868 research outputs found

    Explainable machine learning for labquake prediction using catalog-driven features

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    Recently, Machine learning (ML) has been widely utilized for laboratory earthquake (labquake) prediction using various types of data. This study pioneers in time to failure (TTF) prediction based on ML using acoustic emission (AE) records from three laboratory stick-slip experiments performed on Westerly granite samples with naturally fractured rough faults, more similar to the heterogeneous fault structures in the nature. 47 catalog-driven seismo-mechanical and statistical features are extracted introducing some new features based on focal mechanism. A regression voting ensemble of Long-Short Term Memory (LSTM) networks predicts TTF with a coefficient of determination (R2) of 70% on the test dataset. Feature importance analysis revealed that AE rate, correlation integral, event proximity, and focal mechanism-based features are the most important features for TTF prediction. Results reveal that the network uses all information among the features for prediction, including general trends in high correlated features as well as fine details about local variations and fault evolution involved in low correlated features. Therefore, some highly correlated and physically meaningful features may be considered less important for TTF prediction due to their correlation with other important features. Our study provides a ground for applying catalog-driven to constrain TTF of complex heterogeneous rough faults, which is capable to be developed for real application

    Advanced exploitation of Sentinel-1 data for supporting landslide risk analysis

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    Tesi en modalitat de compendi de publicacionsSatellite Synthetic Aperture Radar Interferometry (InSAR) and Persistent Scatterer Interferometry (PSI) are now consolidated tools for ground movement detection and monitoring. Sentinel-1 (S1) is the first satellite providing free data access and ensuring a regular acquisition worldwide, every 6 days, increasing its potential for long-term monitoring applications. Several regional and national ground motion services are already active, providing products based on S1 data. Soon in 2022 the first European Ground Motion Service (EGMS) will be available and freely provide a displacement map over the whole Europe, with annual updates. This implies a strong expansion of availability of PSI-based displacement maps and an easy access for anyone, with an increasing interest among a wider range of users, including public or governmental institutions, academia, industry, and citizens. The analysis and interpretation of this amount of data is difficult and time consuming, mostly for non-expert InSAR users. The objective of this work is developing methodologies to simplify the operational use of PSI displacement maps, generating derived products with a clear message, easy-to-interpret, and fast to read. We propose a method to be applied over regional scale PSI displacement maps, to fast detect the most significant Active Deformation Areas (ADAs). The ADA map is a first product that allows a fast focusing on the active areas, to prioritize further analysis and investigation. Starting from the ADAs, the potential phenomena are attributed to each area through a preliminary interpretation based on auxiliary data, to derive the Geohazard Activity Map. In this work, a methodology to include the ADA information in the Civil Protection Activities is proposed, with the main output called Vulnerable Elements Activity Maps (VEAM). An application of the VEAM is illustrated in the Canary Islands. Furthermore, the ADA map is used in the Valle d'Aosta Region (Northern of Italy) to generate vulnerability and potential loss maps. Finally, a