Tsunami Prediction Research Articles

Enhancing radar signal processing through LVQ-Kalman fusion: a tsunami prediction perspective

Enhancing radar signal processing through LVQ-Kalman fusion: a tsunami prediction perspective

Prediction of Tsunamis in Indonesia Using an Optimized Neural Network with SMOTE

Tsunamis have the potential to have a large impact on the environment, therefore early detection and preparation for tsunami need to be carried out to reduce the impact of casualties and losses incurred. This research aims to predict tsunami events due to large earthquakes in Indonesia as a form of early detection. The optimized neural network method is used in research to classify tsunami events in Indonesia in 2000-2023 for large earthquakes with strength more than 5 magnitudes. The research results show that the neural network structure formed consists of an input layer, a hidden layer, and an output layer. The results of the evaluation of the neural network model with SMOTE obtained an accuracy value of 99.43%, precision of 96.31%, and an F1 score of 97.86%, which means the resulting model is good. Therefore, an optimized neural networks can be applied as a warning system in various regions to detect potential tsunami events in the future.

Изучение дизъюнктивной сети о. Кунашир (Курильские острова) с целью реконструкции особенностей его тектонического развития

The paper is devoted to the study of the faults on Kunashir Island, focusing on their classification, morphology, kinematics, timing, and causes of formation, which are key to understanding the tectonic evolution of the area. The magma-supporting role of these faults is noted, contributing to the understanding of the distribution of volcanic and plutonic formations in the Great Kuril Ridge. The ore-controlling role of the faults was evaluated, which may contribute to the discovery of new mineral deposits. Additionally, studying these faults is important for improving the prediction of earthquakes and tsunamis. The research methodology includes an integrated approach that combines traditional geological observations with modern remote sensing methods, allowing us to detail the map of the Kunashir Island rupture faults. The results of the work confirm the presence of three systems of tectonic structures that differ in depth of emplacement, length, timing, kinematics, and causes of formation. The study contributes to our understanding of the complex geological structure of Kunashir Island and the entire island arc and provides a basis for further research in this area."

Adjoint Synthesis for Trans‐Oceanic Tsunami Waveforms and Simultaneous Inversion of Fault Geometry and Slip Distribution

AbstractTsunamis propagate over long distances and can cause widespread damage even after crossing ocean basins. Prediction of tsunamis in distant areas based on observations near their sources is critical to mitigating damage. In recent years, the accuracy of numerical models of trans‐oceanic tsunami propagation has improved significantly due to the incorporation of effects such as the solid earth response to tsunami loading and wave dispersion. However, these models are computationally expensive and have not been fully utilized for real‐time prediction. Here, we derive the adjoint operator for the linear set of equations describing deep‐ocean tsunami propagation and show how a pre‐computed database of adjoint states can achieve rapid synthesis of tsunami waveforms at target sites from nonpoint arbitrary tsunami sources. The adjoint synthesis method allows for an exhaustive parameter search for tsunami source estimation. A method for simultaneous inversion of fault geometry and slip distribution using adjoint synthesis with Sequential Monte Carlo method was proposed and applied to the 2012 Haida Gwaii earthquake tsunami. The influence of model accuracy and the amount of observed data on the estimation of tsunami sources and waveforms was examined. It was found that with a highly accurate propagation model, using only a limited amount of observed data produced source and waveform estimates very similar to the final models obtained with much larger data sets. The final inferred fault model involved megathrust slip distributed between the Haida Gwaii trench and the Queen Charlotte fault. The proposed method can also quantify the uncertainty of the waveform forecasts.

Characterization of Underwater Landslides in Krui Waters, Lampung-Indonesia using Shallow Seismic and Sonar Data

Abstract Krui Waters, Lampung, Indonesia, is an area that has the potential for a tsunami disaster. Local tsunamis can be caused by underwater landslides owing to ground movement and gravitational forces. Imaging underwater conditions using sonar and shallow seismic systems is an interesting research topic for geological disaster researchers. Underwater landslides are very important for disaster mitigation, especially for tsunami prediction. This research aims to identify the characteristics of underwater landslides around Krui Waters, Lampung, Indonesia, using imaging analysis methods from sonar data and a sub-bottom profiler (SBP). The underwater landslide phenomenon in this area is still not known in detail, and therefore requires further research. All data were obtained from the results of hydrographic, geological, and geophysical surveys of RV Baruna Jaya IV in 2020. The sonar data shows surface characteristics such as the presence of Furrows (Gravel Waves), Cobble (Boulder), as well as Canyons and landslides or mass movements rocks. SBP data show the characteristics of the seabed in the form of a canyon with a slope greater than 160. Finally, the morphological characteristics and features obtained could possibly be a potential source of local tsunamis owing to the high slope.

