Spectral analysis and inversion of experimental codas

Geophysics ◽  
1993 ◽  
Vol 58 (3) ◽  
pp. 408-418 ◽  
Author(s):  
L. R. Jannaud ◽  
P. M. Adler ◽  
C. G. Jacquin
Keyword(s):  
Spectral Analysis ◽  
Characteristic Length ◽  
Gaussian Model ◽  
Seismic Survey ◽  
Data Sets ◽  
Data Set ◽  
Anisotropic Elastic ◽  
And Inversion ◽  
Good Agreement

A method developed for the determination of the characteristic lengths of an heterogeneous medium from the spectral analysis of codas is based on an extension of Aki’s theory to anisotropic elastic media. An equivalent Gaussian model is obtained and seems to be in good agreement with the two experimental data sets that illustrate the method. The first set was obtained in a laboratory experiment with an isotropic marble sample. This sample is characterized by a submillimetric length scale that can be directly observed on a thin section. The spectral analysis of codas and their inversion yields an equivalent correlation length that is in good agreement with the observed one. The second data set is obtained in a crosshole experiment at the usual scale of a seismic survey. The codas are recorded, analysed, and inverted. The analysis yields a vertical characteristic length for the studied subsurface that compares well with the characteristic length measured by seismic and stratigraphic logs.

scholarly journals Improved retrieval of nitrogen dioxide (NO<sub>2</sub>) column densities by means of MKIV Brewer spectrophotometers

2014 ◽  
Vol 7 (11) ◽  
pp. 4009-4022 ◽  
Author(s):  
H. Diémoz ◽  
A. M. Siani ◽  
A. Redondas ◽  
V. Savastiouk ◽  
C. T. McElroy ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Ad Hoc ◽  
Absorption Spectrometry ◽  
Atmospheric Composition ◽  
Data Sets ◽  
Data Set ◽  
Noise Interference ◽  
Atmospheric Species ◽  
Mass Factor

Abstract. A new algorithm to retrieve nitrogen dioxide (NO2) column densities using MKIV ("Mark IV") Brewer spectrophotometers is described. The method includes several improvements, such as a more recent spectroscopic data set, the reduction of measurement noise, interference by other atmospheric species and instrumental settings, and a better determination of the zenith sky air mass factor. The technique was tested during an ad hoc calibration campaign at the high-altitude site of Izaña (Tenerife, Spain) and the results of the direct sun and zenith sky geometries were compared to those obtained by two reference instruments from the Network for the Detection of Atmospheric Composition Change (NDACC): a Fourier Transform Infrared Radiometer (FTIR) and an advanced visible spectrograph (RASAS-II) based on the differential optical absorption spectrometry (DOAS) technique. To determine the extraterrestrial constant, an easily implementable extension of the standard Langley technique for very clean sites without tropospheric NO2 was developed which takes into account the daytime linear drift of stratospheric nitrogen dioxide due to photochemistry. The measurement uncertainty was thoroughly determined by using a Monte Carlo technique. Poisson noise and wavelength misalignments were found to be the most influential contributors to the overall uncertainty, and possible solutions are proposed for future improvements. The new algorithm is backward-compatible, thus allowing for the reprocessing of historical data sets.

scholarly journals Tsallis Entropy for Assessing Spatial Uncertainty Associated with Mean Annual Runoff of Quaternary Catchments of the Middle Vaal Basin in South Africa

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1050
Author(s):  
Masengo Ilunga
Keyword(s):  
South Africa ◽  
Shannon Entropy ◽  
Annual Runoff ◽  
Spatial Uncertainty ◽  
Data Sets ◽  
Data Set ◽  
Information Index ◽  
Surface Water Resources ◽  
Convergence Point

This study assesses mainly the uncertainty of the mean annual runoff (MAR) for quaternary catchments (QCs) considered as metastable nonextensive systems (from Tsalllis entropy) in the Middle Vaal catchment. The study is applied to the surface water resources (WR) of the South Africa 1990 (WR90), 2005 (WR2005) and 2012 (WR2012) data sets. The q-information index (from the Tsalllis entropy) is used here as a deviation indicator for the spatial evolution of uncertainty for the different QCs, using the Shannon entropy as a baseline. It enables the determination of a (virtual) convergence point, zone of positive and negative uncertainty deviation, zone of null deviation and chaotic zone for each data set. Such a determination is not possible on the basis of the Shannon entropy alone as a measure for the MAR uncertainty of QCs, i.e., when they are viewed as extensive systems. Finally, the spatial distributions for the zones of the q-uncertainty deviation (gain or loss in information) of the MAR are derived and lead to iso q-uncertainty deviation maps.