methodology to derive potential damage maps of the exposed buildings, based on the spatial gradients of movement, is proposed, and applied in a coastal area of the Province of Granada (Spain). A pack of software tools has been developed based on the proposed methods to extract ADA and then classify them to generate a Geohazard Activity Map. The set of tools is called ADATools, it is open-access, easy to use and fast, improving the operational exploitation of PSI regional-scale displacement maps. All the methodologies have been developed in the frame of several European projects (Safety, U-Geohaz, MOMIT and RISKCOAST), and are aimed at supporting the multi-scale territorial management and risk analysis activities, with a specific focus on landslides.La interferometría satelital radar (InSAR) y la interferometría de dispersores persistentes (PSI) son herramientas consolidadas para la detección y el monitoreo de movimientos de la superficie de la Tierra. Sentinel-1 (S1) es el primer satélite que proporciona acceso gratuito a los datos y garantiza una adquisición regular en todo el mundo, cada 6 días, aumentando su potencial para aplicaciones de monitoreo a largo plazo. Varios Ground Motion Services regionales y nacionales ya están activos, proporcionando productos basados en datos S1. Pronto, en 2022, el primer servicio europeo (European Ground Motion Service - EGMS) estará disponible y facilitará libremente un mapa de movimientos de toda Europa, con actualizaciones anuales. Esto implica un aumento de la disponibilidad de mapas de movimientos basados en PSI y un fácil acceso para cualquier persona, con un interés creciente entre una amplia gama de usuarios, incluyendo instituciones públicas o gubernamentales, academias, industrias y ciudadanos. El análisis e interpretación de esta cantidad de datos es difícil y consume mucho tiempo, mayormente para usuarios no expertos en la técnica. El objetivo de este trabajo es desarrollar metodologías para simplificar el uso operativo de los mapas de desplazamiento PSI, generando productos derivados con un mensaje claro, fácil de interpretar, y rápido de leer. Se propone un método para detectar rápidamente las Áreas de Deformación Activas (ADAs) más significativas, a partir de mapas de desplazamiento PSI de escala regional. El mapa de las ADAs es un primer producto que permite un enfoque rápido en las áreas activas, útil para priorizar el análisis y las investigaciones adicionales. A partir de las ADAs, se propone una interpretación preliminar basada en datos auxiliares, que atribuye a cada área el fenómeno que está detrás del movimiento, generando el Geohazard Activity Map (GAM). Después, se propone una metodología para incluir la información de las ADAs en las actividades de protección civil, generando los Vulnerable Element Activity Maps (VEAM), a través de su aplicación en las Islas Canarias. Además, el mapa de las ADAs se utiliza en la región de Valle D'Aosta (norte de Italia) para generar mapas de vulnerabilidad y posibles pérdidas económicas. Finalmente, se propone una metodología para obtener mapas de daños potenciales de los edificios expuestos, basados en los gradientes espaciales de movimiento, y se aplica en un área costera de la provincia de Granada (España). A partir de los métodos propuestos para extraer y clasificar las ADAs, y de otros métodos de análisis existentes, se ha desarrollado un paquete de herramientas, los ADAtools, de acceso abierto, fáciles de usar y rápidas, que optimizan la explotación operativa de los mapas de desplazamiento de escala regional. Todas las metodologías se han desarrollado en el marco de varios proyectos europeos (Safety, U-Geohaz, MOMIT y RISKCOAST), y están dirigidos a apoyar las actividades de gestión territorial y análisis de riesgos, con un enfoque específico a los deslizamientos de tierra.Postprint (published version

