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The background seismic noise can be generated by different sources such as, ocean waves (microseisms), atmospheric disturbances (strong wind and storms), and anthropogenic activities, temperature changes and magnetic field variations. Such disturbances are characterized by specific frequency bands, time occurrence (diurnal and seasonal variation), and site location (close to populated areas or to the coasts). Reducing the pernicious effect of these noise sources is one of the main challenges that seismologists and engineers need to face when designing seismic monitoring networks and, more specifically when selecting the hosting site of a seismic station. A solution to partially attenuate the seismic noise effect is achieved by deploying seismic stations in boreholes. A general law estimating the sufficient depth to gain to detect even low seismic events, highly masked by background noise, is fundamental for defining the capability of microseismic network. Here, we aim to characterize the seismic noise level at S. Potito-Cotignola in the Po Valley, Italy, from January 2019 to December 2021 recorded by a broadband seismic station at surface and a vertical array composed by six short-period three-component seismometers installed at depth ranging between 35 and 285 m in borehole. We compute the amplitude noise reduction as a function of depth for different frequencies and we evaluate the depth dependency of the signal to noise ratio for 18 seismic events, with different magnitude (from −0.1 to 2.9) and hypocentral distances (from 12.9 to 37.2 km). Results show that (1) the dependence of noise level with depth follows a logarithmic empirical trend and (2) most of the selected seismic events show that signal to noise ratio increases with depth. The empirical relationships we estimated can be used to help the design of microseismic monitoring networks in similar geological settings.

DAS technology is particularly suitable for microseismic monitoring application in geothermal environments, especially for the development of Enhanced Geothermal Systems. This acquisition technology still lacks appropriate modeling and analysis tools able to handle such a large amount of data without losing efficiency. Since free DAS datasets are still a rarity, we aim to generate an open-access synthetic (but realistic) DAS dataset that may help the geophysical community to develop “ad hoc” data analysis methods suitable for these datasets. In the presented work we make use of the software 'Salvus' which allows the simulation of DAS data. In particular, it outputs a strain measurement between all points defined as receivers in the simulation. Using the repositories of DAS data collected at the geothermal test site FORGE, in Utah (USA), we tried to simulate realistic DAS acquisition conditions of seismic events related to low-magnitude natural seismic activity from the nearby Mineral Mountains and microseismic events related to hydraulic stimulation operations for the generation of an EGS. In order to obtain realistic synthetic data, we first analyze the spectral properties of real noise waveforms by using the Power Spectral Density Analysis. We model the synthetic noise waveforms using a stochastic approach. Then we add it to the synthetic event traces and compare them with the observed ones. We finally test a new semblance-based event detector on a 1-hour continuous waveforms of synthetic data to evaluate the performance of the detector in different operational conditions (e.g., different noise levels and inter-event times)., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

To design an efficient seismic monitoring infrastructure, the characterization of the background seismic noise level of each potential seismic station installation site is one of the most important data-quality metrics used to evaluate the suitability of such sites to host the seismic network. The background seismic noise can be generated by different sources such as, ocean waves (microseisms), atmospheric turbolences (strong wind and storms), and anthropogenic activities. Such disturbances are characterized by specific frequency bands, time-occurrence (diurnal and seasonal variation), and site location (close to populated area or to the coasts). Reducing the effect of these noise sources is one of the main challenges to face for designing seismic monitoring networks and, more specifically, when selecting the hosting site of a seismic stations. A solution to attenuate the seismic noise effect is obtained by deploying seismic stations in boreholes. The noise level reduction with depth has been observed and studied by different authors, however a general law estimating the sufficient depth to gain is still missing. In this study, we analyse the continuous seismic noise level at S. Potito-Cotignola gas storage in the Po Valley (Northern Italy) recorded from January 2019 to December 2021 by a broadband (BB) seismic station at surface and a vertical array composed by 6-short period 3-components seismometers installed at depth ranging between 35 to 285 m in borehole. We aim to characterize the seismic noise by computing the amplitude noise reduction in terms of dB as a function of depth for different frequencies and the SNR by selecting three seismic events, with different epicentral distance and magnitude. Our results show that the noise level decreases with depth following a logarithmic empirical trend and the lowest magnitude event records the maximum SNR difference between the deepest sensor and the one at the surface. The estimated empirical relationships can be used to help the design microseismic monitoring networks in similar geological settings.

