Seismic Shake-e is a free app that provides valuable data and tools related to earthquakes, covering the stages before, during, and after seismic events. In this text, we describe the main features of the Seismic Shake-e 2.1 (SSe) app, the considerations that guided its development, examples of its use, and the challenges for future versions. Version 1.0 of this app was awarded as one of the winners of EOVALUE: Call for Innovative Apps in environmental and social fields, a project by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. SSe recognizes two user levels: basic and intermediate/advanced. There are six modules for each level. The main topics of these modules for both user types are: (1) Accelerometer Networks (AN), (2) Seismograms Analyzer-e (SAe), (3) Seismic Design of Buildings (SDB), (4) Earthquake Preparedness (EP), (5) Earthquake Early Warning Systems (EEWS) & Tsunami Warning Systems (TWS), and (6) Earthquake Emergency Response & Recovery. The two key modules are AN and SAe: the first explains how to obtain seismic records, and the second provides tools for their analysis. We include some applications of SSe, along with their results and discussion. We also list the advantages of the main modules and discuss potential future developments and improvements. The uniqueness of this work is that we highlight the software’s essential features and demonstrate its applications.

Digital twin prototypes now exist for assessing and aiding disaster response to geophysical hazards, such as volcanic eruptions, tsunamis, and earthquakes. We propose a systematic assessment framework to help determine the accuracy, transparency, reproducibility, and accountability of machine learning components within these emerging prototypes.

The Statewide Southern California Earthquake Center (SCEC) has designed and implemented CyberShake (CS), a high-performance computing (HPC) workflow to undertake Physics-Based Probabilistic Seismic Hazard Analysis (PB-PSHA) in California (CA). Here, we have ported CS from CA to the South Iceland Seismic Zone (SISZ) and the Reykjanes Peninsula Oblique Rift (RPOR), which experience sinistral transform motion and pose a very high earthquake risk to about 2/3 of the Iceland population. We consider a realization of the 3D SISZ-RPOR fault system, where fault areas are estimated from event magnitude through a scaling law (Mai & Beroza, 2017), that fits maximum fault extents observed from slip inversions and surface mappings. The magnitude variability across the modeling region (~63.8°- 64.1°N, ~20°-23°W) is Mw 5-7. In this work, we employ CS to model ~2100 kinematic earthquake ruptures and quantify the resulting ground motion (GM) in terms of Pseudo Spectral Acceleration (PSA) intensity measures. An important computational milestone is the software development of an open-source in-house workflow manager at the Marenostrum Supercomputer that replaces the one used in CA by SCEC based on Pegasus and HTCondor. This new workflow manager handles input data (fault-plane geometries, rupture magnitudes, surface stations for GM recording and hazard studies), orchestrates the execution of CS components, and stores results (particle velocity seismograms and hazard curves). Among these components, the Graves-Pitarka (GP) kinematic rupture generator is used to produce finite-fault source descriptions characterized by a few large asperities. The other important component is the open-source fourth-order finite-difference staggered-grid AWP-ODC earthquake simulation code that allows for reciprocity and efficiently simulates rupture and seismic wave propagation in 3D heterogeneous Earth models. CS uses an adjoint computational procedure in which simulations of wave propagation are performed using a polarized delta source to compute the Strain Green Tensors (SGTs) at each fault point. The convolution of SGTs with GP ruptures yields particle-velocity seismograms at each station. SGT time histories are memory demanding, but the adjoint calculations are completely independent and therefore embarrassing parallel, making CS a highly efficient earthquake simulation tool. In this study, SGTs are constructed using a source frequency range of 0-1.0 Hz, generating ground motion synthetics resolved up to 0.5 Hz. CS rotation-invariant PSA values (3 and 5 sec periods) computed from our study show a good agreement with updated Bayesian ground motion prediction equations (Kowsari et al, 2022). This study is a first step towards a PB-PSHA in the SISZ-RPOR region and to routinely apply Cybershake outside of California.REFERENCES:Mai, M., & Beroza, G. Source scaling properties from finite-fault-rupture models. Bulletin of the Seismological Society of America, 90(3), 604-615, 2000.Kowsari, M., Sonnemann, T., Halldorsson, B., Hrafnkelsson, B., Snæbjörnsson, J. & Jonsson, S. Bayesian inference of empirical ground motion models to pseudo-spectral accelerations of South Iceland Seismic Zone earthquakes based on informative priors. Soil Dynamics and Earthquake Engineering, 132, 106075, 2020.