    Advanced exploitation of Sentinel-1 data for supporting landslide risk analysis

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    Tesi en modalitat de compendi de publicacionsSatellite Synthetic Aperture Radar Interferometry (InSAR) and Persistent Scatterer Interferometry (PSI) are now consolidated tools for ground movement detection and monitoring. Sentinel-1 (S1) is the first satellite providing free data access and ensuring a regular acquisition worldwide, every 6 days, increasing its potential for long-term monitoring applications. Several regional and national ground motion services are already active, providing products based on S1 data. Soon in 2022 the first European Ground Motion Service (EGMS) will be available and freely provide a displacement map over the whole Europe, with annual updates. This implies a strong expansion of availability of PSI-based displacement maps and an easy access for anyone, with an increasing interest among a wider range of users, including public or governmental institutions, academia, industry, and citizens. The analysis and interpretation of this amount of data is difficult and time consuming, mostly for non-expert InSAR users. The objective of this work is developing methodologies to simplify the operational use of PSI displacement maps, generating derived products with a clear message, easy-to-interpret, and fast to read. We propose a method to be applied over regional scale PSI displacement maps, to fast detect the most significant Active Deformation Areas (ADAs). The ADA map is a first product that allows a fast focusing on the active areas, to prioritize further analysis and investigation. Starting from the ADAs, the potential phenomena are attributed to each area through a preliminary interpretation based on auxiliary data, to derive the Geohazard Activity Map. In this work, a methodology to include the ADA information in the Civil Protection Activities is proposed, with the main output called Vulnerable Elements Activity Maps (VEAM). An application of the VEAM is illustrated in the Canary Islands. Furthermore, the ADA map is used in the Valle d'Aosta Region (Northern of Italy) to generate vulnerability and potential loss maps. Finally, a methodology to derive potential damage maps of the exposed buildings, based on the spatial gradients of movement, is proposed, and applied in a coastal area of the Province of Granada (Spain). A pack of software tools has been developed based on the proposed methods to extract ADA and then classify them to generate a Geohazard Activity Map. The set of tools is called ADATools, it is open-access, easy to use and fast, improving the operational exploitation of PSI regional-scale displacement maps. All the methodologies have been developed in the frame of several European projects (Safety, U-Geohaz, MOMIT and RISKCOAST), and are aimed at supporting the multi-scale territorial management and risk analysis activities, with a specific focus on landslides.La interferometría satelital radar (InSAR) y la interferometría de dispersores persistentes (PSI) son herramientas consolidadas para la detección y el monitoreo de movimientos de la superficie de la Tierra. Sentinel-1 (S1) es el primer satélite que proporciona acceso gratuito a los datos y garantiza una adquisición regular en todo el mundo, cada 6 días, aumentando su potencial para aplicaciones de monitoreo a largo plazo. Varios Ground Motion Services regionales y nacionales ya están activos, proporcionando productos basados en datos S1. Pronto, en 2022, el primer servicio europeo (European Ground Motion Service - EGMS) estará disponible y facilitará libremente un mapa de movimientos de toda Europa, con actualizaciones anuales. Esto implica un aumento de la disponibilidad de mapas de movimientos basados en PSI y un fácil acceso para cualquier persona, con un interés creciente entre una amplia gama de usuarios, incluyendo instituciones públicas o gubernamentales, academias, industrias y ciudadanos. El análisis e interpretación de esta cantidad de datos es difícil y consume mucho tiempo, mayormente para usuarios no expertos en la técnica. El objetivo de este trabajo es desarrollar metodologías para simplificar el uso operativo de los mapas de desplazamiento PSI, generando productos derivados con un mensaje claro, fácil de interpretar, y rápido de leer. Se propone un método para detectar rápidamente las Áreas de Deformación Activas (ADAs) más significativas, a partir de mapas de desplazamiento PSI de escala regional. El mapa de las ADAs es un primer producto que permite un enfoque rápido en las áreas activas, útil para priorizar el análisis y las investigaciones adicionales. A partir de las ADAs, se propone una interpretación preliminar basada en datos auxiliares, que atribuye a cada área el fenómeno que está detrás del movimiento, generando el Geohazard Activity Map (GAM). Después, se propone una metodología para incluir la información de las ADAs en las actividades de protección civil, generando los Vulnerable Element Activity Maps (VEAM), a través de su aplicación en las Islas Canarias. Además, el mapa de las ADAs se utiliza en la región de Valle D'Aosta (norte de Italia) para generar mapas de vulnerabilidad y posibles pérdidas económicas. Finalmente, se propone una metodología para obtener mapas de daños potenciales de los edificios expuestos, basados en los gradientes espaciales de movimiento, y se aplica en un área costera de la provincia de Granada (España). A partir de los métodos propuestos para extraer y clasificar las ADAs, y de otros métodos de análisis existentes, se ha desarrollado un paquete de herramientas, los ADAtools, de acceso abierto, fáciles de usar y rápidas, que optimizan la explotación operativa de los mapas de desplazamiento de escala regional. Todas las metodologías se han desarrollado en el marco de varios proyectos europeos (Safety, U-Geohaz, MOMIT y RISKCOAST), y están dirigidos a apoyar las actividades de gestión territorial y análisis de riesgos, con un enfoque específico a los deslizamientos de tierra.Enginyeria del terren

    Numerical simulation of flooding from multiple sources using adaptive anisotropic unstructured meshes and machine learning methods

