Analysis of seismic refraction data is primarily based on interpretation of critical refraction travel times. Application of the principal component analysis pca to hvsr. Seismic interpretation with machine learning geophysical. Feb 23, 2015 principal component analysis pca clearly explained 2015 duration. Fast principal component analysis for stacking seismic. Modal analysis determination of seismic design forces based upon the theoretical response of a structure in its several modes of vibration to excitation. Principal components analysis georgia tech youtube. Spe 56734 principal component analysis applied to 3d seismic.
The loadings were derived from a principal component analysis, a statistical tool capable of reducing problem dimensionality while retaining most of the variance of the original dataset of observations. Principal component analysis for a seismic usability model. Principal component analysis this transform is known as pca the features are the principal components they are orthogonal to each other and produce orthogonal white weights major tool in statistics removes dependencies from multivariate data also known as. Applying principal component analysis to seismic attributes for interpretation of evaporite facies.
Complete the following steps to interpret a principal components analysis. This continues until a total of p principal components have been calculated, equal to the original number of variables. Pdf new interpretation of principal components analysis. It can be used to identify patterns in highly complex datasets and it can tell you. Principal component analysis pca is one of the bestknown multivariate analysis. The other components are computed likewise see appendix a for proof.
A genealogical interpretation of principal components analysis. Principal components analysis spss annotated output. Let us now go back and fine a visual interpretation of equation 1, which you recall was written. Jackson 1991 gives a good, comprehensive, coverage of principal component analysis from a somewhat di. Risk reduction through additional insights to complement interpretation. Principal component analysis and selforganizing maps, authorrocky roden and thomas smith and deborah sacrey, journalinterpretation, year. Most textbooks teach us to perform matrix multiplication by.
Multiattribute analyses employing principal component analysis pca and selforganizing maps are components of a machinelearning interpretation workflow figure 1 that involves the selection of appropriate seismic attributes and the application of these attributes in an unsupervised neural network analysis, also known as a selforganizing. Seismic data is inherently noisy, and is complicated by the presence of an unknown seismic wavelet. Extensive treatments of pca are given by joliffe 2002 for a variety of applications and by preisendorfer 1988, davis 2002 and wilks 2006 in the specific context of geosciences. Pca reduces a large set of seismic attributes to indicate variations in the data. Seismic displays visualization is key to seismic data analysis 3d displays are mostly done interactively using workstations in this plot. Factor analysis and principal component analysis pca. Whereas pca exploits statistical correlation, ica uses statistical independence to glean useful information from data.
Using multidimensional scaling and kernel principal component. Principal and independent component analysis for seismic data. University of bucharest, faculty of geology and geophysics. Principal component analysis is a useful statistical technique that has found many applications, including image compression and pattern. Principal component analysis pca, a linear quantitative technique, has proven to be an excellent approach to understand which seismic attributes or combination of seismic attributes have interpretive significance. Handbook of poststack seismic attributes geophysical references.
Using multidimensional scaling and kernel principal. In pca, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces. Principal and independent component analysis for seismic. The book ends with an overview of how seismic attributes aid data interpretation and. Principal component analysis and selforganizing maps, authorrocky roden and thomas smith and deborah sacrey. Principal component analysis applied to 3d seismic data for reservoir property.
Principal component analysis pca, a linear quantitative technique, has proven to be an. The aim of this work is to investigate the possible use of the principal component analysis pca for the analysis of the multivariate field of hvsr curves. Application of principal component analysis in weighted stacking of seismic data abstract. The quality of stacking will affect the signaltonoise ratio and amplitude fidelity of the stacked image. This manuscript focuses on building a solid intuition for. Marfurt, the university of oklahoma summary seismic attributes are powerful tools that allow interpreters to make a more comprehensive and precise seismic interpretation. This tutorial focuses on building a solid intuition for how and. Keywords principal component analysis multivariate outlier orthogonal factor seismic trace uncorrelated factor. The principal component analysis 16 is defined as a method for. The object of seismic interpretation is to extract all the geologic information possible from the data as it relates to structure, stratigraphy, rock properties, and perhaps reservoir fluid changes in space and time liner, 1999. Principal component analysis pca and selforganizing maps soms provide. Unsupervised seismic facies classification using independent component analysis david luborobles and kurt j. Seismic attributes are an invaluable aid in the interpretation of seismic data.