New exact solutions to the generalized shallow water wave equation

In this work, we study the generalized shallow water wave equation to obtain novel solitary wave solutions. The application of this nonlinear model can be found in tidal waves, weather simulations, tsunami prediction, river and irrigation flows. To obtain the new exact solutions of the considered model, we have applied a novel analytical technique namely [Formula: see text]-expansion method. Using the aforementioned method and computational software, we have obtained different kinds of periodic and singular solitary wave solutions of the generalized shallow water wave equation. The obtained solutions are exponential function and trigonometric function solutions. Using 2D and 3D plots of the wave solutions, the dynamic behaviors of the developed solutions are displayed. The retrieved solutions validated the effectiveness and robustness of the proposed technique.

Comparative Analysis of SVM and RF Algorithms for Tsunami Prediction: A Performance Evaluation Study

Comparative Analysis of SVM and RF Algorithms for Tsunami Prediction: A Performance Evaluation Study

Analysis of Shallow Water Equation Based on Tsunami Simulations: Evidence from the Pacific Ocean

Tsunamis, particularly prevalent in the Pacific Ocean's "Ring of Fire" region, present both a scientific intrigue and a societal concern due to their potential for devastation. Central to understanding and predicting these phenomena is the Shallow Water Equations (SWEs), which describe the horizontal motion of water waves. This study delves into the specific behaviors of tsunamis in the Pacific coast, utilizing comprehensive oceanographic and seismological data from the Pacific Oceanographic Institute (POI) spanning two decades. Through the lens of the SWEs, the impacts of bathymetric features and coastal topography on tsunamis are analyzed from the perspective of propagation, wave period, and frequency. Findings highlighted the significant role of solitons or solitary waves and the destructive force they exert, especially in shallow waters. While the SWE-based models have greatly assisted in developing real-time warning systems, reducing fatalities and damages, they also present certain limitations, e.g., assuming a flat seafloor and neglecting factors such as Earth's rotation, vertical fluid motion, and real-world marine conditions like turbulence. The study underscores the need for continuous refinement of these models, emphasizing the integration of observational data with advanced computational methods to enhance tsunami prediction and preparedness.

Fractional-stochastic shallow water equations and its analytical solutions

In this paper, the stochastic shallow water wave equation with M-Truncated derivative is taken into account. New rational, trigonometric and hyperbolic solutions are produced using the He’s semi-inverse method and modified extended tanh-function method. The solutions found can be used to simulate a wide range of fascinating physical phenomena since the shallow water equation has numerous applications including weather simulations, tsunami prediction, tidal waves, and river irrigation flows. Also, we generalize some previous results. To show how M-Truncated Derivative and white noise affect the analytical solutions of the stochastic shallow water wave equation with M-Truncated derivative, we create different two dimension and three dimension graphs.

Development of early warning system for tsunamis accompanied by collapse of Anak Krakatau volcano, Indonesia

Present tsunami warning systems have been specialized for earthquake-generated tsunamis, but rapidly evaluating the tsunamis caused by volcanic eruptions and/or volcanic sector collapses remains a challenge. In this study, we applied a numerical model to the 2018 Anak Krakatau tsunami event, which was generated by the sector collapse, investigated a tsunami prediction skill by the model, and developed a real-time forecasting method based on a pre-computed database for future tsunamis accompanied by such eruption of Anak Krakatau. The database stores spatiotemporal changes in water surface level and flux, which are simulated under various collapse scenarios, for confined areas in the vicinity of potential source. The areas also cover the locations of observation stations that are virtually placed on uninhabited island surrounding the source area. During an actual volcanic tsunami event, a tsunami is expected to be observed at the observation stations. For real-time tsunami forecasting, the most suitable scenarios to reproduce the observed waveforms are searched quickly in the database. The precomputed results under the identified scenarios are further provided as input for rapid tsunami propagation simulation. Therefore, an effective real-time forecasting can be conducted to densely populated coastal areas located at a considerable distance from the source, such as the coasts of Java and Sumatra. The forecasting performance was examined by applying the method for three hypothetical collapse scenarios assuming different sliding directions. We demonstrated that the tsunamis along the coasts were successfully forecasted. Moreover, we showed that the combination of a pre-computed database and the existence of observation stations near the source area was able to produce appropriate tsunami forecasting for the coastal area even in a volcanic event.

