Tsunami Detection Research Articles

Exploring AI Progress in GNSS Remote Sensing: A Deep Learning Based Framework for Real‐Time Detection of Earthquake and Tsunami Induced Ionospheric Perturbations

AbstractGlobal Navigation Satellite System Ionospheric Seismology investigates the ionospheric response to earthquakes and tsunamis. These events are known to generate Traveling Ionospheric Disturbances (TIDs) that can be detected through GNSS‐derived Total Electron Content (TEC) observations. Real‐time TID identification provides a method for tsunami detection, improving tsunami early warning systems (TEWS) by extending coverage to open‐ocean regions where buoy‐based warning systems are impractical. Scalable and automated TID detection is, hence, essential for TEWS augmentation. In this work, we present an innovative approach to perform automatic real‐time TID monitoring and detection, using deep learning insights. We utilize Gramian Angular Difference Fields (GADFs), a technique that transforms time‐series into images, in combination with Convolutional Neural Networks (CNNs), starting from VARION (Variometric Approach for Real‐time Ionosphere Observation) real‐time TEC estimates. We select four tsunamigenic earthquakes that occurred in the Pacific Ocean: the 2010 Maule earthquake, the 2011 Tohoku earthquake, the 2012 Haida‐Gwaii, the 2015 Illapel earthquake. The first three events are used for model training, whereas the out‐of‐sample validation is performed on the last one. The presented framework, being perfectly suitable for real‐time applications, achieves 91.7% of F1 score and 84.6% of recall, highlighting its potential. Our approach to improve false positive detection, based on the likelihood of a TID at each time step, ensures robust and high performance as the system scales up, integrating more data for model training. This research lays the foundation for incorporating deep learning into real‐time GNSS‐TEC analysis, offering a joint and substantial contribution to TEWS progression.

Spatial and temporal coverage of the cargo ship network for GNSS-based tsunami detection

Tracking changes in sea-surface height with ship-based GNSS can be used to detect tsunamis. One year of navigation data from ships in the Pacific is examined to investigate how well-distributed a cargo-ship network would be for tsunami detection. There is excellent coverage of the most active tsunamigenic zones, with multiple ships predicted within 30-minutes travel time of notable tsunamis. Tsunamigenic regions with low ship density, such as the Southwest Pacific, require a greater percentage of ships participating to ensure sufficient data. The global nature of GNSS and ship routes make this a promising, low-cost approach, to augment tsunami detection.

Detection of Earthquakes and Tsunami through IOT Network

Detection of Earthquakes and Tsunami through IOT Network

Tsunami tide prediction in shallow water using recurrent neural networks: model implementation in the Indonesia Tsunami Early Warning System

Near-field tides prediction for tsunami detection in the coastal area is a significant problem of the cable-based tsunami meter system in north Sipora, Indonesia. The problem is caused by its shallow water condition and the unavailability of an applicable model or research for tsunami detection in this area. The problem foundation of shallow water area is its ambient noise level-dependent property that requires preprocessing to improve its feature representation. Moreover, because this shallow water is close to the land area, we must consider a model that can accommodate low prediction time for a Tsunami Early Warning System. Therefore, we propose a recurrent neural network (RNN) model because of its reliable performance for time series forecasting. Our report evaluates variants of the RNN model (the vanilla RNN, LSTM and GRU models) in tides prediction and z-score analysis for tsunami identification. The GRU model overwhelms the other two variants in error scores and time processed (training and prediction). It can achieve median error score distribution of 7.8×10-5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.8 \ imes 10^{-5}$$\\end{document} on the L1000-P250 configuration with time prediction under 0.1 s. This lower-time prediction is necessary to ensure the early warning system is going well. Moreover, the GRU model can identify all synthetic tsunami tide spikes (compared with the ground truth result) from magnitude 7.2–8.2 by applying a z-score on the GRU’s prediction.