Understanding volcanic activity through time series data analysis is crucial for uncovering the fundamental physical mechanisms governing this natural phenomenon. In this study, we show the application of multifractal and fractal methodologies, along with statistical analysis, to investigate time series associated with volcanic activity. We aim to make use of these approaches to identify significant variations within the physical processes related to changes in volcanic activity. These methodologies offer the potential to identify pertinent changes preceding a high-energy explosion or a significant volcanic eruption. In particular, we apply it to analyze two study cases. First, the evolution of the multifractal structure of volcanic emissions of low, moderate, and high energy explosions applied to Volcán de Colima (México years 2013-2015). The results contribute to obtaining quite evident signs of the immediacy of possible dangerous emissions of high energy, close to 8.0x10^8 J. Additionally, the evolution of the adapted Gutenberg-Richter seismic law to volcanic energy emissions contributes to confirm the results obtained using multifractal analysis. Secondly, we also studied the time series of the Gutenberg-Richter b-parameter of seismic activities associated with volcanic emissions in Iceland, Hawaii, and the Canary Islands, through the concept of Disparity (degree of irregularity), the fractal Hurst exponent, H, and several multifractal parameters. The results obtained should facilitate a better knowledge of the relationships between the activity of volcanic emissions and the corresponding related seismic activities.

Southeast Spain experiences relatively low seismicity rates, characterized by slow seismic deformation. However, historical records highlight the significant impact of moderate to large earthquakes on local communities, such as the 1518 Vera (Almería) earthquake (Mw 6.4) and the 2011 Lorca earthquake (Mw 5.2). Thus, such events pose a considerable seismic risk to the region, in spite of their infrequent occurrence. Given the lack of comprehensive data on this kind of seismic events, this study contributes towards a physics-based seismic hazard model for Southeast (SE) Spain. Specifically, we first develop a broad earthquake rupture forecast (ERF) model that includes potential single- and multi-fault events, and then we use Cybershake to model the maximum-magnitude expected earthquakes along the various fault systems in the region, to obtain 0-1 Hz ground motion simulations. In this ERF model, we integrate a vast amount of regional geological data, including the Quaternary-Active Faults Database of Iberia, historical seismic catalogs, and available paleoseismic data as well. Using Cybershake, a high-performance computing earthquake-modeling platform originally designed for Southern California, we simulate ground-motion time histories from pseudo-dynamic kinematic rupture scenarios on three-dimensional finite faults. Our simulations consider a recently-available tomographic 3D velocity model, but for completeness, we also perform simulations using a 1D average model and explore the differences on the resulting synthetic ground motions. This approach allows to create physics-based rupture scenarios and shake maps, offering an alternative seismic hazard model tailored to SE Spain and setting the basis to update regional seismic hazard assessments. The results provide valuable insights into potentially harmful multi-fault events and scenarios in slow-deforming tectonic settings, contributing to more accurate seismic hazard and risk maps, and informing effective planning, decision-making and response strategies in the region.

The Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) introduces a fully automatic seismic workflow centered on rapidly delivering synthetic assessments of the impact of moderate to large earthquakes throughout physics-based forward simulations. This novel approach links High-Performance Computing (HPC), High-Performance Data Analytics (HPDA), and highly optimized numerical solvers. Its core objective lies in performing numerical simulations either during or right after an earthquake, accomplishing this task within a short timeframe, typically spanning from minutes to a few hours.During multi-node execution, PyCOMPSs orchestrates UCIS4EQ’s distributed tasks and improves its readiness level towards providing an operational service. UCIS4EQ coordinates the execution of multiple seismic sources to account for input and model uncertainties. Its comprehensive scope provides decision-makers with numerical insights into the potential outcomes of post-earthquake emergency scenarios.The UCIS4EQ workflow includes a fast inference service based on location-specific pre-trained machine learning models. Such learned models permit a swift analysis and estimation of the potential damage caused by an earthquake. Leveraging advanced AI capabilities endows our workflow with the ability to rapidly estimate a seismic event's impact. Ultimately it provides valuable support for rapid decision-making during emergencies.Through the integration of high performance computational techniques and pioneering methodologies, our hope is to see UCIS4EQ emerge as a useful instrument to make agile and well-informed post-event decisions in the face of seismic events.With this study, we account for UCIS4EQ's continuous development through a number of case studies. These case studies will shed light on the most recent developments and applications of urgent computing seismic workflow, demonstrating its efficacy in providing rapid and precise insights into earthquake scenarios.

Urgent Computing (UC) refers to the use of High-Performance Computing (HPC) and High-Performance Data Analytics (HPDA) and Artificial Intelligence (AI) modules during or immediately following emergencies. It typically integrates complex end-to-end workflows with scalable computing resources, where multiple model realizations are necessary to account for input and model uncertainties, all under strict time-to-solution constraints. Enabling urgent HPC in unpredictable events such as earthquakes can significantly enhance resilience and response efforts. The temporal horizon for UC usually spans from minutes to a few hours, providing decision-makers with rapid estimates of the potential outcomes of emergency scenarios. In particular, high-resolution synthetic ground motions for earthquakes can complement the tools used by seismological services for impact analysis. Here, the Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) is proposed as an innovative UC seismic workflow designed to rapidly generate synthetic estimates of the consequences (such as synthetic time histories, shakemaps, PGA/PGV, among other proxies) of moderate to large earthquakes (M > 6). Over the last six years, UCIS4EQ has been developed from scratch and received contributions within the framework of three European projects (DT-GEO, eFlows4HPC, and ChEESE CoE). In this work, we demonstrate the technological maturity of UCIS4EQ and its operational readiness in collaboration with the Mexican Seismological Service (SSN). Furthermore, this work addresses the challenges we face to reach operational maturity addressing the specific requirements of a seismological service for an urgent computing framework providing reliable outcomes for decision making with global coverage.

CyberShake is a high-performance computing workflow for kinematic fault-rupture and earthquake ground-motion simulation developed by the Statewide California Earthquake Center to facilitate physics-based probabilistic seismic hazard assessment (PSHA). CyberShake exploits seismic reciprocity for wave propagation by computing strain green tensors along fault planes, which in turn are convolved with rupture models to generate surface seismograms. Combined with a faultwide hypocentral variation of each simulated rupture, this procedure allows for generating ground-motion synthetics that account for realistic source variability. This study validates the platform's kinematic modeling of physics-based seismic wave propagation simulations in Southwest Iceland as the first step toward migrating CyberShake from its original study region in California. Specifically, we have implemented CyberShake workflows to model 2103 fault ruptures and simulate the corresponding two horizontal components of ground-motion velocity on a 5 km grid of 625 stations in Southwest Iceland. A 500-yr-long earthquake rupture forecast consisting of 223 hypothetical finite-fault sources of M-w 5-7 was generated using a physics-based model of the bookshelf fault system of the Southwest Iceland transform zone. For each station, every reciprocal simulation uses 0-1 Hz Gaussian point sources polarized along two horizontal grid directions. Comparison of the results in the form of rotation-invariant synthetic pseudoacceleration spectral response values at 3, 4, and 5 s periods are in good agreement with the Icelandic strong motion data set and a suite of empirical Bayesian ground-motion prediction equations (GMPEs). The vast majority of the physics-based simulations fall within one standard deviation of the mean GMPE predictions, previously estimated for the area. At large magnitudes for which no data exist in Iceland, the synthetic data set may play an important role in constraining GMPEs for future applications. Our results comprise the first step toward comprehensive and physics-based PSHA for Southwest Iceland.

