Weighted average seismic attributes

Geophysics ◽  
2000 ◽  
Vol 65 (1) ◽  
pp. 275-285 ◽  
Author(s):  
Arthur E. Barnes
Keyword(s):  
Quantitative Analysis ◽  
Instantaneous Frequency ◽  
Weighted Average ◽  
Seismic Attributes ◽  
Weighted Averaging ◽  
Instantaneous Power ◽  
Data Similarity ◽  
Velocity Information ◽  
Average Wavelength ◽  
Related Measure

Local weighted averaging of instantaneous seismic attributes improves their interpretability by removing spikes and reducing rapid and confusing variations. Averaging in a window weighted by the instantaneous power produces a local measure that equals a Fourier spectral average, facilitating quantitative analysis. Local 1-D frequency and bandwidth are scalars, but local 2-D and 3-D seismic attributes derive from average vector wavenumbers, which may require velocity information. The direction of the average vector wavenumber provides average dip and azimuth, and its magnitude provides a measure of average wavelength or frequency. A related measure of bandwidth is a scalar in all dimensions; it includes contributions from both instantaneous bandwidth and the variance of instantaneous frequency or wavenumber. It quantifies data similarity.

scholarly journals Site Selection of Waste-to-Energy (WtE) Plant considering Public Satisfaction by an Extended VIKOR Method

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Yunna Wu ◽  
Lei Qin ◽  
Chuanbo Xu ◽  
Shaoyu Ji
Keyword(s):  
Site Selection ◽  
Weighted Average ◽  
Computational Procedure ◽  
Waste To Energy ◽  
Weighted Averaging ◽  
Vikor Method ◽  
The Public ◽  
Evaluation Group ◽  
Whole Life Cycle ◽  
Selection Of

Site selection of waste-to-energy (WtE) plant is critically important in the whole life cycle. Some research has been launched in the WtE plant site selection, but there is still a serious problem called Not In My Back Yard (NIMBY) effect that needs to be solved. To solve the problem, an improved multigroup VIKOR method is proposed to choose the optimal site and compromised sites. In the proposed method, the public satisfaction is fully considered where the public is invited as an evaluation group far more than creating general indicators to represent the public acceptance. First of all, an elaborate criteria system is built to evaluate site options comprehensively and the weights of criteria are identified by Analytic Hierarchy Process (AHP) method. Then, the interval 2-tuple linguistic information is adopted to assess the ratings for the established criteria. The interval 2-tuple linguistic ordered weighted averaging (ITL-OWA) operator is utilized to aggregate the opinions of evaluation committee while the opinions of the public are aggregated using weighted average operator. Finally, a case from south China which shows the computational procedure and the effectiveness of the proposed method is proved. Last but not least, a sensitivity analysis is conducted by comparing the results with different weights of evaluation group assessments.

scholarly journals Induced intuitionistic fuzzy ordered weighted averaging: Weighted average operator and its application to business decision-making

2014 ◽  
Vol 11 (2) ◽  
pp. 839-857 ◽  
Author(s):  
Zeng Shouzhen ◽  
Wang Qifeng ◽  
José Merigó ◽  
Pan Tiejun
Keyword(s):  
Decision Making ◽  
Weighted Average ◽  
Aggregation Operator ◽  
Weighted Averaging ◽  
Business Decision ◽  
Ordered Weighted Averaging ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Weighted Average ◽  
Decision Making Problem ◽  
Selection Of

We present the induced intuitionistic fuzzy ordered weighted averaging-weighted average (I-IFOWAWA) operator. It is a new aggregation operator that uses the intuitionistic fuzzy weighted average (IFWA) and the induced intuitionistic fuzzy ordered weighted averaging (I-IFOWA) operator in the same formulation. We study some of its main properties and we have seen that it has a lot of particular cases such as the IFWA and the intuitionistic fuzzy ordered weighted averaging (IFOWA) operator. We also study its applicability in a decision-making problem concerning strategic selection of investments. We see that depending on the particular type of I-IFOWAWA operator used, the results may lead to different decisions.

scholarly journals Using ground radar overlaps to verify the retrieval of calibration bias estimates from spaceborne platforms

2019 ◽  
Author(s):  
Irene Crisologo ◽  
Maik Heistermann
Keyword(s):  
Moving Average ◽  
Weighted Average ◽  
Tropical Rainfall Measuring Mission ◽  
The Philippines ◽  
Weighted Averaging ◽  
Radar Observations ◽  
Precipitation Measurement ◽  
Formal Procedure ◽  
Calibration Techniques ◽  
Global Precipitation

Abstract. Many institutions struggle to tap the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines which is much affected by partial beam blockage. The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA), in addition to beam blockage. This extension becomes vital for radars that operate at C- or X-band. Correspondingly, the study setup includes a C-band radar which substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.

scholarly journals Seismic Attributes Method for Prediction of Unconsolidated Sand Reservoirs of Heavy Oil