First Open-Coast HF Radar Observations of a 2-Phase Volcanic Tsunami, Tonga 2022

We describe results from coastal radar systems that observed anomalous current flows generated by the volcanic eruption in the Tongan archipelago on 15 January 2022 UTC, reporting the first radar detection of a volcanic tsunami. The eruption caused small tsunamis along the western U.S. Coast, generating some damage in a few harbors. The highest tsunami signal in U.S. tide gauge data from the California coast occurred at Arena Cove, with significant heights detected at Port San Luis and Crescent City. We analyze correlated wave orbital velocity detections by High Frequency (HF) radars along the coast between Gerstle Cove and Santa Barbara. Signals observed by the radars indicate that the event had two phases, each with its own distinct genesis: an initial weak surface disturbance, most likely generated by the wave of atmospheric pressure that moved outward from the blast source at just below the speed of sound, followed by a stronger disturbance that arrived approximately 3.5 h later, matching the arrival time for a wave moving entirely through the water from the volcano to the U.S. West Coast. We conclude that this phase consists of a conventional water wave tsunami and weaker waves generated by the pulse. We also report the detection of a small pulse-generated event off the west coast of Florida. Radar observations are compared with water level measurements at nearby tide gauges and a DART buoy, and with observations of barometric pressure. We point out that a Proudman near-resonance at the Tonga Trench is unlikely to explain the second phase observations. Comparison with tide gauge signals at San Francisco, generated by the Krakatoa eruption in 1883, support our conclusions.

Sow the seeds of tsunami ready community in Indonesia: Lesson learned from Tanjung Benoa, Bali

UNESCO/IOC established the Ocean Decade Tsunami Program within the framework of the UN Decade of Ocean Science 2021–2030. The program is based on two overarching aspects: (1) exploring technological advances to reduce uncertainty and improve the timeliness of tsunami detection, and (2) promoting community preparedness efforts, including implementing the 100% Tsunami Ready for all at-risk coastlines by 2030. In Indonesia, there are 5744 prone to tsunamis villages. Tanjung Benoa, Bali, became the first village in Indonesia to have been recognized by UNESCO/IOC as a Tsunami-Ready. The process went through various stages and involved many parties. In search of leveraging elements to accelerate the process of implementing Tsunami Ready in Indonesia, we took the initiative to study and identify dynamic factors in Tanjung Benoa that are generic in nature and can be applied to other villages. We pinpoint 4 factors that must interplay dynamically within the science-policy-practice continuum interaction in advocating Tsunami Ready in Tanjung Benoa. Those factors are local leaders, stakeholders’ engagement, capacity intervention, and community readiness.

Detection of Landslides and Tsunamis in Douglas Channel and Gardner Canal, British Columbia

In 1975, an underwater landslide in Kitimat Arm at the northern end of Douglas Channel, British Columbia (Figure 1a), triggered tsunami waves that were observed to reach a height of over 8 m at the head of the inlet, destroying a dock and a newly built barge terminal (Bornhold, 1983). Elsewhere in the Douglas Channel region, seafloor deposits attest to previous submarine landslide events (Conway et al., 2012; Stacey et al., 2019), and subaerial landslides of various sizes regularly occur (Maynard et al., 2017). Landslide-generated tsunamis are increasingly recognized as a substantial hazard worldwide, with the potential for extreme wave runup and localized damage, particularly in narrow, steep-sided bays and inlets. In most cases it is not possible to prevent landslides from occurring; however, mitigation efforts can include early landslide detection and the development of tsunami early warning systems using real-time data.

Sustainable Strategy Management Cable-Based Tsunami Detection Equipment Instrumentation

<p>Indonesia adalah salah satu negara di dunia yang sering diguncang bencana alam, salah satunya tsunami. Menjadi penting bagi Indonesia untuk memiliki detektor tsunami kabel atau Indonesia <em>Cable Based Tsunameter</em> (Ina-CBT). Pengembangan Teknologi <em>Tsunami Early Warning System</em> di Indonesia (InaTEWS), Teknologi Ina-TEWS terdiri dari: Ina-BUOY, Ina-CBT dan Tomografi. Ina-CBT adalah teknologi sensor deteksi Tsunami yang bekerja mengukur naik atau turunnya permukaan air laut melalui perubahan tekanan air bawah laut. <em>Cable Based Tsunameter</em> (CBT) efektif dalam melacak kemungkinan tsunami yang terjadi akibat gempa tektonik dan dapat mendeteksi perubahan gelombang kecil di bawah permukaan air. Untuk mendukung pengelolaan <em>Ina-CBT</em> yang berkelanjutan, <em>Ina-CBT</em> membutuhkan dukungan dari sisi regulasi. Penelitian ini menyajikan rumusan strategi pengelolaan berkelanjutan untuk penguatan ekosistem pengoperasian <em>Ina-CBT</em> dan pelembagaan sistem pengelolaan instrumentasi<em> Ina-CBT.</em></p>