Earthquakes are among the most frequent yet unpredictable natural hazards, posing substantial risk to human safety and infrastructure globally, particularly, when large-magnitude earthquakes occur. This highlights the urgent need to develop innovative and alternative methodologies for rapidly assessing the intensity of ground shaking following an earthquake.This study explores the application of the Machine Learning Estimator for Ground Shaking Maps (MLESmap) methodology in New Zealand, a region characterized by high seismic activity.MLESmap utilizes extensive datasets of high-fidelity, physics-based seismic scenarios to rapidly estimate ground-shaking intensity in near real-time following an earthquake. This methodology has demonstrated evaluation times similar to those of empirical ground motion models, while offering superior predictive accuracy in the two previously tested regions: the Los Angeles basin and the South Iceland Seismic Zone (SISZ).To adapt MLESmap for New Zealand’s seismicity, seismic simulations tailored to the unique geological and tectonic context of the region are implemented. Specifically, we use the dataset generated by CyberShake NZ, a probabilistic seismic hazard analysis (PSHA) software developed by the University of Canterbury. Using this software, a total of 11,362 finite-fault rupture simulations were performed across the region and seismic hazard results were calculated on a grid of 27,481 synthetic seismic stations. A ‘forward’ simulation approach was adopted due to the large number of output locations relative to rupture locations, the optimisation of the grid for each rupture and the intention to include plasticity.The expected results aim to demonstrate the applicability of MLESmap to New Zealand, providing ML-based tools for rapid response actions. This study also takes the first steps in applying cascading effects to MLESmap, in order to improve the overall risk assessment and to advance prevention efforts through innovative and multidisciplinary methodologies. ©2023 ChEESE-2P Funded by the European Union. This work has received funding from the European High Performance Computing Joint Undertaking (JU) and Spain, Italy, Iceland, Germany, Norway, France, Finland and Croatia under grant agreement No 101093038.

The South Iceland Seismic Zone (SISZ) and Reykjanes Peninsula Oblique Rift (RPOR) in Southwest Iceland together form one of the two major transform zones in the country that have the greatest capacity for the occurrence of destructive earthquakes. Therefore, in these regions, the seismic hazard is highest and performing a probabilistic seismic hazard assessment (PSHA) is vital as the foundation of earthquake resistant building design and seismic risk mitigation. It is well known both from observations as well as physics-based (PB) modeling of earthquake rupture and near-fault ground motion simulations, that the most damaging part of near-fault seismic motion is the velocity pulse, the large-amplitude and long-period pulse-like ground motions found along the fault and away from the ends of strike-slip faults. Such motions cause intense earthquake action primarily on large buildings, such as hydroelectric power plants, dams, powerlines, bridges and pipelines. However, the data is still too limited to enable the reliable calibration of a physically realistic, yet parsimonious, near-fault model that incorporate such effects into empirical ground motions models (GMMs), thereby allowing their incorporation into a formal PSHA. However, in the recent European H2020 ChEESE project, we established a new 3D finite-fault system model for the SISZ-RPOR system that now has facilitated the simulation of finite-fault earthquake catalogues. Moreover, the catalogues have been implemented into the CyberShake platform, the PB earthquake simulator that was adapted to the characteristics of the SISZ-RPOR earthquakes in the ChEESE project. The seismic ground motions of each earthquake in the catalogue have thus been simulated on a dense grid of 594 near-fault stations in Southwest Iceland. The simulation has been carried out on high-performance computing systems of the Barcelona Supercomputing Centre in Spain. Moreover, the hypocentral locations and slip distributions on each synthetic fault have been varied, resulting in approximately 1 million earthquake-station-specific pairs of synthetic low-frequency and high-amplitude near-fault ground motion time histories. In this study, we analyse this dataset using an artificial neural network to reveal its characteristics in terms of amplitudes and the characteristics of near-fault velocity pulses, capturing all key features of such effects. The results will facilitate the incorporation of the near-fault effects into new near-fault and far-field GMMs, that are a key element of conventional PSHA. This will both enable the near-fault PB-PSHA along with the comparison of PSHA from the synthetic dataset vs. the GMMs. This will usher in a new era of PB-PSHA in Iceland.

