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Data analysis
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Data analysis, also known as analysis of data or data analytics, is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.
Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing on business information.[1] In statistical applications data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.
Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The term data analysis is sometimes used as a synonym for data modeling.
Contents
[hide]
1 The process of data analysis
1.1 Data requirements
1.2 Data collection
1.3 Data processing
1.4 Data cleaning
1.5 Exploratory data analysis
1.6 Modeling and algorithms
1.7 Data product
1.8 Communication
2 Quantitative messages
3 Techniques for analyzing quantitative data
4 Analytical activities of data users
5 Barriers to effective analysis
5.1 Confusing fact and opinion
5.2 Cognitive biases
5.3 Innumeracy
6 Other topics
6.1 Smart buildings
6.2 Analytics and business intelligence
6.3 Education
7 Practitioner notes
7.1 Initial data analysis
7.1.1 Quality of data
7.1.2 Quality of measurements
7.1.3 Initial transformations
7.1.4 Did the implementation of the study fulfill the intentions of the research design?
7.1.5 Characteristics of data sample
7.1.6 Final stage of the initial data analysis
7.1.7 Analysis
7.1.8 Nonlinear analysis
7.2 Main data analysis
7.2.1 Exploratory and confirmatory approaches
7.2.2 Stability of results
7.2.3 Statistical methods
8 Free software for data analysis
9 International data analysis contests
10 See also
11 References
11.1 Citations
11.2 Bibliography
12 Further reading
The process of data analysis[edit]
Data science process flowchart from "Doing Data Science", Cathy O'Neil and Rachel Schutt, 2013
Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses or disprove theories.[2]
Statistician John Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."[3]
There are several phases that can be distinguished, described below. The phases are iterative, in that feedback from later phases may result in additional work in earlier phases.[4]
Data requirements[edit]
The data is necessary as inputs to the analysis are specified based upon the requirements of those directing the analysis or customers who will use the finished product of the analysis. The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers).[4]
Data collection[edit]
Data is collected from a variety of sources. The requirements may be communicated by analysts to custodians of the data, such as information technology personnel within an organization. The data may also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation.[4]
Data processing[edit]
The phases of the intelligence cycle used to convert raw information into actionable intelligence or knowledge are conceptually similar to the phases in data analysis.
Data initially obtained must be processed or organised for analysis. For instance, these may involve placing data into rows and columns in a table format (i.e., structured data) for further analysis, such as within a spreadsheet or statistical software.[4]
Data cleaning[edit]
Once processed and organised, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that data is entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data,[5] deduplication, and column segmentation.[6] Such data problems can also be identified through a variety of analytical techniques. For example, with financial information, the totals for particular variables may be compared against separately published numbers believed to be reliable.[7] Unusual amounts above or below pre-determined thresholds may also be reviewed. There are several types of data cleaning that depend on the type of data such as phone numbers, email addresses, employers etc. Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. Textual data spell checkers can be used to lessen the amount of mistyped words, but it is harder to tell if the words themselves are correct.[8]
Exploratory data analysis[edit]
Once the data is cleaned, it can be analyzed. Analysts may apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data.[9][10] The process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Descriptive statistics such as the average or median may be generated to help understand the data. Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data.[4]
Modeling and algorithms[edit]
Mathematical formulas or models called algorithms may be applied to the data to identify relationships among the variables, such as correlation or causation. In general terms, models may be developed to evaluate a particular variable in the data based on other variable(s) in the data, with some residual error depending on model accuracy (i.e., Data = Model + Error).[2]
Inferential statistics includes techniques to measure relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X) explains the variation in sales (dependent variable Y). In mathematical terms, Y (sales) is a function of X (advertising). It may be described as Y = aX + b + error, where the model is designed such that a and b minimize the error when the model predicts Y for a given range of values of X. Analysts may attempt to build models that are descriptive of the data to simplify analysis and communicate results.[2]
Data product[edit]
A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. An example is an application that analyzes data about customer purchasing history and recommends other purchases the customer might enjoy.[4]
Communication[edit]
Data visualization to understand the results of a data analysis.[11]
Main article: Data visualization
Once the data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative.[4]
When determining how to communicate the results, the analyst may consider data visualization techniques to help clearly and efficiently communicate the message to the audience. Data visualization uses information displays such as tables and charts to help communicate key messages contained in the data. Tables are helpful to a user who might lookup specific numbers, while charts (e.g., bar charts or line charts) may help explain the quantitative messages contained in the data.
Quantitative messages[edit]
Main article: Data visualization
A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time.
A scatterplot illustrating correlation between two variables (inflation and unemployment) measured at points in time.
Author Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message. Customers specifying requirements and analysts performing the data analysis may consider these messages during the course of the process.
Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend.
Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by sales persons (the category, with each sales person a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the sales persons.
Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.
Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show comparison of the actual versus the reference amount.
Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0–10%, 11–20%, etc. A histogram, a type of bar chart, may be used for this analysis.
Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message.
Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.
Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A cartogram is a typical graphic used.[12][13]
Techniques for analyzing quantitative data[edit]
See also: Problem solving
Author Jonathan Koomey has recommended a series of best practices for understanding quantitative data. These include:
Check raw data for anomalies prior to performing your analysis;
Re-perform important calculations, such as verifying columns of data that are formula driven;
Confirm main totals are the sum of subtotals;
Check relationships between numbers that should be related in a predictable way, such as ratios over time;
Normalize numbers to make comparisons easier, such as analyzing amounts per person or relative to GDP or as an index value relative to a base year;
Break problems into component parts by analyzing factors that led to the results, such as DuPont analysis of return on equity.[7]
For the variables under examination, analysts typically obtain descriptive statistics for them, such as the mean (average), median, and standard deviation. They may also analyze the distribution of the key variables to see how the individual values cluster around the mean.
An illustration of the MECE principle used for data analysis.
The consultants at McKinsey and Company named a technique for breaking a quantitative problem down into its component parts called the MECE principle. Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them. The relationship is referred to as "Mutually Exclusive and Collectively Exhaustive" or MECE. For example, profit by definition can be broken down into total revenue and total cost. In turn, total revenue can be analyzed by its components, such as revenue of divisions A, B, and C (which are mutually exclusive of each other) and should add to the total revenue (collectively exhaustive).
