,3, and Horst Stoecker1,2,4* 1Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany 2Institut für Theoretische Physik, Goethe- Universität Frankfurt, 60438 Frankfurt am Main, Germany 3Institute of Geosciences, Goethe- Universität Frankfurt, 60438 Frankfurt am Main, Germany 4GSI Helmholtzzentrum für Schwerionenforschung GmbH, 64291 Darmstadt, Germany 5GNS Science New Zealand *[email protected] Abstract: Earthquakes with magnitude M ≥ 6.5 are potentially destructive events which may cause tremendous devastation, huge economic loss and large numbers of casualties. Models with predictive or forecasting power are still lacking. Nevertheless, the spatial and temporal information of these seismic events can provide important information about the seismic history and the potential future of a region. This paper analyzes the recently updated International Seismological Centre earthquake catalog of body-wave magnitudes, mb, reported for ~313,500 events in the Sunda-arc region during the last 56 years, i.e., from 1964 to 2020. Based on the data, we report a hitherto unreported strong increase in seismicity during the last two decades associated with strong earthquakes with mb ≥ 6.5. A Gaussian Process Regression Analysis of these ISC-data suggests a continuation of this strong rise in number and strength of events with mb ≥ 6.5, in the region. These yearly maxima in the magnitude of the earthquakes also show another unexpected pattern: about every two-to-three years there is a new maximum in the magnitude and also in the number of earthquakes. Furthermore, a noticeable increase is also observed in the yearly number of the events with mb ≥ 6.5. The trend line generated by Auto-Regressive Integrated Moving Average (ARIMA) method suggests continuing increase of such large-magnitude events in the Sunda-arc region during the next decade. Introduction Earthquakes are considered as a major menace among all the natural hazards, affecting many countries worldwide and resulting in huge human losses each year. Single extremely strong earthquake events take up to several hundred thousand lives, the 2004 Mw 9.1 Sumatra earthquake, the 2010 Mw 7.0 Haiti earthquake and the 2011 Mw 9.0 Tohoku earthquake are horrific such examples. Large earthquakes can trigger ecological disasters if they occur close to a dam or a nuclear power plant, e.g., the 2011 Tohoku earthquake, whose subsequent tsunami drowned ~20,000 humans (as reported by the National Police Agency of Japan) and destroyed the Fukushima Dai-ichi nuclear power plant. These catastrophic events are not only collectively responsible for over 500,000 fatalities but also responsible for the destruction of social infrastructure and heritage sites, leading to economic damages exceeding US $200 billion (Kato and Ben-Zion, 2020). In spite of numerous theoretical modeling, observational studies and laboratory simulations, potential patterns underlying strong earthquakes are yet to be deciphered (Kato and Ben-Zion, 2020). Nonetheless, an improved understanding of earthquake occurrences would be beneficial for Early Warning, rapid response and mitigation plans, especially important for sustainable human habitats in earthquake-prone regions. This study focuses on identification of a temporal pattern associated with the earthquakes triggered in Indonesia over the last ~60 years and its surrounding regions. The region is influenced by the Eurasian, Indo-Australian, Philippine and Pacific plates making seismicity, volcanism and orogeny intensely active in the region (see Daly et al., 1991; Hamilton, 1970). The velocities of underflow along Benioff zones reach at least 10 cm/yr along Sumatra and Java forming the zone of subduction, the magmatic zone and the foreland basin (Hamilton, 1970). The southwest Sumatra subduction is part of a long convergent belt extending from the Himalayan front southward through Myanmar, continuing south past the Andaman and Nicobar Islands and Sumatra, south of Java and the Sunda Islands (Sumba, Timor), and then wrapping around towards north (McCaffrey, 2009). For the present analysis, we use the earthquake catalogue reported by the International Seismological Centre, to apply data mining and pattern deciphering of the seismicity. We adopt a classical Machine Learning approach, to quantify the trend associated with both the maximum magnitude of the seismic events, reported each year from 1964 to 2020, as well as a statistical analysis and forecasting of the trendline of the frequency of strong M>6.5 events as function of time. Spatial Distribution and Magnitudes of the Earthquakes The present study region is located between 13° South to 11° North and near the equator between 92° to 166° East. The seismic events triggered in Sunda-Arc region have been continuously recorded since the year 1964 onwards and are reported by the International Seismological Centre (ISC, http://www.isc.ac.uk, accessed in January 2021). The dataset comprises ~313,500 events with the