scholarly journals Novel Approaches to Smoothing and Comparing SELDI TOF Spectra

2005 ◽  
Vol 1 ◽  
pp. 117693510500100 ◽  
Author(s):  
Sreelatha Meleth ◽  
Isam-Eldin Eltoum ◽  
Liu Zhu ◽  
Denise Oelschlager ◽  
Chandrika Piyathilake ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Fourier Transforms ◽  
Area Under The Curve ◽  
Maximum Intensity ◽  
Data Sets ◽  
Intensity Level ◽  
Prominent Feature ◽  
Data Set ◽  
The Third ◽  
Novel Approaches

Background Most published literature using SELDI-TOF has used traditional techniques in Spectral Analysis such as Fourier transforms and wavelets for denoising. Most of these publications also compare spectra using their most prominent feature, ie, peaks or local maximums. Methods The maximum intensity value within each window of differentiable m/z values was used to represent the intensity level in that window. We also calculated the ‘Area under the Curve’ (AUC) spanned by each window. Results Keeping everything else constant, such as pre-processing of the data and the classifier used, the AUC performed much better as a metric of comparison than the peaks in two out of three data sets. In the third data set both metrics performed equivalently. Conclusions This study shows that the feature used to compare spectra can have an impact on the results of a study attempting to identify biomarkers using SELDI TOF data.

Determination of site occupancies by the intermeasurement minimization method. I. Anomalous scattering usage for noncentrosymmetric crystals

2008 ◽  
Vol 41 (1) ◽  
pp. 83-95 ◽  
Author(s):  
Alexander Dudka
Keyword(s):  
Site Occupancy ◽  
Anomalous Scattering ◽  
Data Sets ◽  
Minimization Method ◽  
Data Set ◽  
Occupancy Factor ◽  
Site Occupancy Factor ◽  
Three Samples ◽  
Scattering Contribution

New methods for the determination of site occupancy factors are described. The methods are based on the analysis of differences between intensities of Friedel reflections in noncentrosymmetric crystals. In the first method (Anomalous-Expert) the site occupancy factor is determined by the condition that it is identical for two data sets: (1) initial data without averaging of Friedel intensities and (2) data that are averaged on Friedel pairs after the reduction of the anomalous scattering contribution. In the second method (anomalous anisotropic intermeasurement minimization method, Anomalous-AniMMM) the site occupancy factor is refined to satisfy the condition that the differences between the intensities of Friedel reflections that are reduced on the anomalous scattering contribution must be minimal. The methods were checked for three samples of RbTi1−xZrxOPO4crystals (A,BandC) with KTiOPO4structure, at 295 and 105 K (five experimental data sets). Microprobe measurements yield compositionsxA,B= 0.034 (5) andxC= 0.022 (4). The corresponding site occupancy factors areQA,B= 0.932 (10) andQC= 0.956 (8). Using Anomalous-AniMMM and three independent refinements for the first and second samples, the initial occupancy factor ofQA,B= 0.963 (15) was improved toQA,B= 0.938 (7). Of the three room-temperature data sets, one was improved toQA,B= 0.934 (2). For the third sample and one data set, the initial occupancy factor ofQC= 1.000 was improved toQC= 0.956 (1). The methods improve the Hirshfeld rigid-bond test. It is discussed how the description of chemical bonding influences the site occupancy factor.

scholarly journals Global validation of improved SCIAMACHY scientific ozone limb data using ozonesonde measurements

2015 ◽  
Vol 8 (5) ◽  
pp. 4817-4858
Author(s):  
J. Jia ◽  
A. Rozanov ◽  
A. Ladstätter-Weißenmayer ◽  
J. P. Burrows
Keyword(s):  
Global Scale ◽  
Data Sets ◽  
Vertical Profiles ◽  
Data Set ◽  
Latitude Range ◽  
Ozone Profile ◽  
The Tropics ◽  
Relative Differences ◽  
Significant Underestimation ◽  
Good Agreement