Deep geothermal energy exploitation necessitates establishing effective fluid circulation paths for heat transfer and managing induced earthquake risk. By detecting and characterizing induced microseismic events, we can provide insights into the fracture network growth and the induced earthquake risk during hydraulic stimulation and geothermal production in enhanced geothermal systems (EGS). During hydraulic stimulation, monitoring has to be performed in near-real-time to provide timely information for assessing potential earthquake risk and for adjusting the stimulation plan. In addition, high-precision microseismic event location is vital for evaluating the connectivity of the stimulated reservoir and designing the trajectory of the production wells. However, achieving real-time monitoring and high-resolution location in a single monitoring workflow is challenging due to the low signal-to-noise ratio and short inter-event time of microseismic events.To address these challenges in microseismic monitoring, we build a near-real-time monitoring workflow that integrates machine-learning (ML) techniques for efficient event detection and waveform back-projection methods for high-precision event location. The proposed workflow is designed to utilize various pre-trained ML models to deal with the scarcity issue of training datasets in new EGS sites. We apply the proposed workflow to the microseismic dataset collected at the Utah FORGE geothermal site in a playback mode. Because most pre-trained ML models are trained on local earthquake datasets having larger event magnitudes and lower data sampling rates, we implement and evaluate various strategies, such as re-scaling, re-sampling, and filtering, to enhance the performance of pre-trained models on the microseismic dataset. We compare the obtained ML catalog with a reference catalog built from a conventional workflow consisting of automatic phase picking and manual refinement. Due to the application of ML and waveform back-projection techniques, our workflow can nicely separate microseismic events with very short inter-event times (in terms of a second) and cope with events with significant magnitude/amplitude differences, leading to more reliable event detections. Detailed comparisons show that the accuracy of ML phase identification is comparable to and sometimes even superior to manual picking (with a difference in milliseconds), which contributes to precise event locations.

In the last few years, the number of dense seismic networks deployed around the world has grown exponentially and will continue to grow in the next years, producing larger and larger datasets. Among the different seismological applications where these massive datasets are usually collected microseismic monitoring operations are certainly the most relevant and are a perfect playground for data-intensive techniques. In these applications we generally deal with seismic sequences characterized by a large number of weak earthquakes overlapping each other or with short inter-event times; in these cases, pick-based detection and location methods may struggle to correctly assign picks to phases and events, and errors can lead to missed detections and/or reduced location resolution. Among the seismological data analysis methods recently developed, waveform-based approaches have gained popularity due to their ability to detect and locate earthquakes without the phase picking and association steps. These approaches exploit the information of the entire network to simultaneously detect and locate seismic events, producing coherence matrices whose maximum corresponds to the coordinates of the seismic event. These methods are particularly powerful at locating microseismic events strongly noise-contaminated, but despite their excellent performance as locators, waveform-based methods still show several disadvantages when used as detectors. Waveform-based earthquake detectors strongly depend on the threshold selected for a certain application. If it is too high, small events may be missed; if it is too low, false events might be detected. To solve this problem, deep learning techniques used for the classification of images can be used to remove the dependence on threshold levels during the detection process. When applied to continuous seismic data, waveform staking methods produce coherence matrices with clear patterns that can be used to distinguish true events from false ones (i.e. noise). The coherence matrices for a seismic event generally show a single and well-focused maximum while pure noise waveforms produce blurred images with low coherence values or many poorly focused maxima. Deep Learning algorithms are the perfect tool to classify these kinds of images and improve the detection capability of waveform-based techniques. The aim of this work is the development of a workflow that, through a Convolutional Neural Network (CNN), detects seismic events by classifying the coherence matrices. We aim to train the CNN by feeding them with synthetic coherence matrices. To generate realistic coherence matrices both for events and noise we use a stochastic modeling approach to generate synthetic noise records with the same spectral properties as the observed one. For each synthetic event or pure noise recording, we finally use waveform stacking to generate coherence matrices that will be used to train the CNN. One important feature of the workflow here exposed is that the training is performed entirely on synthetics without the need for large labeled data, often missing when new microseismic networks are deployed. To test the workflow we apply it to the recently released dataset collected in Iceland, within the COSEISMIQ project.

Lockdowns and voluntary social distancing led to significant reduction in people’s mobility. Yet, there is scant evidence on the heterogeneous effects across segments of the population. Using unique mobility indicators based on anonymized and aggregate data provided by Vodafone for Italy, Portugal, and Spain, we find that lockdowns had a larger impact on the mobility of women and younger cohorts. Younger people also experienced a sharper drop in mobility in response to rising COVID-19 infections. Our findings, which are consistent across estimation methods and robust to a variety of tests, warn about a possible widening of gender and inter-generational inequality and provide important inputs for the formulation of targeted policies.

Using high-frequency proxies for economic activity over a large sample of countries, we show that the economic crisis during the first seven months of the COVID-19 pandemic was only partly due to government lockdowns. Economic activity also contracted severely because of voluntary social distancing in response to higher infections. Furthermore, we show that lockdowns substantially reduced COVID-19 cases, especially if they were introduced early in a country’s epidemic. This implies that, despite involving short-term economic costs, lockdowns may pave the way to a faster recovery by containing the spread of the virus and reducing voluntary social distancing. Finally, we document that lockdowns entail decreasing marginal economic costs but increasing marginal benefits in reducing infections. This suggests that tight short-lived lockdowns are preferable to mild prolonged measures.