A Fuzzy-Logic Approach for Optimized and Cost-Effective Early Warning System for Tsunami Detection

With the economic crisis going around the world, a new approach, “build back better”, has been adopted as a recovery package for various systems. The tsunami detection and warning system is one such system, crucial for saving human lives and infrastructure. While designing a tsunami detection system, the social, economic, and geographical circumstances are considered to be vital. This research is focused on designing a low-cost early warning system mainly for underdeveloped countries, which are more prone to tsunami damage due to a lack of any reliable early warning and detection systems. Such countries require proper cost-effective solutions to address these issues. Previous research has shown that the existing systems are either very costly or hard to implement and manage. In this study, we present a wireless sensor networking model, which is an optimized model in terms of cost, delay, and energy consumption. This research contemplates the techniques and advantages of the intelligence of marine animals. We propose a fuzzy logic-based approach for early tsunami detection, using electromagnetic and pressure sensors, based on the behavioral attributes of turtles and real-time values of earthquakes and water levels.

Optimal Tide Gauge Location for Tsunami Validation in The Lembeh Island, North Sulawesi

DOI:10.17014/ijog.9.3.315-327The tsunami early warning system in Bitung does not work optimally, because there is no buoy as a marine equipment for tsunami validation before reaching the coastal area. The lack of buoy can be replaced by placing a tide gauge on the east coast of Lembeh Island. To determine the optimal tide gauge location, the simple additive weighting (SAW) method was used with three criteria. Those three criteria are the potential of tsunami detection, sufficient evacuation time, and an appropriate site for tide gauge installation. Numerical tsunami modeling is used to calculate the first two criteria. The third criterion is a limiting factor, because the tide gauge can only be installed on the dock. Therefore, there were only five candidate locations on the east coast of Lembeh, namely Dorbolang, Pancuran, Posokan, Motto, and Lirang. The result, Posokan is the best location for tide gauge placement with a total score of 2.884. Based on the simulation, an additional tide gauge in Posokan can detect tsunami at the average of 11.4 minutes earlier than use only the tide gauge currently available at Bitung port. It means that people on the coast of Bitung have more evacuation time before the tsunami hits the coastal area

Rancang Bangun Sistem Pendeteksi Tsunami (Studi Kasus Selat Sunda)

Tsunami is not only caused by the strength of tectonic earthquakes with the dislocation of the seafloor, but also by landslides /rocks in shallow waters, or the relatively large fall of meteors/ celestial bodies. Unlike other tsunamis caused by slab collisions, tsunamis in the Sunda Strait tsunami caused by high tides and underwater landslides due to the eruption of krakatoa. Tsunami due to the eruption of Mount Anak Krakatau threatens at any time so that technology is needed to be able to help the community and BMKG monitor activities in the Sunda Strait so as to provide security and comfort of the surrounding residents. Therefore, research was made to measure the height of waves to detect high waves causing tsunamis in the Sunda Strait. In this research, Tsunami detection system using electrical circuits in its testing with submersible sensors as a wave height reader. The result of the sensor height readings read by the sensor in the process uses a Resberry-Pi microcontroller. The height limit set on the sensor produces siren sounds and sends short messages sent to residents of coastal communities of the Sunda Strait. The results showed that the tsunami detection system managed to read the water level in real time accurately according to the calibration equation. The water level reading tool set by the limit is 2 meters with the length of the wave using a maximum time of 90 seconds. The device will send a short message and produce a siren sound when the wave reaches a height of 2 meters for 90 seconds in a row. Sending messages takes 2-5 seconds, if there is a tsunami due to the eruption of Mount Anak Krakatau the evacuation time to the safe zone for coastal communities of the Sunda Strait is still affordable and safe.

