Limitations on digital filtering of the DNAG magnetic data set for the conterminous U.S.

Geophysics ◽  
1993 ◽  
Vol 58 (9) ◽  
pp. 1281-1296 ◽  
Author(s):  
V. J. S. Grauch
Keyword(s):  
Spatial Frequency ◽  
North American ◽  
Digital Filtering ◽  
Magnetic Data ◽  
Data Sets ◽  
Frequency Content ◽  
Long Distance ◽  
High Quality ◽  
Data Set ◽  
The U.S

The magnetic data set compiled for the Decade of North American Geology (DNAG) project presents an important digital data base that can be used to examine the North American crust. The data represent a patchwork from many individual airborne and marine magnetic surveys. However, the portion of data for the conterminous U.S. has problems that limit the resolution and use of the data. Now that the data are available in digital form, it is important to describe the data limitations more specifically than before. The primary problem is caused by datum shifts between individual survey boundaries. In the western U.S., the DNAG data are generally shifted less than 100 nT. In the eastern U.S., the DNAG data may be shifted by as much as 300 nT and contain regionally shifted areas with wavelengths on the order of 800 to 1400 km. The worst case is the artificial low centered over Kentucky and Tennessee produced by a series of datum shifts. A second significant problem is lack of anomaly resolution that arises primarily from using survey data that is too widely spaced compared to the flight heights above magnetic sources. Unfortunately, these are the only data available for much of the U.S. Another problem is produced by the lack of common observation surface between individual pieces of the U.S. DNAG data. The height disparities introduce variations in spatial frequency content that are unrelated to the magnetization of rocks. The spectral effects of datum shifts and the variation of spatial frequency content due to height disparities were estimated for the DNAG data for the conterminous U.S. As a general guideline for digital filtering, the most reliable features in the U.S. DNAG data have wavelengths roughly between 170 and 500 km, or anomaly half‐widths between 85 and 250 km. High‐quality, large‐region magnetic data sets have become increasingly important to meet exploration and scientific objectives. The acquisition of a new national magnetic data set with higher quality at a greater range of wavelengths is clearly in order. The best approach is to refly much of the U.S. with common specifications and reduction procedures. At the very least, magnetic data sets should be remerged digitally using available or newly flown long‐distance flight‐line data to adjust survey levels. In any case, national coordination is required to produce a consistent, high‐quality national magnetic map.

scholarly journals A fast methodology for large-scale focusing inversion of gravity and magnetic data using the structured model matrix and the 2-D fast Fourier transform

2020 ◽  
Vol 223 (2) ◽  
pp. 1378-1397
Author(s):  
Rosemary A Renaut ◽  
Jarom D Hogue ◽  
Saeed Vatankhah ◽  
Shuang Liu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Linear Systems ◽  
Large Scale ◽  
Surface Measurement ◽  
Magnetic Data ◽  
Uniform Grid ◽  
Data Sets ◽  
Inversion Algorithm ◽  
Data Set

SUMMARY We discuss the focusing inversion of potential field data for the recovery of sparse subsurface structures from surface measurement data on a uniform grid. For the uniform grid, the model sensitivity matrices have a block Toeplitz Toeplitz block structure for each block of columns related to a fixed depth layer of the subsurface. Then, all forward operations with the sensitivity matrix, or its transpose, are performed using the 2-D fast Fourier transform. Simulations are provided to show that the implementation of the focusing inversion algorithm using the fast Fourier transform is efficient, and that the algorithm can be realized on standard desktop computers with sufficient memory for storage of volumes up to size n ≈ 106. The linear systems of equations arising in the focusing inversion algorithm are solved using either Golub–Kahan bidiagonalization or randomized singular value decomposition algorithms. These two algorithms are contrasted for their efficiency when used to solve large-scale problems with respect to the sizes of the projected subspaces adopted for the solutions of the linear systems. The results confirm earlier studies that the randomized algorithms are to be preferred for the inversion of gravity data, and for data sets of size m it is sufficient to use projected spaces of size approximately m/8. For the inversion of magnetic data sets, we show that it is more efficient to use the Golub–Kahan bidiagonalization, and that it is again sufficient to use projected spaces of size approximately m/8. Simulations support the presented conclusions and are verified for the inversion of a magnetic data set obtained over the Wuskwatim Lake region in Manitoba, Canada.

