Catalog Data Research Articles

Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction

Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. Taking the southern part of China’s North–South Seismic Belt (20° N~30° N, 96° E~106° E), where strong earthquakes frequently occur, as an example, we used the sliding time window method to calculate 11 seismicity indicators from the earthquake catalog data as the characteristic parameters of the training model, and compared six machine learning models, including the random forest (RF) and long short-term memory (LSTM) models, to select the best-performing LSTM model for predicting the maximum magnitude of an earthquake in the study area in the coming year. The experimental results show that the LSTM model performs exceptionally well in predicting earthquakes of magnitude 5 < ML ≤ 6 within the time window of the test set, with a prediction success rate of 85%. Additionally, the study explores how different time windows, spatial locations, and parameter choices affect model performance. It found that longer time windows and key seismicity parameters, such as the b-value and the square root of total seismic energy, are crucial for improving prediction accuracy. Finally, we propose a magnitude interval-based assessment method to better predict the actual impacts that different magnitudes may cause. This method demonstrates the LSTM model’s potential in predicting moderate to strong earthquakes and offers new approaches for earthquake early warning and disaster mitigation.

Data Cataloging as a Catalyst for Effective Data Governance and Improved Decision-Making

Data governance is a foundational component for organizations managing data as a strategic asset in today’s data- driven environments. Effective data governance ensures data quality, security, accessibility, and regulatory compliance, all of which are crucial for maintaining organizational integrity and competitive advantage. This paper explores how data cataloging, as a core tool within data governance frameworks, facilitates metadata management, data lineage tracking, and role-based access control (RBAC). We analyze the technical mechanisms through which data cataloging contributes to governance objectives, such as maintaining data quality and enabling regulatory compliance. Additionally, this study examines the impact of data cataloging on decision-making within organizations, highlighting improvements in decision accuracy, speed, and accountability. By drawing from examples in sectors such as finance and healthcare, this paper outlines the challenges associated with data cataloging implementation, including integration complexity and scalability issues. The paper concludes with future directions, focusing on artificial intelligence (AI)- enabled metadata tagging and cloud-native cataloging solutions, which promise to enhance data cataloging capabilities and make governance frameworks more agile and scalable. Keywords Data Governance, Data Cataloging, Metadata Management, Data Quality, Data Lineage, Role-Based Access Control (RBAC), Compliance, AI-Driven Cataloging, Cloud-Native Solutions, Data-Driven Decision-Making.

The first step towards FAIR-ness in Bulgarian archaeology: The Archaeological Map of Bulgaria in ARIADNE and ARIADNEplus

Summary This article provides an overview of the participation of the National Archaeological Institute with Museum at the Bulgarian Academy of Sciences (NAIM-BAS) as a partner in both the ARIADNE and ARIADNEplus projects and the SEADDA COST Action. The article examines both the workflow and the results obtained when modifying and mapping part of the national site and monuments information system, also known as the 'Archaeological Map of Bulgaria' (AIS AKB). The article's focus is mainly on the technical preparation of data shared through the ARIADNE portal. The main workflow followed throughout the projects included mapping terms to the Getty AAT thesaurus, adding Bulgarian archaeological chronologies to PeriodO, and mapping metadata to the ARIADNE catalogue data model (ACDM) in ARIADNE and to the AO-cat ontology in ARIADNEplus. In 2016, a mapping of AIS AKB to the CIDOC CRM was also undertaken, and the first steps towards creating the geographic information system, 'Archaeological Map of Bulgaria' (GIS AKB), were taken. The experience gathered by the NAIM-BAS team members throughout the whole project was important for moving towards FAIR-ness in Bulgarian archaeology.

Pf-HaploAtlas: an interactive web app for spatiotemporal analysis of P. falciparum genes.

