ALBERT

Description: 〈span〉〈div〉ABSTRACT〈/div〉On 6 December 2016, an Mw 6.5 earthquake occurred in Pidie Jaya, Aceh, about 30 km to the north of the Sumatran fault (SF) that killed more than 100 people and destroyed ∼3000 buildings. Mainshock focal mechanism inversions using regional Agency for Meteorology, Climatology, and Geophysics of Indonesia (BMKG) broadband data and teleseismic waveform data all indicate a strike‐slip event with a centroid depth of 11–15 km. The observed macrointensity data show that most of the damaged buildings are distributed along the coast, approximately perpendicular to the ruptured fault strike instead of parallel with it. The strong shaking and damage sites are primarily located on the coastal sedimentary soils, highlighting the importance of site conditions in determining risk. We used one‐month data recorded by nine temporal broadband stations to locate aftershocks with grid‐search and double‐difference algorithms, thereby resolving a linear trend of seismicity aligned in a northeast–southwest direction. The refined aftershock locations indicate a left‐lateral rupture that is in agreement with the preliminary finite‐fault slip inversion as well as geomorphic signatures of local geological structure. Using a well‐located ML 4.2 aftershock for path calibration, we relocated the mainshock epicenter with regional 〈span〉P〈/span〉‐wave arrivals. The refined epicenter falls within the cloud of the well‐located aftershocks, whereas locations from the global and regional catalogs are located 10–20 km away. Aftershock focal mechanisms determined by the first motion reveal similar solutions as the mainshock. This earthquake sequence ruptured a previously unidentified fault that is either located at the west of the fault that produced the 1967 Mw 6.1 earthquake sequence or is actually at the same fault. The Pidie Jaya earthquake and other off‐SF events suggest strong distributed crustal deformation in Aceh, highlighting the need for better understanding of active faulting and seismic hazard in this region.〈/span〉

Description: The Van (Eastern Anatolia, Turkey) earthquake occurred on Sunday, October 23, 2011 with a moment magnitude of 7.2. The tectonics of this region is characterized by strike–slip faulting on the Bitlis Suture Zone, and thrusting in the Zagros fold and thrust belt. Using high-rate (1 second) GPS data from permanent GNSS stations from the CORS-TR network, co-seismic displacements of eleven stations were determined using precise point positioning during this earthquake. We used the time series of coordinate changes for fourteen CORS-TR stations, and calculated the crust movements before and after the earthquake. According to the PPP solutions computed using high frequency GPS data to determine the co-seismic motions of stations, we conclude for the Van earthquake an occurrence time of 10:41:22 (UTC). No pre-seismic horizontal movement of stations at the level more than 5 mm before the earthquake could be observed. That means that no kinematic warning or prediction before the earthquake exists. Along an east–west horizontal line north of the Van Sea with a length of about 100 km, the northern part of this line experienced extension of 0.2–1 ppm in a NW–SE direction. The southern part experienced N–S shortening of 0.5–1.5 ppm. The N–S shortening we estimated geodetically matches well with the N–S shortening and thrust focal mechanism derived independently using seismic data by the USGS. Co-seismic surface displacements derived from the GPS data are consistent with the teleseismic source model given by the USGS. The geodetic source model derived from the GPS data reproduces the same moment magnitude and centroid as the teleseismic model, but shows a higher spatial resolution of the slip distribution. We also analyzed the post-seismic surface displacements derived from the GPS data within the first two weeks after the mainshock. No reasonable slip distribution on the co-seismic fault plane could be found, indicating that the sources for the early post-seismic deformation might come from the widely scattered aftershocks.

Description: The static surface deformation can be recovered from strong motion records. Compared to satellite-based measurements such as GPS or InSAR, the advantage of strong motion records is that they have the potential to provide real-time coseismic static displacements. The use of these valuable data was optimized for the moment magnitude estimation. A centroid grid search method was introduced to calculate the moment magnitude by using1 model. The method to data sets was applied of the 2011, Mw 9.0 Tohoku earthquake, the 2004, Mw 9.0 Sumatra earthquake, the 2010, Mw 8.8 Maule earthquake, the 2003, Mw 8.3 Tokachi-Oki earthquake, the 2010, Mw 7.8 Mentawai earthquake and the 2007, Mw 7.7 Tocopilla earthquake. The method calculates reasonable moment magnitude using static surface displacement data even only from single station. This method can be done very rapidly within approximately 5 minutes. It provides crucial information e.g. for making tsunami early warning decision.

