Uncertainty Quantification for Earthquake and Tsunami Risk Analysis with Application to Valparaíso, Chile

Real-time seismic activity monitoring and earthquake risk assessment have long been critical aspects of disaster mitigation and urban resilience planning. Traditional models based on statistical or geophysical methods often fail to capture the complexity and dynamic nature of seismic phenomena. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized predictive modeling by enabling data-driven approaches for seismic signal analysis, ground motion prediction, and structural vulnerability assessment. This review paper presents a comprehensive synthesis of recent advancements in AI-driven seismic risk models, emphasizing their capacity for real-time forecasting and rapid decision-making. We examine state-of-the-art frameworks that integrate heterogeneous datasets—including geospatial, sensor-based, and historical seismic data—for enhanced situational awareness. The review also identifies the methodological strengths, comparative efficiencies, and current limitations of various AI techniques in earthquake risk analysis. Moreover, we highlight significant research gaps, such as model interpretability and integration with IoT-based monitoring systems, while discussing challenges like data sparsity, model generalization, and real-time computational constraints. A detailed comparative study and summary of recent works provide clarity on prevailing trends. We conclude by outlining future research directions involving hybrid AI models, high-resolution sensing, and multi-hazard frameworks. This paper serves as a valuable resource for researchers and policymakers focused on advancing earthquake preparations using cutting-edge AI innovations.

Recently developed methods of risk analysis are capable of modeling variations in earthquake hazard and its resultant damage to urban development. This type of risk analysis can provide useful information for land use decisions. Our article describes a probabilistic approach to earthquake risk analysis, which begins by estimating and mapping the expected level of ground motion over a study area. Areas subject to the earthquake-induced secondary hazards of landsliding and liquefaction are identified and combined with ground motion to describe the overall hazard. Damage estimates (by type of structure) can be produced for either the existing land use pattern or alternative future scenarios; that information can be used to evaluate alternative land use patterns in terms of their potential for earthquake damage. To illustrate the approach we use a case study application of earthquake risk analysis techniques to an area in San Luis Obispo County, California. We summarize the reactions of several pract...

The practical anti-earthquake optimum design of civil infrastructure network system

The utilization of Geographic Information System (GIS) technology in spatial planning for disaster mitigation is a novel approach to developing regional spatial planning documents in Indonesia, particularly in the Pidie Jaya district. This district is situated along the fault line on the island of Sumatra, a tectonically active zone highly susceptible to seismic activity. This research employs a methodology that integrates spatial data analysis, and GIS modeling to comprehensively understand the potential and impact of earthquakes in the area. Initial data used for analysis was sourced from the 2016 earthquake in Pidie Jaya district. The analysis results were incorporated into GIS to create precise and detailed maps depicting earthquake vulnerability and hazard. Subsequently, a meticulous spatial planning strategy is formulated, encompassing recommendations for land use, building standards, and disaster mitigation infrastructure. This strategy is designed to offer practical and effective guidance for local governments in reducing earthquake disaster risks and significantly contribute to formulating sustainable disaster mitigation policies. The findings of this paper are expected to provide a substantial contribution to earthquake disaster mitigation efforts, not only in Indonesia but also globally.

Local governments rarely use risk analysis, the most sophisticated level of hazard assessment, to inform their planning and development decisions. But new tools are rapidly becoming available to accomplish such analysis. In this paper we present an example of an earthquake risk analysis for Los Angeles County, using available land-use maps, a probabilistic earthquake hazard model developed by the Southern California Earthquake Center, and the Federal Emergency Management Agency's new HAZUS earthquake loss estimation software. We computed the annual expected loss owing to earthquakes and the spatial variation of this risk. The analysis shows that the annual long-term earthquake risk in Los Angeles County, as a result of direct structural and nonstructural damage, is $388 million per year. We also investigated the extent to which planned future land-use growth would affect this risk estimate. We found that planned growth of 14.2% would result in an increase in annual risk to $449.5 million, a 15.8% increase over the risk to current land uses. Because of ever increasing disaster costs, planners need to be able to evaluate the risks that their communities face, both in the present and in the future. It is particularly important for planners to be sure that they are not disproportionately planning future growth for hazardous locations. In this paper we describe some ways in which to perform such evaluations, by using tools that have only recently become available.

It may be no exaggeration to claim that this most recent quaddrenium has seen more controversy and thus more progress in understanding the physics of earthquakes than any in recent memory. The most interesting development has clearly been the emergence of a large community of condensed matter physicists around the world who have begun working on the problem of earthquake physics. These scientists bring to the study of earthquakes an entirely new viewpoint, grounded in the physics of nucleation and critical phenomena in thermal, magnetic, and other systems. Moreover, a surprising technology transfer from geophysics to other fields has been made possible by the realization that models originally proposed to explain self‐organization in earthquakes can also be used to explain similar processes in problems as disparate as brain dynamics in neurobiology (Hopfield, 1994), and charge density waves in solids (Brown and Gruner, 1994). An entirely new sub‐discipline is emerging that is focused around the development and analysis of large scale numerical simulations of the dynamics of faults. At the same time, intriguing new laboratory and field data, together with insightful physical reasoning, has led to significant advances in our understanding of earthquake source physics. As a consequence, we can anticipate substantial improvement in our ability to understand the nature of earthquake occurrence. Moreover, while much research in the area of earthquake physics is fundamental in character, the results have many potential applications (Cornell et al., 1993) in the areas of earthquake risk and hazard analysis, and seismic zonation.