Analysts may use robust statistical measurements to solve certain analytical problems. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that state of affairs is true or false. For example, the hypothesis might be that "Unemployment has no effect on inflation", which relates to an economics concept called the Phillips Curve. Hypothesis testing involves considering the likelihood of Type I and type II errors, which relate to whether the data supports accepting or rejecting the hypothesis.
Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate (Y)?"). This is an attempt to model or fit an equation line or curve to the data, such that Y is a function of X.
Necessary condition analysis (NCA) may be used when the analyst is trying to determine the extent to which independent variable X allows variable Y (e.g., "To what extent is a certain unemployment rate (X) necessary for a certain inflation rate (Y)?"). Whereas (multiple) regression analysis uses additive logic where each X-variable can produce the outcome and the X's can compensate for each other (they are sufficient but not necessary), necessary condition analysis (NCA) uses necessity logic, where one or more X-variables allow the outcome to exist, but may not produce it (they are necessary but not sufficient). Each single necessary condition must be present and compensation is not possible.
Analytical activities of data users[edit]
Users may have particular data points of interest within a data set, as opposed to general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points.[14][15][16][17]
#
Task
General
Description
Pro Forma
Abstract
Examples
1 Retrieve Value Given a set of specific cases, find attributes of those cases. What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}? - What is the mileage per gallon of the Ford Mondeo?
- How long is the movie Gone with the Wind?
2 Filter Given some concrete conditions on attribute values, find data cases satisfying those conditions. Which data cases satisfy conditions {A, B, C...}? - What Kellogg's cereals have high fiber?
- What comedies have won awards?
- Which funds underperformed the SP-500?
3 Compute Derived Value Given a set of data cases, compute an aggregate numeric representation of those data cases. What is the value of aggregation function F over a given set S of data cases? - What is the average calorie content of Post cereals?
- What is the gross income of all stores combined?
- How many manufacturers of cars are there?
4 Find Extremum Find data cases possessing an extreme value of an attribute over its range within the data set. What are the top/bottom N data cases with respect to attribute A? - What is the car with the highest MPG?
- What director/film has won the most awards?
- What Marvel Studios film has the most recent release date?
5 Sort Given a set of data cases, rank them according to some ordinal metric. What is the sorted order of a set S of data cases according to their value of attribute A? - Order the cars by weight.
- Rank the cereals by calories.
6 Determine Range Given a set of data cases and an attribute of interest, find the span of values within the set. What is the range of values of attribute A in a set S of data cases? - What is the range of film lengths?
- What is the range of car horsepowers?
- What actresses are in the data set?
7 Characterize Distribution Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute’s values over the set. What is the distribution of values of attribute A in a set S of data cases? - What is the distribution of carbohydrates in cereals?
- What is the age distribution of shoppers?
8 Find Anomalies Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers. Which data cases in a set S of data cases have unexpected/exceptional values? - Are there exceptions to the relationship between horsepower and acceleration?
- Are there any outliers in protein?
9 Cluster Given a set of data cases, find clusters of similar attribute values. Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}? - Are there groups of cereals w/ similar fat/calories/sugar?
- Is there a cluster of typical film lengths?
10 Correlate Given a set of data cases and two attributes, determine useful relationships between the values of those attributes. What is the correlation between attributes X and Y over a given set S of data cases? - Is there a correlation between carbohydrates and fat?
- Is there a correlation between country of origin and MPG?
- Do different genders have a preferred payment method?
- Is there a trend of increasing film length over the years?
11 Contextualization [17] Given a set of data cases, find contextual relevancy of the data to the users. Which data cases in a set S of data cases are relevant to the current users' context? - Are there groups of restaurants that have foods based on my current caloric intake?
Barriers to effective analysis[edit]
Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis.
Confusing fact and opinion[edit]
You are entitled to your own opinion, but you are not entitled to your own facts.
Daniel Patrick Moynihan
Effective analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion, or test hypotheses. Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. For example, in August 2010, the Congressional Budget Office (CBO) estimated that extending the Bush tax cuts of 2001 and 2003 for the 2011–2020 time period would add approximately $3.3 trillion to the national debt.[18] Everyone should be able to agree that indeed this is what CBO reported; they can all examine the report. This makes it a fact. Whether persons agree or disagree with the CBO is their own opinion.
As another example, the auditor of a public company must arrive at a formal opinion on whether financial statements of publicly traded corporations are "fairly stated, in all material respects." This requires extensive analysis of factual data and evidence to support their opinion. When making the leap from facts to opinions, there is always the possibility that the opinion is erroneous.
Cognitive biases[edit]
There are a variety of cognitive biases that can adversely affect analysis. For example, confirmation bias is the tendency to search for or interpret information in a way that confirms one's preconceptions. In addition, individuals may discredit information that does not support their views.
Analysts may be trained specifically to be aware of these biases and how to overcome them. In his book Psychology of Intelligence Analysis, retired CIA analyst Richards Heuer wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions. He emphasized procedures to help surface and debate alternative points of view.[19]
Innumeracy[edit]
Effective analysts are generally adept with a variety of numerical techniques. However, audiences may not have such literacy with numbers or numeracy; they are said to be innumerate. Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques.[20]
For example, whether a number is rising or falling may not be the key factor. More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy (GDP) or the amount of cost relative to revenue in corporate financial statements. This numerical technique is referred to as normalization[7] or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation (i.e., comparing real vs. nominal data) or considering population increases, demographics, etc. Analysts apply a variety of techniques to address the various quantitative messages described in the section above.
Analysts may also analyze data under different assumptions or scenarios. For example, when analysts perform financial statement analysis, they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock. Similarly, the CBO analyzes the effects of various policy options on the government's revenue, outlays and deficits, creating alternative future scenarios for key measures.
Other topics[edit]
Smart buildings[edit]
A data analytics approach can be used in order to predict energy consumption in buildings.[21] The different steps of the data analysis process are carried out in order to realise smart buildings, where the building management and control operations including heating, ventilation, air conditioning, lighting and security are realised automatically by miming the needs of the building users and optimising resources like energy and time.