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    Over the past few decades, urban floods have been gaining more attention due to their increase in frequency. To provide reliable flooding predictions in urban areas, various numerical models have been developed to perform high-resolution flood simulations. However, the use of high-resolution meshes across the whole computational domain causes a high computational burden. In this thesis, a 2D control-volume and finite-element (DCV-FEM) flood model using adaptive unstructured mesh technology has been developed. This adaptive unstructured mesh technique enables meshes to be adapted optimally in time and space in response to the evolving flow features, thus providing sufficient mesh resolution where and when it is required. It has the advantage of capturing the details of local flows and wetting and drying front while reducing the computational cost. Complex topographic features are represented accurately during the flooding process. This adaptive unstructured mesh technique can dynamically modify (both, coarsening and refining the mesh) and adapt the mesh to achieve a desired precision, thus better capturing transient and complex flow dynamics as the flow evolves. A flooding event that happened in 2002 in Glasgow, Scotland, United Kingdom has been simulated to demonstrate the capability of the adaptive unstructured mesh flooding model. The simulations have been performed using both fixed and adaptive unstructured meshes, and then results have been compared with those published 2D and 3D results. The presented method shows that the 2D adaptive mesh model provides accurate results while having a low computational cost. The above adaptive mesh flooding model (named as Floodity) has been further developed by introducing (1) an anisotropic dynamic mesh optimization technique (anisotropic-DMO); (2) multiple flooding sources (extreme rainfall and sea-level events); and (3) a unique combination of anisotropic-DMO and high-resolution Digital Terrain Model (DTM) data. It has been applied to a densely urbanized area within Greve, Denmark. Results from MIKE 21 FM are utilized to validate our model. To assess uncertainties in model predictions, sensitivity of flooding results to extreme sea levels, rainfall and mesh resolution has been undertaken. The use of anisotropic-DMO enables us to capture high resolution topographic features (buildings, rivers and streets) only where and when is needed, thus providing improved accurate flooding prediction while reducing the computational cost. It also allows us to better capture the evolving flow features (wetting-drying fronts). To provide real-time spatio-temporal flood predictions, an integrated long short-term memory (LSTM) and reduced order model (ROM) framework has been developed. This integrated LSTM-ROM has the capability of representing the spatio-temporal distribution of floods since it takes advantage of both ROM and LSTM. To reduce the dimensional size of large spatial datasets in LSTM, the proper orthogonal decomposition (POD) and singular value decomposition (SVD) approaches are introduced. The performance of the LSTM-ROM developed here has been evaluated using Okushiri tsunami as test cases. The results obtained from the LSTM-ROM have been compared with those from the full model (Fluidity). Promising results indicate that the use of LSTM-ROM can provide the flood prediction in seconds, enabling us to provide real-time flood prediction and inform the public in a timely manner, reducing injuries and fatalities. Additionally, data-driven optimal sensing for reconstruction (DOSR) and data assimilation (DA) have been further introduced to LSTM-ROM. This linkage between modelling and experimental data/observations allows us to minimize model errors and determine uncertainties, thus improving the accuracy of modelling. It should be noting that after we introduced the DA approach, the prediction errors are significantly reduced at time levels when an assimilation procedure is conducted, which illustrates the ability of DOSR-LSTM-DA to significantly improve the model performance. By using DOSR-LSTM-DA, the predictive horizon can be extended by 3 times of the initial horizon. More importantly, the online CPU cost of using DOSR-LSTM-DA is only 1/3 of the cost required by running the full model.Open Acces

    The June 2020 Aniangzhai landslide in Sichuan Province, Southwest China: slope instability analysis from radar and optical satellite remote sensing data

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    A large, deep-seated ancient landslide was partially reactivated on 17 June 2020 close to the Aniangzhai village of Danba County in Sichuan Province of Southwest China. It was initiated by undercutting of the toe of this landslide resulting from increased discharge of the Xiaojinchuan River caused by the failure of a landslide dam, which had been created by the debris flow originating from the Meilong valley. As a result, 12 townships in the downstream area were endangered leading to the evacuation of more than 20000 people. This study investigated the Aniangzhai landslide area by optical and radar satellite remote sensing techniques. A horizontal displacement map produced using cross-correlation of high-resolution optical images from Planet shows a maximum horizontal motion of approximately 15 meters for the slope failure between the two acquisitions. The undercutting effects on the toe of the landslide are clearly revealed by exploiting optical data and field surveys, indicating the direct influence of the overflow from the landslide dam and water release from a nearby hydropower station on the toe erosion. Pre-disaster instability analysis using a stack of SAR data from Sentinel-1 between 2014 and 2020 suggests that the Aniangzhai landslide has long been active before the failure, with the largest annual LOS deformation rate more than 50 mm/yr. The 3-year wet period that followed a relative drought year in 2016 resulted in a 14% higher average velocity in 2018–2020, in comparison to the rate in 2014–2017. A detailed analysis of slope surface kinematics in different parts of the landslide indicates that temporal changes in precipitation are mainly correlated with kinematics of motion at the head part of the failure body, where an accelerated creep is observed since spring 2020 before the large failure. Overall, this study provides an example of how full exploitation of optical and radar satellite remote sensing data can be used for a comprehensive analysis of destabilization and reactivation of an ancient landslide in response to a complex cascading event chain in the transition zone between the Qinghai-Tibetan Plateau and the Sichuan Basin. © 2021, The Author(s)