2015 ◽  
Vol 8 (1) ◽  
pp. 14-18 ◽  
Author(s):  
Lei Zhang ◽  
Donghui Zhu ◽  
Xuejuan Zhang
Keyword(s):  
Heavy Oil ◽  
Instantaneous Frequency ◽  
Seismic Attributes ◽  
Oil Reservoirs ◽  
Unconsolidated Sediments ◽  
Burial Depth ◽  
High Porosity ◽  
Reservoir Prediction ◽  
Impedance Inversion ◽  
Heavy Oil Reservoirs

Heavy crude oil is known as oil that is highly viscous and of a higher density than that of conventional oil. Sand reservoirs containing heavy oil generally consist of unconsolidated sediments deposited at a shallow burial depth, with high porosity and permeability. In seismic exploration, acoustic impedance inversion is a commonly used tool in reservoir prediction. However, due to the unconsolidated characteristic of heavy oil reservoirs, the wave impedance difference between heavy oil sandstones and mudstones becomes less apparent, thus limiting the ability of impedance inversion to accurately characterize the reservoir. Therefore we must expand our characterization of the target heavy oil reservoirs to include correlation analysis of different seismic attributes to the unconsolidated reservoir thickness. The results show that there has a strong correlation between the seismic attribute value of instantaneous frequency and unconsolidated reservoir thickness, more than other seismic attributes in the target strata. Thus the instantaneous frequency attribute can be used to predict qualitatively the lateral distribution of unconsolidated reservoirs, which in turn, indicates the vertical variation of thickness for the unconsolidated reservoirs. By using frequency attributes which are sensitive to unconsolidated sediments, coupling with additional geologic information, we can predict the distribution of sedimentary facies accurately in the study area, which results in a more reliable prediction for the lateral and vertical distributions of heavy oil reservoirs.

scholarly journals Pythagorean Fuzzy Soft Einstein Ordered Weighted Average Operator in Sustainable Supplier Selection Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Rana Muhammad Zulqarnain ◽  
Imran Siddique ◽  
Shahzad Ahmad ◽  
Aiyared Iampan ◽  
Goran Jovanov ◽  
...  
Keyword(s):  
Decision Making ◽  
Membership Function ◽  
Weighted Average ◽  
Aggregation Operator ◽  
Soft Set ◽  
Weighted Averaging ◽  
Accurate Estimation ◽  
Sustainable Supply Chain ◽  
Fuzzy Soft Set ◽  
Sustainable Supplier Selection

Pythagorean fuzzy soft set (PFSS) is the most influential and operative extension of the Pythagorean fuzzy set (PFS), which contracts with the parametrized standards of the substitutes. It is also a generalized form of the intuitionistic fuzzy soft set (IFSS) and delivers a well and accurate estimation in the decision-making (DM) procedure. The primary purpose is to prolong and propose ideas related to Einstein’s ordered weighted aggregation operator from fuzzy to PFSS, comforting the condition that the sum of the degrees of membership function and nonmembership function is less than one and the sum of the squares of the degree of membership function and nonmembership function is less than one. We present a novel Pythagorean fuzzy soft Einstein ordered weighted averaging (PFSEOWA) operator based on operational laws for Pythagorean fuzzy soft numbers. Furthermore, some essential properties such as idempotency, boundedness, and homogeneity for the proposed operator have been presented in detail. The choice of a sustainable supplier is also examined as an essential part of sustainable supply chain management (SSCM) and is considered a crucial multiattribute group decision-making (MAGDM) issue. In some MAGDM problems, the relationship between alternatives and uncertain environments will be the main reason for deficient consequences. We have presented a novel aggregation operator for PFSS information to choose sustainable suppliers to cope with those complex issues. The Pythagorean fuzzy soft number (PFSN) helps to represent the obscure information in such real-world perspectives. The priority relationship of PFSS details is beneficial in coping with SSCM. The proposed method’s effectiveness is proved by comparing advantages, effectiveness, and flexibility among the existing studies.

scholarly journals A Single-Valued Neutrosophic Linguistic Combined Weighted Distance Measure and Its Application in Multiple-Attribute Group Decision-Making

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 275 ◽  
Author(s):  
Chengdong Cao ◽  
Shouzhen Zeng ◽  
Dandan Luo
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Distance Measure ◽  
Group Decision ◽  
Weighted Average ◽  
Weighted Averaging ◽  
Low Carbon ◽  
Linguistic Distance ◽  
Weighted Distance ◽  
Multiple Attribute

The aim of this paper is to present a multiple-attribute group decision-making (MAGDM) framework based on a new single-valued neutrosophic linguistic (SVNL) distance measure. By unifying the idea of the weighted average and ordered weighted averaging into a single-valued neutrosophic linguistic distance, we first developed a new SVNL weighted distance measure, namely a SVNL combined and weighted distance (SVNLCWD) measure. The focal characteristics of the devised SVNLCWD are its ability to combine both the decision-makers’ attitudes toward the importance, as well as the weights, of the arguments. Various desirable properties and families of the developed SVNLCWD were contemplated. Moreover, a MAGDM approach based on the SVNLCWD was formulated. Lastly, a real numerical example concerning a low-carbon supplier selection problem was used to describe the superiority and feasibility of the developed approach.