Robust automatic event detection and location is central to real-time earthquake monitoring. With the increase of computing power and data availability, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, ML-based classical workflows still face challenges when applied to the analysis of microseismic data. These seismic sequences are often characterized by short interevent times and/or low signal-to-noise ratio (SNR). Full waveform methods that do not rely on phase picking and association are suitable for processing such datasets, but are computationally costly and lack clear event identification criteria, which is not ideal for real-time processing. To leverage the advantages of both the methods, we propose a new workflow—MAchine Learning aided earthquake MIgration location (MALMI), which integrates ML and waveform migration to perform automated event detection and location. The new workflow uses a pretrained ML model to generate continuous phase probabilities that are then backprojected and stacked to locate seismic sources using migration. We applied the workflow to one month of continuous data collected in the Hengill geothermal area of Iceland to monitor induced earthquakes around two geothermal production sites. With a ML model (EQ-Transformer) pretrained using a global distribution of earthquakes, the proposed workflow automatically detects and locates 250 additional seismic events (accounting for 36% events in the obtained catalog) compared to a reference catalog generated using the SeisComP software. Most of the new events are microseismic events with a magnitude less than 0. Visual inspection of the waveforms of the newly detected events indicates that they are real seismic events of low SNR and are only reliably recorded by very few stations in the array. Further comparison with the conventional migration method based on short-term average over long-term average confirms that MALMI can produce much clearer stacked images with higher resolution and reliability, especially for events with low SNR. The workflow is freely available on GitHub, providing an automated tool for simultaneous event detection and location from continuous seismic data.

Population ageing in advanced economies could have significant macroeconomic implications, unless more individuals choose to participate in labour markets. In this context, the steep increase in th...

High levels of economic policy uncertainty in various parts of the world revamped the de- bate about its impact on economic activity. With increasingly stronger economic, fi nancial, and political ties among countries, economic agents have more reasons to be vigilant of for- eign economic policy. Employing heterogeneous panel structural vector autoregressions, this paper tests for spillovers from economic policy uncertainty on other countries' economic ac- tivity. Furthermore, using local projections, the paper zooms in on shocks originating in the United States, Europe, and China. Our results suggest that economic policy uncertainty re- duces growth in real output, private consumption, and private investment, and that spillovers from abroad account for about two-thirds of the negative effect. Moreover, uncertainty in the United States, Europe, and China reduces economic activity in the rest of the world, with the effects being mostly felt in Europe and the Western Hemisphere.

Advanced economies are in the midst of a major demographic transition, with the number of elderly rising precipitously relative to the working-age population. Yet, despite the acceleration in demographic shifts in the past decade, advanced economies experienced markedly different trajectories in overall labor force participation rates and the workforce attachment of men and women. Using a cohort-based model of labor force participation for 17 advanced economies estimated over the 1985{2016 period, we document a significant role of common patterns of participation over the life cycle and shifts in these patterns across generations for aggregate labor supply, especially in the case of women. The entry of new cohorts of women led to upward shifts in the age participation profile, boosting aggregate participation rates. However, this process plateaued in most advanced economies, with signs of reversal in some. Using the model's results to forecast future participation trends, we project sizable declines in aggregate participation rates over the next three decades due to the aging of the population. Illustrative simulations show that implementing policies encouraging labor supply can help attenuate but may not fully offset demographic pressures.

In this study, we present a new approach based on recent advances in deep learning for rapid source-parameter estimation of microseismic earthquakes. The seismic inversion is represented in compact form by two convolutional neural networks, with individual feature extraction, and a fully connected neural network, for feature aggregation, to simultaneously obtain moment tensor and spatial location of microseismic sources. The neural network algorithm encapsulates the information about the relationship between seismic waveforms and underlying point-source mechanisms and locations allowing rapid inversion (within a small fraction of a second) once input data are available. A key advantage of the algorithm is that it can be trained using synthesized seismic data only, so it is directly applicable to scenarios where there are insufficient real data for training including temporary seismic networks and hydraulic stimulation experiments, for example. Moreover, we find that the method is robust with respect to perturbations such as observational noise and data incompleteness (missing stations). We apply the new approach on a database of small magnitude (M ≤ 2) earthquakes recorded at the Hellisheiði geothermal field in the Hengill area, Iceland, which is the demonstration site in the EU-GEOTHERMICA project COSEISMIQ (http://www.coseismiq.ethz.ch). For the examined events, the model achieves very good agreement with the inverted solutions determined through standard methodology. The new approach offers great potential for automatic and rapid real-time information on microseismic sources in a deep geothermal context and can be viably used for microseismic monitoring tasks in general.

Microseismic monitoring plays a fundamental role for the risk assessment and management of industrial activities related to the exploitation of georesources. In such application, microseismic monitoring is performed in real-time.One of the most widely distributed and used tools for seismic monitoring is SeisComP, a software package for automatic data acquisition and processing in real-time or during post-processing developed by the German Research for Geosciences (GFZ).In this work, we show how SeisComP can be optimized for real-time data-processing for microseismic monitoring of an Underground Gas Storage field in Northern Italy.We analysed 2-years of continuous seismic data recorded by a network composed of 15 (surface and borehole) stations. In order to improve the accuracy of earthquakes location, after processing seismic data in real-time, we used Joint Hypocentral Inversion techniques to compute a 1D velocity model (both for P and S waves) for the surrounding area of gas storage field. Then, we extracted a P 3D velocity model at reservoir scale, based on the migration velocity from a 3D seismic reflection survey. The Vp model is then converted to Vs by using an average Vp/Vs value extracted from the 1D velocity model and well-logs.Finally, we compared the different velocities models by analysing earthquakes location obtained with each model.For the events located in the inner area, our comparison shows a systematic location improvement (both in terms of RMS and waveform coherence) with the 3D model. For events outside that area, the optimized 1D model performs better than the initial model (both in terms of RMS and waveform coherence). Our processing routine for this seismic network is the first application in Italy where a 3D velocity model is fully integrated within the real-time microseismic monitoring operations, as suggested by the Italian Guideline for Microseismicity Monitoring on Industrial activities.