DEEP LEARNING TO PREDICT TSUNAMI HEIGHT AT THE SHORELINE USING OCEAN BOTTOM PRESSURE DATA

Real-time tsunami prediction is a required component of a tsunami warning system. Several advances have been made to improve prediction in the tsunami warning process, including precomputed databases and the assimilation of deep-ocean observations (DART buoys) into numerical modeling (Bernard and Titov, 2015). These improvements aim to accurately and quickly predict the time and height of the tsunami wave impact. Here, two deep learning models (DLM) are developed to predict the maximum tsunami height at a local/long shoreline from four time series observations of ocean bottom pressure data.

Accurate tsunami wave prediction using long short-term memory based neural networks

Novel tsunami prediction models based on deep learning technique are used for tsunami wave prediction. The Long Short-Term Memory (LSTM) model and its variants are compared with numerical models and observation data. Three different types of tsunami prediction, namely single tsunami prediction at the same location, single tsunami prediction at different locations, and different tsunami prediction at different locations are considered. Different input–output ratios are examined. The error indicators are utilized to evaluate the reliability and accuracy. It is found that the input–output ratio 2:1 is most suitable for tsunami prediction. However, more inputs may not bring better prediction results under the same input–output ratio. Results show that our proposed Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Networks–Long Short-Term Memory(CNN–LSTM) models are superior to the National Oceanic and Atmospheric Administration (NOAA) numerical model in prediction. This may be because the observed tsunami data contains rich information, and the deep learning model can better grasp various information hidden in the data, thus giving excellent prediction results.

Prediction of tsunami of resistance to some antibiotics is not far-fetched which used during COVID-19 pandemic.

One of the most tragic events in recent history was the COVID-19 outbreak, which has caused thousands of deaths. A variety of drugs were prescribed to improve the condition of patients, including antiparasitic, antiviral, antibiotics, and anti-inflammatory medicines. It must be understood, however, that COVID-19 is like a tip of an iceberg on the ocean, and the consequences of overuse of antibiotics are like the body of a mountain under water whose greatness has not yet been determined for humanity, and additional study is needed to understand them. History of the war between microbes and antimicrobial agents has shown that microbes are intelligent organisms that win over antimicrobial agents over time through many acquired or inherent mechanisms. The key terms containing "COVID-19," "Severe acute respiratory syndrome coronavirus-2," "SARS-CoV2," "Antibiotic Resistance," "Coronavirus," "Pandemic," "Antibiotics," and "Antimicrobial Resistance" were used for searching in PubMed, Scopus, and Google Scholar databases. The COVID-19 pandemic has resulted in an increased prescription of antibiotics. Infections caused by secondary or co-bacterial infections or beneficial bacteria in the body can be increased as a result of this amount of antibiotic prescription and exposure to antibiotics. Antibiotic resistance will likely pose a major problem in the future, especially for last resort antibiotics. In order to address the antibiotic resistance crisis, it is imperative that researchers, farmers, veterinarians, physicians, public and policymakers, pharmacists, other health and environmental professionals, and others collaborate during and beyond this pandemic.

The Soliton Solutions of the Stochastic Shallow Water Wave Equations in the Sense of Beta-Derivative

The stochastic shallow water wave equation (SSWWE) in the sense of the beta-derivative is considered in this study. The solutions of the SSWWE are obtained using the F-expansion technique with the Riccati equation and He’s semi-inverse method. Since the shallow water equation has many uses in ocean engineering, including river irrigation flows, tidal waves, tsunami prediction, and weather simulations, the solutions discovered can be utilized to represent a wide variety of exciting physical events. We create many 2D and 3D graphs to demonstrate how the beta-derivative and Brownian motion affect the analytical solutions of the SSWWE.