Analytics and business intelligence[edit]
Main article: Analytics
Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions." It is a subset of business intelligence, which is a set of technologies and processes that use data to understand and analyze business performance.[22]
Education[edit]
Analytic activities of data visualization users
In education, most educators have access to a data system for the purpose of analyzing student data.[23] These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system and making key package/display and content decisions) to improve the accuracy of educators’ data analyses.[24]
Practitioner notes[edit]
This section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a Wikipedia article.
Initial data analysis[edit]
The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:[25]
Quality of data[edit]
The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms, n: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.
Test for common-method variance.
The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.[26]
Quality of measurements[edit]
The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature.
There are two ways to assess measurement: [NOTE: only one way seems to be listed]
Analysis of homogeneity (internal consistency), which gives an indication of the reliability of a measurement instrument. During this analysis, one inspects the variances of the items and the scales, the Cronbach's α of the scales, and the change in the Cronbach's alpha when an item would be deleted from a scale[27]
Initial transformations[edit]
After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase.[28]
Possible transformations of variables are:[29]
Square root transformation (if the distribution differs moderately from normal)
Log-transformation (if the distribution differs substantially from normal)
Inverse transformation (if the distribution differs severely from normal)
Make categorical (ordinal / dichotomous) (if the distribution differs severely from normal, and no transformations help)
Did the implementation of the study fulfill the intentions of the research design?[edit]
One should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups.
If the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in sample.
Other possible data distortions that should be checked are:
dropout (this should be identified during the initial data analysis phase)
Item nonresponse (whether this is random or not should be assessed during the initial data analysis phase)
Treatment quality (using manipulation checks).[30]
Characteristics of data sample[edit]
In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis phase.
The characteristics of the data sample can be assessed by looking at:
Basic statistics of important variables
Scatter plots
Correlations and associations
Cross-tabulations[31]
Final stage of the initial data analysis[edit]
During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.
Also, the original plan for the main data analyses can and should be specified in more detail or rewritten.
In order to do this, several decisions about the main data analyses can and should be made:
In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method?
In the case of missing data: should one neglect or impute the missing data; which imputation technique should be used?
In the case of outliers: should one use robust analysis techniques?
In case items do not fit the scale: should one adapt the measurement instrument by omitting items, or rather ensure comparability with other (uses of the) measurement instrument(s)?
In the case of (too) small subgroups: should one drop the hypothesis about inter-group differences, or use small sample techniques, like exact tests or bootstrapping?
In case the randomization procedure seems to be defective: can and should one calculate propensity scores and include them as covariates in the main analyses?[32]
Analysis[edit]
Several analyses can be used during the initial data analysis phase:[33]
Univariate statistics (single variable)
Bivariate associations (correlations)
Graphical techniques (scatter plots)
It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:[34]
Nominal and ordinal variables
Frequency counts (numbers and percentages)
Associations
circumambulations (crosstabulations)
hierarchical loglinear analysis (restricted to a maximum of 8 variables)
loglinear analysis (to identify relevant/important variables and possible confounders)
Exact tests or bootstrapping (in case subgroups are small)
Computation of new variables
Continuous variables
Distribution
Statistics (M, SD, variance, skewness, kurtosis)
Stem-and-leaf displays
Box plots
Nonlinear analysis[edit]
Nonlinear analysis will be necessary when the data is recorded from a nonlinear system. Nonlinear systems can exhibit complex dynamic effects including bifurcations, chaos, harmonics and subharmonics that cannot be analyzed using simple linear methods. Nonlinear data analysis is closely related to nonlinear system identification.[35]
Main data analysis[edit]
In the main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.[36]
Exploratory and confirmatory approaches[edit]
In the main analysis phase either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are tested.
Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.[37]
Stability of results[edit]
It is important to obtain some indication about how generalizable the results are.[38] While this is hard to check, one can look at the stability of the results. Are the results reliable and reproducible? There are two main ways of doing this:
Cross-validation: By splitting the data in multiple parts we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well.
Sensitivity analysis: A procedure to study the behavior of a system or model when global parameters are (systematically) varied. One way to do this is with bootstrapping.
Statistical methods[edit]
Many statistical methods have been used for statistical analyses. A very brief list of four of the more popular methods is:
General linear model: A widely used model on which various methods are based (e.g. t test, ANOVA, ANCOVA, MANOVA). Usable for assessing the effect of several predictors on one or more continuous dependent variables.
Generalized linear model: An extension of the general linear model for discrete dependent variables.
Structural equation modelling: Usable for assessing latent structures from measured manifest variables.
Item response theory: Models for (mostly) assessing one latent variable from several binary measured variables (e.g. an exam).
Free software for data analysis[edit]
DevInfo – a database system endorsed by the United Nations Development Group for monitoring and analyzing human development.
ELKI – data mining framework in Java with data mining oriented visualization functions.
KNIME – the Konstanz Information Miner, a user friendly and comprehensive data analytics framework.
Orange – A visual programming tool featuring interactive data visualization and methods for statistical data analysis, data mining, and machine learning.
PAST – free software for scientific data analysis
PAW – FORTRAN/C data analysis framework developed at CERN
R – a programming language and software environment for statistical computing and graphics.
ROOT – C++ data analysis framework developed at CERN
SciPy and Pandas – Python libraries for data analysis
International data analysis contests[edit]
Different companies or organizations hold a data analysis contests to encourage researchers utilize their data or to solve a particular question using data analysis. A few examples of well-known international data analysis contests are as follows.
Kaggle competition held by Kaggle[39]
LTPP data analysis contest held by FHWA and ASCE[40].[41]
See also[edit]
statistics portal
Actuarial science
Analytics
Big data
Business intelligence
Censoring (statistics)
Computational physics
Data acquisition
Data blending
Data governance
Data mining
Data Presentation Architecture
Data science
Digital signal processing
Dimension reduction
Early case assessment
Exploratory data analysis
Fourier analysis
Machine learning
Multilinear PCA
Multilinear subspace learning
Multiway data analysis
Nearest neighbor search
Nonlinear system identification
Predictive analytics
Principal component analysis
Qualitative research
Scientific computing
Structured data analysis (statistics)
System identification
Test method
Text analytics
Unstructured data
Wavelet
References[edit]
Citations[edit]
Jump up ^ Exploring Data Analysis
^ Jump up to: a b c Judd, Charles and, McCleland, Gary (1989). Data Analysis. Harcourt Brace Jovanovich. ISBN 0-15-516765-0.
