Natural Events Research Articles

Analysing the use of OpenAerialMap images for OpenStreetMap edits

ABSTRACT OpenAerialMap (OAM) is a crowdsourcing platform for uploading, hosting, sharing, displaying, searching, and downloading openly licensed Earth image data from around the world. OAM was launched with the primary goal to facilitate rapid disaster response mapping after natural events, such as floods or hurricanes. Images contributed to OAM can be used as a background layer in OpenStreetMap (OSM) editors to edit map features, such as buildings or facilities, which may have been affected by such events. This study analyzes how the provision of OAM images is associated with changes in underlying editing patterns of OSM features by comparing the number of edited OSM objects between 2 weeks before and 2 weeks after an image has been shared through OAM. The comparison also involves other aspects of OSM editing patterns, including geometry types and OSM primary feature types of edited objects, type of feature editing operations, edits per user, and contribution distribution across continents. Results show that the number of point features added to OSM more than quadrupled within the first 2 weeks after OAM image upload compared to that of 2 weeks before, and that the number of ways added almost doubled. This suggests that the OSM community utilizes provided OAM images for OSM map updates in the respective geographic areas. The study provides a showcase which demonstrates how information from one crowdsourcing platform (OAM) can be used to enhance the data quality of another (OSM) with regards to completeness and timeliness.

Stones that Tell Stories

When a historical building, monument, or cultural heritage artifact is affected by a natural or anthropic event, the first reaction is to recover and restore to maintain our heritage for the next generations. If they are made of stone, some are completely destroyed, but it is often still possible to recognize the facts that made them a cultural item. In many cases, the marks of violent actions are visible on the stone, especially if the impacts came from a non-massive destructive weapon, such as rifle bullet marks or cannon marks from centuries ago, with a lower level of destruction than today. These marks are also part of our heritage and, as a reminder of the history of our cities and countries for generations, it is recommended that they be preserved as a testimony to avoid the repetition of unnecessary violence. This is the aim of the stones that tell stories.

Leveraging domain-adapted transformer based models for disaster-specific tweet classification

Social media platforms play vital roles in disseminating information during crisis situations. Many rescue agencies, media outlets, and volunteers regularly monitor this data to identify and analyze disasters, ultimately mitigating life risks. However, effectively categorizing these messages based on information types is crucial for enhancing the situational awareness of emergency responders. This paper addresses the challenge of analyzing informal crisis-related social media texts by classifying disaster event tweets into 10 humanitarian categories associated with 19 major natural disaster events. We fine-tune seven state-of-the-art pre-trained transformer models and compare their performance with the recently introduced domain-specific models, i.e., CrisisTransformers. We empirically found that CrisisTransformers outperform seven strong baseline transformer models in classifying disaster-specific tweets from the HumAID dataset, achieving a macro-averaged F1 score of 0.77. Our work contributes to the crisis computing field by improving the classification of disaster-related tweets and enhancing the capabilities of emergency responders and disaster management organizations.

The Mind Travels on the Waves of Fantasy to Pursue Universal Evolution

A positive aspect of quantum mechanics has been that we have become familiar with wave sizes. Declined in the various forms that these can assume, we offer a probabilistic approach in the study of phenomena that occur in nature. Whether it is sound waves, electromagnetic waves or gravitational waves, whether they collapse when we measure phenomena, they present us with a completely different view than we have so far conceived of natural events. The maximum systems that have accompanied us to this day, and that have represented the foundation of our knowledge, have failed in favor of a philosophy that projects us into an evanescent abstract dimension, and not usable with the common senses of which we are endowed. In this work we present a synthesis of wave concepts, adopting them to a possible theoretical interpretation of how even our feeling and our knowledge can travel on the special waves of fantasy, and at the same time offer a personal reinterpretation of natural phenomena in harmony with the wave-like conception.