    Urban morphology analysis by remote sensing and gis technique, case study: Georgetown, Penang

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    This paper was analysed the potential of applications of satellite remote sensing to urban planning research in urban morphology. Urban morphology is the study of the form of human settlements and the process of their formation and transformation. It is an approach in designing urban form that considers both physical and spatial components of the urban structure. The study conducted in Georgetown, Penang purposely main to identify the evolution of urban morphology and the land use expansion. In addition, Penang is well known for its heritage character, especially in the city of Georgetown with more than 200 years of urban history. Four series of temporal satellite SPOT 5 J on year 2004, 2007, 2009 and 2014 have been used in detecting an expansion of land use development aided by ERDAS IMAGINE 2014. Three types of land uses have been classified namely build-up areas, un-built and water bodies show a good accuracy with achieved above 85%. The result shows the built-up area significantly increased due to the rapid development in urban areas. Simultaneously, this study provides an understanding and strengthening a relation between urban planning and remote sensing applications in creating sustainable and resilience of the city and future societies as well

    Influence of summertime mesoscale convective systems on the heat balance and surface mixed layer dynamics of a large Amazonian hydroelectric reservoir

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    We evaluated the impacts of summertime mesoscale convective systems (MCS) on the heat balance and diel surface mixed layer (SML) dynamics of the Brazilian Amazon's Tucuruí Hydroelectric Reservoir (THR). We used a synergistic approach that combines in situ data, remote sensing data, and three-dimensional (3-D) modeling to investigate the typical behavior of the components of the heat balance and the SML dynamics. During the study period (the austral summer of 2012–2013), 22 days with MCS activity were identified. These events occurred approximately every 4 days, and they were most frequent during January (50% of the observations). An analysis of local meteorological data showed that when MCS occur, the environmental conditions at THR change significantly (p-value < 0.01). The net longwave flux, which was the heat balance component most strongly impacted by MCS, increased more than 32% on days with MCS activity. The daily integrated heat balance became negative (−54 W m−2) on MCS days, while the balance was positive (19 W m−2) on non-MCS days. In response to the changes in the heat balance, the SML dynamics changed when a MCS was over the THR. The SML depth was typically 28% higher on the days with MCS (∼1.6 m) compared with the days without MCS (∼1.3 m). The results indicate that MCS are one of the main meteorological disturbances driving the heat balance and the mixing dynamics of Amazonian hydroelectric reservoirs during the summer. These events may have implications for the water quality and greenhouse gas emissions of Amazonian reservoirs

    A Methodology to Detect and Update Active Deformation Areas Based on Sentinel-1 SAR Images

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    This work is focused on deformation activity mapping and monitoring using Sentinel-1 (S 1) data and the DInSAR (Differential Interferometric Synthetic Aperture Radar) technique. The main goal is to present a procedure to periodically update and assess the geohazard activity (volcanic activity, landslides and ground-subsidence) of a given area by exploiting the wide area coverage and the high coherence and temporal sampling (revisit time up to six days) provided by the S-1 satellites. The main products of the procedure are two updatable maps: the deformation activity map and the active deformation areas map. These maps present two different levels of information aimed at different levels of geohazard risk management, from a very simplified level of information to the classical deformation map based on SAR interferometry. The methodology has been successfully applied to La Gomera, Tenerife and Gran Canaria Islands (Canary Island archipelago). The main obtained results are discussed.Geomatics Division, Centre Tecnològic de Telecomunicacions de Catalunya, EspañaEarth Sciences Department, University of Firenze, ItalyGeohazards InSAR laboratory and Modelling Group, Instituto Geológico y Minero de España, EspañaCentro Nacional de Información Geográfica, Instituto Geográfico Nacional, EspañaUnidad de Granada, Instituto Geológico y Minero de España, Españ
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