Jump up ^ John Tukey-The Future of Data Analysis-July 1961
^ Jump up to: a b c d e f g O'Neil, Cathy and, Schutt, Rachel (2013). Doing Data Science. O'Reilly. ISBN 978-1-449-35865-5.
Jump up ^ Clean Data in CRM: The Key to Generate Sales-Ready Leads and Boost Your Revenue Pool Retrieved 29th July, 2016
Jump up ^ "Data Cleaning". Microsoft Research. Retrieved 26 October 2013.
^ Jump up to: a b c Perceptual Edge-Jonathan Koomey-Best practices for understanding quantitative data-February 14, 2006
Jump up ^ Hellerstein, Joseph (27 February 2008). "Quantitative Data Cleaning for Large Databases" (PDF). EECS Computer Science Division: 3. Retrieved 26 October 2013.
Jump up ^ Stephen Few-Perceptual Edge-Selecting the Right Graph For Your Message-September 2004
Jump up ^ Behrens-Principles and Procedures of Exploratory Data Analysis-American Psychological Association-1997
Jump up ^ Grandjean, Martin (2014). "La connaissance est un réseau" (PDF). Les Cahiers du Numérique. 10 (3): 37–54. doi:10.3166/lcn.10.3.37-54.
Jump up ^ Stephen Few-Perceptual Edge-Selecting the Right Graph for Your Message-2004
Jump up ^ Stephen Few-Perceptual Edge-Graph Selection Matrix
Jump up ^ Robert Amar, James Eagan, and John Stasko (2005) "Low-Level Components of Analytic Activity in Information Visualization"
Jump up ^ William Newman (1994) "A Preliminary Analysis of the Products of HCI Research, Using Pro Forma Abstracts"
Jump up ^ Mary Shaw (2002) "What Makes Good Research in Software Engineering?"
^ Jump up to: a b "ConTaaS: An Approach to Internet-Scale Contextualisation for Developing Efficient Internet of Things Applications". ScholarSpace. HICSS50. Retrieved May 24, 2017.
Jump up ^ "Congressional Budget Office-The Budget and Economic Outlook-August 2010-Table 1.7 on Page 24" (PDF). Retrieved 2011-03-31.
Jump up ^ "Introduction". cia.gov.
Jump up ^ Bloomberg-Barry Ritholz-Bad Math that Passes for Insight-October 28, 2014
Jump up ^ González-Vidal, Aurora; Moreno-Cano, Victoria (2016). "Towards energy efficiency smart buildings models based on intelligent data analytics". Procedia Computer Science. 83 (Elsevier): 994–999. doi:10.1016/j.procs.2016.04.213.
Jump up ^ Davenport, Thomas and, Harris, Jeanne (2007). Competing on Analytics. O'Reilly. ISBN 978-1-4221-0332-6.
Jump up ^ Aarons, D. (2009). Report finds states on course to build pupil-data systems. Education Week, 29(13), 6.
Jump up ^ Rankin, J. (2013, March 28). How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help. Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.
Jump up ^ Adèr 2008a, p. 337.
Jump up ^ Adèr 2008a, pp. 338-341.
Jump up ^ Adèr 2008a, pp. 341-342.
Jump up ^ Adèr 2008a, p. 344.
Jump up ^ Tabachnick & Fidell, 2007, p. 87-88.
Jump up ^ Adèr 2008a, pp. 344-345.
Jump up ^ Adèr 2008a, p. 345.
Jump up ^ Adèr 2008a, pp. 345-346.
Jump up ^ Adèr 2008a, pp. 346-347.
Jump up ^ Adèr 2008a, pp. 349-353.
Jump up ^ Billings S.A. "Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains". Wiley, 2013
Jump up ^ Adèr 2008b, p. 363.
Jump up ^ Adèr 2008b, pp. 361-362.
Jump up ^ Adèr 2008b, pp. 361-371.
Jump up ^ "The machine learning community takes on the Higgs". Symmetry Magazine. July 15, 2014. Retrieved 14 January 2015.
Jump up ^ Nehme, Jean (September 29, 2016). "LTPP International Data Analysis Contest". Federal Highway Administration. Retrieved October 22, 2017.
Jump up ^ "Data.Gov:Long-Term Pavement Performance (LTPP)". May 26, 2016. Retrieved November 10, 2017.
Bibliography[edit]
Adèr, Herman J. (2008a). "Chapter 14: Phases and initial steps in data analysis". In Adèr, Herman J.; Mellenbergh, Gideon J.; Hand, David J. Advising on research methods : a consultant's companion. Huizen, Netherlands: Johannes van Kessel Pub. pp. 333–356. ISBN 9789079418015. OCLC 905799857.
Adèr, Herman J. (2008b). "Chapter 15: The main analysis phase". In Adèr, Herman J.; Mellenbergh, Gideon J.; Hand, David J. Advising on research methods : a consultant's companion. Huizen, Netherlands: Johannes van Kessel Pub. pp. 357–386. ISBN 9789079418015. OCLC 905799857.
Tabachnick, B.G. & Fidell, L.S. (2007). Chapter 4: Cleaning up your act. Screening data prior to analysis. In B.G. Tabachnick & L.S. Fidell (Eds.), Using Multivariate Statistics, Fifth Edition (pp. 60–116). Boston: Pearson Education, Inc. / Allyn and Bacon.
Further reading[edit]
Wikiversity has learning resources about Data analysis
Adèr, H.J. & Mellenbergh, G.J. (with contributions by D.J. Hand) (2008). Advising on Research Methods: A Consultant's Companion. Huizen, the Netherlands: Johannes van Kessel Publishing.