Single sign-on is critical for the usability of distributed systems. While there are several authentication mechanisms which support single sign-on (e.g. Kerberos and X.509), it may be difficult to modify a particular legacy application to utilize an authentication scheme other than username/password. Asimple solution for single sign-on involves transmitting a user's password over the network. However, it is undesirable to expose a user's private password in an insecure environment. This paper describes our effort to create "session passwords" which are short-lived passwords transmitted in lieu of a user's private password. Our implementation utilizes the MyProxy X.509 credential service as an authentication service. We demonstrate our solution in the MAEviz application portal, a Java Web Start application for earthquake risk management and analysis.

For coastal areas located close to offshore seismic sources, earthquake risk is inherently multi-hazard: intense ground shaking and tsunami inundation can occur in rapid succession, with very short lead times for protective actions. In these near-field settings, traditional Tsunami Early Warning Systems (TEWS), which rely on seismic source parameters available several minutes after origin time, may provide alerts that are only marginally earlier than tsunami arrival, limiting their effectiveness.In this study, we evaluate the integration of rapid earthquake magnitude and location estimates from QuakeUp, an impact-based Earthquake Early Warning System (EEWS), into a tsunami early warning workflow. QuakeUp processes real-time seismic observations to issue initial earthquake alerts within a few seconds, based on fast estimates of magnitude, location, and potential damage zone, supporting immediate risk mitigation for ground shaking. These estimates are then simultaneously used to initialize Probabilistic Tsunami Forecasting (PTF), enabling a coordinated multi-hazard warning strategy in which earthquake and tsunami risks are addressed within a unified, time-critical framework.We present a real test case to quantify the earliness and accuracy of EEWS-derived source parameters and assess their impact on tsunami forecasting. A hindcast of the 30 October 2020 Mw 7.0 Aegean Sea earthquake, whose tsunami reached nearby coastlines in approximately 10 minutes, shows that the EEWS delivers stable and accurate hypocenter and magnitude estimates about 40 seconds after origin time. These estimates are comparable to those provided by the operational Early-Est system, which become available only after several minutes in the Mediterranean region. When used to initialize a PTF procedure, the EEWS-based source characterization yields coastal runup estimates in reasonable agreement with observations, despite the substantially reduced warning latency.To further assess the robustness of the results obtained in the real case, we analyze a second scenario based on 150 simulated seismogram datasets for earthquakes in the Messina Strait (Southern Italy), a region characterized by high exposure and extremely short tsunami travel times. For events with moment magnitudes between 6.0 and 7.0, the analysis confirms that the integrated EEWS–TEWS workflow can provide reliable source estimates within one minute, supporting its applicability to near-field tsunami early warning. This study provides the first demonstration of a combined EEWS–TEWS approach for near-coastal tsunamigenic events, highlighting its potential for dual risk mitigation within a multi-hazard early warning perspective. Future work will focus on testing performance in an operational setting, including the effects of network latencies and configuration-dependent efficiency. In addition, the EEWS technique employed, by providing a preliminary mapping of the most damaged zone, offers a promising perspective for extracting early constraints on seismic source geometry. This information could further reduce uncertainty in near-field tsunami inundation forecasts.

The awareness on the threat to society represented by the seismic risk is increasing due to several reasons. The impact of several destructive earthquakes having occurred during the last decades has made people more sensitive to this issue. The activities conducted worldwide under the auspices of IDNDR (International Decade for Natural Disaster Reduction) and WSSI (World Seismic Safety Initiative) contributed considerably to this too. Direct experience showed how high the toll of human lives can be, especially in less developed countries (Tangshan, 1976), but also that the most advanced and best protected countries of our time still have much to do in order to avoid heavy losses (Northridge, 1994, Kobe, 1995).KeywordsRisk AnalysisReturn PeriodSeismic RiskRisk MitigationSeismogenic ZoneThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Disaster risk analysis quantifies potential damages and losses using accurate hazard, exposure, and vulnerability information. Risk reduction managers rely on disaster risk analysis to make informed decisions towards promoting greater resilience. However, current approaches lack key features for proactive decision-making, such as accounting for uncertainty in hazard modeling, time-dependent processes affecting vulnerability, and highlighting successes and benefits of risk reduction. This dissertation makes three main contributions to the field of regional disaster risk analysis. First, I develop a framework for calibrating hazard parameters (inversion) and estimating the response of a hazard system (forward estimation) using limited and uncertain spatial data. This addresses gaps in current inversion-forward estimation approaches, which includes not accounting for varying uncertainties and spatial distribution in data, as well as the assumptions of the cost function. As a test case, I implement the framework on a tephra dispersion model using thickness observations of varying uncertainty to reconstruct past volcanic eruption characteristics and associated tephra fallout. Results show consistent improvements in model predictive performance for both inversion and forward models. Secondly, I develop a computational framework for modeling time-dependent physical vulnerability in regional risk analysis. This study addresses a gap in regional earthquake risk analysis by accounting for processes that increase vulnerability (e.g. structural deterioration) and policies that mitigate it (e.g. regional-scale structural retrofit). The method provides a flexible tool for decision-makers to investigate the outcomes of various mitigation decisions. Finally, I propose a counterfactual probabilistic risk analysis framework to quantify and highlight the benefits of effective disaster risk reduction interventions. This approach addresses a key challenge in risk management: that successful mitigations can go unnoticed (often because they are successful), making it difficult to incentivise proactive risk reduction decisions. Through counterfactual analysis (i.e. imagining the ‘what-if’ scenarios in disaster risk), the thesis shows how to calculate the probabilistic benefits of interventions, highlighting and celebrating their success.