Large earthquakes are among the most destructive natural phenomena. After a large-magnitude event occurs, a crucial task for hazard assessment is to rapidly and accurately estimate the ground shaking intensities in the affected region. To satisfy real-time constraints, ground shaking is traditionally evaluated with empirical relations called Ground Motion Prediction Equations (GMPE) which can be combined with local amplification factors and early data recordings, when available. Given their nature, GMPEs can be inaccurate to model rarely observed earthquakes, such as large earthquakes. Furthermore, even for very populated databases, GMPEs are characterized by large variances, as earthquakes of similar magnitude and location may have very different outcomes related to complex fault phenomena and wave physics. The ML Estimator for Ground Shaking maps (MLESmap) workflow is proposed as a novel procedure that exploits the predictive power of ML algorithms to estimate ground acceleration values a few seconds after a large earthquake occurs. The inferred model can produce peak (spectral) ground motion maps for quasi-real-time applications. Due to its fast assessment, it can further be used to explore uncertainties quickly and reliably. MLESmap is based upon large databases of physics-based seismic scenarios to feed the algorithms. Our approach (i.e. simulate, train, deploy) can help produce the next generation of ground shake maps, capturing physical information from wave propagation (directivity, topography, site effects) at the velocity of simple empirical GMPE. In this work, we will present the MLESmap workflow, its precision, and a use case.

CyberShake (CS) is a high-performance computing workflow for Probabilistic Seismic Hazard Assessment (PSHA) developed by the Statewide California Earthquake Center. Here, we employ CS to generate a set of 2103 fault ruptures and simulate the corresponding two horizontal velocity components time histories of ground motion (GM) on a 5-km grid of 625 stations in Southwest Iceland (SI). The ruptures were defined on a new synthetic time-independent 500-year catalog consisting of 223 hypothetical finite-fault sources of 5-7, generated using a new physics-based bookshelf fault system model in the SI transform zone. This fault system model and rupture realizations enable the CS time-independent physics-based approach to PSHA in the region. The study aims to migrate CS to SI and validate its kinematic fault rupture, anelastic wave propagation and ground motion simulations. Toward this goal, we use CS to generate multiple finite-fault rupture variations for each hypothetical fault. CS exploits seismic reciprocity for wave propagation by computing Strain Green Tensors along fault planes, which in turn are convolved with rupture models to generate GM seismograms. For each GM recording station, every adjoint simulation uses a 0-1 Hz Gaussian point source polarized along one horizontal grid direction. Comparison of the results in the form of rotation-invariant synthetic pseudo-acceleration spectral response values at 2, 3 and 5 sec periods are in very good agreement with the Icelandic strong-motion dataset, and a suite of new empirical Bayesian ground motion prediction equations (GMPEs). The vast majority of the CS results fall within one standard deviation of the mean GMPE predictions, previously estimated for the area. Importantly, at large magnitudes for which no data exists in Iceland, the CS dataset may play an important role in constraining the GMPEs for future applications.

