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Time series applied to volcanic data: A review and application to Fuego volcano. Rüdiger Escobar Wolf MTU, September 2010. Outline:. What are time series and where do they come from? In what way are time series useful and what can they tell us?

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time series applied to volcanic data a review and application to fuego volcano

Time series applied to volcanic data:A review and application to Fuego volcano.

Rüdiger Escobar Wolf

MTU, September 2010.

outline
Outline:
  • What are time series and where do they come from?
  • In what way are time series useful and what can they tell us?
  • How can we analyze time series and what methods are out there?
  • The Fuego case: different domains and different time scales.
  • The larger context of Fuego
what are time series and where do they come from
What are time series and where do they come from?
  • A variable “varying” in time.
  • The domain of the variable: physical continuous magnitudes
where do time series come from
Where do time series come from?
  • Measuring and recording the time-varying “variable”.
  • Discretization.
  • Sampling resolution.
  • Reliability & uncertainty.
  • How well do we know the variable? I. e. the size of an eruption?
degradation with time and size
Degradation with time and size.
  • Older and smaller events are more difficult to record.
  • Dataset or “catalogue” completeness. How far back do I think my dataset is reliable for a given size of event?
  • Implications for the statistical properties of the dataset.
slide11

4

3

VEI

2

1

1900

2000

1500

1600

1700

1800

Year

measuring and recording the time variable
Measuring and recording the time variable
  • Sampling resolution.
  • Reliability & uncertainty.
  • Aggregating data.
dating
Dating…
  • Some geologist are so boring that they end up dating rocks…
  • Dating prehistoric events: 40Ar/39Ar, 14C, K-Ar, U series, etc…
  • Point vs. interval data, and how to combine them.
the time of historical events
The time of historical events
  • Chronicles and their interpretation
  • Facts, myths and everything in between.
slide18

The year of [fifteen hundred] eighty one, on December twenty six, the volcano started to throw fire more than usual, and it was so much what it threw, and with such a fury, the next day of December twenty seven, through a mouth that it has in the highest part, that from the abundant ash that came out, the air became black and thick, such that people couldn’t see each other [in Antigua Guatemala?]…

…that ash reached many leagues from Guatemala, in the province of Xoconusco, where the trees were found to be covered by it…

The next month of January, at the beginning of the year [fifteen hundred] eighty two, on the fourteenth of that month, the same volcano started to throw so much fire, that a great mishap was feared, because in the twenty four hours that the fury lasted, one couldn’t see anything from the volcano but rivers of fire and very large rocks made embers, which came out of the volcanoes mouth and came down with enormous fury and impetus…

That fire caused much damage from the coast to the southeast, where it ruined a pueblo de indios named San Pedro, two leagues from [Antigua] Guatemala, although there were no deaths, because it happened during the day, and prevented by fear, all the indios escaped with time, abandoning their homes…

Ciudad – Real , Antonio de, 1873, Relacion breve y verdadera de algunas cosas de las muchas que sucedieron al Padre Fray Alonso Ponce en las provincias de la Nueva España, siendo comisario general de aquellas partes. Tomo I. Imprenta de la Viuda de Calero. Madrid.

the problem of defining discrete events for continuous variables
The problem of defining discrete events for continuous variables
  • Different approaches:
  • Values over threshold
  • Local peaks
in what way are time series useful and what can they tell us
In what way are time series useful and what can they tell us?
  • From very straightforward, i. e. simple trends…
  • To very complex, i. e. cyclic behavior, etc.
correlation between time series
Correlation between time series
  • If they share some phenomenological (causal?) relationship, chances are that they may vary together (co-vary)
  • Volcanologists (and other geo-scientists) try this a lot!
stochastic nature and statistical structure of some time series data
Stochastic nature and statistical structure of some time series data
  • Some element of randomness but not completely unpredictable
  • The question is “what can be predicted or forecasted”?
  • Parametric vs. Non-parametric
probability distributions and fitting of data
Probability distributions and fitting of data
  • What is the “distribution of the population from which the sampled set comes from”?
  • How to fit?
  • MLE.
assumptions and elegant lies
Assumptions and elegant lies…
  • Can we just assume certain things about the distribution of the population? E. g. Normality, independence, Poisson process?
  • Is this assumption valid/justified? How do we know?
  • Stationary vs. Non-stationary time series.
a note on time series and early warning
A note on time series and early warning…
  • The “ideal” model of increasing risk, acceptable risk threshold and warning/response to that threshold.
  • Some “real world details”.
crisis time path development posibilities
Crisis time path (development) posibilities

A lethal

eruption

WILL

happen

soon.