A Numerical Study of SMART Cables Potential in Marine Hazard Early Warning for the Sumatra and Java Regions

We present results from a series of exploratory numerical experiments based on ocean bottom pressure and seismic data from a simulated linear array of SMART cable stations off the trench in the Sumatra-Java region. We use six rupture scenarios to calculate tsunami propagation using hydrodynamic simulations. Through these experiments we show that such an addition would result in up to several hours of improvement in the detection of earthquakes and tsunamis compared to the existing (minimal) DART systems in the northern and southern Indian Ocean. By simulating tsunamis from 58 submarine landslide scenarios in the region, we show that the SMART system can provide invaluable information in early warning against landslide tsunamis. We also calculate seismic phase arrival times from six source scenarios at existing seismic stations and our proposed SMART cables. Statistical analysis of our results shows that inclusion of such a SMART array can improve the important network parameters for the detection, evaluation and locating of seismic events.

An agent-based model of short-notice tsunami evacuation in Waikiki, Hawaii

Evacuation planning is an essential part of transportation resilience. It is necessary to understand transportation systems and human behavior to plan for safe evacuation. This study estimates populations exposed to harm, clearance times and fatalities for short-notice evacuation for a catastrophic tsunami event. An Agent-Based Model (ABM) is developed and tested for Waikiki, Hawaii, with three travel modes: pedestrian, bicycle, and motor vehicles. The modeled environment includes the road network, tsunami inundation zones, and building footprints with vertical and horizontal evacuation destinations. Significant loss of life (38,760 persons) is estimated to occur with a catastrophic short-notice tsunami event. A new bridge over the Ala Wai Canal will create an additional exit route that could save 13,860 lives. Shortening tsunami detection and alert times by half would save 4,510 lives over the baseline, do-nothing scenario. Vertical evacuation is most effective and is estimated to save 55,770 lives. In addition to the importance to evacuation planning, the research using ABM supports longer term investment in transportation imnprovements, mitigation and adaptation.

Algoritme deteksi kedatangan tsunami otomatis untuk sistem observasi tinggi muka air laut

The automatic tsunami detection algorithm needs to be put in the sea level observation system to give society a quick warning when a tsunami happens. This study designs an automatic tsunami detection algorithm consisting of three sub-algorithm: spike elimination, gap data filling, and tsunami detection. Spike elimination and gap data filling are used to improve the sea level data, which is often disturbed by spikes and gap data due to electronic factors. This algorithm was tested using time-series tide gauge data that contain tsunami waveforms in Indonesia from 2007 to 2019. About 54.52 % of 409 spikes have been eliminated while the gap data were successfully filled. Furthermore, tsunami detection, which uses DART (Deep-ocean Assessment and Reporting of Tsunamis) and TEDA (Tsunami Early Detection Algorithm) methods, can detect 7 of 10 tsunami waveforms. However, there are three undetected tsunamis and one false detection. This algorithm has an average delay of 7.7 minutes in detection time.

Infrasound sound source localization for early tsunami detection

Early detection of tsunami needed for sufficient evacuation time, which the existing method using buoy can not accommodate. In this report, we investigated low-frequency signal (infrasound) as a cue for tsunami detection. We used Bayesian infrasound source localization (BISL) method for predicting events and direction of the tsunami based on prior data. The method consists of three processes such as detection, association, and localization. All processes are done to infrasound data that are recorded from the microphone array. The recorded infrasound data at Minahasa, Indonesia used for evaluating the proposed method. The result indicates that BISL can predict latitude, longitude, and incident time of the tsunami based on the true value of the real tsunami event.

Optimal placement of tsunami sensors with depth constraint.

Tsunamis are destructive natural disasters that can cause severe damage to property and the loss of many lives. To mitigate the damage and casualties, tsunami warning systems are implemented in coastal areas, especially in locations with high seismic activity. This study presents a method to identify the placement of near-shore detection sensors by minimizing the tsunami detection time, obtained by solving the two-dimensional shallow water equations (SWE). Several benchmark tests were done to establish the robustness of the SWE model, which is solved using a staggered finite volume method. The optimization problem is solved using particle swarm optimization (PSO). The proposed method is applied to different test problems. As an application, the method is used to find the optimal location of a detection sensor using data from the 2018 Palu tsunami. Our findings show that detection time can be significantly reduced through the strategic placement of tsunami sensors.