Monitoring the genomic evolution of Plasmodium falciparum-the most widespread and deadliest of the human-infecting malaria species-is critical for making decisions in response to changes in drug resistance, diagnostic test failures, and vaccine effectiveness. The MalariaGEN data resources are the world's largest whole genome sequencing databases for Plasmodium parasites. The size and complexity of such data is a barrier to many potential end users in both public health and academic research. A user-friendly method for accessing and exploring data on the genetic variation of P. falciparum would greatly enable efforts in studying and controlling malaria. We developed Pf-HaploAtlas, a web application enabling exploratory data analysis of genomic variation without requiring advanced technical expertise. The app provides analysis-ready data catalogues and visualisations of amino acid haplotypes for all 5,102 core P. falciparum genes. Pf-HaploAtlas facilitates comprehensive spatial and temporal exploration of genes and variants of interest by using data from 16,203 samples, from 33 countries, and spread between the years 1984 and 2018. The scope of Pf-HaploAtlas will expand with each new MalariaGEN Plasmodium data release. Pf-HaploAtlas is available online for public use at https://apps.malariagen.net/pf-haploatlas, which allows users to download the underlying amino acid haplotype data for further analyses, and its source code is freely available on GitHub under the MIT licence at https://github.com/malariagen/pf-haploatlas.

Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data

Spatially and temporally discontinuous canopy height footprints collected by NASA's GEDI (Global Ecosystem Dynamics Investigation) mission are accessible on the Google Earth Engine (GEE) cloud computing platform. This study introduces an open-source, user-friendly, code-free GEE web application called Canopy Height Mapper (CH-GEE), available at https://ee-calvites1990.projects.earthengine.app/view/ch-gee, which automatically generates (10 m) high-resolution canopy height maps for a specific area by integrating GEDI with multi-source remote sensing data: Copernicus and topographical data from the GEE data catalogue. CH-GEE generates local-to-country scale calibrated canopy height maps worldwide using machine learning algorithms and leveraging the GEE platform's big data and cloud computing capabilities. CH-GEE allows customization of geographic area, algorithms and time windows for GEDI and predictors. Canopy heights generated by CH-GEE were validated using the Italian National Forest Inventory across 110,000 km2 at multiple scales (Country-based R-squared = 0.89, RMSE = 17%). CH-GEE's accuracy and scalability make it suitable for forest monitoring.

The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023

Abstract The Sunda–Banda arc transition zone features the collision of the Indo-Australian oceanic plate and the Australian continent, resulting in intricate geological and geodynamic conditions. Tectonic activity in this region is shaped by the convergence of multiple major plates, including the Indo-Australian oceanic plate and the Eurasian plate. The crustal structure along the Sunda–Banda arc transition zone is complex and influenced by various factors such as subduction, continental collision, and volcanic activity. The tectonic complexity of the region in eastern Indonesia makes it an interesting area for study. In this research, International Seismological Centre-Engdahl-van der Hilst-Buland catalogue data from 1964 to 2020 were used, which include recorded information on 69.705 earthquake events from 1.185 recording stations and consist of 2.943.974 P phases. Resolution testing was performed using various velocity grids, and optimal results were obtained with a medium resolution of ∼100 km × 100 km × 80 km for the inversion process. The tomographic inversion analysis provided valuable insights into subsurface structures within Earth’s crust and mantle up to a depth of approximately 750 km. The occurrence of deep earthquakes in the study area has provided valuable insights into complex dynamics associated with subduction and plate tectonics. The results of the tomographic inversion analysis reveal that earthquakes are concentrated in areas with high-velocity anomalies, indicating intense tectonic activity near the subduction zone. This study offers the perspective on the structural complexities and earthquake origins in the Sunda–Banda arc transition zone following the 2023 Mw 7.1 Bali Sea earthquake, which occurred on August 29, 2023, at 02:55:32 UTC + 7, approximately 163 km northeast of Lombok, Indonesia. This earthquake was caused by slab pull activity from the Australian Plate and involved a combination of downward and oblique-normal movement. These characteristics indicate the convergence and interaction between tectonic plates in the subduction process occurring in the Bali Sea area. As a result, there have been frequent occurrences of various tectonic and volcanic activities including earthquakes of different magnitudes. These results highlight the significance of the high-velocity anomaly connected to this occurrence, offering valuable insights into seismic behaviour and tectonic phenomena in the region. The findings of this study indicate that the deep earthquakes in the Bali Sea may be induced by faulting due to the transformation of metastable olivine into denser spinel at significant depths, along with shear instability caused by phase transitions within Earth’s mantle layers. This theory proposes that stress-induced changes in phase can initiate shear instabilities and subsequently lead to deep earthquakes.

Remote Sensing and GIS in Natural Resource Management: Comparing Tools and Emphasizing the Importance of In-Situ Data

Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales.

