An Optimized Array Configuration of Tsunami Observation Network Off Southern Java, Indonesia

Abstract

Historical records have proven that the southern coasts of Java, Indonesia, are prone to tsunamis. The existing tsunami observing system using bottom pressure gauges, also known as tsunameters, composes a global network but is too sparse for regional tsunami forecasts. The nearest tsunameter to Indonesia is located approximately 500‐km offshore, which is not very useful for the area, particularly for a tsunami source in eastern part of the Sunda megathrust. Here we propose a methodology to optimally place offshore tsunameters with the main consideration of operational costs, which is proportional to the number of observation points. We use a stochastic slip model of earthquakes with Mw 8.8–9.2 to generate multiple tsunami source realizations representing possible future events in the region. Based on the sources, we then identify a potential zone where an early tsunami detection is feasible. Furthermore, empirical orthogonal functions derived from tsunami simulations are used to determine the initial locations for tsunameter placement inside the specified zone. Finally, we apply an optimization scheme to improve the initial locations that facilitates both tsunami forecasts and source characterizations through a tsunami waveform inversion analysis. Our result indicates that six tsunameters are sufficient to efficiently cover the major seismogenic region in the study area.

A Tsunami Warning System Based on Offshore Bottom Pressure Gauges and Data Assimilation for Crete Island in the Eastern Mediterranean Basin

“…For the three MGSs, that is, the 2013‐like event, the 1956‐like event, and the 2017‐like event, the variances of the first EOF mode are 36.8%, 51.1%, and 41.2%, respectively. Since the variance of the first EOF mode is considerably larger than that of subsequent EOF modes (e.g., the second and third modes), using only the first EOF mode is sufficient to quantify the total energy of the tsunami dynamics in deep water (Mulia et al, 2019). It can be seen that most OBPGs are located at the areas with a large absolute EOF value for at least one MGS (Figures 4a–4c).…”

Potential deployment of offshore bottom pressure gauges and adoption of data assimilation for tsunami warning system in the western Mediterranean Sea

“…7 Comparison of the observations (black lines), simulation (blue lines) and forecasted (data assimilation; red lines) waveforms at four PoIs for the third (a; three OBPGs) and fourth (b; two OBPGs) scenarios. The time window for assimilation is 20 min (dashed vertical line) strike, epicenter) combined with numerous OBPGs arrangements (number, alignment, spacing) (Mulia et al 2017(Mulia et al , 2019 are necessary to determine the optimum OBPGs network design for the WMB which can address tsunami threats from all four tsunamigenic zones in this basin. This was out of the scope of this study because we here focused on a single case study considering one tsunamigenic zone (i.e., NAC) and one real tsunami event (i.e., the May 2003 tsunami).…”

Implications for megathrust earthquakes and tsunamis from seismic gaps south of Java Indonesia

“…Our multidisciplinary study, which includes seismic and geodetic data analyses and tsunami wave height modelling, clearly reveal the presence of seismic gaps off the southern coast of Java that could be the source of future great earthquakes with tsunamigenic potential. Although this is only one possible scenario, with mitigating circumstances such as slow slip potentially reducing the likelihood and severity of such an event, it nevertheless seems prudent to support recent calls 13 , 25 for additional submarine and sea level instruments for the relatively sparse InaTEWS network in south Java, to help protect the many people living in the coastal areas.…”