Event Location Research Articles

Systematic disparities in reporting on community firearm violence on local television news in Philadelphia, PA, USA

ObjectiveTo better understand how community firearm violence (CFV) is communicated to the public, we aimed to identify systematic differences between the characteristics of shooting victims and events covered on television news and all shootings in Philadelphia, PA, a city with escalating CFV incidence. MethodsWe compiled a stratified sample of local television news clips covering shootings that occurred in Philadelphia aired on two randomly selected days per month from January-June 2021 (n = 154 clips). We coded the clips to determine demographic and geographic information about the shooting victims and events and then matched coded shootings with corresponding shootings in the Philadelphia police database. We compared characteristics of shooting victims and shooting event locations depicted in television clips (n = 62) with overall characteristics of shootings in Philadelphia during the study period (n = 1082). ResultsCompared to all individuals shot, victims whose shootings were covered on local television news more likely to be children and more likely to be shot in a mass shooting. The average median household income of shooting locations featured on television was significantly higher than the median household income across all shooting locations ($60,302 for television shootings vs. $41,233 for all shootings; p = 0.002). Shootings featured on television occurred in areas with lower rates of income inequality and racialized economic segregation compared to all shooting locations. ConclusionsTelevision news outlets in Philadelphia systematically over-reported shootings of children, mass shootings, and shootings that occurred in neighborhoods with higher median household income, less socioeconomic inequality, and lower rates of racialized economic segregation.

REVEAL: A Global Full-Waveform Inversion Model

ABSTRACT We present REVEAL, a global-scale, transversely isotropic full-waveform inversion model. REVEAL builds upon the earlier construction of the long-wavelength Earth (LOWE) model by lowering the minimum period from 100 to 33 s and by more than doubling the number of included earthquakes to 2366. In the course of 305 quasi-Newton iterations, REVEAL assimilated a total of 6,005,727 unique three-component waveforms. The inversion method rests on the combination of a stochastic mini-batch optimization and wavefield-adapted spectral-element meshes. Although the former naturally exploits redundancies in the data set, the latter reduces the cost of wavefield simulations by reducing the effective dimension of the numerical mesh. As a consequence, the average cost of an iteration in this inversion is only around 0.62% of an iteration that uses the complete data set with a standard cubed-sphere-type mesh. We calculated 3D synthetic seismograms using a graphics processing unit-accelerated spectral-element wave propagation solver, accommodating the effects of anelasticity, topography, bathymetry, ocean loading, and ellipticity. For a diverse range of global wavepaths, REVEAL predicts complete three-component seismograms at 33 s period that have not been included in the inversion. This generalization to unseen data suggests applications of REVEAL in event location and characterization, as well as in ground-motion modeling.

Experimental Study on the Butterfly Shape of the Plastic Zone around a Hole near Rock Failure

The precursor of rock failure around a hole has always been one of the research hotspots in the field of rock mechanics, and the distribution of the plastic zone is often adopted to reflect the location and form of rock failure. The shape of the plastic zone around a hole before rock failure can guide the mechanism of and early warning methods for geotechnical engineering disasters, while previous theoretical research and numerical simulation results show that the shape of the plastic zone around the hole is butterfly shaped under specific stress, which is referred to as butterfly failure theory. Studies also indicate that the butterfly shape of the plastic zone around a hole is considered to be the main cause of many disasters, which signifies the importance of studying the morphology of the plastic zone near rock failure. Therefore, this study is committed to finding the specific shape of the plastic zone near rock failure through relatively accurate and a high number of AE event location results, and the final experimental results show that the plastic zone around the hole is basically a butterfly shape near rock failure. This study verifies the correctness of the butterfly failure theory and provides an important reference for the study of geotechnical engineering disaster mechanisms and monitoring methods. The fact that the plastic zone in the early stage of rock failure in this study tends to be butterfly shaped preliminarily indicates the fractal law of rock failure. In the moment before rock failure, the distribution of AE events is more regular, which leads to large-scale collapse type failure.

Globe-LFMC 2.0, an enhanced and updated dataset for live fuel moisture content research.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Causes of Death in the Presence of Law Enforcement in Johnson County, Iowa, 2011-2020.