Photometric, astrometric, and kinematical characteristics of two Xuyi’s open clusters utilizing Gaia DR3 data

This study reports and examines two poorly studied open star clusters surveyed from XSTPS-GAC, namely Xuyi 15 and Xuyi 23, using the Gaia DR3 catalog data. We identified 570 (Xuyi 15) and 824 (Xuyi 23) stars as highly probable astrometric members with membership probability higher than 60%. By fitting King’s profile on radial density profiles (RDPs), we estimated both core and limiting radii and found rc about 1.95 ± 0.07 (Xuyi 15) and 4.55 ± 0.47 (Xuyi 23) while rlim are about 8.55 ± 2.92 (Xuyi 15) and 16.56 ± 4.07 (Xuyi 23) in pc units. We constructed CMDs for both clusters and calculated the fundamental parameters using the best-fitted isochrones of metallicity Z=0.03 and log age (yr) = 9.0 ± 0.05. The heliocentric distances are determined to be 7.413±0.90 kpc and 6.223±0.80 kpc for the clusters Xuyi 15 and Xuyi 23, respectively, using the isochrone fitting method. We have also obtained distances as 7.05-0.25+0.65 and 6.90-0.30+0.72 kpc for both clusters using the Bailer-Jones method. Moreover, total mass (MC;M⊙) was deduced with mass-luminosity relation (MLR) as 684 ± 26 and 1158 ± 34 and luminosity function (LF) concluded the absolute (MG; mag) of 4.05 ± 0.05 and 3.42 ± 0.04 for Xuyi 15 and Xuyi 23 clusters respectively. The overall mass function reflects the slopes (α) for Salpeter’s value (2.35) within the uncertainty (i.e., αXuyi15 = 2.52 ± 0.06 and αXuyi23 = 2.34 ± 0.05). The present study and the dynamical analysis for different evolving times demonstrate that the clusters are dynamically relaxed, where the dynamical evolution parameter (τ) is much greater than one. Finally, we computed their coherent convergent points with AD-diagram method, thus (Ao,Do) are (99o.27 ± 0o.11, 20o.91 ± 0o.22; Xuyi 15) and (123o.34 ± 0o.09, -58o.27 ± 0o.13; Xuyi 23).

Exploring Spatial and Temporal Patterns in the Debrecen Solar Faculae Database: Part II

In this study, utilizing data from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager–Debrecen Faculae Data Catalogue, we investigated solar facula distribution over timescales much larger than the individual lifetimes of faculae. We aimed to better comprehend the behavior of the Sun’s global magnetic field, where faculae play a role as markers. (1) Along the latitudinal direction, we observed the following in the appearance of faculae. (i) Short-term periods with the Carrington rotation (27.3 days) and some of its harmonics (approximately 13.5 and 9 day periods) are identified. In the northern hemisphere, there is also a faint 7 day period. (ii) We also identified longer periods. These longer periods, approximately between 150–300 and 300–500 days, exhibit a diminishing trend from the solar equator to the poles in both hemispheres. (2) Analyzing the distribution and evolution of the longitudinal coordinates of the faculae, we identified two pairs of active longitudes that are separated by 180° from each other.

Применение машинного обучения методом случайного леса и систем управления большими пространственными данными для восстановления рядов данных вегетационных индексов

This article discusses the content and results of the work devoted to the development of a machine learning model that allows for data incompleteness recovery using cloud computing. The problem is considered using the example of a study devoted to data modeling to fill in missing values of vegetation indices based on open data catalogs of cloud computing platforms. The proposed methodology is based on the use of a multi-year periodic sampling of vegetation index values and model training on large amounts of data to improve the quality of series reconstruction. The approach indicated in the work allows for higher accuracy than using classical interpolation methods for data recovery, which makes the modeled values suitable for use in solving various practical problems. The proposed method is implemented using the example of restoring the values of the Normalized Difference Vegetation Index used for monitoring and evaluating the state of vegetation cover. Arrays of values obtained from the catalogs of the Google Earth Engine cloud environment intended for processing and analyzing data from remote sensing of the Earth (on the territory of the central part of the Novgorod Region) were used as initial data. To accelerate the learning process of the model and increase efficiency and productivity, the capabilities of the Google Colaboratory platform were used, which made it possible not to use local computing capacity and do not use specialized software in the study. This approach can be adapted to reconstruct other indexes or resolve data incompleteness in various subject areas, which emphasizes its versatility and potential practical application.