ASTM International (2002). Manual on Presentation of Data and Control Chart Analysis, MNL 7A, ISBN 0-8031-2093-1
Chambers, John M.; Cleveland, William S.; Kleiner, Beat; Tukey, Paul A. (1983). Graphical Methods for Data Analysis, Wadsworth/Duxbury Press. ISBN 0-534-98052-X
Chekanov, S. (2016) Numeric Computation and Statistical Data Analysis on the Java Platform, Springer. ISBN 978-3-319-28531-3
Fandango, Armando (2008). Python Data Analysis, 2nd Edition. Packt Publishers.
Juran, Joseph M.; Godfrey, A. Blanton (1999). Juran's Quality Handbook, 5th Edition. New York: McGraw Hill. ISBN 0-07-034003-X
Lewis-Beck, Michael S. (1995). Data Analysis: an Introduction, Sage Publications Inc, ISBN 0-8039-5772-6
NIST/SEMATECH (2008) Handbook of Statistical Methods,
Pyzdek, T, (2003). Quality Engineering Handbook, ISBN 0-8247-4614-7
Richard Veryard (1984). Pragmatic Data Analysis. Oxford : Blackwell Scientific Publications. ISBN 0-632-01311-7
Tabachnick, B.G.; Fidell, L.S. (2007). Using Multivariate Statistics, 5th Edition. Boston: Pearson Education, Inc. / Allyn and Bacon, ISBN 978-0-205-45938-4
Vance (September 8, 2011). "Data Analytics: Crunching the Future". Bloomberg Businessweek. Retrieved 26 September 2011.
Hair, Joseph (2008). Marketing Research 4th ed. McGraw Hill. Data Analysis: Testing for Association ISBN 0-07-340470-5
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Mantle plume
From Wikipedia, the free encyclopedia
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A superplume generated by cooling processes in the mantle (LVZ=low-velocity zone)[1]
A mantle plume is an upwelling of abnormally hot rock within the Earth's mantle, first proposed by J. Tuzo Wilson in 1963.[2] As the heads of mantle plumes can partly melt when they reach shallow depths, they are often invoked as the cause of volcanic hotspots, such as Hawaii or Iceland, and flood basalts such as the Deccan and Siberian traps. Some such volcanic regions lie far from tectonic plate boundaries, while others represent unusually large-volume volcanism near plate boundaries or in large igneous provinces.
A mantle plume is posited to exist where hot rock nucleates at the core-mantle boundary and rises through the Earth's mantle becoming a diapir in the Earth's crust.[3] The currently active volcanic centers are known as hotspots. In particular, the concept that mantle plumes are fixed relative to one another, and anchored at the core-mantle boundary, would provide a natural explanation for the time-progressive chains of older volcanoes seen extending out from some such hot spots, such as the Hawaiian–Emperor seamount chain.
The hypothesis of mantle plumes from depth is not universally accepted as explaining all such volcanism. It has required progressive hypothesis-elaboration leading to variant propositions such as mini-plumes and pulsing plumes. Another hypothesis for unusual volcanic regions is the "Plate model". This proposes shallower, passive leakage of magma from the mantle onto the Earth's surface where extension of the lithosphere permits it, attributing most volcanism to plate tectonic processes, with volcanoes far from plate boundaries resulting from intraplate extension.[4]
Contents
[hide]
1 Concepts
1.1 Chemistry, heat flow and melting
2 The lower mantle and the core
3 Evidence for the theory
3.1 Linear volcanic chains
3.2 Noble gas and other isotopes
3.3 Geophysical anomalies
3.4 Geochemistry
3.5 Seismology
4 Suggested mantle plume locations
5 Alternative hypotheses
5.1 The plate hypothesis
5.2 The impact hypothesis
5.3 Comparison of the hypotheses
6 See also
7 References
8 External links
Concepts[edit]
In 1971, geophysicist W. Jason Morgan further developed the hypothesis of mantle plumes. In this hypothesis, convection in the mantle transports heat from the core to the Earth's surface in thermal diapirs. In this concept, two largely independent convective processes occur in the mantle: the broad convective flow associated with plate tectonics, which is driven primarily by the sinking of cold plates of lithosphere back into the mantle asthenosphere, and mantle plumes, which carry heat upward in narrow, rising columns, driven by heat exchange across the core-mantle boundary. The latter type of convection is postulated to be independent of plate motions.
The sizes and occurrence of mushroom mantle plumes can be predicted easily by transient instability theory developed by Tan and Thorpe.[5][6] The theory predicts mushroom mantle plumes of about 2000 km diameter with a critical time[clarification needed] of about 830 Myr for a core mantle heat flux of 20 mW/m2, while the cycle time[clarification needed] is about 2 Gyr.[7] The number of mantle plumes is predicted to be about 17.
The plume hypothesis was studied using laboratory experiments conducted in small fluid-filled tanks in the early 1970s.[8] Thermal or compositional fluid-dynamical plumes produced in that way were presented as models for the much larger postulated mantle plumes. On the basis of these experiments, mantle plumes are now postulated to comprise two parts: a long thin conduit connecting the top of the plume to its base, and a bulbous head that expands in size as the plume rises. The entire structure is considered to resemble a mushroom. The bulbous head of thermal plumes forms because hot material moves upward through the conduit faster than the plume itself rises through its surroundings. In the late 1980s and early 1990s, experiments with thermal models showed that as the bulbous head expands it may entrain some of the adjacent mantle into the head.
When a plume head encounters the base of the lithosphere, it is expected to flatten out against this barrier and to undergo widespread decompression melting to form large volumes of basalt magma. It may then erupt onto the surface. Numerical modelling predicts that melting and eruption will take place over several million years.[9] These eruptions have been linked to flood basalts, although many of those erupt over much shorter time scales (less than 1 million years). Examples include the Deccan traps in India, the Siberian traps of Asia, the Karoo-Ferrar basalts/dolerites in South Africa and Antarctica, the Paraná and Etendeka traps in South America and Africa (formerly a single province separated by opening of the South Atlantic Ocean), and the Columbia River basalts of North America. Flood basalts in the oceans are known as oceanic plateaus, and include the Ontong Java plateau of the western Pacific Ocean and the Kerguelen Plateau of the Indian Ocean.