Over the past decade, West Sumatra experienced a major earthquake event with significant risk. Disaster risk analysis needs to be done with the aim of minimizing disaster risk. This study aims to model the distribution risk of building houses damages and economic losses. Risk analysis is carried out quantitatively with probabilistic and deterministic approaches with faults and subduction zone sources based on Seismic Hazard Analysis (SHA) method. The use of probabilistic and deterministic approaches is done for providing a better risk analysis perspective and regarding to earthquake disaster management policy making. The results found that the earthquake risks are greater from fault earthquake sources compared with subduction earthquake sources.

First, this chapter describes policy implications derived from an economic analysis of the Japanese housing market from four points of view: (1) changes in real estate prices and the impacts on the macro economy; (2) the relationship between housing type and economic welfare; (3) an economic analysis of earthquake risk and the real estate market; and (4) the change in household behavior after the 2011 Tohoku earthquake and tsunami. Next, this chapter describes future research topics from three points of view: (i) Designing a Housing Finance System, (ii) Liquidity and Provision in the Rental Housing Market, (iii) Earthquake Risk and Disaster Prevention Polies.

EaRL—Software for Earthquake Risk, Loss and Lifecycle Analysis

Bu çalışmada, Değirmendere beldesinde, Coğrafi Bilgi Sistemleri (CBS) temelli, depreme yönelik afet yönetimi zarar azaltma amacıyla bir sistem geliştirilmiştir. Geliştirilen sistem için öncelikle bölgede yapılmış olan çalışmalar, 17 Ağustos 1999 depreminde bölgede oluşan zararlar ve sebepleri incelenmiş ve bölgede depremde yapıları etkileyen yerbilimleri verilerine dayanarak risk oranları ve yerbilimleri verilerinin alt kategorilerinin etki oranlarına göre alt risk oranları belirlenmiştir. Bölgenin depreme yönelik tehlike analizinin yapmakta kullanılan bu verilerin CBS ortamında değerlendirilebilmesi için Analitik Hiyerarşik Süreç (AHS) yöntemi ile ağırlıklandırılması yapılmıştır. Analiz birimi olarak ‘alan’ ı esas alan diğer CBS çalışmalardan farklı olarak, tüm veriler, yapı birimlerinin öznitelik değeri olarak CBS ortamına aktarılmıştır. Bölgede yaşayan nüfus ile ilgili demografik bilgiler Nüfus Vatandaşlık Genel Müdürlüğü’nden elde edilmiş ve yine yapılara ait veri olarak CBS ortamına aktarılmıştır.Sonrasında da CBS ortamında tehlike analizleri yapılarak bölgedeki riskli yapılar ve risk oranları belirlenmiştir. Diğer afet yönetimi çalışmalarından farklı olarak, riskli yapılarda yaşayan nüfus verileri de değerlendirilmiş ve bunlara yönelik önerilerde bulunulmuştur.

This study aims to map the risk of earthquake disasters in Botteng Village, Mamuju Regency, after the devastating earthquake measuring 6.2 on the Richter scale that hit the area on January 15 2021. The earthquake caused heavy damage, around 90% of the 350 houses were seriously damaged, causing many fatalities and injuries. This research uses a combined methodology from the Disaster Imagination Game (DIG) and Geographic Information Systems (GIS) to conduct a comprehensive earthquake risk analysis in the region. This participatory approach enables community involvement in identifying vulnerabilities and assessing risk levels while utilizing spatial analysis to effectively visualize data. The research results show that most of the Botteng Village area has a high risk of earthquake disasters, namely 1,507.13 hectares potentially at high risk, 978.94 hectares at medium risk, and 663,177 hectares at low risk. These findings underscore the critical need to increase public awareness and disaster preparedness, as well as improve mitigation strategies for effective disaster risk reduction in the future. The implications of this research are very significant for local governments and disaster management institutions, in mitigating disasters by integrating community perspectives with GIS technology.