ETL vs ELT: Evolving Approaches to Data Integration

This article explores the evolution of data integration approaches, focusing on the transition from Extract, Transform, Load (ETL) to Extract, Load, Transform (ELT) methodologies. It examines the characteristics, advantages, and challenges of both approaches in the context of modern data management requirements. The article discusses the factors driving the shift towards ELT, including cloud adoption, big data growth, real-time analytics demands, and the rise of data lake architectures. Additionally, it presents best practices for implementing ELT, covering areas such as data cataloging, version control, data quality checks, performance optimization, and security compliance. Through analysis of industry trends and real-world use cases, the article provides insights into why organizations are increasingly adopting ELT frameworks and the considerations involved in this transition.

Higher Precision Is Not Always Better: Search Algorithm and Consumer Engagement

On decentralized e-commerce platforms, search algorithms play a critical role in matching buyers and sellers. A typical search algorithm routinely refines and improves its catalog of data to increase search precision, but the effects of a more precise search are little known. We evaluate such effects via a 2019 quasiexperiment on a world-leading e-commerce platform in which the search algorithm refined some product categories into finer subgroups to allocate consumer queries to more relevant product listings. Our data cover millions of consumers’ search and purchase behaviors over six months across multiple search sessions and product categories, enabling us to investigate trade-offs over time and across categories. We find that a more precise search algorithm improves consumers’ click-through and purchase rates drastically and instantaneously, but it comes at the cost of a significant decrease in consumer engagement and unplanned purchases over a longer time horizon. On average, consumers who used to spend more time searching now conduct 5.5% fewer searches, spend 4.1% less time on the platform, and decrease their spending on related categories by 2.2% in the week after the search precision increases. Our examination of the mechanisms behind these consequences calls for more careful search algorithm designs that account for not only instant conversion based on search precision but also consumer engagement and sellers’ strategic responses in the longer horizon. This paper was accepted by Anindya Ghose, information systems. Funding: This work was supported by the Key Program of the National Natural Science Foundation of China [Grants 72141305 and 72192803] and the Ministry of Education, People’s Republic of China (Fundamental Research Funds for the Central Universities), both to L. Fang. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00478 .

No evidence for interstellar fireballs in the CNEOS database

Context. The detection of interstellar meteors, especially meteorite-dropping meteoroids, would be transformative, as this would enable direct sampling of material from other stellar systems on Earth. One candidate is the fireball observed by U.S. government sensors on January 8, 2014. It has been claimed that fragments of this meteoroid have been recovered from the ocean floor near Papua New Guinea and that they support an extrasolar origin. Based on its parameters reported in the Center for Near Earth Object Studies (CNEOS) catalog, the fireball exhibits a hyperbolic excess velocity that indicates an interstellar origin; however, the catalog does not report parameter uncertainties. Aims. To achieve a clear confirmation of the fireball’s interstellar origin, we assessed the underlying error distributions of the catalog data. Our aim was also to confirm whether the fragments of this meteoroid survived passage through the atmosphere and assess all conditions needed to unambiguously determine the fragments’ origin. Methods. We approached the investigation of the entire catalog using statistical analyses and modeling, and we provide a comprehensive analysis of the individual hyperbolic CNEOS cases. Results. We have developed several independent arguments indicating substantial uncertainties in the velocity and radiant position of the CNEOS events. We determined that all the hyperbolic fireballs exhibit significant deviations from the majority of the events in one of their velocity components, and we show that such mismeasurements can produce spurious parameters. According to our estimation of the speed measurement uncertainty for the catalog, we found that it is highly probable that such a catalog containing only Sun-bound meteors would show at least one event that appears highly unlikely to be Sun-bound. We also establish that it is unlikely that any fragments from a fireball traveling at the high inferred velocities could survive passage through the atmosphere. When assuming a much lower velocity, some fragments of this meteoroid could survive; however, they would be of a common Solar System origin and thus highly probable to be indistinguishable from the quantity of other local micrometeorites that have gradually accumulated on the sea floor. Conclusions. We conclude that there is no evidence in the CNEOS data to confirm or reject the interstellar origin of any of the nominally hyperbolic fireballs in the CNEOS catalog. Therefore, the claim of an interstellar origin for the fireball recorded over Papua New Guinea in 2014 remains unsubstantiated. We have also gathered arguments that refute the claim that the collected spherules from the sea floor originated in the body of this fireball.