Objectives. To examine sudden and unexpected or trauma-related deaths that occurred in the presence of law enforcement in Johnson County, Iowa, between 2011 and 2020. Methods. We identified deaths in the presence of law enforcement using definitions from the National Association of Medical Examiners. We obtained data, including demographics, cause and manner of death, toxicology results, and circumstances and location of event leading to death, from comprehensive medical examiner investigative reports. Results. There were 165 deaths that occurred in the presence of law enforcement: 114 were from a known disease, and 51 were either trauma related or the sudden, unexpected initial presentation of a previously unrecognized disease. Three deaths occurred in the context of physical restraint by law enforcement. Suicide was the leading manner of death among trauma-related deaths; the means of suicide was predictable based on in-custody (hanging) or precustody (firearm) circumstances. Conclusions. Our findings highlight the potential role of medical examiners and coroners in improving completeness of data on reporting death in the presence of law enforcement to public health agencies. (Am J Public Health. 2024;114(6):642-650. https://doi.org/10.2105/AJPH.2024.307616).

Investigating the Congestion Levels on a Mesoscopic Scale During Outdoor Events

In event management, preventing excessive overcrowding is not only essential for providing comfort but also crucial for ensuring safety. However, understanding the crowd dynamics of participants in outdoor events can be challenging. One of the primary reasons is the limited availability of sensing systems suitable for outdoor use. Challenges include the need for power outlets and adapting to dynamic environmental conditions and unclear event boundaries. Consequently, there is still uncertainty about which measurements can be conducted to scientifically manage crowding based on sound principles. Therefore, there is a need for systems that are capable of discerning spatial and temporal heterogeneity in density and accurately estimating the number of people in regions of interest in both sparse and congested areas. In this study, we propose a novel approach for measuring and understanding crowd states at outdoor events. We designed a highly portable measurement system utilizing Bluetooth technology to monitor crowd density in real time, ensuring uninterrupted data collection even in remote event locations. This system stands out for its ability to operate effectively under diverse weather and lighting conditions without power outlets, making it highly adaptable to various outdoor settings. In our experiments, conducted at four distinct outdoor event locations, we used a 360° camera and LiDARs to validate the system. For instance, we deployed the system at 40-m intervals in a shopping district during a high-density parade. This deployment enabled us to capture the movement of the crowd and estimate the total number of people within the district. A key finding was the system’s capability to detect temporal and spatial congestion in both sparse and crowded areas. The system’s potential to estimate crowd sizes and manage diverse outdoor events marks an advancement over traditional methods like cameras and LiDARs.

Multi-model ensemble of frost risks across East Asia (1850–2100)

Frost events can cause the deterioration of a wide range of heritage materials, including stone, brick and earth. In a warming world, the frequency and location of frost events is likely to change, affecting the conservation strategies required at heritage sites. We use a multi-model ensemble approach to investigate three types of frost events in East Asia: freeze–thaw cycles; deep frost days and wet frosts. The study uses nine CMIP6 models for the period 1850 to 2100, with future projections run under the SPS585 scenario. Additional analysis is undertaken for five specific 2° ✕ 2° areas located across East Asia. The three frost event parameters are spatially and temporally distinct. A decrease in all three frost parameters is found in Japan, South Korea and East China, with some areas projected to have no frost events by the end of the twenty-first century. However, Northwest China is distinctive as wet frosts are projected to increase over the twenty-first century, while on the Tibetan plateau of Southwest China, freeze–thaw cycles are projected to increase. This suggests that except in some localised regions, heritage managers can focus on risks other than frost weathering in developing plans to address climate change.Graphical