Alternative Clustering

2017 ◽  
pp. 1-12 ◽  
Author(s):  
Avinash Navlani ◽  
V. B. Gupta
Keyword(s):  
Research Problem ◽  
Research Community ◽  
Data Sets ◽  
Complex Data ◽  
High Quality ◽  
Data Set ◽  
Alternative Clustering ◽  
Complex Data Sets ◽  
Data Objects ◽  
Community Clustering

In the last couple of decades, clustering has become a very crucial research problem in the data mining research community. Clustering refers to the partitioning of data objects such as records and documents into groups or clusters of similar characteristics. Clustering is unsupervised learning, because of unsupervised nature there is no unique solution for all problems. Most of the time complex data sets require explanation in multiple clustering sets. All the Traditional clustering approaches generate single clustering. There is more than one pattern in a dataset; each of patterns can be interesting in from different perspectives. Alternative clustering intends to find all unlike groupings of the data set such that each grouping has high quality and distinct from each other. This chapter gives you an overall view of alternative clustering; it's various approaches, related work, comparing with various confusing related terms like subspace, multi-view, and ensemble clustering, applications, issues, and challenges.

TANK VESSEL PETROLEUM TRANSPORTATION AS A PREDICTOR OF MARINE OIL SPILLS

2005 ◽  
Vol 2005 (1) ◽  
pp. 143-147
Author(s):  
Daniel R. Norton
Keyword(s):  
Marine Environment ◽  
Oil Spills ◽  
Strong Relationship ◽  
The United States ◽  
Coast Guard ◽  
Army Corps ◽  
Data Sets ◽  
Data Set ◽  
Annual Volume ◽  
The U.S

ABSTRACT The annual volume of oil spilled into the marine environment by tank vessels (tank barges and tanks hips) is analyzed against the total annual volume of oil transported by tank vessels in order to determine any correlational relationship. U.S. Coast Guard data was used to provide the volume of oil (petroleum) spilled into the marine environment each year by tank vessels. Data from the U.S. Army Corps of Engineers and the U.S. Department of Transportation's (US DOT) National Transportation Statistics (NTS) were used for the annual volume of oil transported via tank vessels in the United States. This data is provided in the form of tonnage and ton-miles, respectively. Each data set has inherent benefits and weaknesses. For the analysis the volume of oil transported was used as the explanatory variable (x) and the volume of oil spilled into the marine environment as the response variable (y). Both data sets were tested for correlation. A weak relationship, r = −0.38 was found using tonnage, and no further analysis was performed. A moderately strong relationship, r = 0.79, was found using ton-miles. Further analysis using regression analysis and a plot of residuals showed the data to be satisfactory with no sign of lurking variables, but with the year 1990 being a possible outlier.

Rapid multi-scale analysis of near-surface geophysical anomaly maps: Application to an archaeo-geophysical data set

Geophysics ◽  
2020 ◽  
pp. 1-41 ◽  
Author(s):  
Jens Tronicke ◽  
Niklas Allroggen ◽  
Felix Biermann ◽  
Florian Fanselow ◽  
Julien Guillemoteau ◽  
...  
Keyword(s):  
Random Noise ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Engineering Practice ◽  
Data Sets ◽  
Stripe Pattern ◽  
Data Set ◽  
Near Surface ◽  
Multi Scale ◽  
Topographic Data

In near-surface geophysics, ground-based mapping surveys are routinely employed in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. The resulting geophysical anomaly maps of, for example, magnetic or electrical parameters are usually interpreted to laterally delineate subsurface structures such as those related to the remains of past human activities, subsurface utilities and other installations, hydrological properties, or different soil types. To ease the interpretation of such data sets, we propose a multi-scale processing, analysis, and visualization strategy. Our approach relies on a discrete redundant wavelet transform (RWT) implemented using cubic-spline filters and the à trous algorithm, which allows to efficiently compute a multi-scale decomposition of 2D data using a series of 1D convolutions. The basic idea of the approach is presented using a synthetic test image, while our archaeo-geophysical case study from North-East Germany demonstrates its potential to analyze and process rather typical geophysical anomaly maps including magnetic and topographic data. Our vertical-gradient magnetic data show amplitude variations over several orders of magnitude, complex anomaly patterns at various spatial scales, and typical noise patterns, while our topographic data show a distinct hill structure superimposed by a microtopographic stripe pattern and random noise. Our results demonstrate that the RWT approach is capable to successfully separate these components and that selected wavelet planes can be scaled and combined so that the reconstructed images allow for a detailed, multi-scale structural interpretation also using integrated visualizations of magnetic and topographic data. Because our analysis approach is straightforward to implement without laborious parameter testing and tuning, computationally efficient, and easily adaptable to other geophysical data sets, we believe that it can help to rapidly analyze and interpret different geophysical mapping data collected to address a variety of near-surface applications from engineering practice and research.

