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Contributions to Tsunami Detection b...

Moran, Patrick J.

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Contributions to Tsunami Detection by High Frequency Radar.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Contributions to Tsunami Detection by High Frequency Radar./
作者:	Moran, Patrick J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	154 p.
附註:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
標題:	Ocean engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10792712
ISBN:	9780355861723

Contributions to Tsunami Detection by High Frequency Radar.
Moran, Patrick J.

Contributions to Tsunami Detection by High Frequency Radar. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 154 p.

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--University of Rhode Island, 2018.

This thesis is comprised of three separate manuscripts, detailing recent work into the detection of Tsunamis in coastal waters using High Frequency (HF) coastal radar systems. The overarching focus of the three manuscripts is the development and performance analysis of a tsunami detection technique from using synthetic radar data and synthetic, simulated tsunamis, to using recorded raw radar data combined with synthetic tsunamis, and the comparison of this work against another published method. The proposed detection algorithm tests for fluctuations in correlations of HF measurements of the sea surface, and has been named the "Time Correlation Algorithm" or TCA. The first two manuscripts are published journal papers, with the third currently being edited for submittal.

ISBN: 9780355861723Subjects--Topical Terms:

660731
Ocean engineering.

Contributions to Tsunami Detection by High Frequency Radar.
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500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Stephan T. Grilli.
502 $a Thesis (M.S.)--University of Rhode Island, 2018.
520 $a This thesis is comprised of three separate manuscripts, detailing recent work into the detection of Tsunamis in coastal waters using High Frequency (HF) coastal radar systems. The overarching focus of the three manuscripts is the development and performance analysis of a tsunami detection technique from using synthetic radar data and synthetic, simulated tsunamis, to using recorded raw radar data combined with synthetic tsunamis, and the comparison of this work against another published method. The proposed detection algorithm tests for fluctuations in correlations of HF measurements of the sea surface, and has been named the "Time Correlation Algorithm" or TCA. The first two manuscripts are published journal papers, with the third currently being edited for submittal.
520 $a The first manuscript covers work based on a realistic case study, a simulated radar signal and two simulated tsunamis are used in order to validate the operation of the TCA modeled on an existing radar system in Tofino Canada and realistic tsunami threats to the area. The TCA is defined as correlations between radar cells connected along an intersecting wave ray, shifted in time by the long wave propagation time along the ray, c equal to √gh. The correlation is taken over a long time window, on the order of 10--15 minutes, in order to capture a meaningful portion of the tsunami wave, and average out the correlation values of smaller period waves. The correlation in the radar signal between cells is expected to be a near uniform value of one when no tsunami is present, this is due to the general lock of other naturally occurring oceanic waves or patterns with the same period of tsunamis. The first synthetic tsunami was modeled on a Mw 9.1 far-field source based in the Semidi Subduction Zone (SSZ). It was demonstrated that the TCA is able to detect extremely small currents, less than 10 cm/s, in the presence of strong, random background currents, up to 35 cm/s. The second was a near field submarine mass failure (SMF) tsunami located just off of the coast of the modeled radar station.
520 $a Manuscript two continues the development of the TCA, by replacing the simulated radar signal with recorded radar data from the WERA station in Tofino CA which the simulated signal was based on, and expanding to a third tsunami source. The new tsunami source is a potential meteo-tsunami which occurred on October 14, 2016, and provides an opportunity or detection of a real tsunami event (albeit in an offline a posteriori analysis). When applied to the raw data it was found that the radar signal itself exhibited a high level of self correlation, thought to be an artifact from the signal processing software; namely range-gating and beam forming. A new slightly modified TCA was therefore developed which contrasts the average correllation along a portion of a wave ray against the correlation of the same portion, taken one hour prior. This modified version of the TCA demonstrated detection of the simulated SSZ tsunami and SMF tsunami using the recorded radar signal from several different days, representative of using varying oceanic and meteorological conditions to test the robustness of the algorithm. An initial detection threshold for the method was also determined, and using a few days of data the method for determining a more robust confidence in detection was demonstrated. The major conclusions are the function of the TCA on real data, and with a variety of different, realistic threats to the area.
520 $a The final manuscript compares the TCA with another published detection method, the "Q-Factor" algorithm. The Q-Factor uses the measurements recorded by coastal radar stations in the from of traditionally radially inverted currents. These currents are derived from the Doppler spectra of the backscattered radar signal. By using an empirically derived pattern recognition algorithm, the Q-Factor tests for fluctuations in surface currents across bathymetry bands indicative of a tsunami. In this manuscript the raw radar data and simulated tsunami sources used to test the TCA are again used to provide a direct comparison of the two. Additionally several aspects from the TCA are borrowed to generate a modified Q-Factor and test whether a hybridization of the two methods results in any performance improvements. Its concluded that the TCA operates more reliably over a variety of meteorological, oceanic, and operating conditions, although a more thorough analysis (especially of signal quality) must be completed before true conclusions can be drawn. The Q-Factor is also unable to detect the SMF, demonstrating an important limitation of the system, something that must be taken into account with local threats when considering an algorithm to be used in a specific area. (Abstract shortened by ProQuest.).
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