Optical properties and molecular compositions of dissolved organic matter in multiple runoff components during rainfalls on the karst hillslope

Understanding the chemical composition, origin, and molecular structure of dissolved organic matter (DOM) in multi-interface runoff is essential for comprehending the fate of laterally transported DOM in complex soil-epikarst systems of karst hillslopes. Limited information, however, is available for the optical properties and molecular compositions of the transported OM in multiple runoff components on the karst hillslope in relation to land-uses and soil thicknesses. In this study, we conducted a study to observe the changes in the quantity and quality of DOM in multiple interface flow (surface, subsurface, and epikarst) during natural rainfall events in 2022 in karst hillslopes that are covered by different land uses (cropland and shrubland) and soil thicknesses (with mean depths of 66.0 cm for deeper soil and 35.4 cm for shallower soil) in the karst region of southwest China. chemcial compositions of runoff DOM were determined by optical analysis and microbial compositions in runoff were inferred with high-throughput sequencing. The results showed that the soil-epikarst structure was controlling the runoff DOM quantity and quality during rainfall events. A decrease in the aromaticity, humification, unsaturation, and oxidation degree and an increase in carbohydrate, aminosugars, protein, and lipid compounds were found from surface to epikarst flow, indicating that plant-and soil-derived carbon decreased, while the microbially-derived carbon increased. The results were further comfirmed by the higher bacterial richness and diversity, along with fungal diversity in the epikarst flow compared to other runoff components. The bio-labile protein materials (C2) were the most important component of runoff DOM output in karst hillslopes. In surface and subsurface flow, rainfall amount, runoff rate, and discharge significantly affected the DOM concentration and quality during rainfalls, indicating that the dynamics of DOM in runoff from karst hillslopes were predominantly influenced by hydrological processes. Furthermore, the runoff DOM quality in cropland was dominated by lower unsaturation and oxidation degrees and higher protein component, compared to those in shrubland. The compositions of DOM in runoff from hillslope plots with thicker soils were primarily characterized by microbially-derived materials. Our findings were conducive to understanding the mechanism governing the migration of DOM quality and quantity in discharge during multi-interface hydrological processes on karst hillslopes.

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

Circumstances to be ascertained during the investigation of high profile sexual criminal offenses

In the article has been emphasized that the circumstances to be ascertained are a mandatory structural element of the methodology of investigating high-profile sexual criminal offenses, because their establishment, in turn, is a prerequisite for achieving the goal of criminal procedural knowledge and solving the tasks of criminal proceedings. It has been given the list of circumstances to be ascertained during the pre-trial investigation of such types of high-profile sexual criminal offenses as: rape and sexual violence committed by a group of persons and/or parents, persons who replace them, and/or against minors; molestation of a child for sexual purposes; conducting a spectacular event of a sexual nature with the participation of a minor. It has been found that different approaches to the systematization of the circumstances to be ascertained during the pre-trial investigation have been formed in forensic science. They are mainly determined by the specifics of the mechanism of committing a specific type of criminal offense, which undoubtedly affects the formation of investigative and other evidentiary information. An analysis of scientific approaches to determining the range of circumstances to be ascertained (established) during the investigation of sexual criminal offenses has been carried out. It has been proposed to systematize the circumstances to be ascertained during the pre-trial investigation of high-profile sexual criminal offenses taking into account the mixed (criminal procedural and forensic) criterion. The following groups of circumstances to be ascertained during the pre-trial investigation of high-profile sexual criminal offenses have been singled out and characterized: circumstances characterizing the event of a criminal offence; circumstances characterizing the person of the offender; circumstances characterizing the person of the victim; other circumstances.

An economic evaluation of eptinezumab for the preventive treatment of migraine in the UK, with consideration for natural history and work productivity

