Aditya Riadi Gusman

Fault slip distribution of the 2014 Iquique, Chile, earthquake estimated from ocean‐wide tsunami waveforms and GPS data

We applied a new method to compute tsunami Greens functions for slip inversion of the 1 April 2014 Iquique earthquake using both near-field and far-field tsunami waveforms. Inclusion of the effects of the elastic loading of seafloor, compressibility of seawater, and the geopotential variation in the computed Greens functions reproduced the tsunami traveltime delay relative to long-wave simulation and allowed us to use far-field records in tsunami waveform inversion. Multiple time window inversion was applied to tsunami waveforms iteratively until the result resembles the stable moment rate function from teleseismic inversion. We also used GPS data for a joint inversion of tsunami waveforms and coseismic crustal deformation. The major slip region with a size of 100 km × 40 km is located downdip the epicenter at depth ~28 km, regardless of assumed rupture velocities. The total seismic moment estimated from the slip distribution is 1.24 × 1021 N m (Mw 8.0).

A methodology for near‐field tsunami inundation forecasting: Application to the 2011 Tohoku tsunami

Existing tsunami early warning systems in the world can give either one or a combination of estimated tsunami arrival times, heights, or qualitative tsunami forecasts before the tsunami hits near-field coastlines. A future tsunami early warning system should be able to provide a reliable near-field tsunami inundation forecast on high-resolution topography within a short time period. Here we describe a new methodology for near-field tsunami inundation forecasting. In this method, a precomputed tsunami inundation and precomputed tsunami waveform database is required. After information about a tsunami source is estimated, tsunami waveforms at nearshore points can be simulated in real time. A scenario that gives the most similar tsunami waveforms is selected as the site-specific best scenario and the tsunami inundation from that scenario is selected as the tsunami inundation forecast. To test the algorithm, tsunami inundation along the Sanriku Coast is forecasted by using source models for the 2011 Tohoku earthquake estimated from GPS, W phase, or offshore tsunami waveform data. The forecasting algorithm is capable of providing a tsunami inundation forecast that is similar to that obtained by numerical forward modeling but with remarkably smaller CPU time. The time required to forecast tsunami inundation in coastal sites from the Sendai Plain to Miyako City is approximately 3 min after information about the tsunami source is obtained. We found that the tsunami inundation forecasts from the 5 min GPS, 5 min W phase, 10 min W phase fault models, and 35 min tsunami source model are all reliable for tsunami early warning purposes and quantitatively match the observations well, although the latter model gives tsunami forecasts with highest overall accuracy. The required times to obtain tsunami forecast from the above four models are 8 min, 9 min, 14 min, and 39 min after the earthquake, respectively, or in other words 3 min after receiving the source model. This method can be useful in developing future tsunami forecasting systems with a capability of providing tsunami inundation forecasts for locations near the tsunami source area.

Tsunami data assimilation of Cascadia seafloor pressure gauge records from the 2012 Haida Gwaii earthquake

We use tsunami waveforms recorded on a dense array of seafloor pressure gauges offshore Oregon and California from the 2012 Haida Gwaii, Canada, earthquake to simulate the performance of two different real-time tsunami-forecasting methods. In the first method, the tsunami source is first estimated by inversion of recorded tsunami waveforms. In the second method, the array data are assimilated to reproduce tsunami wavefields. These estimates can be used for forecasting tsunami on the coast. The dense seafloor array provides critical data for both methods to produce timeliness (>30 min lead time) and accuracy in both timing and amplitude (>94% confidence) tsunami forecasts. Real-time tsunami data on dense arrays and data assimilation can be tested as a possible new generation tsunami warning system.

Comparative study of two tsunamigenic earthquakes in the Solomon Islands: 2015 Mw 7.0 normal‐fault and 2013 Santa Cruz Mw 8.0 megathrust earthquakes

The July 2015 Mw 7.0 Solomon Islands tsunamigenic earthquake occurred ~40 km north of the February 2013 Mw 8.0 Santa Cruz earthquake. The proximity of the two epicenters provided unique opportunities for a comparative study of their source mechanisms and tsunami generation. The 2013 earthquake was an interplate event having a thrust focal mechanism at a depth of 30 km while the 2015 event was a normal-fault earthquake occurring at a shallow depth of 10 km in the overriding Pacific Plate. A combined use of tsunami and teleseismic data from the 2015 event revealed the north dipping fault plane and a rupture velocity of 3.6 km/s. Stress transfer analysis revealed that the 2015 earthquake occurred in a region with increased Coulomb stress following the 2013 earthquake. Spectral deconvolution, assuming the 2015 tsunami as empirical Greens function, indicated the source periods of the 2013 Santa Cruz tsunami as 10 and 22 min.

Tsunamis from the 29 March and 5 May 2015 Papua New Guinea earthquake doublet (M w 7.5) and tsunamigenic potential of the New Britain trench

We characterized tsunamis from the 29 March and 5 May 2015 Kokopo, Papua New Guinea, Mw 7.5 earthquake doublet. Teleseismic body wave inversions using various rupture velocities (Vr) showed similar source-time functions and waveform agreements, but the spatial distributions of the slips were different. The rupture durations were ~45 and ~55 s for the March and May events, with their peaks at ~25 and at ~17 s, respectively. Tsunami simulations favored source models with Vr = 1.75 and 1.50 km/s for the March and May earthquakes. The largest slip on the fault was similar (2.1 and 1.7 m), but the different depths and locations yielded maximum seafloor uplift of ~0.4 and ~0.2 m. Tsunami simulation from hypothetical great earthquakes (M 8.4 and 8.5) on the New Britain trench showed that tsunami amplitudes may reach up to 10 m in Rabaul, but most tsunami energy was confined within the Solomon Sea.