The narrow vertical pipe, or conduit, postulated to connect the plume head to the core-mantle boundary, is viewed as providing a continuous supply of magma to a fixed location, often referred to as a "hotspot". As the overlying tectonic plate (lithosphere) moves over this hotspot, the eruption of magma from the fixed conduit onto the surface is expected to form a chain of volcanoes that parallels plate motion.[10] The Hawaiian Islands chain in the Pacific Ocean is the type example. Interestingly, it has recently been discovered that the volcanic locus of this chain has not been fixed over time, and it thus joined the club of the many type examples that do not exhibit the key characteristic originally proposed.[11]
The eruption of continental flood basalts is often associated with continental rifting and breakup. This has led to the hypothesis that mantle plumes contribute to continental rifting and the formation of ocean basins. In the context of the alternative "Plate model", continental breakup is a process integral to plate tectonics, and massive volcanism occurs as a natural consequence when it onsets.[12]
The current mantle plume theory is that material and energy from Earth's interior are exchanged with the surface crust in two distinct modes: the predominant, steady state plate tectonic regime driven by upper mantle convection, and a punctuated, intermittently dominant, mantle overturn regime driven by plume convection.[3] This second regime, while often discontinuous, is periodically significant in mountain building[13] and continental breakup.[14]
Chemistry, heat flow and melting[edit]
Hydrodynamic simulation of a single "finger" of the Rayleigh–Taylor instability, a possible mechanism for plume formation.[15] In the third and fourth frame in the sequence, the plume forms a "mushroom cap". Note that the core is at the top of the diagram and the crust is at the bottom.
See also: Rayleigh–Taylor instability
Earth cross-section showing location of upper (3) and lower (5) mantle, D″-layer (6), and outer (7) and inner (9) core
The chemical and isotopic composition of basalts found at hotspots differs subtly from mid-ocean-ridge basalts. This geochemical signature arises from the mixing of near-surface materials such as subducted slabs and continental sediments, in the mantle source. There are two competing interpretations for this. In the context of mantle plumes, the near-surface material is postulated to have been transported down to the core-mantle boundary by subducting slabs, and to have been transported back up to the surface by plumes. In the context of the Plate hypothesis, subducted material is mostly re-circulated in the shallow mantle and tapped from there by volcanoes.
The processing of oceanic crust, lithosphere, and sediment through a subduction zone decouples the water-soluble trace elements (e.g., K, Rb, Th) from the immobile trace elements (e.g., Ti, Nb, Ta), concentrating the immobile elements in the oceanic slab (the water-soluble elements are added to the crust in island arc volcanoes). Seismic tomography shows that subducted oceanic slabs sink as far as the bottom of the mantle transition zone at 650 km depth. Subduction to greater depths is less certain, but there is evidence that they may sink to mid-lower-mantle depths at about 1,500 km depth.
The source of mantle plumes is postulated to be the core-mantle boundary at 3,000 km depth.[16] Because there is little material transport across the core-mantle boundary, heat transfer must occur by conduction, with adiabatic gradients above and below this boundary. The core-mantle boundary is a strong thermal (temperature) discontinuity. The temperature of the core is approximately 1,000 degrees Celsius higher than that of the overlying mantle. Plumes are postulated to rise as the base of the mantle becomes hotter and more buoyant.
Plumes are postulated to rise through the mantle and begin to partially melt on reaching shallow depths in the asthenosphere by decompression melting. This would create large volumes of magma. The plume hypothesis postulates that this melt rises to the surface and erupts to form "hot spots".
The lower mantle and the core[edit]
Calculated Earth's temperature vs. depth. Dashed curve: Layered mantle convection; Solid curve: Whole mantle convection.[17]
The most prominent thermal contrast known to exist in the deep (1000 km) mantle is at the core-mantle boundary. Mantle plumes were originally postulated to rise from this layer because the "hot spots" that are assumed to be their surface expression were thought to be fixed relative to one another. This required that plumes were sourced from beneath the shallow asthenosphere that is thought to be flowing rapidly in response to motion of the overlying tectonic plates. There is no other known major thermal boundary layer in the deep Earth, and so the core-mantle boundary was the only candidate.
The base of the mantle is known as the D″ layer, a seismological subdivision of the Earth. It appears to be compositionally distinct from the overlying mantle, and may contain partial melt.
Two very large, broad, large low-shear-velocity provinces, exist in the lower mantle under Africa and under the central Pacific. It is postulated that small plumes rise from their surface or their edges. Their low seismic velocities were thought to suggest that they are relatively hot, although it has recently been shown that their low wave velocities are due to high density caused by chemical heterogeneity.[18][19]
Evidence for the theory[edit]
Various lines of evidence have been cited in support of mantle plumes. There is some confusion regarding what constitutes support, as there has been a tendency to re-define the postulated characteristics of mantle plumes after observations have been made.[4]
Some common and basic lines of evidence cited in support of the theory are linear volcanic chains, noble gases, geophysical anomalies and geochemistry.
Linear volcanic chains[edit]
The age-progressive distribution of the Hawaiian-Emperor seamount chain has been explained as a result of a fixed, deep-mantle plume rising into the upper mantle, partly melting, and causing a volcanic chain to form as the plate moves overhead relative to the fixed plume source.[16] Other "hot spots" with time-progressive volcanic chains behind them include Réunion, the Chagos-Laccadive Ridge, the Louisville Ridge, the Ninety East Ridge and Kerguelen, Tristan, and Yellowstone.
An intrinsic aspect of the plume hypothesis is that the "hot spots" and their volcanic trails have been fixed relative to one another throughout geological time. Whereas there is evidence that the chains listed above are time-progressive, it has, however, been shown that they are not fixed relative to one another. The most remarkable example of this is the Emperor chain, the older part of the Hawaii system, which was formed by migration of volcanic activity across a geo-stationary plate.[11]
Many postulated "hot spots" are also lacking time-progressive volcanic trails, e.g., Iceland, the Galapagos, and the Azores. Mismatches between the predictions of the hypothesis and observations are commonly explained by auxiliary processes such as "mantle wind", "ridge capture", "ridge escape" and lateral flow of plume material.