Message from the Winner

We are very honored to receive the prestigious JDR Award for the Most Cited Paper 2022. The winning paper, “Real-Time Tsunami Prediction System Using DONET,” discusses a system that uses data from the Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET), which was installed in the rupture areas of the 1944 Tonankai and 1946 Nankai earthquakes in order to instantly generate and visualize tsunami prediction information. The forecast information from this system consists of tsunami arrival time, maximum tsunami height, tsunami inundation area, and inundation depth distribution. The system, which visualizes and distributes forecast information for areas where users need it as a supplement to the tsunami information provided nationwide by the Japan Meteorological Agency, has been introduced in Wakayama Prefecture, Mie Prefecture, Chiba Prefecture, and the city of Owase, as well as by Chubu Electric Power Co., Inc. After DONET was installed in the Tonankai rupture area, we were considering the possibility of using DONET data regionally, and we developed the system as results of discussions with Wakayama Prefecture and Chubu Electric Power Co., Inc. Under the concept that the users themselves would be operating the system, we intended to make the system as simple and easy to understand as possible, and to reduce costs by minimizing the number of hardware units. We also considered making the system flexible and scalable, recognizing that each user has a different way of how to use the tsunami forecast information. We were able to receive this award thanks to the cooperation of the people who were involved in many discussions with us during the process of establishing the concept. We will not become complacent going forward, but will continue to improve our system in ways that reflect the opinions of the users. We would like to thank everyone who has cited this paper with their interests.

Real‐Time Prediction of Alongshore Near‐Field Tsunami Runup Distribution From Heterogeneous Earthquake Slip Distribution

AbstractReal‐time tsunami prediction is necessary for tsunami forecasting. Although tsunami forecasting based on a precomputed tsunami simulation database is fast, it is difficult to respond to earthquakes that are not in the database. As the computation speed increases, various alternatives based on physics‐based models have been proposed. However, physics‐based models still require several minutes to simulate tsunamis and can have numerical stability issues that potentially make them unreliable for use in forecasting—particularly in the case of near‐field tsunamis. This paper presents a data‐driven model called the tsunami runup response function for finite faults (TRRF‐FF) model that can predict alongshore near‐field tsunami runup distribution from heterogeneous earthquake slip distribution in less than a second. Once the TRRF‐FF model is trained and calibrated based on a discrete set of tsunami simulations, the TRRF‐FF model can predict alongshore tsunami runup distribution from any combination of finite fault parameters. The TRRF‐FF model treats the leading‐order contribution and the residual part of the alongshore tsunami runup distribution separately. The interaction between finite faults is modeled based on the leading‐order alongshore tsunami runup distribution. We validated the TRRF‐FF modeling approach with more than 200 synthetic tsunami scenarios in eastern Japan. We further explored the performance of the TRRF‐FF model by applying it to the 2011 Tohoku (Japan) tsunami event. The results show that the TRRF‐FF model is more flexible, occupies much less storage space than a precomputed tsunami simulation database, and is more rapid and reliable than real‐time physics‐based numerical simulation.

Fractal soliton solutions for the fractal-fractional shallow water wave equation arising in ocean engineering

The generalized shallow water wave equation is an important mathematical model that is used to elaborate ocean engineering, weather simulations, tsunami prediction and tidal currents. In this work, the generalized (3 + 1)-dimensional fractal-fractional shallow water wave equation (FFSWWE) is investigated where fractal-fractional derivative is taken in the conformable derivative sense. Some new fractal soliton solutions of FFSWWE are successfully derived by the fractal-fractional variational wave method (FFVWM), which is a new mathematical technology. This new method has the advantages of being simple, efficient, and direct. The 3D graphics that describe these new fractal soliton solutions that were obtained are tremendously important for improving our understanding of physical oceanography.