A Descriptive Analysis of the Fastest Race Courses for Triathletes

Background: For Ironman® triathlon, it has been reported that most of the fnishers and the fastest women and men in Ironman® Hawaii originated from the United States of America (USA). We have, however, no knowledge ofwhere the fastest race courses in the Ironman® 70.3 triathlon took place. We aim to analyse where the Ironman® 70.3 races were held and where the fastest split and overall race times were achieved. Methods: The athletes’ sex, age group, country of origin, and split times for swimming, running, cycling, and transitioning were obtained from the official Ironman® website. To investigate the locations of the fastest Ironman® 70.3 competitions between 2004 and 2020, a full sample of 852,721 qualifying records throughout 197 different event locations was processed. These race records were aggregated by location, and each location’s split and full finish times were calculated. Data analysis was performed first for the full sample (all race records), and then for an elite sub-sample consisting of the top 100 males and top 100 females records in each location. Results: For the full sample, the fastest overall race times were achieved in Ironman® 70.3 Zell am See (Austria). For the top 100 athletes sub-sample, the Ironman® 70.3 European Championship Elsinore and Ironman® 70.3 World Championship were the fastest courses. Conclusion: These results are useful for athletes’ strategic planning and inform event organisers about the strengths of different courses, aiding in the optimisation and promotion of future Ironman® 70.3 races worldwide. Keywords: endurance, performance, half-distance Ironman®, multi-sport, running.

Tornado damage in forest ecosystems of the United States

Changing climate dynamics have been forecasted worldwide, including in some regions of the United States (U.S.), leading to potentially significant damage to forest ecosystems. To better understand how natural disturbances may affect forests, we analyzed trends in frequency and area of tornado damage for 1995–2021, damage area within forested ecosystems, and assessed data limitations for the contiguous U.S. for 1950–2021. We found that recent, more uniform data collection periods from 1995-present showed stable to declining numbers of tornadoes nationally. However, our results indicate that the southeastern U.S. has experienced significant increases in total and forest area damaged by tornadoes as compared to other regions over the last three decades. The northeastern U.S. also showed a slight increase in damage, while all other regions showed no significant trends. This indicates a potential shift in the geographic location of tornadic events from the Midwest towards the Southeast, which has a larger area of commercial timberland. Additionally, forest area damage has increased 36 %, as estimated by total tornado track area, over the most recent 20 years. Increased tornado damage in forests suggests increased financial risks for the wood products sector and landowners in recent decades, and we recommend that large, institutional landowners monitor and assess natural disturbances (occurrence, extent, severity, and financial consequences). These findings highlight the need to improve risk assessment methodologies and support research that informs both pre- and post-disturbance forest management practices.

Detecting and locating chemical intrusion in water distribution systems using 911 calls

ABSTRACT Intentional or unintentional chemical contamination of water distribution systems (WDSs) could have severe health and socio-economic consequences. High potency chemicals constituting, in essence, “super poisons” have the potential to be used in such intrusion scenarios. Some of these contaminants are capable of killing the victim in less than 1 h. Due to their high toxicity levels and short time from exposure to onset of symptoms, 911 call centers are likely the first point of contact for victims or their families with the authorities. Information such as 911 calls could be used to identify the ongoing event and potential intrusion locations. In this way, such emergency calls could function as an intrusion warning system. This study employs network hydraulic modelling to synthesize the 911 call patterns in the aftermath of such events. It then defines the scenarios as a multi-label pattern recognition problem. The synthesized data then was used to train a Convolutional Neural Network (CNN). The trained AI was applied to a real-world WDS with approximately 4000 km of pipe and 26,000 demand nodes. The results indicated that CNN is capable of accurately recognizing the pattern and pinpointing the originating location of the intrusion with an accuracy greater than 93%.

Intracortical recordings reveal vision-to-action cortical gradients driving human exogenous attention

Exogenous attention, the process that makes external salient stimuli pop-out of a visual scene, is essential for survival. How attention-capturing events modulate human brain processing remains unclear. Here we show how the psychological construct of exogenous attention gradually emerges over large-scale gradients in the human cortex, by analyzing activity from 1,403 intracortical contacts implanted in 28 individuals, while they performed an exogenous attention task. The timing, location and task-relevance of attentional events defined a spatiotemporal gradient of three neural clusters, which mapped onto cortical gradients and presented a hierarchy of timescales. Visual attributes modulated neural activity at one end of the gradient, while at the other end it reflected the upcoming response timing, with attentional effects occurring at the intersection of visual and response signals. These findings challenge multi-step models of attention, and suggest that frontoparietal networks, which process sequential stimuli as separate events sharing the same location, drive exogenous attention phenomena such as inhibition of return.