Abstract. In this manuscript, the latest SCIAMACHY limb ozone scientific vertical profiles, namely the current V2.9 and the upcoming V3.0, are extensively compared with ozone sonde data from the WOUDC database. The comparisons are made on a global scale from 2003 to 2011, involving 61 sonde stations. The retrieval processors used to generate V2.9 and V3.0 data sets are briefly introduced. The comparisons are discussed in terms of vertical profiles and stratospheric partial columns. Our results indicate that the V2.9 ozone profile data between 20–30 km is in good agreement with ground based measurements with less than 5% relative differences in the latitude range of 90° S–40° N (with exception of the tropical Pacific region where an overestimation of more than 10% is observed), which corresponds to less than 5 DU partial column differences. In the tropics the differences are within 3%. However, this data set shows a significant underestimation northwards of 40° N (up to ~15%). The newly developed V3.0 data set reduces this bias to below 10% while maintaining a good agreement southwards of 40° N with slightly increased relative differences of up to 5% in the tropics.

scholarly journals TRANSFORMATION OF RANDOM BREAKING WAVES ON SURF BEAT

1986 ◽  
Vol 1 (20) ◽  
pp. 9 ◽  
Author(s):  
William R. Dally ◽  
Robert G. Dean
Keyword(s):  
Surf Zone ◽  
Wave Model ◽  
Breaking Waves ◽  
Wave Transformation ◽  
Random Wave ◽  
Data Sets ◽  
Data Set ◽  
Wave Decay ◽  
Mean Water Level ◽  
Good Agreement

Based on a previous study by the authors of regular breaking waves in the surf zone, a model for random wave transformation across the nearshore region is developed. The results of a laboratory investigation of the effect of a steady opposing current on the wave decay process are presented and a proposed governing equation verified. Surf beat effects on wave transformation are then included in the model by representing the long wave as a temporally and spatiallyvarying current and mean water level. The concept of an equivalent water depth, which contains the effect of the current, is introduced and then included in a stochastic form in the random wave model. Surf beat is found to noticeably increase the decay of the root mean square wave height, especially in the inner surf where the beat is strongest. Comparison of the models to two field data sets show very good agreement for Hotta and Mizuguchi (1980), but rather poor for Thornton and Guza (1983). Possible explanations for the unexpected behavior of the second data set, pertaining to filtering, are discussed. Finally, a possible explanation for the dependence of random wave decay on deepwater steepness, noted by Battjes and Stive (1985), is presented.

scholarly journals Novel Automated K-means++ Algorithm for Financial Data Sets

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guoyu Du ◽  
Xuehua Li ◽  
Lanjie Zhang ◽  
Libo Liu ◽  
Chaohua Zhao
Keyword(s):  
Linear Time ◽  
Sparse Matrix ◽  
Data Sets ◽  
Volume Data ◽  
Text Data ◽  
Data Set ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Initial Cluster

The K-means algorithm has been extensively investigated in the field of text clustering because of its linear time complexity and adaptation to sparse matrix data. However, it has two main problems, namely, the determination of the number of clusters and the location of the initial cluster centres. In this study, we propose an improved K-means++ algorithm based on the Davies-Bouldin index (DBI) and the largest sum of distance called the SDK-means++ algorithm. Firstly, we use the term frequency-inverse document frequency to represent the data set. Secondly, we measure the distance between objects by cosine similarity. Thirdly, the initial cluster centres are selected by comparing the distance to existing initial cluster centres and the maximum density. Fourthly, clustering results are obtained using the K-means++ method. Lastly, DBI is used to obtain optimal clustering results automatically. Experimental results on real bank transaction volume data sets show that the SDK-means++ algorithm is more effective and efficient than two other algorithms in organising large financial text data sets. The F-measure value of the proposed algorithm is 0.97. The running time of the SDK-means++ algorithm is reduced by 42.9% and 22.4% compared with that for K-means and K-means++ algorithms, respectively.

scholarly journals Comparison of 14C Ages Between LSC and AMS Measurements of Choukai Jindai Cedar Tree Rings at 2600 cal BP

Radiocarbon ◽  
2010 ◽  
Vol 52 (3) ◽  
pp. 895-900 ◽  
Author(s):  
Yui Takahashi ◽  
Hirohisa Sakurai ◽  
Kayo Suzuki ◽  
Taiichi Sato ◽  
Shuichi Gunji ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tree Rings ◽  
Liquid Scintillation ◽  
Measurement Methods ◽  
Scintillation Counting ◽  
Age Difference ◽  
Data Sets ◽  
Data Set ◽  
The Difference ◽  
Good Agreement