Noble gas and other isotopes[edit]
Main article: Helium-3
Helium-3 is a primordial isotope that formed in the Big Bang. Very little is produced, and little has been added to the Earth by other processes since then.[20] Helium-4 includes a primordial component, but it is also produced by the natural radioactive decay of U and Th. Over time, He in the upper atmosphere is lost into space. Thus, the Earth has become progressively depleted in He, and 3He is not replaced as 4He is. As a result, the ratio 3He/4He in the Earth has decreased over time.
Unusually high 3He/4He have been observed in some, but not all, "hot spots". In mantle plume theory, this is explained by plumes tapping a deep, primordial reservoir in the lower mantle, where the original, high 3He/4He ratios have been preserved throughout geologic time.[21] In the context of the Plate hypothesis, the high ratios are explained by preservation of old material in the shallow mantle. Ancient, high 3He/4He ratios would be particularly easily preserved in materials lacking U or Th, so 4He was not added over time. Olivine and dunite, both found in subducted crust, are materials of this sort.[20]
Other elements, e.g. osmium, have been suggested to be tracers of material arising from near to the Earth's core, in basalts at oceanic islands. However, so far conclusive proof for this is lacking.[22]
Geophysical anomalies[edit]
Diagram showing a cross section though the Earth's lithosphere (in yellow) with magma rising from the mantle (in red). The crust may move relative to the plume, creating a track.
The plume hypothesis has been tested by looking for the geophysical anomalies predicted to be associated with them. These include thermal, seismic, and elevation anomalies. Thermal anomalies are inherent in the term "hotspot". They can be measured in numerous different ways, including surface heat flow, petrology, and seismology. Thermal anomalies produce anomalies in the speeds of seismic waves, but unfortunately so do composition and partial melt. As a result, wave speeds cannot be used simply and directly to measure temperature, but more sophisticated approaches must be taken.
Seismic anomalies are identified by mapping variations in wave speed as seismic waves travel through Earth. A hot mantle plume is predicted to have lower seismic wave speeds compared with similar material at a lower temperature. Mantle material containing a trace of partial melt (e.g., as a result of it having a lower melting point), or being richer in Fe, also has a lower seismic wave speed and those effects are stronger than temperature. Thus, although unusually low wave speeds have been taken to indicate anomalously hot mantle beneath "hot spots", this interpretation is ambiguous.[4] The most commonly cited seismic wave-speed images that are used to look for variations in regions where plumes have been proposed come from seismic tomography. This method involves using a network of seismometers to construct three-dimensional images of the variation in seismic wave speed throughout the mantle.[23]
Seismic waves generated by large earthquakes enable structure below the Earth's surface to be determined along the ray path. Seismic waves that have traveled a thousand or more kilometers (also called teleseismic waves) can be used to image large regions of Earth's mantle. They also have limited resolution, however, and only structures at least several hundred kilometers in diameter can be detected.
Seismic tomography images have been cited as evidence for a number of mantle plumes in Earth's mantle.[24] There is, however, vigorous on-going discussion regarding whether the structures imaged are reliably resolved, and whether they correspond to columns of hot, rising rock.[25]
The mantle plume hypothesis predicts that domal topographic uplifts will develop when plume heads impinge on the base of the lithosphere. An uplift of this kind occurred when the north Atlantic Ocean opened about 54 million years ago. Some scientists have linked this to a mantle plume postulated to have caused the breakup of Eurasia and the opening of the north Atlantic, now suggested to underlie Iceland. Current research has shown that the time-history of the uplift is probably much shorter than predicted, however. It is thus not clear how strongly this observation supports the mantle plume hypothesis.
Geochemistry[edit]
Basalts found at oceanic islands are geochemically distinct from those found at mid-ocean ridges and volcanoes associated with subduction zones (island arc basalts). "Ocean island basalt" is also similar to basalts found throughout the oceans on both small and large seamounts (thought to be formed by eruptions on the sea floor that did not rise above the surface of the ocean). They are also compositionally similar to some basalts found in the interiors of the continents (e.g., the Snake River Plain).
In major elements, ocean island basalts are typically higher in iron (Fe) and titanium (Ti) than mid-ocean ridge basalts at similar magnesium (Mg) contents. In trace elements, they are typically more enriched in the light rare-earth elements than mid-ocean ridge basalts. Compared to island arc basalts, ocean island basalts are lower in alumina (Al2O3) and higher in immobile trace elements (e.g., Ti, Nb, Ta).
These differences result from processes that occur during the subduction of oceanic crust and mantle lithosphere. Oceanic crust (and to a lesser extent, the underlying mantle) typically becomes hydrated to varying degrees on the seafloor, partly as the result of seafloor weathering, and partly in response to hydrothermal circulation near the mid-ocean-ridge crest where it was originally formed. As oceanic crust and underlying lithosphere subduct, water is released by dehydration reactions, along with water-soluble elements and trace elements. This enriched fluid rises to metasomatize the overlying mantle wedge and leads to the formation of island arc basalts. The subducting slab is depleted in these water-mobile elements (e.g., K, Rb, Th, Pb) and thus relatively enriched in elements that are not water-mobile (e.g., Ti, Nb, Ta) compared to both mid-ocean ridge and island arc basalts.
Ocean island basalts are also relatively enriched in immobile elements relative to the water-mobile elements. This, and other observations, have been interpreted as indicating that the distinct geochemical signature of ocean island basalts results from inclusion of a component of subducted slab material. This must have been recycled in the mantle, then re-melted and incorporated in the lavas erupted. In the context of the plume hypothesis, subducted slabs are postulated to have been subducted down as far as the core-mantle boundary, and transported back up to the surface in rising plumes. In the plate hypothesis, the slabs are postulated to have been recycled at shallower depths – in the upper few hundred kilometers that make up the upper mantle. However, the plate hypothesis is inconsistent with both the geochemistry of shallow asthenosphere melts (i.e., Mid-ocean ridge basalts) and with the isotopic compositions of ocean island basalts.