Grid-search method for short-term over long-term average parameter tuning: an application to Stromboli explosion quakes

The collection of a significant catalogue of seismo-volcanic data involves the selection of relevant parts of raw signals, which can be automatised by using the short-term over long-term average (STA/LTA) method. The STA/LTA method employs the “Characteristic Function” to describe a section of a seismic record in terms of trace amplitude and first-time difference. This function is calculated in a short-term and long-term window; the ratio between the two windows defines a quantity that is controlled through threshold values, i.e., trigger on and trigger off. These threshold values indicate whether there is an increase in the energy in the seismic signal compared to the background noise. The common approach to the selection of the STA/LTA values is the adoption of literature-suggested ones. This could be a limitation as there may be cases in which a choice adapted to a specific raw signal may significantly help in the extraction of the relevant parts. To overcome the possible drawbacks of a non-adaptive choice imposed by such standard literature values, in this study, we propose a methodology for the automatic selection of STA/LTA values that can optimise the extraction of explosion quakes (EQs) from a seismo-volcanic raw signal. The values are obtained through a grid search over an index named quality–numerosity index (QNI) that measures the accordance in the automatic cuts and the consequent number of triggered seismo-volcanic events with the ones suggested by a human expert. The method was applied in the volcano domain for the specific application of the explosion quake signal extraction at Stromboli volcano. The experiments were conducted by selecting a subset of the dataset as training where to search for the best values, which were subsequently adopted in a test set. The results prove that the values suggested by our approach significantly improve the quality of the relevant part compared to the one extracted by adopting the values indicated in the literature. The methodology presented in this study can be applied to a wider typology of signals of volcanic, seismic, and other origin, potentially becoming a widely used approach in parameter optimisation processes.

A novel classification framework for genome-wide association study of whole brain MRI images using deep learning.

Genome-wide association studies (GWASs) have been widely applied in the neuroimaging field to discover genetic variants associated with brain-related traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on univariate quantitative features summarized from brain images. On the other hand, powerful deep learning technologies have dramatically improved our ability to classify images. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying genetic variants that lead to detectable nuances on Magnetic Resonance Images (MRI). For a specific single nucleotide polymorphism (SNP), if MRI images labeled by genotypes of this SNP can be reliably distinguished using machine learning, we then hypothesized that this SNP is likely to be associated with brain anatomy or function which is manifested in MRI brain images. We applied this strategy to a catalog of MRI image and genotype data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI) consortium. From the results, we identified novel variants that show strong association to brain phenotypes.

Algorithmic Identification of the Precursory Scale Increase Phenomenon in Earthquake Catalogs

Abstract The precursory scale increase (Ψ) phenomenon describes the sudden increase in rate and magnitude in a precursory area AP, at precursor time TP, and with precursor magnitude MP prior to the upcoming large earthquake with magnitude Mm. Scaling relations between the Ψ variables form the basis of the “Every Earthquake a Precursor According to Scale” (EEPAS) earthquake forecasting model. EEPAS is a well-established space–time point process model that forecasts large earthquakes in the medium term, that is, the coming months to decades, depending on Mm. In Aotearoa New Zealand, EEPAS contributes to hybrid models for public earthquake forecasting and to the source model of time-varying seismic hazard models, including the latest revision of the National Seismic Hazard Model. The Ψ phenomenon was recently shown not to be unique for a given earthquake, with smaller precursory areas AP associated with larger precursor times TP and vice versa. This trade-off between AP and TP has also been found for the spatial and temporal distributions of the EEPAS models. Detailed analysis of the Ψ phenomenon has so far been limited by the manual and labor-intensive procedure of identifying Ψ in earthquake catalogs. Here, we introduce two algorithms to automatically detect Ψ and apply them to real and simulated earthquake catalog data. By randomizing the catalog and removing aftershocks, we confirm that the Ψ phenomenon is a feature of space–time earthquake clustering prior to major earthquakes. Multiple Ψ identifications confirm the trade-off between AP and TP, and the scaling relations for both real and simulated catalogs are consistent with the original scaling relations on which EEPAS is based. We identify opportunities for future work to refine the algorithms and apply them to physics-based simulated catalogs to enhance the understanding of Ψ. A better understanding of Ψ has the potential to improve forecasting of large upcoming earthquakes.