Connecting onshore-offshore Campos Basin structures: Interpretation of high-resolution airborne magnetic data

2014 ◽  
Vol 2 (4) ◽  
pp. SJ35-SJ45 ◽  
Author(s):  
Juarez Lourenço ◽  
Paulo T. L. Menezes ◽  
Valeria C. F. Barbosa
Keyword(s):  
Source Rocks ◽  
Magnetic Data ◽  
Data Sets ◽  
Hydrocarbon Migration ◽  
Data Set ◽  
Campos Basin ◽  
Turbidite Sedimentation ◽  
Edge Detection Method ◽  
Transfer Faults ◽  
Airborne Magnetic

We interpreted northwest-trending transfer faults whose extensions are not entirely mapped in the Precambrian basement of the onshore and offshore Campos Basin. To enhance the subtle northwest–southeast lineaments not clearly seen in the total-field data, we reprocessed and merged two airborne magnetic data sets aiming at producing a single merged magnetic data set. Next, we applied a directional filter to these integrated magnetic data. Finally, we applied a multiscale edge detection method to these filtered data. This combination allowed the detection of edges and ridges that are used to produce several northwest–southeast lineations. We interpreted these northwest-trending lineations as magnetic expressions of transfer faults that cut across the onshore adjacent basement of the Campos Basin to the shallow and deep Campos Basin waters. These interpreted northwest-trending faults suggested the continuity of the known northwest-striking transfer faults in the deep Campos Basin waters toward the shallow Campos Basin waters and the adjacent continent. Moreover, our interpreted northwest-trending faults revealed the control of several known oilfields in the Campos Basin. This result supported the hypothesis of the influence of the northwest–southeast-trending transfer faults on the petroleum system of Campos Basin, which were reactivated in the Tertiary providing a pathway for the turbidite sedimentation, reworking, and redistribution of several deepwater reservoirs. In addition, it was hypothesized that this faulting system controlled the hydrocarbon migration paths from the presalt source rocks through salt windows into basal suprasalt layers.

scholarly journals A multi-decade record of high-quality fCO<sub>2</sub> data in version 3 of the Surface Ocean CO<sub>2</sub> Atlas (SOCAT)

2016 ◽  
Author(s):  
Dorothee C. E. Bakker ◽  
Benjamin Pfeil ◽  
Camilla S. Landa ◽  
Nicolas Metzl ◽  
Kevin M. O'Brien ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Surface Water ◽  
Data Collection ◽  
Data Availability ◽  
Data Sets ◽  
Science Data ◽  
High Quality ◽  
Data Set ◽  
Surface Ocean ◽  
Biogeochemical Models

Abstract. The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.5 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.4 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) "Living Data" publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014).

scholarly journals Investigating the Role of Culture on Negative Emotion Expressions in the Wild

2021 ◽  
Vol 15 ◽  
Author(s):  
Emma Hughson ◽  
Roya Javadi ◽  
James Thompson ◽  
Angelica Lim
Keyword(s):  
Emotion Recognition ◽  
North American ◽  
Affective Computing ◽  
Negative Emotion ◽  
Negative Emotions ◽  
Support Vector ◽  
Data Sets ◽  
Social Signals ◽  
Data Set ◽  
In The Wild

Even though culture has been found to play some role in negative emotion expression, affective computing research primarily takes on a basic emotion approach when analyzing social signals for automatic emotion recognition technologies. Furthermore, automatic negative emotion recognition systems still train data that originates primarily from North America and contains a majority of Caucasian training samples. As such, the current study aims to address this problem by analyzing what the differences are of the underlying social signals by leveraging machine learning models to classify 3 negative emotions, contempt, anger and disgust (CAD) amongst 3 different cultures: North American, Persian, and Filipino. Using a curated data set compiled from YouTube videos, a support vector machine (SVM) was used to predict negative emotions amongst differing cultures. In addition a one-way ANOVA was used to analyse the differences that exist between each culture group in-terms of level of activation of underlying social signal. Our results not only highlighted the significant differences in the associated social signals that were activated for each culture, but also indicated the specific underlying social signals that differ in our cross-cultural data sets. Furthermore, the automatic classification methods showed North American expressions of CAD to be well-recognized, while Filipino and Persian expressions were recognized at near chance levels.