Description: We recover coseismic static surface deformation by double integration of strong motion accelerometric data. Compared to GPS measurement, the advantage of strong motion data is that they have the potential to provide real-time coseismic static displacements. Strong motion data, however, has the classic problem of baseline offsets which produce unrealistic displacements after double integration is applied. We adopted a bilinear line fitting of empirical baseline correction method to overcome such problem. We investigate the improvement methods of baseline correction that constrain the maximum flatness of the displacement trace and use the cumulative energy ratio as a threshold. We apply the methods to data sets of the 2003, Mw 8.3 Tokachi-Oki earthquake, the 2007, Mw 7.7 Tocopilla earthquake, the 2010, Mw 7.8 Mentawai earthquake and the 2011, Mw 9.0 Tohoku earthquake. We show that, in general, the results of strong motion derived displacements are comparable to nearby GPS data for most data sets, although for far-field data the method may lead to poor results. It confirms that cumulative energy ratio is appropriate to be used as a threshold of baseline correction method. The very large and very good quality of boreholes strong motion data of the Tohoku earthquake gives opportunity to investigate the method deeply. We analyze the dependency of the method on hypocenter distance, magnitude and rupture model of the earthquake. We found that the method has a strong dependency on the given parameters, particularly on hypocenter distance. We also show that the method should be distinguished for horizontal and vertical components. Using our improvement method in this study, the deviations of vector length between strong motion derived displacements and nearby GPS data either for horizontal or vertical components, are significantly minimized. Further study, we optimize the use of valuable rapid static displacement data obtained from strong motion or GPS near-source station. We introduce a centroid grid search method to calculate the moment magnitude by using Okada (1985) model. Our method calculates reasonable moment magnitude using data even only from single station. This method can be done very rapidly within about 5 minutes. It provides crucial information e.g. for making tsunami warning decision.

Description: We recover the static displacement by double integration of strong-motion accelerometric data. A baseline correction is applied to prevent numerical instabilities in the double integration and hence unrealistic displacements. We adopted the relatively robust method of baseline correction that has been introduced by Wu and Wu (2007) which is based on the flatness idea. In order to allow analysis of the waveforms in near real-time, we modified the method to obtain the time points which indicates maximum flatness value automatically. Our modification can speed up the procedure of baseline correction and determines the flatness value from the whole trace instead of part of the trace. We applied the technique to data of the 25 October 2010, Mw 7.8 Mentawai earthquake. The digital strong motion accelerograph network installed in Indonesia produced a large data set from this earthquake. In fact, this is the first large “tsunami earthquake” recorded by this network since it was installed after Sumatra earthquake in 2004. These data offer the opportunity to calculate the coseismic displacement of the Mentawai earthquake. For this earthquake, displacements of up to 60 cm were derived. The method appears to be stable enough to be applied in an automated fashion in near real-time. A near real-time determination of static displacement could potentially provide crucial information to assess the tsunami hazard very early after the earthquake occurrence.

Description: For the first time a dense seismic network (AcehSeis) consisting of 30 short-period seismic stations was installed for 10 months from July 2014 in order to record crustal earthquakes in Central Aceh, Northern Sumatra, following the hazardous earthquake with magnitude Mw. 6.1, July 2nd, 2013. The objective of this research is to investigate the active fault characteristics derived from local earthquake data combined with a digital elevation model. The network recorded more than 1790 local events occurring along the main Great Sumatran Fault (GSF) and its secondary faults. Local earthquakes with azimuthal gap angles greater than 180° were excluded to avoid non-unique earthquake locations. Therefore from all recorded earthquakes, 1127 local earthquakes were used for further seismological analysis. A method of simultaneous earthquake relocation and 1D velocity determination was applied to obtain better location of earthquakes. The output of the first relocated seismicity was then relocated again by using a double difference relocation method. After relocation, earthquakes are concentrated in a narrow zone along the active fault, indicating the reliability of the earthquake location. We then determined the focal mechanism of earthquakes, based on the first polarity picks. The seismicity distribution correlates with the well-known GSF and its main secondary faults. Earthquake locations also clearly revealed several active segments which include the Batee, Beutong, Tangse-Geumpang, Lampahan, Aceh and Seulimeum, and Nisam faults. Also less-known faults as the Pamue and Atu Lintang-Peusangan faults are imaged. In Northern Aceh an unknown earthquake cluster associated with an active fault, the Nisam Fault was detected, which is also visible in the topography. The seismicity along GSF is offset at around 96.7 E, constrained by the nearly N-S striking Atu Lintang-Peusangan Fault confirming the segmentation of GSF at this longitude. The Aceh segment and the Seulimum Fault in the northernmost region of the network are both active and clearly imaged by the seismicity. The Pamue and the Lampahan Faults seem to be the splay of GSF as indicated by identical strike direction as the GSF. The Lampahan fault is characterized by NW-SE seismicity lineation, which is in agreement with the focal mechanisms. The Laut Tawar Lake could be a part of the Lampahan Fault that is also offset at ∼96.7 E at the line of the Atu Lintang-Peusangan fault. The Burni Geureudong Fault on the other hand strikes ENE-WSW which suggests a conjugate fault of the Lampahan Fault.