Monthly Maximum Magnitude Prediction in the North–South Seismic Belt of China Based on Deep Learning

The North–South Seismic Belt is one of the major regions in China where strong earthquakes frequently occur. Predicting the monthly maximum magnitude is of significant importance for proactive seismic hazard defense. This paper uses seismic catalog data from the North–South Seismic Belt since 1970 to calculate and extract multiple seismic parameters. The monthly maximum magnitude is processed using Variational Mode Decomposition (VMD) with sample segmentation to avoid information leakage. The decomposed multiple modal data and seismic parameters together form a new dataset. Based on these datasets, this paper employs four deep learning models and four time windows to predict the monthly maximum magnitude, using prediction accuracy (PA), False Alarm Rate (FAR), and Missed Alarm Rate (MR) as evaluation metrics. It is found that a time window of 12 generally yields better prediction results, with the PA for Ms 5.0–6.0 earthquakes reaching 77.27% and for earthquakes above Ms 6.0 reaching 12.5%. Compared to data not decomposed using VMD, traditional error metrics show only a slight improvement, but the model can better predict short-term trends in magnitude changes.

3-D P-wave velocity structure of the upper mantle beneath eastern Indonesia from body wave tomography

Eastern Indonesia's tectonic setting is well known for its complexity and intense seismic activity. Controlled by several major and minor plates, including the Eurasian, Australian, and Pacific plates, this region is famous for its U-shaped subduction system beneath the Banda Arc. To better understand the architecture of the underlying structure in this region, we performed body-wave travel time tomography using ten years of catalog data provided by the Indonesian Agency for Meteorology, Climatology, and Geophysics. We utilize 9729 events in total, from which 46,446 P-wave arrival times were extracted. We used a double difference method to relocate the initial event catalog, which produced a pattern of seismicity consistent with a curved subduction system. Our tomographic model reveals a high velocity band between 90 and 240 km depth in the upper mantle, which is interpreted to be a concave dipping lithospheric slab that is parallel to the present-day Banda arc. Our results also show that lithosphere subducting from the north and south starts to collide at a depth of 300–350 km and becomes shallower further east. Apparent discontinuities in the high velocity band and a corresponding lack of seismicity supports the presence of a slab tear to the west of Seram. A dipping high velocity structure that is present from south to north beneath the island of Timor represents a subducting slab that dips more steeply beyond a depth of 150–200 km, which appears consistent with slab roll-back. Our tomographic model also shows evidence of back arc thrusting to the north of Sumbawa and Flores Islands in the form of a south-dipping higher velocity band at shallow depth. Furthermore, our tomographic models also reveal the possible presence of underthrust continental forearc in the form of a thin higher velocity anomaly that connects the backarc thrust and northward dipping lithosphere slab in the Timor area. Finally, a zone of low velocity above the higher velocity slab is clearly seen beneath Seram Island at a depth of ∼100 km and may represent a partial melting zone.

Differential Rotation Rates of Recurrent Sunspot Groups Lasting Two or Three Passages in the Debrecen Photoheliographic Data Catalogue

Differential Rotation Rates of Recurrent Sunspot Groups Lasting Two or Three Passages in the Debrecen Photoheliographic Data Catalogue

РАЗРАБОТКА ПРОГРАММЫ РАСЧЕТА ПАРАМЕТРОВ СХЕМЫ ЗАМЕЩЕНИЯ ТРЕХФАЗНОГО АСИНХРОННОГО ЭЛЕКТРОДВИГАТЕЛЯ

The article is devoted to developing a program for calculating the parameters of a T-shaped equivalent circuit of three-phase asynchronous electric motors. The relevance of this work is due to the need to obtain reliable results of computer simulation modelling of electric drive systems in software packages. The aim of the study is to implement in a convenient software interface an analytical technique for calculating the parameters of an asynchronous motor equivalent circuit with minimal deviations from the catalogue data. For the software calculation of the equivalent circuit parameters the paper considers analytical approximation and iterative counting techniques, since they do not require experiments on industrial electrical equipment. A comparative analysis of such techniques is performed using the example of a three-phase asynchronous motor brand BA280S4 with a power of 110 kW. The results of the compar-ative analysis show that the highest reliability of the mechanical and electromechanical characteristics is achieved in the case of using the iterative counting technique with minimizing the objective function of the sum of the weighted squares of the deviations of the control point values from the catalogue data. The authors implement the program for calculating the equivalent circuit parameters according to the selected calculation method in the Embarcadero Delphi environment. The reliable values of the electric motor parameters calculated by the program will allow for computer to model electric drive systems that best matches real objects.