scholarly journals Petrophysically and geologically guided multi-physics inversion using a dynamic Gaussian mixture model

2020 ◽  
Vol 224 (1) ◽  
pp. 40-68 ◽  
Author(s):  
Thibaut Astic ◽  
Lindsey J Heagy ◽  
Douglas W Oldenburg
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Quantitative Information ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Data Sets ◽  
Data Set ◽  
Geophysical Surveys ◽  
Geological Information

SUMMARY In a previous paper, we introduced a framework for carrying out petrophysically and geologically guided geophysical inversions. In that framework, petrophysical and geological information is modelled with a Gaussian mixture model (GMM). In the inversion, the GMM serves as a prior for the geophysical model. The formulation and applications were confined to problems in which a single physical property model was sought, and a single geophysical data set was available. In this paper, we extend that framework to jointly invert multiple geophysical data sets that depend on multiple physical properties. The petrophysical and geological information is used to couple geophysical surveys that, otherwise, rely on independent physics. This requires advancements in two areas. First, an extension from a univariate to a multivariate analysis of the petrophysical data, and their inclusion within the inverse problem, is necessary. Secondly, we address the practical issues of simultaneously inverting data from multiple surveys and finding a solution that acceptably reproduces each one, along with the petrophysical and geological information. To illustrate the efficacy of our approach and the advantages of carrying out multi-physics inversions coupled with petrophysical and geological information, we invert synthetic gravity and magnetic data associated with a kimberlite deposit. The kimberlite pipe contains two distinct facies embedded in a host rock. Inverting the data sets individually, even with petrophysical information, leads to a binary geological model: background or undetermined kimberlite. A multi-physics inversion, with petrophysical information, differentiates between the two main kimberlite facies of the pipe. Through this example, we also highlight the capabilities of our framework to work with interpretive geological assumptions when minimal quantitative information is available. In those cases, the dynamic updates of the GMM allow us to perform multi-physics inversions by learning a petrophysical model.

Travel Characteristics of Urban Freight Vehicles and Their Effects on Emission Factors

2005 ◽  
Vol 1941 (1) ◽  
pp. 89-98
Author(s):  
Annie Protopapas ◽  
Arun Chatterjee ◽  
Terry Miller ◽  
Jerry Everett
Keyword(s):  
Commercial Vehicle ◽  
Emission Factors ◽  
Data Sets ◽  
Data Set ◽  
Average Speed ◽  
Driving Pattern ◽  
Vehicle Data ◽  
Input Parameters ◽  
Vehicle Usage ◽  
The U.S

This study collected local commercial vehicle data in Knox County, Tennessee, from the U.S. Postal Service (USPS) and two companies engaged in package pickup and delivery (PUD). Another urban commercial vehicle data set with a wider spectrum of freight companies was obtained from North Carolina for comparative analysis. The two data sets were analyzed to develop two sets of values for input parameters for the U.S. Environmental Protection Agency's MOBILE6 model. Statistical tests permitted four aggregated vehicle usage classes to be formed. Two runs of MOBILE6 modeled the two commercial vehicle data sets in their entirety. Four additional runs modeled each vehicle usage class individually through the use of average speed and starts per day specific to the driving pattern of each class. Differences between the values of input parameters and emission factors based on data collected by this study and those based on the default values of MOBILE6 are discussed. Commercial vehicles examined by this study indicated higher annual mileage accumulation rates than the default values. Also their vehicle miles traveled and engine start distributions by hour of day varied considerably from the default values, occurring primarily between the two daily peak traffic periods (morning and evening). The study found higher volatile organic compounds (VOCs), carbon monoxide (CO), and nitrogen oxide emission factors than in default runs for USPS vehicles, whose driving pattern resulted in lower-than-default average speed. Higher VOC and CO emission factors were found for gasoline and diesel package PUD vehicles due to lower-than-default average speeds and higher-than-default starts per day.

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