BackgroundMigraine is a highly prevalent neurological disease with a substantial societal burden due to lost productivity. From a societal perspective, we assessed the cost-effectiveness of eptinezumab for the preventive treatment of migraine.MethodsAn individual patient simulation of discrete competing events was developed to evaluate eptinezumab cost-effectiveness compared to best supportive care for adults in the United Kingdom with ≥ 4 migraine days per month and prior failure of ≥ 3 preventive migraine treatments. Individuals with sampled baseline characteristics were created to represent this population, which comprised dedicated episodic and chronic migraine subpopulations. Clinical efficacy, utility, and work productivity inputs were based on results from the DELIVER randomised controlled trial (NCT04418765). Timing of natural history events and treatment holidays—informed by the literature—were simulated to unmask any natural improvement of the disease unrelated to treatment. The primary outcomes were monthly migraine days, migraine-associated costs, quality-adjusted life years (QALYs), incremental cost-effectiveness ratio, and net monetary benefit, each evaluated over a 5-year time horizon from 2020. Secondary analyses explored a lifetime horizon and an alternative treatment stopping rule.ResultsTreatment with eptinezumab resulted in an average of 0.231 QALYs gained at a saving of £4,894 over 5 years, making eptinezumab dominant over best supportive care (i.e., better health outcomes and less costly). This result was confirmed by the probabilistic analysis and all alternative assumption scenarios under the same societal perspective. Univariate testing of inputs showed net monetary benefit was most sensitive to the number of days of productivity loss, and monthly salary.ConclusionsThis economic evaluation shows that from a societal perspective, eptinezumab is a cost-effective treatment in patients with ≥ 4 migraine days per month and for whom ≥ 3 other preventive migraine treatments have failed.Trial registrationN/A.Graphical

The Interplay of Climate and Disaster in Men's Stories of the 2016 Kaikōura Earthquake in Aotearoa New Zealand1

This paper contributes to the emerging field of men, masculinities, and disasters by drawing on narratives of men's accounts of the 2016 Kaikōura earthquake, including how stories of the earthquake intersect with experiences and understandings of extreme weather and climate change. A qualitative methodology was employed, and semi‐structured interviews were conducted with 19 men who experienced the 7.8 magnitude earthquake. This article offers an examination of the complexity of disaster experiences and recovery, as well as how people make sense of hazards and risks. We argue that ongoing exposure to climate hazards informed participant's responses to other infrequent natural hazard events, such as the Kaikōura earthquake. The research identified that men construct their own understandings and responses to natural hazards through a hierarchy of risk perception and probability based on personal experience.

Seismic Discrimination Between Nuclear Explosions and Natural Earthquakes using Multi-Machine Learning Techniques

AbstractIn the field of seismic signal analysis, it is of utmost importance to accurately differentiate between earthquakes and underground nuclear explosions. As a contribution for the verification regime of the Comprehensive Nuclear Test Ban Treaty (CTBT), Various methods have been employed for this purpose, including Complexity, Spectral ratio, mb—Ms (body wave and surface wave magnitudes), and corner frequency of P and S waves. These discrimination techniques have been examined to manually identify natural seismic events from nuclear explosions across different regions worldwide, such as China, India, Pakistan, North Korea, and the United States. To gather the necessary data, a comprehensive dataset comprising nuclear explosions and earthquakes of the same magnitude range (4 ≤ mb ≤ 6.5) of 35 seismic events from 1945 to 2017 has been compiled from the International Research Institute for Seismology (IRIS) using broadband and long period seismic stations. The objective of this study is to employ a range of linear and nonlinear Machine Learning (ML) models with the aim of automatically distinguishing between underground nuclear explosions and large earthquakes to enhance the accuracy of manual feature extraction. For this purpose, time domain waveforms and different classifier techniques focused on feature extraction have been used. The ML models employed include logistic regression, K-nearest neighbours classifier, decision tree classifier, random forest classifier, voting classifier, and Naive Bayes. The outcomes of the ROC and AUC analyses were employed to validate the validity of our proposed discrimination algorithm. The results show that the Random Forest Classifier is the most effective model, obtaining 100% accuracy in the case of feature extraction, while the best model for the time domain waveform classifier that achieved 75.5% accuracy is the voting classifier.

Estimating the natural disaster ınter-event time defition (NIETD) to define compound natural disasters in South Korea

Previous study on natural disaster events has been conducted under the assumption that each event is independent of others, which underestimates the risk of natural disasters by ignoring interactions between events. To solve this problem, the concept of compound natural disaster (CND) which is the combination of events has been proposed; however, there is no quantitative method for defining CND. The aims of this study are to examine the estimation methods for IETD (Inter-Event Time Definition), which separates continuous rainfalls into independent rainfall events, and to define NIETD (Natural disaster Inter-Event Time Definition), which is a criterion for determining the independence of natural disasters. This study used the method of average annual number of events for estimating NIETD. Two natural disasters can be defined as CND if the duration between them is less than the NIETD. We estimated the NIETD as 8 days using natural disasters that occurred in South Korea and identified a total of 89 CNDs of 14 different types such as consecutive rainfall events. The largest number of CNDs was caused by the combination of rainfall and typhoon, which also resulted in the most damage. To examine the randomness of event occurrences, we applied a bootstrapping approach and found that there is no evidence of randomness. The frequency analysis showed that CNDs consisting of rainfall and typhoon (7.6years), and consecutive rainfalls (9.4years) had overwhelmingly more frequent occurrences than other types. The CND definition and concept proposed in this study could be useful in the research on CND.