information of latitude, longitude, origin time, focal depth and magnitude of the events. Saturation effects associated with the body wave magnitudes (mb) are certainly relevant for mb ≥ 6 and may lead to an underestimation of earthquake strength as measured by energy release or rupture area (Geller, 1976; Howell Jr, 1981; Kanamori, 1983; Giardini, 1988). Additionally, there are some potential limitations due to the quality of the recorded data, methods and guidelines followed in the estimation of mb, as well as distribution of reporting stations (Giardini, 1988). Previous researchers have investigated the reliability and consistency of the ISC catalogue reporting with time since 1963 and concluded that the consideration of the events with reported body wave magnitude greater than 4.5 to be safe (Giardini, 1988; Habermann, 1982). As the ISC-catalogue has predominantly reported body-wave magnitudes for this region, we base the temporal analysis on this type of magnitude. Events reported without information on mb, spatial coordinates and temporal information are excluded from the analysis. The spatial distribution of the seismic events with mb ≥ 4.5 reported is shown in Figure 1 along with the pie-charts of focal depth distribution. The distribution of the focal depth of the seismic events for each magnitude range is shown with different colors in addition to a pie- chart for better understanding of focal depth distribution in the region. On the western side, the seismicity is observed along the collision of the Eurasian and the Indian plates which form a subduction zone, the so-called Sunda Trench. Parallel to this Sunda Trench is the ~1900 km long Sumatran Fault which has a long history of many damaging earthquakes (McCaffrey, 2009). Towards the eastern side, active deformation takes place within a complex Suture Zone (linear belt of intense deformation) which includes several relatively small fault lines and subduction zones (Hall, 2009). Figure 1: Spatial distribution of seismic events reported between 1964-2020 by the International Seismological Centre for magnitudes in the range of mb ≥ 6.5, 6.5 > mb ≥ 6.0, 6.0 > mb ≥ 5.5 and 5.5 > mb ≥ 4.5. At least 70% of the earthquakes reported in these magnitude ranges are shallow in nature with their corresponding focal depth in the range of 0-70 km Figure 1 exhibits a dominance of shallow seismic events reported with focal depths in the range of 0-70 km. This is observed for all magnitude ranges and is likely related to the more brittle nature of the crust in shallow regions. This dominance depletes due to the changes in rheology with increasing depths. Although the events look more or less uniformly distributed for focal depth below ~250 km, two separate clusters with deep focal depths are observed beyond this depth. One of the two clusters represent the tectonic activity of the plates of the Java region while other deep earthquakes are observed due to the complex tectonics along the line of collision of Sunda plate with Philippine Sea plate and Caroline plate (McCaffrey, 2009). Two narrow clusters of high magnitude events with mb > 6 are also observed below 500 km depth. Temporal Observation, Analysis and Discussion Due to generally improved availability of seismic data, establishment of new seismographic stations, and better global coverage, ISC has an enhanced reporting of seismic events (Storchak et al., 2015). Timelines showing the steep growth in the numbers of reporting for the present study region is shown in Figure 2. The number of events reported with mb ≥ 3.5 are shown in a stacked bar graph. Figure 2: Number of earthquakes reported between 1964-2020 in the study region. Data were obtained from the International Seismological Centre website (http://www.isc.ac.uk) As expected, a strong increase in the number of low magnitude events is observed over time in the reported data. This is not surprising, as it is probably due to the significant increase of both the quality and number of sensors employed over the last five decades. In contrast to this, the sudden increases in the number of events with mb ≥ 6.5 detected in the dataset, after 1982 and after 2000, are surprising. In order to analyse the time dependence of the number and the strength of the seismic events in the region, a non-parametric, generic supervised machine learning method, the Gaussian Processes Regression (GPR) is used, based on the Bayesian approach. GPR not only works well with small datasets, but also provides uncertainty measurements. The motivation to use GPR here, instead of a linear or an exponential regression, is to avoid assuming an underlying functional form that might influence the identifying the predominant trend. The Gaussian Process Regressor implements Gaussian processes which is a stochastic approach such that every finite collection of those random variables has a multivariate normal distribution for regression purposes.
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