Chaoyong Peng

Development of an UAS for post-earthquake disaster surveying and its application in Ms7.0 Lushan Earthquake, Sichuan, China

The main objective of early impact analysis after a disaster is to produce georeferenced data about the affected areas, in support of humanitarian action. Crucial information is the identification of the disaster areas and the estimation of the number of people involved. Satellite imageries are mainly used as input data for early impact analysis in medium and large scale map. Analyses aimed at defining the damages of infrastructure and/or to facilities require suitable data, such as high resolution satellite images. Unfortunately, satellite images are not always available in a few days after the event. Therefore in situ surveys are preferred. Innovations in Unmanned Aircraft System (UAS) have allowed them to become valuable tools in capturing and assessing the extents and amount of damages. Flexibility, safety, ease of operation, and relatively low-cost of ownership and operation facilitate UAS implementation in disaster situations. In this paper, an example of UAS was developed for rapidly obtaining disaster information. Data acquisition at specified scales was successfully performed with the chosen fixed-wing UAS. For the image analysis, a photogrammetric workflow was applied to cope with the very high resolution of the images acquired without ground control points. Tests showed that the system plays an important role in the work of investigating and gathering information about disaster in epicentral areas of the earthquake, such as road detection, secondary disaster investigation, and rapid disaster evaluation. It can effectively provide earthquake information to salvation headquarters for swiftly developing the relief measures and improving the efficiency of emergency rescue.

A new type of tri-axial accelerometers with high dynamic range MEMS for earthquake early warning

Earthquake Early Warning System (EEWS) has shown its efficiency for earthquake damage mitigation. As the progress of low-cost Micro Electro Mechanical System (MEMS), many types of MEMS-based accelerometers have been developed and widely used in deploying large-scale, dense seismic networks for EEWS. However, the noise performance of these commercially available MEMS is still insufficient for weak seismic signals, leading to the large scatter of early-warning parameters estimation. In this study, we developed a new type of tri-axial accelerometer based on high dynamic range MEMS with low noise level using for EEWS. It is a MEMS-integrated data logger with built-in seismological processing. The device is built on a custom-tailored Linux 2.6.27 operating system and the method for automatic detecting seismic events is STA/LTA algorithms. When a seismic event is detected, peak ground parameters of all data components will be calculated at an interval of 1s, and c-Pd values will be evaluated using the initial 3s of P wave. These values will then be organized as a trigger packet actively sent to the processing center for event combining detection. The output data of all three components are calibrated to sensitivity 500 counts/cm/s2. Several tests and a real field test deployment were performed to obtain the performances of this device. The results show that the dynamic range can reach 98dB for the vertical component and 99dB for the horizontal components, and majority of bias temperature coefficients are lower than 200g/C. In addition, the results of event detection and real field deployment have shown its capabilities for EEWS and rapid intensity reporting. A new type of tri-axial accelerometers with high dynamic range MEMS for EEWS.The device is a MEMS-integrated data logger with built-in seismological processing.Peak ground parameters calculated at an interval of 1s after an event occurrence.Its dynamic range can reach 99dB and temperature coefficients are <200g/C.Test results demonstrated its capabilities for EEWS and rapid intensity reporting.

New τc regression relationship derived from all P wave time windows for rapid magnitude estimation

Two issues related to the average period τc early-warning parameter are the magnitude saturation effect on large earthquakes and considerable scatter for small earthquakes. To reduce the effect of these two issues on earthquake early-warning systems, we introduce a new τc regression relationship derived from all P wave time windows (PTWs) in high-pass filtered (T = 0.075 Hz) strong-motion data for three damaging moderate-to-large earthquakes. Our results show that this relationship provides a better and more stable magnitude prediction than those derived from 3 s PTW without a saturation effect on large earthquakes with M < 7.5. It is expected that fewer false alerts (those outside the magnitude uncertainty tolerance) would be issued. Additionally, a reduction of the initial PTWs to 1–2 s and evolutionary calculation with an expanding window allow more lead time for small-to-moderate events.