Radiocarbon ages of Choukai Jindai cedar tree rings growing in the excess era of 14C concentrations during 2757–2437 cal BP were measured using 2 types of 14C measurement methods, i.e. liquid scintillation counting (LSC) and accelerator mass spectrometry (AMS). The difference between the 2 methods is 3.7 ± 5.2 14C yr on average for 61 single-year tree rings, indicating good agreement between the methods. The Choukai data sets show a small sharp bump with an average 14C age of 2497.1 ± 3.0 14C yr BP during 2650–2600 cal BP. Although the profile of the Choukai LSC data set compares well with that of IntCal04, having a 14C age difference of 4.6 ± 5.3 14C yr on average, the Choukai LSC 14C ages indicate variability against the smoothed profile of IntCal04.

Determination of Optimal Clusters Using a Genetic Algorithm

2008 ◽  
pp. 98-117 ◽  
Author(s):  
Tushar ◽  
Tushar ◽  
Shibendu Shekhar Roy ◽  
Dilip Kumar Pratihar
Keyword(s):  
Genetic Algorithm ◽  
Threshold Value ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Fcm Algorithm ◽  
Data Points ◽  
The Relationship

Clustering is a potential tool of data mining. A clustering method analyzes the pattern of a data set and groups the data into several clusters based on the similarity among themselves. Clusters may be either crisp or fuzzy in nature. The present chapter deals with clustering of some data sets using Fuzzy C-Means (FCM) algorithm and Entropy-based Fuzzy Clustering (EFC) algorithm. In FCM algorithm, the nature and quality of clusters depend on the pre-defined number of clusters, level of cluster fuzziness and a threshold value utilized for obtaining the number of outliers (if any). On the other hand, the quality of clusters obtained by the EFC algorithm is dependent on a constant used to establish the relationship between the distance and similarity of two data points, a threshold value of similarity and another threshold value used for determining the number of outliers. The clusters should ideally be distinct and at the same time compact in nature. Moreover, the number of outliers should be as minimum as possible. Thus, the above problem may be posed as an optimization problem, which will be solved using a Genetic Algorithm (GA). The best set of multi-dimensional clusters will be mapped into 2-D for visualization using a Self-Organizing Map (SOM).

Big commercial microwave link data: Detecting rain events with deep learning

2020 ◽  
Author(s):  
Julius Polz ◽  
Christian Chwala ◽  
Maximilian Graf ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Rain Rate ◽  
Data Sets ◽  
Data Set ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Signal Fluctuation ◽  
Rain Events ◽  
Microwave Link ◽  
Good Agreement

&lt;p&gt;Commercial microwave links (CMLs) can be used for quantitative precipitation estimation. The measurement technique is based on the exploitation of the close to linear relationship between the attenuation of the signal level by rainfall and the path averaged rain rate. At a temporal resolution of one minute, the signal level of almost 4000 CMLs distributed all over Germany is being recorded since August 2017, resulting in one of the biggest CML data sets available for scientific purposes. A crucial step for retrieving rainfall information from this large CML data set is to accurately detect rainy periods in the time-series, a process which is hampered by strong signal fluctuations, occasionally occurring even when there is no rain. In our study, we evaluate the performance of convolutional neural networks (CNNs) to distinguish between rainy and non-rainy signal fluctuations by recognizing their specific patterns. CNNs make use of many layers and local connections of neurons to recognize patterns independent of their location in the time-series. We designed a custom CNN architecture consisting of a feature extraction and classification part with 20 layers of neurons and 1.4&amp;#8201;x&amp;#8201;10&lt;sup&gt;5&lt;/sup&gt; trainable parameters. To train the model and validate the results we refer to the gauge-adjusted radar product RADOLAN-RW, provided by the German meteorological service. Despite not being an absolute truth, it provides robust information about rain events at the CML locations at an hourly time resolution. With only 400 CMLs used for training and 3504 for validation, we find that CNNs can learn to recognize different signal fluctuation patterns and generalize well to sensors and time periods not used for training. Overall we find a good agreement between the CML and weather radar derived rainfall information by detecting on average 87&amp;#8201;% of all rainy and 91&amp;#8201;% of all non-rainy periods.&lt;/p&gt;

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