Education of Science for Preschool Children through Experiential Approach

Science education for preschool children through experiential approach is a highly feasible and promising topic in promoting the holistic development of children from their early years. Therefore, educating science through experience is an effective method to encourage curiosity and develop skills of preschool children. Preschoolers enjoy exploring and learning through experience. Experiential approach helps children understand natural phenomena around them in a natural and interesting way. This promotes the development of logical thinking, curiosity, and observation skills from an early age. The experiential approach allows children to engage with the real world, from observing natural events to practicing simple experiments. Research has established six steps for implementing effective science education for preschool children.

Short-term changes in mental health help-seeking behaviors following exposure to multiple social stressors and a natural disaster

In 2020, unprecedented circumstances led to significant mental health consequences. Individuals faced mental health stressors that extended beyond the devastating impact of the COVID-19 pandemic, including widespread social unrest following the murder of George Floyd, an intense hurricane season in the Atlantic, and the politically divisive 2020 election. The objective of this analysis was to consider changes in help-seeking behavior following exposure to multiple social stressors and a natural disaster. Data from Crisis Text Line (CTL), a national text-based mental health crisis counseling service, was used to determine how help-seeking behavior changed in the wake of each event. Wilcoxon rank sum tests assessed changes in help-seeking behavior for each event in 2020 as compared to the same period in 2019. AutoRegressive Integrated Moving Average (ARIMA) models examined if changes in crisis conversation volumes following each event differed. Higher median conversation volumes noted for the COVID-19 pandemic (+1 to +5 conversations), Hurricane Laura (+1 to +7 conversations) and the 2020 Election (+1 to +26 conversations). ARIMA models show substantial increases in help-seeking behavior following the declaration of a national emergency for the COVID-19 pandemic (+4.3 to +38.2%) and following the 2020 election (+3 to +24.44%). Our analysis found that the mental health response following social stressors may be distinct from natural events, especially when natural disasters occur in the context of multiple social stressors. This analysis adds to the growing body of literature considering the mental health impact of exposure to multiple co-occurring societal stressors, like police violence and a global pandemic.

Multi-rate modulation encoding via unsupervised learning for audio event detection

Technologies in healthcare, smart homes, security, ecology, and entertainment all deploy audio event detection (AED) in order to detect sound events in an audio recording. Effective AED techniques rely heavily on supervised or semi-supervised models to capture the wide range of dynamics spanned by sound events in order to achieve temporally precise boundaries and accurate event classification. These methods require extensive collections of labeled or weakly labeled in-domain data, which is costly and labor-intensive. Importantly, these approaches do not fully leverage the inherent variability and range of dynamics across sound events, aspects that can be effectively identified through unsupervised methods. The present work proposes an approach based on multi-rate autoencoders that are pretrained in an unsupervised way to leverage unlabeled audio data and ultimately learn the rich temporal dynamics inherent in natural sound events. This approach utilizes parallel autoencoders that achieve decompositions of the modulation spectrum along different bands. In addition, we introduce a rate-selective temporal contrastive loss to align the training objective with event detection metrics. Optimizing the configuration of multi-rate encoders and the temporal contrastive loss leads to notable improvements in domestic sound event detection in the context of the DCASE challenge.