Seismology[edit]
In 2015, based on data from 273 large earthquakes, researchers compiled a model based on full waveform tomography, requiring the equivalent of 3 million hours of supercomputer time.[26] Due to computational limitations, high-frequency data still could not be used, and seismic data remained unavailable from much of the seafloor.[26] Nonetheless, vertical plumes, 400 C hotter than the surrounding rock, were visualized under many hotspots, including the Pitcairn, Macdonald, Samoa, Tahiti, Marquesas, Galapagos, Cape Verde, and Canary hotspots.[27] They extended nearly vertically from the core-mantle boundary (2900 km depth) to a possible layer of shearing and bending at 1000 km.[26] They were detectable because they were 600–800 km wide, more than three times the width expected from contemporary models.[26] Many of these plumes are in the large low-shear-velocity provinces under Africa and the Pacific, while some other hotspots such as Yellowstone were less clearly related to mantle features in the model.[28]
The unexpected size of the plumes leaves open the possibility that they may conduct the bulk of the Earth's 44 terawatts of internal heat flow from the core to the surface, and means that the lower mantle convects less than expected, if at all. It is possible that there is a compositional difference between plumes and the surrounding mantle that slows them down and broadens them.[26]
Suggested mantle plume locations[edit]
An example of plume locations suggested by one recent group.[29] Figure from Foulger (2010).[4]
Many different localities have been suggested to be underlain by mantle plumes, and scientists cannot agree on a definitive list. Some scientists suggest that several tens of plumes exist,[29] whereas others suggest that there are none.[4] The theory was really inspired by the Hawaiian volcano system. Hawaii is a large volcanic edifice in the center of the Pacific Ocean, far from any plate boundaries. Its regular, time-progressive chain of islands and seamounts superficially fits the plume theory well. However, it is almost unique on Earth, as nothing as extreme exists anywhere else. The second strongest candidate for a plume location is often quoted to be Iceland, but according to opponents of the plume hypothesis its massive nature can be explained by plate tectonic forces along the mid-Atlantic spreading center.
Mantle plumes have been suggested as the source for flood basalts.[30][31] These extremely rapid, large scale eruptions of basaltic magmas have periodically formed continental flood basalt provinces on land and oceanic plateaus in the ocean basins, such as the Deccan Traps,[32] the Siberian Traps[33] the Karoo/Ferrar flood basalts of Gondwana,[34] and the largest known continental flood basalt, the Central Atlantic magmatic province (CAMP).[35]
Others,[36] have pointed out the coincidence of many continental flood basalt events with continental rifting. This is consistent with a system that tends toward equilibrium, as matter rises in a mantle plume, other material is drawn down into the mantle causing rifting.[36]
Alternative hypotheses[edit]
In parallel with the mantle plume model, two alternative explanations for the observed phenomena have been considered: the plate hypothesis and the impact hypothesis.
The plate hypothesis[edit]
The plate hypothesis suggests that "anomalous" volcanism results from lithospheric extension that permits melt to rise passively from the asthenosphere beneath. It is thus the conceptual inverse of the plume hypothesis, attributing volcanism to shallow, near-surface processes associated with plate tectonics, rather than active processes arising at the core-mantle boundary. The plate hypothesis argues that deep mantle plumes causing surface, time-progressive volcanism do not exist[clarification needed (Is paper really a claim to exclusive universality?)][not in citation given].[37]
Lithospheric extension is attributed to processes related to plate tectonics. These processes are well understood at mid-ocean ridges, where most of Earth's volcanism occurs. It is less commonly recognised that the plates themselves deform internally, and can permit volcanism in those regions where the deformation is extensional. Well-known examples are the Basin and Range Province in the western USA, the East African Rift valley, and the Rhine Graben. Under this hypothesis, variable volumes of magma are attributed to variations in chemical composition (large volumes of volcanism corresponding to more easily molten mantle material) rather than to temperature differences.
The plate hypothesis thus attributes all[dubious – discuss] of Earth's volcanism to a single process – plate tectonics – rather than to two independent processes (plumes and plate tectonics), but does not address issues of core–mantle heat and/or material transfer.[dubious – discuss]
Under the umbrella of the plate hypothesis, the following sub-processes, all of which can contribute to permitting surface volcanism, are recognised:[4]
Continental break-up;
Fertility at mid-ocean ridges;
Enhanced volcanism at plate boundary junctions;
Small-scale sublithospheric convection;
Oceanic intraplate extension;
Slab tearing and break-off;
Shallow mantle convection;
Abrupt lateral changes in stress at structural discontinuities;
Continental intraplate extension;
Catastrophic lithospheric thinning;
Sublithospheric melt ponding and draining.
The impact hypothesis[edit]
In addition to these processes, impact events such as ones that created the Addams crater on Venus and the Sudbury Igneous Complex in Canada are known to have caused melting and volcanism. In the impact hypothesis, it is proposed that some regions of hotspot volcanism can be triggered by certain large-body oceanic impacts which are able to penetrate the thinner oceanic lithosphere, and flood basalt volcanism can be triggered by converging seismic energy focused at the antipodal point opposite major impact sites.[38] Impact-induced volcanism has not been adequately studied and comprises a separate causal category of terrestrial volcanism with implications for the study of hotspots and plate tectonics.
Comparison of the hypotheses[edit]
In 1997 it became possible using seismic tomography to image submerging tectonic slabs penetrating from the surface all the way to the core-mantle boundary.[39]
For the Hawaii hotspot, long-period seismic body wave diffraction tomography provided evidence that a mantle plume is responsible, as had been proposed as early as 1971.[40] For the Yellowstone hotspot, seismological evidence began to converge from 2011 in support of the plume model, as concluded by James et al., "we favor a lower mantle plume as the origin for the Yellowstone hotspot."[37][41] Data acquired through Earthscope, a program collecting high-resolution seismic data throughout the contiguous United States has accelerated acceptance of a plume underlying Yellowstone.[42][43]
Although there is strong evidence that at least two deep mantle plumes rise to the core-mantle boundary, confirmation that other hypotheses can be dismissed may require similar tomographic evidence for other hot spots.
See also[edit]
Delamination (geology)
Epeirogenic movement
Hotspot (geology)
Mantle convection
Orogeny
Plume tectonics
Volcano
Verneshot
References[edit]
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External links[edit]
Seismic-tomography image of Yellowstone mantle plume
Large Igneous Provinces Commission
mantleplumes.org - mantle-plume skeptic website managed and maintained by Gillian R. Foulger
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