Estimate of tsunami source using optimized unit sources and including dispersion effects during tsunami propagation: The 2012 Haida Gwaii earthquake

We apply a genetic algorithm (GA) to find the optimized unit sources using dispersive tsunami synthetics to estimate the tsunami source of the 2012 Haida Gwaii earthquake. The optimal number and distribution of unit sources gives the sea surface elevation similar to that from our previous slip distribution on a fault using tsunami data, but different from that using seismic data. The difference is possibly due to submarine mass failure in the source region. Dispersion effects during tsunami propagation reduce the maximum amplitudes by up to 20% of conventional linear long wave propagation model. Dispersion effects also increase tsunami travel time by approximately 1 min per 1,300 km on average. The dispersion effects on amplitudes depend on the azimuth from the tsunami source reflecting the directivity of tsunami source, while the effects on travel times depend only on the distance from the source.

Rupture process of the 2016 Wharton Basin strike‐slip faulting earthquake estimated from joint inversion of teleseismic and tsunami waveforms

The 2016 Wharton Basin strike-slip faulting earthquake generated a small tsunami that was clearly recorded at deep ocean stations. Teleseismic inversions were made on four different nodal planes suggested by GCMT and W-phase solutions. Tsunami waveforms computed for the north-south striking westward dipping plane were most similar to the observations. With this fault plane, joint inversions of teleseismic and tsunami waveforms were used to estimate the kinematic rupture process. The weights of teleseismic and tsunami waveforms were found to make both datasets equally contribute to the solution. Misfit analysis of both datasets favored a rupture model with rupture front speed of 2.0 km/s. The seismic moment was estimated to be 7.69 × 1020 Nm (Mw 7.9), most of it was released within 30 s and it peaked at 15 s. Subfaults with slip larger than 5 m were located from near surface to 30 km depth.

Optimum Sea Surface Displacement and Fault Slip Distribution of the 2017 Tehuantepec Earthquake (Mw 8.2) in Mexico Estimated From Tsunami Waveforms

17 The 2017 Tehuantepec earthquake (Mw 8.2) was the first great normal fault event 18 ever instrumentally recorded to occur in the Middle America Trench. The earthquake 19 generated a tsunami with an amplitude of 1.8 m (height=3.5 m) in Puerto Chiapas, Mexico. 20 Tsunami waveforms recorded at coastal tide gauges and offshore buoy stations were used to 21 estimate the optimum sea surface displacement without assuming any fault. Our optimum sea 22 surface displacement model indicated that the maximum uplift of 0.5 m is located near the 23 trench and the maximum subsidence of 0.8 m on the coastal side near the epicenter. We then 24 estimated the fault slip distribution that can best explain the optimum sea surface 25 displacement assuming ten different fault geometries. The best model suggests that a 26 compact region of large slip (3 – 6 m) extends from a depth of 30 km to 90 km, centered at a 27 depth of 60 km. 28 29

Method to Determine Appropriate Source Models of Large Earthquakes Including Tsunami Earthquakes for Tsunami Early Warning in Central America

Large earthquakes, such as the Mw 7.7 1992 Nicaragua earthquake, have occurred off the Pacific coasts of El Salvador and Nicaragua in Central America and have generated distractive tsunamis along these coasts. It is necessary to determine appropriate fault models before large tsunamis hit the coast. In this study, first, fault parameters were estimated from the W-phase inversion, and then an appropriate fault model was determined from the fault parameters and scaling relationships with a depth dependent rigidity. The method was tested for four large earthquakes, the 1992 Nicaragua tsunami earthquake (Mw7.7), the 2001 El Salvador earthquake (Mw7.7), the 2004 El Astillero earthquake (Mw7.0), and the 2012 El Salvador–Nicaragua earthquake (Mw7.3), which occurred off El Salvador and Nicaragua in Central America. The tsunami numerical simulations were carried out from the determined fault models. We found that the observed tsunami heights, run-up heights, and inundation areas were reasonably well explained by the computed ones. Therefore, our method for tsunami early warning purpose should work to estimate a fault model which reproduces tsunami heights near the coast of El Salvador and Nicaragua due to large earthquakes in the subduction zone.

Preparing for the Future Nankai Trough Tsunami: A Data Assimilation and Inversion Analysis From Various Observational Systems

The future Nankai Trough tsunami is one of the imminent threats to the Japanese coastal communities that could potentially cause a catastrophic event. As a part of the countermeasure efforts for such an occurrence, this study analyzes the efficacy of combining tsunami data assimilation (DA) and waveform inversion (WI). The DA is used to continuously refine a wave field model whereas the WI is used to estimate the tsunami source. We consider a future scenario of the Nankai Trough tsunami recorded at various observational systems, including ocean bottom pressure (OBP) gauges, global positioning system (GPS) buoys, and ship height positioning data. Since most of the OBP gauges are located inside the source region, the recorded tsunami signals exhibit significant offsets from surface measurements due to coseismic seafloor deformation effects. Such biased data are not applicable to the standard DA, but can be taken into account in the WI. On the other hand, the use of WI for the ship data may not be practical because a considerably large precomputed tsunami database is needed to cope with the spontaneous ship locations. The DA is more suitable for such an observational system as it can be executed sequentially in time and does not require precomputed scenarios. Therefore, the combined approach of DA and WI allows us to concurrently make use of all observational resources. Additionally, we introduce a bias correction scheme for the OBP data to improve the accuracy, and an adaptive thinning of observations to determine the efficient number of observations.