Tsunami Impact Prediction System Based on TsunAWI Inundation Data

It is very important for tsunami early warning systems to provide inundation predictions within a short period of time. Inundation is one of the factors that directly cause destruction and damage from tsunamis. This research proposes a tsunami impact prediction system based on inundation data analysis. The inundation data used in this analysis were obtained from the tsunami modeling called TsunAWI. The inundation data analysis refers to the coastal forecast zones for each city/regency that are currently used in the Indonesia Tsunami Early Warning System (InaTEWS). The data analysis process comprises data collection, data transformation, data analysis (through GIS analysis, predictive analysis, and simple statistical analysis), and data integration, ultimately producing a pre-calculated inundation database for inundation prediction and tsunami impact prediction. As the outcome, the tsunami impact prediction system provides estimations of the flow depth and inundation distance for each city/regency incorporated into generated tsunami warning bulletins and impact predictions based on the Integrated Tsunami Intensity Scale (ITIS-2012). In addition, the system provides automatic sea level anomaly detection from tide gauge sensors by applying a tsunami detection algorithm. Finally, the contribution of this research is expected to bring enhancements to the tsunami warning products of InaTEWS.

Tsunami detection by GPS-derived ionospheric total electron content

To unravel the relationship between earthquake and tsunami using ionospheric total electron content (TEC) changes, we analyzed two Chilean tsunamigenic subduction earthquakes: the 2014 Pisagua Mw 8.1 and the 2015 Illapel Mw 8.3. During the Pisagua earthquake, the TEC changes were detected at the GPS sites located to the north and south of the earthquake epicenter, whereas during the Illapel earthquake, we registered the changes only in the northward direction. Tide-gauge sites mimicked the propagation direction of tsunami waves similar to the TEC change pattern during both earthquakes. The TEC changes were represented by three signals. The initial weaker signal correlated well with Acoustic Rayleigh wave (AWRayleigh), while the following stronger perturbation was interpreted to be caused by Acoustic Gravity wave (AGWepi) and Internal Gravity wave (IGWtsuna) induced by earthquakes and subsequent tsunamis respectively. Inevitably, TEC changes can be utilized to evaluate earthquake occurrence and tsunami propagation within a framework of multi-parameter early warning systems.

Real-Time Tsunami Data Assimilation of S-Net Pressure Gauge Records during the 2016 Fukushima Earthquake

Abstract The 2016 Fukushima earthquake (M 7.4) generated a moderate tsunami, which was recorded by the offshore pressure gauges of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net). We used 28 S-net pressure gauge records for tsunami data assimilation and forecasted the tsunami waveforms at four tide gauges on the Sanriku coast. The S-net raw records were processed using two different methods. In the first method, we removed the tidal components by polynomial fitting and applied a low-pass filter. In the second method, we used a real-time tsunami detection algorithm based on ensemble empirical mode decomposition to extract the tsunami signals, imitating real-time operations for tsunami early warning. The forecast accuracy scores of the two detection methods are 60% and 74%, respectively, for a time window of 35 min, but they improve to 89% and 94% if we neglect the stations with imperfect modeling or insufficient offshore observations. Hence, the tsunami data assimilation approach can be put into practice with the help of the real-time tsunami detection algorithm.

Application of Singular Spectrum Analysis in Deep-Ocean Tide Reconstruction and Prediction

This study utilized the Singular Spectrum Analysis (SSA) approach to perform time series orthogonalization and demonstrated its use by analyzing vertical tidal acceleration and sea level time series from different deep-ocean locations. This method quantifies astronomical variations by using decomposed vertical tidal acceleration to reconstruct and predict deep-ocean tide. The results show that each decomposed vertical tidal acceleration can be associated with the decomposed sea level having at least 5 astronomical variations. Their associated energies can also be used to diagnose the change of the oceanic tide response to tidal acceleration. Performance evaluation also shows that this method can give comparable reconstruction accuracy and slightly better prediction accuracy compared to the harmonic analysis-based method. It is indicated that the proposed method is accurate enough to be applied in a tsunami detection algorithm. The results also indicate that the proposed method is stable enough to provide unpropagated prediction residuals.