The determination of seismic moment tensors (MTs) for microseismicity poses challenges because of both the large number of events that are typically recorded, and their low signal to noise ratio. In recent years, automated moment tensor inversion methods have become more and more accurate, but an objective evaluation of their performance is often problematic due to the absence of site-specific, reference databases for comparison. In this study, we build a database of manually inverted MTs for the recent COSEIMIQ project, using the well-tested FociMT/HybridMT inversion method. COSEISMIQ focussed on microseismic monitoring in the Hellisheiði geothermal field, in the Hengill region, southern Iceland, where a dense network of 33 temporary seismic stations was deployed during 2018-2021, offering an ideal case study for microseismic MT inversion.As a first step, we test the efficacy and possible pitfalls of the manual MT inversion on both a realistic and a simplified synthetic events waveform database. After careful, repeated manual tests, we observe that the inversion is robust across widely different choices of frequency band, but can be triggered to fail by not including key stations in some rare source-station geometries.We then analyse the real data from the COSEISMIQ experiment, using previously located events from a large, recently developed microseismic catalog of the area. By running preliminary inversions of a subset of events in the centre of the deployment, we are able to pinpoint pre-processing steps that have a key effect on the MT inversion. We find that in strong noise conditions such as in the Hengill region, the order and phase of the used frequency filter are fundamental parameters in correctly processing the P-wave onset used later for inversion.After fine-tuning the event preprocessing, we select a larger subset of 197 events with magnitude > 0.8 from the catalog across the whole COSEISMIQ area, including several seismicity clusters at the edge of the deployment. We then pick all 197 events and invert them first with FociMT, then cluster the events based on their location using K-means clustering, and finally re-invert each cluster using HybridMT. The clustered inversion using HybridMT changes some MT solutions significantly, reducing the intra-cluster MT variance for most clusters. Interestingly, some event clusters show increased variance after the HybridMT inversion, suggesting that these include substantially different source mechanisms within a small area.This new database of carefully inverted MT solutions can now be used as a test dataset to evaluate the performance of automated inversion tools.

We present preliminary results on a moment tensor inversion workflow for Distributed Acoustic Sensing (DAS). It makes use of a fast-marching Eikonal solver and synthetically modeled data. The study specifically focuses on borehole settings for geothermal sites. Distributed Acoustic Sensing measures the wavefield with high spatial and temporal resolution. In borehole settings, individual DAS traces generally prove to be noisier than co-located geophones, whereas the densely spaced DAS shot-gathers show features that would have otherwise been missed by the commonly more sparsely distributed geophone chains. For example, the coherency in the DAS records shows the polarity reversals of the arriving wavefield in great detail, which can help constrain the moment tensor of the seismic source. The synthetic tests encompass different source types and source positions relative to the deployed fiber to assess moment tensor resolvability. Further tests include the addition of a three-component seismometer at different positions to investigate an optimal network configuration, as well as various noise conditions to mimic real data. The synthetic tests are tailored to prepare for the data from future microseismicity monitoring with DAS in the conditions of the Utah FORGE geothermal test site, Utah, USA. The proposed method aims at improving amplitude-based moment tensor inversion for DAS deployed in downhole or underground lab contexts.

On 2021 March 27th an Mw 5.2 earthquake occurred in the Adriatic Sea, between the Italia and Croatian coast. The earthquake sequence lasted for several months and consisted of more than 150 seismic events with a magnitude above 2. Analyzing offshore seismic sequences is challenging both for the lack of optimal seismic monitoring networks and detailed enough velocity models. These conditions strongly limit the data analysis procedures, leading to inaccurate results that may have severe effects on the identification of the seismogenic structure associated with the seismic sequence, bringing to wrong seismo-tectonic interpretations, with direct consequences in the seismic hazard assessment of an area. In this study, we analyze the March 2021 Mw 5.2 earthquake sequence that occurred in the Adriatic Sea with recently developed location techniques. Our workflow allows achieving a higher location accuracy, even when dealing with suboptimal monitoring conditions. We analyze this dataset using waveform-based location techniques and a recently developed location technique based on Distance Geometry Solvers (DGS). This last approach uses inter-event distances between earthquake pairs estimated at one or two seismic stations to get high-resolution locations of seismicity clusters. The application of such techniques led to different improvements in locating the seismic sequence, which is more clustered and clearly shows an N-S trending compatible with the geological setting of the area.

