Dataset Information

A possible space-based tsunami early warning system using observations of the tsunami ionospheric hole.

ABSTRACT: Ionospheric plasma disturbances after a large tsunami can be detected by measurement of the total electron content (TEC) between a Global Positioning System (GPS) satellite and its ground-based receivers. TEC depression lasting for a few minutes to tens of minutes termed as tsunami ionospheric hole (TIH) is formed above the tsunami source area. Here we describe the quantitative relationship between initial tsunami height and the TEC depression rate caused by a TIH from seven tsunamigenic earthquakes in Japan and Chile. We found that the percentage of TEC depression and initial tsunami height are correlated and the largest TEC depressions appear 10 to 20?minutes after the main shocks. Our findings imply that Ionospheric TEC measurement using the existing ground receiver networks could be used in an early warning system for near-field tsunamis that take more than 20?minutes to arrive in coastal areas.

SUBMITTER: Kamogawa M

PROVIDER: S-EPMC5131353 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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A possible space-based tsunami early warning system using observations of the tsunami ionospheric hole.

Kamogawa Masashi M Orihara Yoshiaki Y Tsurudome Chiaki C Tomida Yuto Y Kanaya Tatsuya T Ikeda Daiki D Gusman Aditya Riadi AR Kakinami Yoshihiro Y Liu Jann-Yenq JY Toyoda Atsushi A

Scientific reports 20161201

Ionospheric plasma disturbances after a large tsunami can be detected by measurement of the total electron content (TEC) between a Global Positioning System (GPS) satellite and its ground-based receivers. TEC depression lasting for a few minutes to tens of minutes termed as tsunami ionospheric hole (TIH) is formed above the tsunami source area. Here we describe the quantitative relationship between initial tsunami height and the TEC depression rate caused by a TIH from seven tsunamigenic earthqu ...[more]

PMID: 27905487

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Project description:Tsunamis are natural phenomena that, although occasional, can have large impacts on coastal environments and settlements, especially in terms of loss of life. An accurate, detailed and timely assessment of the hazard is essential as input for mitigation strategies both in the long term and during emergencies. This goal is compounded by the high computational cost of simulating an adequate number of scenarios to make robust assessments. To reduce this handicap, alternative methods could be used. Here, an enhanced method for estimating tsunami time series using a one-dimensional convolutional neural network model (1D CNN) is considered. While the use of deep learning for this problem is not new, most of existing research has focused on assessing the capability of a network to reproduce inundation metrics extrema. However, for the context of Tsunami Early Warning, it is equally relevant to assess whether the networks can accurately predict whether inundation would occur or not, and its time series if it does. Hence, a set of 6776 scenarios with magnitudes in the range [Formula: see text] 8.0-9.2 were used to design several 1D CNN models at two bays that have different hydrodynamic behavior, that would use as input inexpensive low-resolution numerical modeling of tsunami propagation to predict inundation time series at pinpoint locations. In addition, different configuration parameters were also analyzed to outline a methodology for model testing and design, that could be applied elsewhere. The results show that the network models are capable of reproducing inundation time series well, either for small or large flow depths, but also when no inundation was forecast, with minimal instances of false alarms or missed alarms. To further assess the performance, the model was tested with two past tsunamis and compared with actual inundation metrics. The results obtained are promising, and the proposed model could become a reliable alternative for the calculation of tsunami intensity measures in a faster than real time manner. This could complement existing early warning system, by means of an approximate and fast procedure that could allow simulating a larger number of scenarios within the always restricting time frame of tsunami emergencies.

Dataset Information

A possible space-based tsunami early warning system using observations of the tsunami ionospheric hole.

Publications

A possible space-based tsunami early warning system using observations of the tsunami ionospheric hole.

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