Enhancing Freeze Tolerance of Field-grown Lettuce with Salicylic Acid, Ascorbic Acid, and Calcium Chloride

With the climate continuing to change, specialty crop growers are particularly at risk for economic loss caused by erratic weather conditions, excess heat and rain, and frost damage. Although tolerant to cold, lettuce (Lactuca sativa) is a major horticultural crop at risk for exposure to freezing temperatures in late fall or early spring in the Midwest. Exogenous applications of salicylic acid, ascorbic acid, and calcium chloride have been shown to improve abiotic stress in plants, particularly with freezing tolerance. This research investigated the effects of the exogenous application of salicylic acid, ascorbic acid, and calcium chloride, in varying concentrations, on field-grown lettuce. The study was conducted at the Iowa State University Horticulture Research Station, Ames, IA, USA. Weekly applications of salicylic acid (0.5 and 1.0 mM), ascorbic acid (0.5 and 1.0 mM), and calcium chloride (10 and 20 mM) were applied until plants were a marketable size. A control treatment (no spray) was also included. Data regarding plant size, yield, leaf area, leaf number, plant dry weight, plant nutrient analysis, and freeze tolerance were collected. Freeze tolerance assessments were conducted through laboratory-simulated freeze events and the evaluation of natural in-field freeze events. Freeze injury was quantified through the electrolyte leakage method. Marketable weights in 2020 and 2021 were statistically similar, except for 1.0 mM salicylic acid in 2020, which showed a significantly lower marketable weight and head diameter. Trends during both years showed that stress protectant applications had the most effective freeze protection at −12 °C in laboratory-simulated freeze events. Calcium chloride at 20 mM had the highest protection at −12 °C, with 13.3% and 24.0% less injury compared with the control in 2020 and 2021, respectively. Salicylic acid 1.0 mM at −12 °C had 4.7% and 23.7% less injury compared with control during these two years, respectively. Ascorbic acid 1.0 mM at −12 °C also showed 6.8% and 9.5% less injury than the control in 2020 and 2021, respectively. Similar trends were observed after in-field freezing events, with 20 mM calcium chloride providing the highest protection against frost in 2020 and 2021. These findings advance the understanding of the capabilities of stress protectants on lettuce as chemical primers after freezing events. This research highlights the benefits of ascorbic acid, salicylic acid, and calcium chloride as stress protectants and encourages future research and further exploration of concentrations, methods of application, and timing of application of products.

Improving Natural Science Learning Outcomes on Natural Events Material Through Cooperative Learning Models of Teams Games Tournament Type for Class V Elementary School Students

Purpose of the study: This research is an effort to improve the learning outcomes of class V students in Natural Sciences subjects with a teams games tournament type cooperative learning model.
 Methodology: This research is classroom action research. The subjects of this research were 21 class V students, consisting of 8 boys and 13 girls. This research was carried out in 3 cycles, each cycle consisting of four stages, namely planning, implementation, observation and reflection. The data collection methods used were interviews, observation, documentation and tests. Data was analyzed statistically using a percentage formula.
 Main Findings: The research results show that the Teams Games Tournament type cooperative learning model can improve learning outcomes on natural events material for class V students. Improvement from pre-cycle to cycle III. The increase from pre-cycle to cycle I was 19%, from cycle I to cycle II was 24%, from cycle II to cycle III was 23%. The number of students who completed the pre-cycle was 5 students or 24% with an average score of 47.61. The number of students who completed Cycle I was 9 students or 43% with an average score of 55.71. In cycle II there were 14 students or 67% of students completed the study with an average score of 68.09, and in cycle III there were 19 students or 90% students completed the study with an average score of 74.28.
 Novelty/Originality of this study: This research contributes to the development of education and can provide new information on how to overcome problems that arise in the teaching and learning process. Especially in science subjects, especially to improve student learning outcomes in class V natural events material in science subjects.

A Cross-Decadal Change in the Fish and Crustacean Community of Lower Yaquina Bay, Oregon, USA