Distributed Acoustic Sensing (DAS) is becoming increasingly popular in microseismic monitoring operations. Fiber-optic cables such as conventional telecommunication or built-for-purpose cables can be turned into a dense array of geophones that samples seismic wavefields continuously for several kilometers. DAS is particularly interesting for microseismic monitoring of geothermal systems since it does not have the same temperature limitations as standard electronic equipment. The sensing fiber can therefore be installed at high-temperature reservoir conditions and in the same well that is being stimulated. Because of these advantages, the distance between the detecting sensor and the induced seismicity can be minimized, maximizing the detection capability. Typical DAS acquisition samples the wavefield at about 1 m spacing and sampling frequencies of 1 kHz or higher. Unfortunately, standard seismological techniques are not capable of exploiting this high spatial density of sensors, hence they are ineffective in processing this kind of data. Here we propose a semblance-based seismic event detection method that fully exploits the characteristics of the DAS data. The detection identifies seismic events by looking at waveform coherence along hyperbolas while changing the curvature and position of the vertex. The method returns a time series of coherence values and, if these values are higher than a determined threshold, it catches a seismic event. First we test the detector with synthetic data resembling a realistic setup. Finally, we validate the detector by applying it to real DAS data from the Utah FORGE site in the US. This work is supported by the EU-Geothermica DEEP project.

Automatic event detection and location is key to real-time earthquake monitoring. With the increase of computing power and labeled data, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, classical workflows using ML as phase pickers still face challenges for seismic events of short inter-event time or low signal-to-noise ratio (SNR). Full waveform methods that do not rely on phase pick and association are suitable for processing these events, but are computationally costly and can lack clear event identification criteria, which is not ideal for real-time processing. To leverage the advantages of both methods, we propose a new workflow, MALMI, which integrates ML and waveform migration to perform automated event detection and location. The new workflow uses a pre-trained ML model to generate continuous phase probabilities that are then back-projected and stacked to locate seismic sources using migration.We applied the workflow to a microseismic monitoring dataset collected in a borehole at the Utah FORGE geothermal laboratory site. The proposed workflow can automatically detect and locate induced microseismic events from continuous geophone recordings. Different ML models are evaluated for detection capability and phase classification accuracy. We expect that better performance should be possible if a customized ML model re-trained using local dataset would be used in the MALMI workflow. Further comparison with conventional migration methods confirms that MALMI can produce much clearer stacked images with higher resolution and reliability, especially for events with low SNR. The workflow is freely available on GitHub, providing a complementary tool for automated event detection and location from continuous data.

The linearity of the relationship between income inequality and economic development has been long questioned. While theory provides arguments for which the shape of the relationship may be positive for low levels of inequality and negative for high ones, most of the empirical literature assumes a linear specification finding conflicting results. Employing an innovative empirical approach, robust to endogeneity, we find pervasive evidence of nonlinearities. In particular, similar to the debt-overhang literature, we identify an inequality-overhang level, in that the slope of the relationship between income inequality and economic development switches from positive to negative at a net Gini coefficient of about 27 per cent. We also find that in an environment characterized by widespread financial inclusion and high income concentration, rising income inequality has a larger negative impact on economic development because banks may curtail credit to customers at the lower end of the income distribution. On the positive side, a sufficiently high female labor participation can act as a shock absorber reducing such a negative impact, possibly through a more efficient allocation of resources.

Economic theory offers several explanations as to why shifting expectations about future economic activity affect current demand. Abstracting from whether changes in expectations originate from swings in beliefs or fundamentals, we test empirically whether more optimistic or pessimistic potential output forecasts trigger short-term fluctuations in private consumption and investment. Relying on a dataset of actual data and forecasts for 89 countries over the 1990-2022 period, we find that private economic agents learn from different sources of in- formation about future potential output growth, and adjust their current demand accordingly over the two years following the shock in expectations. To provide a theoretical foundation to the empirical analysis, we also propose a simple Keynesian model that highlights the role of expectations about long-term output in determining short-term economic activity.

The sharp, long-lasting decline in oil prices in 2014–16 tested the resilience of oil exporters. We examine the degree to which economic fundamentals entering the oil price decline explain the impact on economic growth across oil exporting economies, and derive policy implications as to what factors help to mitigate the negative effects. We find that pre-existing fundamentals account for about half of the cross-country variation in the impact of the shock. Oil exporters that weathered the shock better tended to have a stronger fiscal position, higher foreign currency liquidity buffers, a more diversified export base, a history of price stability, and a more flexible exchange rate regime. Within this group of countries, the impact of the shock is not found to be related to the size of oil exports, or the share of oil in fiscal revenue or economic activity.