Probabilistic tsunami hazard assessment based on the Gutenberg–Richter law in eastern Shikoku, Nankai subduction zone, Japan

Earthquake and tsunami predictions comprise huge uncertainties, thus necessitating probabilistic assessments for the design of defense facilities and urban planning. In recent years, computer development has advanced probabilistic tsunami hazard assessments (PTHAs), where hazard curves show the exceedance probability of the maximum tsunami height. However, owing to the lack of historical and geological tsunami records, this method is generally insufficient for validating the estimated hazard curves. The eastern coast of Shikoku in the Nankai subduction zone, Japan, is suitable for validation because tsunami records from historical Nankai Trough earthquakes are available. This study evaluated PTHAs by comparing the tsunami hazard curves and exceedance frequencies of historical Nankai Trough tsunamis. We considered 3480 earthquake scenarios representing the rupture patterns of past Nankai earthquakes and calculated all tsunamis. The probability of earthquake occurrence was based on the Gutenberg–Richter law. We considered uncertainty in tsunami calculations with astronomical tide variations. The estimated tsunami hazard curves are consistent with the exceedance frequencies obtained from historical tsunamis. In addition, sensitivity tests indicate the significance of the earthquake slip heterogeneity and tsunami defense facilities in PTHAs. We also extended the PTHAs to tsunami inundation maps in high resolution and proposed an effective new method for reducing the tsunami computation load.Graphical

Optimizing a Model of Coseismic Rupture for the 22 July 2020MW7.8 Simeonof Earthquake by Exploiting Acute Sensitivity of Tsunami Excitation Across the Shelf Break

AbstractThe Shumagin seismic gap along the Alaska Peninsula experienced a major,MW7.8, interplate thrust earthquake on 22 July 2020. Several available finite‐fault inversions indicate patchy slip of up to 4 m at 8–48 km depth. There are differences among the models in peak slip and absolute placement of slip on the plate boundary, resulting from differences in data distributions, model parameterizations, and inversion algorithms. Two representative slip models obtained from inversions of large seismic and geodetic data sets produce very different tsunami predictions at tide gauges and deep‐water pressure sensors (DART stations), despite having only secondary differences in slip distribution. This is found to be the result of the acute sensitivity of the tsunami excitation for rupture below the continental shelf in proximity to an abrupt shelf break. Iteratively perturbing seismic and geodetic inversions by constraining fault model extent along dip and strike, we obtain an optimal rupture model compatible with teleseismicPandSHwaves, regional three‐component broadband and strong‐motion seismic recordings, hr‐GNSS time series and static offsets, as well as tsunami recordings at DART stations and regional and remote tide gauges. Slip is tightly bounded between 25 and 40 km depth, the up‐dip limit of slip in the earthquake is resolved to be well‐inland of the shelf break, and the rupture extent along strike is well‐constrained. The coseismic slip increased Coulomb stress on the shallow plate boundary extending to the trench, but the frictional behavior of the megathrust below the continental slope remains uncertain.

Rapid and quantitative uncertainty estimation of coseismic slip distribution for large interplate earthquakes using real-time GNSS data and its application to tsunami inundation prediction

This study proposes a new method for the uncertainty estimation of coseismic slip distribution on the plate interface deduced from real-time global navigation satellite system (GNSS) data and explores its application for tsunami inundation prediction. Jointly developed by the Geospatial Information Authority of Japan and Tohoku University, REGARD (REal-time GEONET Analysis system for Rapid Deformation monitoring) estimates coseismic fault models (a single rectangular fault model and slip distribution model) in real time to support tsunami prediction. The estimated results are adopted as part of the Disaster Information System, which is used by the Cabinet Office of the Government of Japan to assess tsunami inundation and damage. However, the REGARD system currently struggles to estimate the quantitative uncertainty of the estimated result, although the obtained result should contain both observation and modeling errors caused by the model settings. Understanding such quantitative uncertainties based on the input data is essential for utilizing this resource for disaster response. We developed an algorithm that estimates the coseismic slip distribution and its uncertainties using Markov chain Monte Carlo methods. We focused on the Nankai Trough of southwest Japan, where megathrust earthquakes have repeatedly occurred, and used simulation data to assume a Hoei-type earthquake. We divided the 2951 rectangular subfaults on the plate interface and designed a multistage sampling flow with stepwise perturbation groups. As a result, we successfully estimated the slip distribution and its uncertainty at the 95% confidence interval of the posterior probability density function. Furthermore, we developed a new visualization procedure that shows the risk of tsunami inundation and the probability on a map. Under the algorithm, we regarded the Markov chain Monte Carlo samples as individual fault models and clustered them using the k-means approach to obtain different tsunami source scenarios. We then calculated the parallel tsunami inundations and integrated the results on the map. This map, which expresses the uncertainties of tsunami inundation caused by uncertainties in the coseismic fault estimation, offers quantitative and real time insights into possible worst-case scenarios.Graphical