scholarly journals Crustal field recovery and secular variation from regional and global models over Albania

2018 ◽  
Vol 61 (1) ◽  
Author(s):  
Klaudio Peqini ◽  
Beko Duka ◽  
Guido Dominici
Keyword(s):  
Secular Variation ◽  
Global Models ◽  
Crustal Field

scholarly journals Crustal geomagnetic field and secular variation by regional and global models for Austria

2018 ◽  
Vol 111 (1) ◽  
pp. 48-63
Author(s):  
Klaudio Peqini ◽  
Bejo Duka ◽  
Ramon Egli ◽  
Barbara Leichter
Keyword(s):  
Secular Variation ◽  
Geomagnetic Field ◽  
Rate Of Change ◽  
Aeromagnetic Data ◽  
Model Bias ◽  
Global Models ◽  
Normal Field ◽  
Core Field ◽  
Crustal Field ◽  
Predicted Values

Abstract Using 12-year-long series of data (2001-2012) from geomagnetic observatories and repeat stations in Austria and its neighboring countries, a regional spatial-temporal (ST) model is developed based on the polynomial expansion consisting of latitude, longitude, and time of the geomagnetic field components and total magnetic field F. Additionally, we have used three different global models (CHAOS-5, POMME-9, and EMM2015), which are built on spherical harmonics up to a maximum degree Lmax and give the core field and crustal field separately. The normal field provided by the ST model and its “model bias”, which comprise the residuals of the differences between measured and predicted values, are calculated and the respective maps are shown. The residuals are considered an estimate of the local crustal field. In the case of global models, we have applied for each of these three methods to calculate the “model bias”: residuals of the differences between observed values and predicted values of the model, residuals of the differences between observed values and core field values of the model, and the average bias for the period 2001-2012. The normal field of the region of Austria provided by each global model is also calculated. Generally, the regional and global models yield relatively similar crustal fields for the Austrian region, especially when the first method is used. The normal fields calculated by them are in good agreement with each other. Each of the global models directly provides the crustal field, and they are compared with the aeromagnetic data provided by aeromagnetic surveys over the Austrian region. The ST model is in better agreement with aeromagnetic data. We have also analyzed the secular variation over the region, which is calculated from the rate of change of normal field given by the ST and global models.

Exploring the Futures of the Ummah

1998 ◽  
Vol 15 (1) ◽  
pp. 1-30
Author(s):  
Sohail Lnayatullah
Keyword(s):  
Turning Point ◽  
Ways Of Knowing ◽  
Global Models ◽  
Limits To Growth ◽  
Islamic World ◽  
Poststructural Theory ◽  
The Future

This article is both a critique of ways of approaching the future and a presentation of scenarios of the Islamic world a generation ahead. The critique covers various global models, including The Club of Rome's classic Limits to Growth (L TG), 1 Mankind at the Turning Point (MTP), and World 2000, and other approaches to the understanding of the future. Drawing from poststructural theory, we ask: What is missing, who does the analysis privilege, and what epistemological frames or ways of knowing are accentuated, are made primary, by the models used? What can the Islamic world learn from these models? We attempt to go a step further than merely asking the Marxist class question of who benefits financially. For us, the issue is deeper. We are concerned with what knowledge frames and (more appropriately, from an Islamic per­spective) what civilizational frames are privileged, are considered more important. An appendix presents recommendations focused on making the Islamic urrunah more future oriented. However, global models are only one way of approaching or under­standing the future. There are other ways of approaching the study of the future from which can be derived specific assertions about issues, trends, and scenarios as to the likely and possible shape of the future. We also inquire into the utility of these models for better understanding the future of the Islamic ummah. We conclude with visions of the future of the ummah ...

Spectral damping scaling factors for horizontal components of ground motions from subduction earthquakes using NGA-Subduction data

2021 ◽  
pp. 875529302110279
Author(s):  
Sanaz Rezaeian ◽  
Linda Al Atik ◽  
Nicolas M Kuehn ◽  
Norman Abrahamson ◽  
Yousef Bozorgnia ◽  
...  
Keyword(s):  
Ground Motions ◽  
Damping Ratio ◽  
Spectral Shape ◽  
Parametric Models ◽  
Scaling Factors ◽  
Global Models ◽  
Motion Models ◽  
Subduction Earthquakes ◽  
Crustal Earthquakes ◽  
Subduction Zone Earthquakes