Pre-print: Ground Motion Shaking Predictions Based on Machine Learning and Physics-based Simulations Earthquakes constitute a major threat to human lives and infrastructure, hence it is crucial to quickly assess the intensity of ground motions after a major seismic event. Rapid estimation of the intensity of ground vibrations is essential to assess the impact after a major earthquake occurs. The Machine Learning Estimator for Ground Shaking Maps (MLESmap) introduces an innovative approach that harnesses the predictive capabilities of Machine Learning (ML) algorithms, utilizing high-quality physics-based seismic scenarios. MLESmap aims to provide ground intensity measures within seconds following an earthquake. The inferred information can produce shaking maps of the ground providing quasi-real-time affectation information to help us explore uncertainties quickly and reliably. To develop the MLESmap technology, we used ground-motion simulations generated by the CyberShake platform. Originally designed for Southern California, this physics-based Probabilistic Seismic Hazard Methodology was migrated to the South Iceland Seismic Zone recently. Our methodology follows a three-step process: simulation, training, and deployment. By employing this approach, we can generate the next generation of ground shake maps, incorporating essential physical information derived from wave propagation, such as directivity, topography, and site effects. Remarkably, the evaluation times for MLESmap are comparable to empirical Ground Motion Models, whereas the predictive capacity of the former is superior for the Mw > 5 earthquakes. In this work, we present the application of the MLESmap methodology in South West Iceland.

The evolution of multifractal structures in various physical processes, such as climatology, seismology, or volcanology, serves as a crucial tool for detecting changes in corresponding phenomena. In this study, we explore the evolution of the multifractal structure of volcanic emissions with varying energy levels (observed at Colima, Mexico, during the years 2013-2015) to identify clear indicators of imminent high-energy emissions nearing 8.0x108 J. These indicators manifest through the evolution of six multifractal parameters: the central H & ouml;lder exponent (alpha 0); the maximum and minimum H & ouml;lder exponents (alpha max, alpha min); the multifractal amplitude (W=alpha max-alpha min); the multifractal asymmetry (gamma=[alpha max-alpha 0]/[alpha 0-alpha min]); and the complexity index (CI), calculated as the sum of the normalized values of alpha 0, W, and gamma. Additionally, the results obtained from adapting the Gutenberg-Richter seismic law to volcanic energy emissions, along with the corresponding skewness and standard deviation of the volcanic emission data, further support the findings obtained through multifractal analysis. These results, derived from multifractal structure analysis, adaptation of the Gutenberg-Richter law to volcanic emissions, and basic statistical parameters, hold significant relevance in anticipating potential volcanic episodes of high energy. Such anticipation can be further quantified using an appropriate forecasting algorithm.

When large earthquakes happen, first responders need fast and accurate information regarding their impact. UCIS4EQ is an urgent computing platform that estimates ground shaking based on high-performance parallel 3D simulations. In this work, we present the PyCOMPSs implementation of UCIS4EQ towards urgent high-performance computing with a particular focus on providing a malleability mechanism. This allows UCIS4EQ to scan for data updates during runtime and dynamically incorporate said data into its execution. This involves, in particular, leveraging several HPC jobs. The implementation has been validated with data from the Samos Izmir earthquake. The malleable version of the workflow can reduce the use of computational resources by 40% while reducing the variability of the results and keeping the same time-to-solution as a non-malleable run.

After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.

The Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) is proposed as a novel Urgent Computing (UC) seismic workflow that focuses on short-time reports of synthetic estimates of the consequences of moderate to large earthquakes. UC combines High-Performance Computing (HPC), High-Performance Data Analytics (HPDA), and optimized solvers to perform numerical simulations during or immediately after emergency situations, typically within a few minutes to a few hours. Complex edge-to-end UC workflows coordinate the execution of multiple model realizations to account for input and model uncertainties and can provide decision-makers with numerical estimates of the outcomes of emergency scenarios, such as earthquakes addressed by UCIS4EQ. UCIS4EQ is being driven toward operational maturity thanks to the technological and scientific developments within the eFlows4HPC project. Based on containerised micorservices, this workflow is fully orchestrated by the PyCOMPSs workflow manager to automatically prepare and manage physics-based deterministic simulation suites for rapid synthetic results. Through pre-computed and on-the-fly simulations, UCIS4EQ delivers estimates of relevant ground motion parameters, such as peak ground velocity, peak ground acceleration, or shaking duration, with very high spatial resolution. The physics-based engine includes pre-trained Machine Learning (ML) models fed with pre-computed simulation databases, as well as deterministic 3D simulations on demand, providing results in minutes and hours, respectively. The combined results, when well-calibrated, could lead to a new generation of ground shaking maps that complement GMPEs for rapid hazard assessment. To demonstrate the potential use of UC in seismology, in this work we show the UCIS4EQ simulation of the M7.1 Puebla earthquake that occurred in central Mexico on the 19th of September 2017. With a hypocentre at 18.40ºN, 98.72ºW and 57 km depth, the Puebla earthquake was located about 150 km southeast from Mexico City. Identified as a severe event (VIII) in the Modified Mercalli Intensity scale, it resulted in a total of 370 killed and around 6000 injured, as well as structural damages, downed telephone lines, and ruptured gas mains.