Neural networks for seismic principal components analysis. A very important classification technique in seismic interpretation. Seismic attribute analysis analysis of unconventional resource plays reservoir geology thickness and lateral extent mineralogy porosity and permeability geochemistry total organic content toc maturity and kerogen richness geomechanics acoustic impedance inversion youngs modulus poissons ratio. Not all the tools in the data analytics component analysis, selforganizing mapping, fuzzy logic, support vector machines, neural networks, etc. Seismic attribute analysis and the use of unsupervised neural networks and principal component analysis in unconventional and conventional reservoirs. Principal component analysis pca is a technique that is useful for the compression and classification of data. To be effective, it is necessary to teach geologists and engineers a bit of geophysics, and geophysicists and engineers a bit of. Download citation geologic pattern recognition from seismic attributes.
Lower triassic jialingjiang formation, sichuan basin, china suyun hu, wenzhi zhao, zhaohui xu, hongliu zeng, qilong fu, lei jiang, shuyuan shi, zecheng wang, and wei liu. Churning seismic attributes with principal component analysis. Over the past two decades, the industry has seen significant advancements in interpretation capabilities, strongly driven by increased computer power and associated. If raw data are used, the procedure will create the original correlation matrix or covariance matrix, as specified by the user.
Machine learning and seismic interpretation seg wiki. This is achieved by transforming to a new set of variables, the principal components pcs, which are uncorrelated, and which are ordered so that the rst few retain most of the variation present in all of the original variables. The emerson seismic interpretation suite is an integrated, modern interpretation and visualization system designed to carry out all seismic interpretation tasks, including structural, stratigraphic, and volume interpretation. Principal component analysis pca in order to focus the data relevant to multiple bands in one band for better visibility from sharp edges band ratio to observe minerals and other complications associated with the fault in this section, the techniques used in this project are discussed. Pdf application of principal component analysis in weighted.
Application of principal component analysis in weighted stacking of seismic data article pdf available in ieee geoscience and remote sensing letters 148. This paper investigates seismic signatures of thin shalysand reservoirs with statistical attributes extracted using multidimensional scaling mds and kernel principal component analysis kpca. Fast principal component analysis for stacking seismic data. Pdf application of principal component analysis in. Seismic data interpretation using the hough transform and principal component analysis. Sabeti 9 proposed an approach for seismic facies interpretation using kmeans. Principal component analysis pca is a mainstay of modern data analysis a black box that is widely used but sometimes poorly understood. Principal component analysis highlighted how structural class is the most relevant parameter, followed by construction timespan. Vertical axis is the twoway reflection travel time, horizontal axes give the collocated sourcereceiver coordinates, color represents reflection amplitudes the colorcoded upper surface is the water bottom.
Application of principal component analysis in weighted. A case study from the bohai bay basin, china hongtao zhu, hongliu zeng, xianghua yang, and yawen he. New interpretation of principal components analysis applied to all points in the space of the standardized primary variables, then all points in the principal component space will be obtained. Index terms data compression, eigenvectors, generalized hebbian algorithm, neural network, principal component analysis pca, ricker wavelets, seismic interpretation. Seismic attributes are an invaluable aid in the interpretation of. The size of the proportion can help you decide whether the principal component is important enough to retain. Pca is a useful statistical technique that has found application in. The higher the proportion, the more variability that the principal component explains. Theres a little variance along the second component now the yaxis, but we can drop this component entirely without significant loss of information. Principal component analysis the central idea of principal component analysis pca is to reduce the dimensionality of a data set consisting of a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. Principal and independent component analysis pca and ica are two ideas which are very much related. The application of principal components analysis to seismic. Faults and fractures detection in 2d seismic data based on principal component analysis poorandokht soltani a, mehrdad soleimani a. The use and interpretation of principal component analysis.
Singular value decomposition and principal component analysis 1 chapter 5 singular value decomposition and principal component analysis in a practical approach to microarray data analysis d. The goal of this paper is to dispel the magic behind this black box. Principal component analysis 3 because it is a variable reduction procedure, principal component analysis is similar in many respects to exploratory factor analysis. Microzonation seismic zoning, generally by use of maps, for land areas smaller than regions shown in typical seismic code maps, but larger than individual building sites. For example, a principal component with a proportion of 0. Jul 27, 2017 suyun hu, wenzhi zhao, zhaohui xu, hongliu zeng, qilong fu, lei jiang, shuyuan shi, zecheng wang, wei liu. Interpolation of seismic data using a principal component analysis pocs approach scott keating and kris innanen abstract projection onto convex sets, or pocs, is a simple, straightforward method of interpolation which hinges on a few basic assumptions. Formerly, a book, now available on cdrom, this title was written to be an introduction to the geologic interpretation of seismic data. A simple principal component analysis example brian russell, august, 2011.