Hybrid Balanced Justified Treynor ratio

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saeid Tajdini ◽  
Mohsen Mehrara ◽  
Reza Tehrani
Keyword(s):  
Regime Switching ◽  
Weighted Average ◽  
First Grade ◽  
Weighted Averaging ◽  
Markov Switching Model ◽  
Final Model ◽  
Content Type ◽  
Switching Model ◽  
The Third ◽  
Markov Regime Switching Model

PurposeRisk and return are the most important components in the financial and investment world and the existence of a better balance between them with the goal of the best solution for investing in different assets has always been studied and discussed by researchers. For this purpose in this study introduced the Hybrid Balanced Justified Treynor ratio (HBJTR) criterion.Design/methodology/approachThis study introduced the HBJTR criterion, which has three major attributes, including combination of both the frequency and severity of the risk using Markov regime switching model which was modeled on the Justified Beta (Jßi). The second is the merger of data of both the cycles of boom and recession, which was modeled on the Hybrid Justified Treynor Ratio (HJTR). The third was the balancing act in two periods of boom and recession, which was introduced on the HBJTR model.FindingsBased on a weighted averaging of the Justified Treynor ratio of both the cycles of boom and recession, which was introduced by the HJTR term in this study, the superiority in the first grade related to the two indexes were sugar index (0.0096) and insurance index (0.0053). Finally, using the final model in this study, namely HBJTR, the overall advantage was the defensive index, i.e. the insurance index of 1.23.Originality/valueIn other words, the HBJTRi criterion consists of three steps: first, the Justified Beta (Jßi) and Justified Treynor ratio of each index using two regimes of Markov switching model were calculated for each of the cycles of boom and recession separately according to formulas 8 and 9. In the second step, the weighted average was taken from both Justified Treynor ratios of boom and recession cycles, which was called the HJTR. In the third step, to calculate the HBJTR criterion

scholarly journals Using ground radar overlaps to verify the retrieval of calibration bias estimates from spaceborne platforms

2020 ◽  
Vol 13 (2) ◽  
pp. 645-659
Author(s):  
Irene Crisologo ◽  
Maik Heistermann
Keyword(s):  
Moving Average ◽  
Weighted Average ◽  
Tropical Rainfall Measuring Mission ◽  
The Philippines ◽  
Weighted Averaging ◽  
Radar Observations ◽  
Precipitation Measurement ◽  
Formal Procedure ◽  
Calibration Techniques ◽  
Global Precipitation

Abstract. Many institutions struggle to tap into the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar (GR) is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines, which is greatly affected by partial beam blockage. The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA) in addition to beam blockage. This extension becomes vital for radars that operate at the C or X band. Correspondingly, the study setup includes a C-band radar that substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.

A theory of palaeoclimate reconstruction

2020 ◽  
Author(s):  
Mengmeng Liu ◽  
Iain Colin Prentice ◽  
Cajo ter Braak ◽  
Sandy Harrison
Keyword(s):  
Climate Models ◽  
Compositional Data ◽  
Likelihood Function ◽  
Multinomial Distribution ◽  
Weighted Average ◽  
Weighted Averaging ◽  
Northern Eurasia ◽  
Data Set ◽  
Best Estimate ◽  
Special Case

<p>Past climate states can be used to test climate models for present-day changes and future responses. Past states can be reconstructed from fossil assemblages, and WA-PLS (weighted averaging–partial least squares) is one of the most widely used statistical methods to do this. However, WA-PLS has a marked bias. Whatever biotic indicator is being used, reconstructed climate values are artificially compressed and biased towards the centre of the range used for calibration.</p><p>Here we developed an improvement of the method, derived rigorously from theory. It makes three assumptions:</p><p>a) the theoretical abundance of each taxon follows a Gaussian (unimodal) curve with respect to each climate variable considered;</p><p>b) the abundances of taxa are compositional data, so they sum to unity and follow a multinomial distribution;</p><p>c) the best estimate of the climate value at the site to be reconstructed maximizes the log-likelihood function – in other words, it minimizes the difference between theoretical and actual abundances as assessed by the likelihood criterion.</p><p>The best estimate of the climate value is approximated by a tolerance-weighted version of the weighted average in which the abundances of taxa are weighted by the inverse square of their tolerances (a measure of the range of environments in which a taxon is found). WA-PLS thus corresponds to the special case when all taxon tolerances are equal. The fact that this special case is far from reality generally is part of the the cause of the “compression and bias”. The new method can be applied using the existing functions for WA-PLS in the R package rioja. We show that it greatly reduces the compression bias in reconstructions based on a large modern pollen data set from Europe, northern Eurasia and the Middle East.</p>

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