The effusive–explosive energy emission process in a volcano is a dynamic and complex physical phenomenon. The importance of quantifying this complexity in terms of the physical and mathematical mechanisms that govern these emissions should be a requirement for deciding to apply a possible forecasting strategy with a sufficient degree of certainty. The complexity of this process is determined in this research by means of the reconstruction theorem and statistical procedures applied to the effusive–explosive volcanic energy emissions corresponding to the activity in the Volcán de Colima (western segment of the Trans-Mexican Volcanic Belt) along the years 2013–2015. The analysis is focused on measuring the degree of persistence or randomness of the series, the degree of predictability of energy emissions, and the quantification of the degree of complexity and “memory loss” of the physical mechanism throughout an episode of volcanic emissions. The results indicate that the analysed time series depict a high degree of persistence and low memory loss, making the mentioned effusive–explosive volcanic emission structure a candidate for successfully applying a forecasting strategy.

Abstract. A time series of effusive-explosive volcanic emissions of energy by the volcanic activity in Volcán de Colima (Western segment of Trans-Mexican volcanic belt, years 2013–2015) is analysed from the point of view of the reconstruction theorem, being considered several fractal computational procedures, such as the Hurst exponent (persistence, anti-persistence or randomness of the series), Lyapunov exponents and Kaplan-Yorke dimension (degree of forecasting difficulty on the series of energy emissions) and the correlation integral of the series, being obtained the Kolmogorov entropy and the embedding dimension (quantification of the degree of complexity and “loss of memory” of the physical mechanism along an episode of volcanic emissions). The analysed series have been chosen by applying the Gutenberg-Richter law to the logarithm of the complete energy emissions, being discarded those not accomplishing the mentioned law. Definitively, a series of 6182 explosive events and its seismic energy along years 2013–2015 has been selected. The reconstruction theorem algorithm is applied to the whole emission series, to 6 consecutive segments of 1000 emissions and to 21 moving window series of 2000 data lengths and shifts of 200 data, being also introduced and validated some examples of a nowcasting strategy. The main objective of this paper is to quantify the complexities concerning the physical and mathematical mechanisms governing these emissions, which are necessary for possible forecasting of explosive volcanic energy emissions with an enough degree of certainty.

Abstract After large-magnitude earthquakes occur, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking intensities in the affected region. The intensity response can be obtained through computationally demanding simulations of wave propagation phenomena, which are accurate but still slow to produce timely results. To satisfy real-time constraints, ground shaking is traditionally evaluated with approximated Ground Motion Models (GMMs) that can drastically limit the accuracy of the estimated intensity map. In this work, one of the largest existent datasets (with more than 100M simulated seismograms), from CyberShake Study 15.4 developed by the Southern California Earthquake Center covering the Los Angeles basin region, was used to train and validate several Machine Learning (ML) strategies as an alternative to GMMs. Our predictions using the ML-based EStimator for ground-shaking maps (MLESmap) strategy outperform GMM solutions requiring similar evaluation times. We show a reduction in the median of the root mean square error (RMSE) up to 45\% for a set of unseen samples of the synthetic database, and a reduction in RMSE between 11\% and 88\% for real historical earthquakes relative to GMM predictions.