Despite advanced seismological methods, source characterization for micro-seismic events remains challenging since current inversion and modelling of high-frequency waveforms are complex and time consuming. For a real-time application like induced-seismicity monitoring, these methods are slow for true real-time information because they require repeated evaluation of the often computationally expensive forward operation. Moreover, because of the low amplitude and high-frequency content of the recorded micro-seismic signals, routine inversion procedure can become unstable and manual parameter tuning is often required. Therefore, real-time and automatic source inversion procedures are difficult and not standard. A more promising alternative to the current inversion methods for rapid source parameter inversion is to use a deep-learning neural network model that is calibrated on a data set of past and/or possible future observations. Such data-driven model, once trained, offers the potential for rapid real-time information on seismic sources in a monitoring context.In this study, we investigate how a supervised deep-learning model trained on a data set of synthetic seismograms can be used to rapidly invert for source parameters. The inversion is represented in compact form by a convolutional neural network which yields seismic moment tensor. In other words, a neural-network algorithm is trained to encapsulate the information about the relationship between observations and underlying point-source models. The learning-based model allows rapid inversion once seismic waveforms are available. Moreover, we find that the method is robust with respect to perturbations such as observational noise and missing data. In this study, we seek to demonstrate that this approach is viable for micro-seismicity real-time estimation of source parameters. As a demonstration test, we plan to apply the new approach to data collected at the geothermal field system in the Hengill area, Iceland, within the framework of the COSEISMIQ project funded through the EU GEOTHERMICA programme.

Currently the capability of detecting earthquakes with decreasing magnitudes demands efficient source localization, especially in seismic monitoring. This work is a step towards automatic high-resolution earthquake localization in a seismic monitoring setup that makes use of Distributed Acoustic Sensing (DAS) as its primary measuring technique. With DAS, the dense spatial sampling of the seismic wavefield leads to an improvement of both event detection and localization of earthquakes. The advantage of DAS is easy and cost-effective deployment compared to traditional seismic instruments (especially in boreholes). However, the single-component nature and the large storage requirements of DAS data demand novel methods for efficient analysis of the recorded events.We apply a new seismic event location method to DAS data, based on a distance geometry problem in biochemistry for protein structure determination (HADES1). From the distances between individual earthquakes and a seismic station, the relative distance between the events can be computed. This approach allows us to first determine the relative location of earthquakes within a seismic cluster, and subsequently position the cluster in its correct absolute location. The technique has already been successfully applied for a single traditional seismometer. The densely spaced channels in DAS measurements accommodate accurate relative distance computation, without the ability to constrain the azimuth of the seismic cluster. Therefore, after finding the relative locations within the cluster, the position and orientation of the cluster with respect to the fiber-optic cable is calculated by minimizing the difference between observed and calculated P- and S-wave first arrival times, using a grid search approach (multi-event location). In this way, the absolute locations of all earthquakes present in the cluster are found efficiently. We first test this DAS-adapted method on synthetics, then we will move towards a real data application.1 HADES: https://github.com/wulwife/HADES

This report summarizes the seismicity in Switzerland and surrounding regions in the years 2017 and 2018. In 2017 and 2018, the Swiss Seismological Service detected and located 1227 and 955 earthquakes in the region under consideration, respectively. The strongest event in the analysed period was the ML 4.6 Urnerboden earthquake, which occurred in the border region of cantons Uri, Glarus and Schwyz on March 6, 2017. The event was the strongest earthquake within Switzerland since the ML 5.0 Vaz earthquake of 1991. Associated ground motions indicating intensity IV were reported in a radius up to about 50 km and locally approached intensity VI in the region close to the epicentre. Derived focal mechanisms and relative hypocentre relocations of the immediate aftershocks image a NNW–SSE striking sinistral strike-slip fault. Together with other past events in this region, the Urnerboden earthquake suggests the existence of a system of sub-parallel strike-slip faults, likely within in the uppermost crystalline basement of the eastern Aar Massif. A vigorous earthquake sequence occurred close to Château-d'Oex in the Préalpes-Romandes region in western Switzerland. With a magnitude of ML 4.3, the strongest earthquake of the sequence occurred on July 1, 2017. Focal mechanism and relative relocations of fore- and aftershocks image a NNE dipping normal fault in about 4 km depth. Two similarly oriented shallow normal-fault events occurred between subalpine Molasse and Préalpes units close to Châtel-St-Denis and St. Silvester in 2017/18. Together, these events indicate a domain of NE–SW oriented extensional to transtensional deformation along the Alpine Front between Lake Geneva in the west and the Fribourg Fault in the east. The structural complexity of the Fribourg Fault is revealed by an ML 2.9 earthquake near Tafers in 2018. The event images a NW–SE striking fault segment within the crystalline basement, which might be related to the Fribourg Fault Zone. Finally, the ML 2.8 Grenchen earthquake of 2017 provides a rare example of shallow thrust faulting along the Jura fold-and-thrust belt, indicating contraction in the northwestern Alpine foreland of Switzerland., Swiss Journal of Geosciences, 114 (1), ISSN:1661-8734, ISSN:1661-8726