Natural environmental change, anthropogenic development, and inter-annual variability can affect the ecology of estuarine fish and invertebrates. Yaquina Bay, Oregon, a well-studied estuary, has undergone intense development, as well as deep-draft dredging during the latter half of the 20th century, resulting in the alteration of ~45% of the lower estuary’s natural shoreline. In 1967, the United States Environmental Protection Agency (USEPA) conducted a 21-month survey of Yaquina Bay to characterize the demersal fishes and epibenthic crustaceans that occupy the bay. From 2003 to 2005, we conducted a 25-month survey to replicate that work and provide a comparative snapshot of the demersal fish and epibenthic crustacean community in the bay. A comparison of the trawl survey datasets reveals a 91% decline in total catch per unit effort (CPUE) between surveys, as well as a decline in multiple measures of biodiversity. Furthermore, the fishes and crustaceans of Yaquina Bay have experienced a shift in species dominance from demersal fishes in the late 1960s to epibenthic crustaceans in the 2000s, marked most notably by a nine-fold increase in the Dungeness crab CPUE. While this work does not establish a causal relationship between changes in the demersal communities of this West Coast estuary and human or natural events, it does document substantial changes in both the diversity and total abundance of animals in that community over a three-plus decade period of development and environmental variability. Hence, this forms a second baseline for continued long-term monitoring.

Natural Emergencies and Some Causes of Their Occurrence: a Review

In recent decades, nature has seen many anomalies with the environment, climate, and the processes taking place. This leads to a change in the temperature of the environment, the formation of meteorological processes that are not characteristic of a certain part of the planet and the triggering of emergency situations of a natural nature. The study seeks to fill the gap regarding the trends in the influence of man-made activities on the occurrence of natural disasters, their scale of distribution and consequences. The current study methodology is a combination of a literature review and bibliometric analysis to obtain the relevant impact of man-made activities to the extent of occurrence and impact of natural disasters. This report examines the main extraordinary processes that are observed around us and cause damage and departure from the normal way of life of people and the rest of the living world. It was found that many researchers indirectly confirm that anthropogenic activities can provoke negative natural events, or contribute to a significant increase in the scale of a dangerous natural phenomenon. Some of the reasons that cause them, usually independent of human activity or inaction, is also indicated. Several research works have noted that climate change over the past few decades and global warming are contributing to an increase in the frequency of natural disasters such as earthquakes, floods, tsunamis, landslides, etc. But we need to change our behaviour to minimize causing such phenomena.

A Deep Parallel Hybrid Fusion Model for disaster tweet classification on Twitter data

Disaster tweet classification has gained significant attention in natural language processing (NLP) due to its potential to aid disaster response and emergency management. The goal of disaster tweet classification is to automate the identification of informative tweets containing information related to various types of disasters, such as floods, earthquakes, wildfires, and more. This classification task plays a crucial role in real-time monitoring, situational awareness, and timely response coordination during emergency situations. In this context, we propose a Deep Parallel Hybrid Fusion Model (DPHFM) that combines features from Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) as base learners. The features extracted from these base learners are combined using a fusion mechanism and then reconstructed for input to a meta-learner for making predictions. The DPHFM is trained on disaster datasets, such as crisisMMD, which consists of seven natural disaster events. The model underwent thorough evaluation using various metrics, demonstrating an average performance improvement of 90% to 96%. Furthermore, the proposed model’s performance surpassed that of other state-of-the-art models, showcasing its potential for disaster tweet classification using deep learning techniques.

Using Moment Tensor Inversions for Rapid Seismic Source Detection and Characterization: Application to the North Korean Nuclear Tests

AbstractThe rapid detection and source characterization of any type of seismic events including earthquakes and nuclear explosions is one of the missions of many seismological laboratories. Most often, the techniques used are based on phase picking and amplitude measurements for detecting and locating events, and for estimating magnitudes. From these parameters, especially for anthropogenic sources, event screening is then done empirically, and this may lead to misinterpretations of the source nature. However, it is known that seismic waveform inversion for the determination of the moment tensor has proven to be a reliable source physics-based method for event characterization. Here, we present a technique already used for earthquake monitoring in tectonically active regions, and we test it on very shallow natural and anthropogenic events recorded in the vicinity of the Democratic People’s Republic of Korea (DPRK). From a grid of potential locations and by scanning continuous seismic waveforms, it is possible to implement a rapid detector of seismic events providing the full information of the sources (origin time, location, magnitude, mechanism, and source decomposition). We show its overall performances on all past DPRK nuclear tests and regional earthquakes. From such an approach fast complete event screening is achieved. Source uncertainties can also be estimated. This stand-alone detector and identifier of seismic events may help monitoring seismological agencies to provide a rapid and complete alert for any events within a region of interest before more in-depth discrimination analysis can be run.