In fact, the steps followed when conducting a principal component analysis are virtually identical to those followed when conducting an exploratory factor analysis. Seismic interpretation of tectonic and paleogeomorphologic controls on sediment dispersal patterns in a continental rift basin. Journal of geophysics and engineering, volume 15, issue 2, april 2018, pages 295306. Investigations thus far indicate the information can be reduced to 10% of the original data base size. Principal component analysis for a seismic usability model of. Author summary genetic variation in natural populations typically demonstrates structure arising from diverse processes including geographical isolation, founder events, migration, and admixture. The central idea of principal component analysis pca is to reduce the dimensionality of a data set consisting of a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. Pdf seismic data interpretation using the hough transform. The handbook of poststack seismic attributes is a general reference for. The second component is computed under the constraint of being orthogonal to the.
This tutorial focuses on building a solid intuition for how and why principal component analysis works. Cu u, where is the symmetric covariance matrix, is an eigenvector and. The purpose is to reduce the dimensionality of a data set sample by finding a new set of variables, smaller than the original set of variables, that nonetheless retains most. Principal components analysis, like factor analysis, can be preformed on raw data, as shown in this example, or on a correlation or a covariance matrix.
Some machine learning applications in seismic interpretation. Application of the principal component analysis pca to. Methodological analysis of principal component analysis pca method. Seismic interpretation better tools, better understanding, in less time. The application of principal components analysis to. The original version of this chapter was written several years ago by chris dracup. Seismic data interpretation using the hough transform and principal component analysis this article has been downloaded from iopscience.
Usually we analyze p wave refraction data, but s wave data occasionally recorded. Principal component analysis pca and selforganizing maps soms provide multiattribute analyses that have proven to be an excellent pattern recognition approach in the seismic interpretation workflow. Methodological analysis of principal component analysis. The second principal component is calculated in the same way, with the condition that it is uncorrelated with i. Dec 16, 2016 multiattribute analyses employing principal component analysis pca and selforganizing maps are components of a machinelearning interpretation workflow figure 1 that involves the selection of appropriate seismic attributes and the application of these attributes in an unsupervised neural network analysis, also known as a selforganizing. The object of seismic interpretation is to extract all the geologic. Principal component analysis pca as one of the most popular multivariate data analysis methods. A tutorial on principal component analysis derivation. Apr 02, 2018 principal component analysis, is one of the most useful data analysis and machine learning methods out there. Pdf principal component analysis as a tool for enhanced well. Characterizing thin shalysand reservoirs using seismic data can be challenging due to limited seismic resolution. Unsupervised seismic facies classification using independent. The theoreticians and practitioners can also benefit from a detailed description of the pca applying on a certain set of data.
Principal component analysis pca is a mathematical analytical approach that extracts the. It is a linear mathematical method that reduces large sets of seismic attributes to a smaller set of attributes that still have most of the variations of the larger set. Faults and fractures detection in 2d seismic data based on. The application of principal components analysis takes advantage of the high degree of redundancy in the seismic data set to determine its statistical behavior and reduce it to its essential features. Interpret the key results for principal components analysis. Key output includes the eigenvalues, the proportion of variance that the component explains, the coefficients, and several graphs. This tutorial is designed to give the reader an understanding of principal components analysis pca. Interpolation of seismic data using a principal component. Seismic interpreters can be geologists, geophysicists, and even engineers.
If the pdf is narrow, entropy is low, and if the pdf is broad, entropy is high. Optimal stacking of multiple data sets plays a significant role in many scientific domains. Principal component analysis and selforganizing maps interpretation of seismic. Pca is a statistical approach used for reducing the number of variables which is most widely used in face recognition. Principal component analysis pca is a mainstay of modern data analysis a black box that is widely used but poorly understood.
Geo expro seismic interpretation with machine learning. Interpretation of seismic reflection data routinely involves powerful. One technique commonly used to uncover such structure is principal components analysis, which identifies the primary axes of variation in data and projects the samples onto these axes in a graphically. Principal component analysis pca results from 12 seismic attributes. Pdf geologic pattern recognition from seismic attributes. Geologic pattern recognition from seismic attributes. Principal component analysis is the most widely used method of multivariate data analysis owing to the simplicity of its algebra and to its straightforward interpretation. Seismic interpretation with machine learning geophysical insights.
Interpretation of seismic reflection data involves powerful multiplecpu computers, advanced visualization techniques, and generation of. The theorem is applied to the interpretation of a fault seismogram and the uniform property of other seismograms. Lower triassic jialingjiang formation, sichuan basin, china. The purpose is to reduce the dimensionality of a data set sample by finding a new set of variables, smaller than the original set of variables, that nonetheless retains most of the samples information. A simple principal component analysis example brian.
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