Reykjavik is almost entirely heated by geothermal energy. Yet, recent growth of the city significantly increased the heat demand. Past experiences in Iceland's capital region showed that hydraulic stimulation of existing geothermal wells is suited to improve hydraulic performance and energy supply. However, fluid injection may also trigger felt or even damaging earthquakes, which are of concern in populated areas and pose a significant risk to stimulation operations. Consequently, soft stimulation concepts have been developed to increase geothermal well performance while minimizing environmental effects such as induced seismicity. In a demonstration project of hydraulic soft stimulation in October 2019, more than 20.000 m³ of water were injected into well RV-43 in Reykjavik in multiple stages and with different injection schemes. The hydraulic performance of the well was improved without inducing felt seismicity. An a priori seismic risk assessment was conducted and for the first time the risk was continuously updated by an adaptive traffic light system supported by a sophisticated realtime microseismic monitoring. Our results confirm that it is possible to improve the performance of geothermal wells in Reykjavik and worldwide with acceptable technical, economic, and environmental risks. Here we provide an overview of the entire stimulation project including site description, stimulation design, zonal isolation, logging, seismic risk assessment and mitigation measures, realtime seismic, hydraulic and chemical monitoring, and stimulation results and challenges., Geothermics, 96, ISSN:0375-6505

Using high-frequency proxies for economic activity over a large sample of countries, we show that the economic crisis during the first seven months of the COVID-19 pandemic was only partly due to government lockdowns. Economic activity also contracted because of voluntary social distancing in response to higher infections. We also show that lockdowns can substantially reduce COVID-19 infections, especially if they are introduced early in a country's epidemic. Despite involving short-term economic costs, lockdowns may thus pave the way to a faster recovery by containing the spread of the virus and reducing voluntary social distancing. Finally, we document that lockdowns entail decreasing marginal economic costs but increasing marginal benefits in reducing infections. This suggests that tight short-lived lockdowns are preferable to mild prolonged measures.

We show that domestic production networks shape worker flows between firms. Data on the universe of firm-to-firm transactions for the Dominican Republic, matched with employer-employee records, reveals that about 20 percent of workers who change firms move to a buyer or supplier of their original firm. This is a considerably larger share than would be implied by a random allocation of movers to firms. We find considerable gains associated with this form of hiring: higher worker wages, lower job separation rates, faster firm productivity growth, and faster coworker wage growth. Hiring workers from a supplier is followed by a rising share of purchases from that supplier. These findings indicate that human capital is easily transferable along the supply chain and that human capital accumulated while working at a firm is complementary with the intermediate products/services produced by that firm.

Geothermal systems in the vicinity of the Hengill volcano, SW Iceland, started to be exploited for electrical power and heat production since the late 1960s, and today the two largest operating geothermal power plants are located at the Nesjavellir and the Hellisheidi. This area is a complex tectonic and geothermal site, being located at the triple junction between the Reykjanes Peninsula (RP), the Western Volcanic Zone (WVZ), and the South Iceland Seismic Zone (SISZ). The region is seismically highly active with several thousand earthquakes located yearly. The origin of such earthquakes may be either natural or anthropogenic. The analysis of microseismicity can provide useful information on natural active processes in tectonic, geothermal and volcanic environments as well as on physical mechanisms governing induced events. Here, we investigate the microseismicity occurring in Hengill area to understand physical source mechanisms and the origin of these microseismic events. We use a very dense broadband monitoring network deployed since November 2018 with support of the GEOTHERMICA project COSEISMIQ and apply robust and full-waveform based methods for earthquake location, clustering analysis and source mechanism determination. Our dataset consists of about 637 events with ML ranging between 0.8 and 4.7 from December 2018 to January 2019. We use this rich and large dataset for testing a workflow for automated processing. Earthquake location and clustering analysis show that seismicity is spatially clustered, with shallower events at the center of geothermal site in proximity to geothermal plants, and deeper earthquakes in the southern part of the study area. Most of our moment tensors can suggest the influence of geothermal activity and geothermal energy exploitation operations on the subsurface. This work is supported by the COSEISMIQ project of the EU GEOTHERMICA program .

Sound regional policies are essential for balanced and sustained economic growth. The interaction of federal and regional policies with cross-regional structural differences affects human and physical capital formation, the business climate, private investment, market depth, and competition. This paper summarizes the main elements of Russia’s fiscal federalism, describes the channels through which it operates, and assesses the effectiveness of regional transfers in reducing regional disparities. The results suggest that federal transfers to regions contributed to reducing disparities arising from heterogeneous regional tax bases and fiscal revenues. This allowed regions with initially lower per capita income to increase human and physical capital at higher rates. There is little evidence for transfers contributing to increased cross-regional growth synchronization. The results also suggest that federal transfers did not significantly improve regional fiscal sustainability, a conclusion that is supported by the lack of convergence in per capita real income across Russian regions in the last 15 years.

This paper presents new evidence on the behavior of saving in the world, by extending previous empirical research in several dimensions. After extensively surveying the relevant theoretical and empirical literature, the paper reports estimates of saving determinants relying on the newly constructed and largest available database covering 165 countries over 1981–2012. The empirical specification includes determinants not considered in the literature, explores differences in saving behavior nesting the 2008–10 crisis period and four different country groups, searches for commonalities across key saving aggregates (national, private, household, and corporate saving rates), and is subject to a robustness analysis based on different estimation techniques. The results confirm in part existing research, but also shed light on some ambiguous or contradictory findings and highlight the role of neglected determinants. Compared to the literature, we find a larger number of significant determinants, changes across periods and country groups, and similarities across different saving aggregates.