Branding Strategy Rumah Keramik Tanah Baru To Increase Tourists' Motivation To Visit

This research aims to analyze the branding strategies carried out by rumah keramik tanah baru and their implementation to increase tourist motivation to visit. Based on data from survey results via Instagram, the Tanah Baru Ceramic House still does not introduce its identity as a tourist attraction, namely educational tourism, culinary tourism, wedding event locations, and so on. Data was obtained through interviews with managers, employees, and visitors to Rumah Keramik, as well as through literature studies, journals, e-books, and official websites. Data analysis using Nvivo and SWOT analysis. The research results show that tourists are motivated because of the activities they can do, namely making ceramics. The ceramic house created a branding strategy to build brand awareness by creating a direct touch through the logo and tagline as the identity of the ceramic house. There is a driving factor to increase tourist motivation, namely implementing the "Fun With Clay" tour package promotion system. However, obstacles also influence the motivation to visit the rumah keramik, namely the health crisis, infrastructure and competitors

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.

Numerical investigation of the stress regime effect on injection-induced fault reactivation and associated seismicity

The occurrence of many seismic events in energy production projects is caused by fluid injection-induced reactivation of critically stressed faults. The distribution of local stress regime has a significant influence on natural earthquakes, while its role in controlling injection-induced fault slip behavior and seismicity magnitude remains poorly understood. In this paper, we present a three-dimensional hydro-mechanical coupled fault reactivation study using unified pipe-interface element method (UP-IEM). The accuracy and reliability of the numerical method are evaluated and confirmed by numerical verification and actual observation data in Pohang EGS project, South Korea. The relationship between fault aseismic slip, seismic slip and seismicity magnitude is analyzed. Results show that both aseismic and seismic slip have different spatiotemporal distribution with the variation of stress regime. The seismic slip zone will exceed the pressurized area with the increase of initial slip tendency under the strike-slip-faulting stress regime (σH>σv>σh). Compared with fluid pressure, the seismic slip is mainly determined by a sudden increase of the shear stress at the aseismic slip zone boundary. In addition, the location of the microseismic events moves towards the fluid pressure propagation front. The larger slip area and seismicity magnitude are easier to be induced under strike-slip-faulting stress regime than thrust-faulting stress regime (σH>σh>σv). The tensile failure and higher permeability of the fault induced at injection phase for normal-faulting stress regime (σv>σH>σh) leads to a greater seismogenic hazard at the shut-in stage.

Quantitative prediction of surrounding rock deformation via an energy-based damage model combining with microseismic monitoring

In this study, combined with microseismic (MS) monitoring and numerical simulation, a novel energy-based damage model is proposed for quantitatively predicting the surrounding rock deformation for the stability analysis of underground engineering. First, the rock damage range is delineated based on MS event location and source radius. Second, seismic efficiency is introduced to calculate the actual released energy, and the total releasable energy density and total stored energy density are derived from element stress information and stress–strain curves in numerical simulation. Finally, a novel energy-based damage model with definite physical significance is developed based on the above-mentioned three forms of energy. Our new model introduces MS source parameters into the classical damage variable based on dissipated energy, accurately reflecting the qualitative relationship between them, which is highly applicable to practical engineering. Moreover, this model comprehensively reflects the damage degree variation of surrounding rocks from in-situ stress state to different excavation stages and describes real-time deterioration of rock mechanical parameters. In addition, a new approach based on seismology formulation is applied to solve seismic efficiency, without requiring reference to the destruction situation. To validate our proposed model, a three-dimensional (3D) numerical simulation embedding the damage model is conducted to investigate the surrounding rock deformation behavior of underground caverns in the Lianghekou hydropower station. A reasonable consistency is observed between the simulated displacement field and source parameters distribution obtained from MS monitoring and field failure. Furthermore, compared to the original model without a damage variable, the energy-based damage model greatly improves the deformation prediction precision.

Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial–temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial–temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial–temporal regulation of this process.