This article develops global models of damping scaling factors (DSFs) for subduction zone earthquakes that are functions of the damping ratio, spectral period, earthquake magnitude, and distance. The Next Generation Attenuation for subduction earthquakes (NGA-Sub) project has developed the largest uniformly processed database of recorded ground motions to date from seven subduction regions: Alaska, Cascadia, Central America and Mexico, South America, Japan, Taiwan, and New Zealand. NGA-Sub used this database to develop new ground motion models (GMMs) at a reference 5% damping ratio. We worked with the NGA-Sub project team to develop an extended database that includes pseudo-spectral accelerations (PSA) for 11 damping ratios between 0.5% and 30%. We use this database to develop parametric models of DSF for both interface and intraslab subduction earthquakes that can be used to adjust any subduction GMM from a reference 5% damping ratio to other damping ratios. The DSF is strongly influenced by the response spectral shape and the duration of motion; therefore, in addition to the damping ratio, the median DSF model uses spectral period, magnitude, and distance as surrogate predictor variables to capture the effects of the spectral shape and the duration of motion. We also develop parametric models for the standard deviation of DSF. The models presented in this article are for the RotD50 horizontal component of PSA and are compared with the models for shallow crustal earthquakes in active tectonic regions. Some noticeable differences arise from the considerably longer duration of interface records for very large magnitude events and the enriched high-frequency content of intraslab records, compared with shallow crustal earthquakes. Regional differences are discussed by comparing the proposed global models with the data from each subduction region along with recommendations on the applicability of the models.

scholarly journals Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Sabrina Sanchez ◽  
Johannes Wicht ◽  
Julien Bärenzung
Keyword(s):  
Magnetic Field ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Wave Number ◽  
Main Magnetic Field ◽  
Candidate Model ◽  
Uncertainty Estimates ◽  
Geomagnetic Field Models ◽  
Dipole Configuration

Abstract The IGRF offers an important incentive for testing algorithms predicting the Earth’s magnetic field changes, known as secular variation (SV), in a 5-year range. Here, we present a SV candidate model for the 13th IGRF that stems from a sequential ensemble data assimilation approach (EnKF). The ensemble consists of a number of parallel-running 3D-dynamo simulations. The assimilated data are geomagnetic field snapshots covering the years 1840 to 2000 from the COV-OBS.x1 model and for 2001 to 2020 from the Kalmag model. A spectral covariance localization method, considering the couplings between spherical harmonics of the same equatorial symmetry and same azimuthal wave number, allows decreasing the ensemble size to about a 100 while maintaining the stability of the assimilation. The quality of 5-year predictions is tested for the past two decades. These tests show that the assimilation scheme is able to reconstruct the overall SV evolution. They also suggest that a better 5-year forecast is obtained keeping the SV constant compared to the dynamically evolving SV. However, the quality of the dynamical forecast steadily improves over the full assimilation window (180 years). We therefore propose the instantaneous SV estimate for 2020 from our assimilation as a candidate model for the IGRF-13. The ensemble approach provides uncertainty estimates, which closely match the residual differences with respect to the IGRF-13. Longer term predictions for the evolution of the main magnetic field features over a 50-year range are also presented. We observe the further decrease of the axial dipole at a mean rate of 8 nT/year as well as a deepening and broadening of the South Atlantic Anomaly. The magnetic dip poles are seen to approach an eccentric dipole configuration.

scholarly journals NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering

2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Dongsheng Li ◽  
Haodong Liu ◽  
Chao Chen ◽  
Yingying Zhao ◽  
Stephen M. Chu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Optimization Problems ◽  
Empirical Studies ◽  
Large Datasets ◽  
Model Learning ◽  
Global Models ◽  
Convex Optimization Problems ◽  
Memory Network ◽  
Real World Datasets ◽  
Performance Tradeoff

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a performance tradeoff among users/items, i.e., not all users/items are perfectly fitted by the global models due to the hard non-convex optimization problems in CF algorithms. Ensemble learning can address this issue by learning multiple diverse models but usually suffer from efficiency issue on large datasets or complex algorithms. In this article, we keep the intermediate models obtained during global model learning as the snapshot models, and then adaptively combine the snapshot models for individual user-item pairs using a memory network-based method. Empirical studies on three real-world datasets show that the proposed method can extensively and significantly improve the accuracy (up to 15.9% relatively) when applied to a variety of existing collaborative filtering methods.

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