Understanding the sources of inflation persistence is crucial for monetary policy. This paper provides an assessment of the influence of inflation expectations' anchoring on the persistence of inflation. We construct an index of inflation expectations' anchoring using survey-based inflation forecasts for 45 economies since 1989. We then study the response of consumer prices to terms-of-trade shocks and find that these shocks have a significant and persistent effect on consumer price inflation when expectations are poorly anchored. By contrast, inflation reacts by less and returns quickly to its pre-shock level when expectations are strongly anchored.

Public health spending is low in emerging and developing economies relative to advanced economies and health outputs and outcomes need to be substantially improved. Simply increasing public expenditure in the health sector, however, may not significantly affect health outcomes if the efficiency of this spending is low. This paper quantifies the inefficiency of public health expenditure and the associated potential gains for emerging and developing economies using a stochastic frontier model that controls for the socioeconomic determinants of health, and provides country-specific estimates. The results suggest that African economies have the lowest efficiency. At current spending levels, they could boost life expectancy up to about five years if they followed best practices.

Poor performance of the electricity sector remains a drag to economic efficiency and a bottleneck to economic activity in many low-income countries. This paper proposes a number of models that account for different equilibria (some better, some worse) of the electricity sector. They show how policy choices (affecting insolvency prospects or related to rules for electricity dispatching or tariff setting), stochastic generation costs, and initial conditions, affect investment in generation and electricity supply. They also show how credible (non-credible) promises of stronger enforcement to reduce theft result in larger (smaller) electricity supply, lower (higher) government subsidies, and lower (higher) tariffs and distribution losses, which in turn affect economic activity. To illustrate these findings, the paper reviews the experience of Haiti, a country stuck in a bad equilibrium of insufficient supply, high prices, and electricity theft; and that of Nicaragua, which is gradually transitioning to a better equilibrium of the electricity sector.

A crucial issue to characterize hydraulic fractures is the robust, accurate and automated detection and location of acoustic emissions (AE) associated with the fracture nucleation and growth process. Waveform stacking and coherence analysis techniques are here adapted using massive datasets with very high sampling (1 MHz) from a hydraulic fracturing experiment that took place 410 m below surface in the Aspo Hard Rock Laboratory (Sweden). We present the results obtained during the conventional, continuous water injection experiment Hydraulic Fracture 2. The resulting catalogue is composed of more than 4000 AEs. Frequency–magnitude distribution from AE magnitudes (MAE) reveals a high b value of 2.4. The magnitude of completeness is also estimated approximately MAE 1.1, and we observe an interval range of MAE between 0.77 and 2.79. The hydraulic fractures growth is then characterized by mapping the spatiotemporal evolution of AE hypocentres. The AE activity is spatially clustered in a prolate ellipsoid, resembling the main activated fracture volume (~105 m3), where the lengths of the principal axes (a = 10 m; b = 5 m; c = 4 m) define its size and its orientation can be estimated for a rupture plane (strike ~123°, dip ~60°). An asymmetric rupture process regarding to the fracturing borehole is clearly exhibited. AE events migrate upwards covering the depth interval between 404 and 414 m. After completing each injection and reinjection phase, the AE activity decreases and appears located in the same area of the initial fracture phase, suggesting a crack-closing effect.

Due to the deep socioeconomic implications, induced seismicity is a timely and increasingly relevant topic of interest for the general public. Cases of induced seismicity have a global distribution and involve a large number of industrial operations, with many documented cases from as far back to the beginning of the twentieth century. However, the sparse and fragmented documentation available makes it difficult to have a clear picture on our understanding of the physical phenomenon and consequently in our ability to mitigate the risk associated with induced seismicity. This review presents a unified and concise summary of the still open questions related to monitoring, discrimination, and management of induced seismicity in the European context and, when possible, provides potential answers. We further discuss selected critical European cases of induced seismicity, which led to the suspension or reduction of the related industrial activities.

The rapid increase in energy demand in the city of Reykjavik has posed the need for an additional supply of deep geothermal energy. The deep-hydraulic (re-)stimulation of well RV-43 on the peninsula of Geldinganes (north of Reykjavik) is an essential component of the plan implemented by Reykjavik Energy to meet this energy target. Hydraulic stimulation is often associated with fluid-induced seismicity, most of which is not felt on the surface but which, in rare cases, can be a nuisance to the population and even damage the nearby building stock. This study presents a first-of-its-kind pre-drilling probabilistic induced seismic hazard and risk analysis for the site of interest. Specifically, we provide probabilistic estimates of peak ground acceleration, European microseismicity intensity, probability of light damage (damage risk), and individual risk. The results of the risk assessment indicate that the individual risk within a radius of 2 km around the injection point is below 0.1 micromorts, and damage risk is below 10−2, for the total duration of the project. However, these results are affected by several orders of magnitude of variability due to the deep uncertainties present at all levels of the analysis, indicating a critical need in updating this risk assessment with in situ data collected during the stimulation. Therefore, it is important to stress that this a priori study represents a baseline model and starting point to be updated and refined after the start of the project.