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

Transhumant Pastoralism, Climate Change, and Conflict in Africa

Abstract We consider the effects of climate change on seasonally migrant populations that herd livestock—i.e. transhumant pastoralists—in Africa. Traditionally, transhumant pastoralists benefit from a cooperative relationship with sedentary agriculturalists whereby arable land is used for crop farming in the wet season and animal grazing in the dry season. Rainfall scarcity can disrupt this arrangement by inducing pastoral groups to migrate to agricultural lands before the harvest, causing conflict to emerge. We examine this hypothesis by combining ethnographic information on the traditional locations of transhumant pastoralists and sedentary agriculturalists with high-resolution data on the location and timing of rainfall and violent conflict events in Africa from 1989 to 2018. We find that reduced rainfall in the territory of transhumant pastoralists leads to conflict in neighbouring areas. Consistent with the proposed mechanism, the conflicts are concentrated in agricultural areas; they occur during the wet season and not the dry season; and they are due to rainfall’s impact on plant biomass growth. Since pastoralists tend to be Muslim and agriculturalists Christian, this mechanism accounts for a sizable proportion of the rapid rise in religious conflict observed in recent decades. Regarding policy responses, we find that development aid projects tend not to mitigate the effects that we document. By contrast, the effects are reduced when transhumant pastoralists have greater power in national government, suggesting that more equal political representation is conducive to peace.

Nation-branding Opportunities Through Sport Mega-events: Tourism Impact from FIFA World Cup in South Africa 2010 and Qatar 2022

Sport mega-events can be powerful agents in the imaging, re-imaging and branding of places, especially for emerging mega-event host nations. South Africa was the first African country to host the FIFA World Cup in 2010 and is a good case of how nation-branding opportunities were leveraged to showcase its global engagement, re-emergence post-apartheid, and its competence as a tourism and mega-event host. Similarly, Qatar’s hosting of the 2022 FIFA World Cup is a region-first for the Middle East. Hosting the World Cup is aligned to Qatar’s strategic leveraging of sport to build a new image, acquire global recognition as a world-class venue for major events, sport sponsorships, and state-of-the-art stadiums. Yet Qatar, like South Africa, was confronted with significant detractors and negative media coverage in the lead-up to its hosting. This paper compares nation-brand perceptions collected from international visitors to the respective mega-events. The studies aimed to identify any changes to these brand perceptions because of their visit to these nations. It utilises the same instrument and method; with the survey instrument for 2010 adapted for Qatar to increase its relevance. A total of 561 (South Africa) and 421 (Qatar) international visitors were interviewed at event and tourist locations, using a spatially based, purposive sampling approach. The findings reveal how visitor perceptions changed in both instances. It reveals nuances in profiling mega-event sport tourists and contrasts expected nation-branding legacies for host nations, especially in emerging nation contexts. 

Microseismic Event Location with Dual Vertical DAS Arrays: Insights from the FORGE 2022 Stimulation

Abstract We investigate the resolvability of a microseismic event location given a recording array composed of vertical distributed acoustic sensing (DAS) boreholes. We use a modified source-scanning algorithm that takes into account both P and S waves. We transform the brightness maps it produces into probability density functions (PDFs), over which we carry out a resolution and uncertainty analysis. We apply this approach to microseismic events recorded by two vertical DAS boreholes as part of the Frontier Observatory for Research in Geothermal Energy (FORGE) project. We show that for the specific acquisition geometry in FORGE, the horizontal location of the events cannot be determined, but their depth can, similar to results obtained with a single borehole. Using synthetic examples, we show that the recording array’s geometry is the limiting factor in the determination of the horizontal location. We investigate various possible recording geometries composed of idealized DAS-like vertical boreholes with varying locations and depths. We find that, besides the number of recordingd boreholes, their depth is the main factor influencing the location estimation uncertainty. The number and position of the boreholes mainly influence the spatial distribution of the PDF, whereas the boreholes’ depth mainly influences its size. Despite the simplicity of our analysis, it highlights the influence of the monitoring array design for microseismic events’ locating using vertical DAS arrays.