1

Highly concave curve. This case is unlikely to be maintained for a prolonged period of time because of accustomization and desensitizing. It usually tends to become case III over time.

II

Tends to…

III

Highly convex curve. This case represents the “sudden” or “surprise” scenario, in which warning and evacuation actions can be severely limited (even impossible) due to the short time available to carry them out. This is arguably the worst case.

IV

Peak and decrease curve. This case reflects and initial increase in the perceived risk, followed by a rapid decrease, either due to a decrease of the observed activity, or due to a process of desensitizing.

Perceived risk (probability?)

Linear increase curve. This case represents a steady increase in the perceived risk, pointing towards the actual occurrence of the eruption. This is arguably the best case.

I

A lethal

eruption

will NOT

happen

soon.

0

Time

Progressively narrowing time window for evacuation. As the window narrows the options for action decrease and the evacuation becomes more difficult.

The lethal

event

HAPPENS

crisis time path development posibilities1
Crisis time path (development) posibilities

In real life, the changes in perceived risk don’t happen as a continuously varying function of time. They tend to happen as jumps or drops (discontinuities) associated to the occurrence of key events and findings (e. g. the initiation of the eruption or the issue of a warning).

A lethal

eruption

WILL

happen

soon.

1

Perceived risk (probability?)

A lethal

eruption

will NOT

happen

soon.

0

Time

Progressively narrowing time window for evacuation. As the window narrows the options for action decrease and the evacuation becomes more difficult.

The lethal

event

HAPPENS

crisis time path development posibilities2
Crisis time path (development) posibilities

A lethal

eruption

WILL

happen

soon.

1

II

III

IV

Perceived risk (probability?)

I

A lethal

eruption

will NOT

happen

soon.

0

Time

Progressively narrowing time window for evacuation. As the window narrows the options for action decrease and the evacuation becomes more difficult.

The lethal

event

HAPPENS

how can we analyze time series and what methods are out there
How can we analyze time series and what methods are out there?
  • Organizing dataset for analysis.
  • Database for large, multidimensional datasets… or just a simple table (the simplest database) for small, low dimensionality datasets.
tools and platforms
Tools and platforms…
  • From the very basic (and limited!): Excel…
  • ...to the more complex (and powerful!) Matlab, R, etc…
  • Level of automatization and available (built in) tools…
depending on how big and good your dataset is you can apply different tools
Depending on how big and good your dataset is you can apply different tools.
  • Borrowing tools from signal processing community (electronic and communications engineering, seismologists, etc):
  • Time vs. frequency domain.
  • Auto and cross-correlation.
  • Fourier and Laplace transformations.
stochastic structure and statistical methods
Stochastic structure and statistical methods
  • Parametric: Choosing a distribution and searching for a “best fit”.
  • Hypothesizing on why they fit the distribution or how the fit can be interpreted, for instance in physical terms.
the recent 2002 2007 fuego dataset1
The recent (2002 – 2007) Fuego dataset

Parameter

k = 1.55 > 0

Agingprocess?

summarizing
Summarizing…
  • Prehistoric data: very sparse and uncertain…
  • goes back to 230 ka, but most relevant for the last 3.5 ka.
  • A “fairly good” (detailed and time precise) historical record.
  • Some issues with interpretation for assessing the size, explosivity, and other relevant characteristics of the events.
combined data sources
Combined data sources:
  • Pre-historic: dating and deposit assessment.
  • Historic – older: Accounts from witnesses and scientific reports.
  • Historic – recent: INSIVUMEH bulletins, OVFUEGO / INSIVUMEH records of lava flow lengths and number of daily eruptions, GOES and MODIS / MODVOLC thermal data, RSAM from INSIVUMEH and J. Lyons, SE-CONRED bulletins and personal notes.
some caveats
Some caveats…
  • Non-stationary: turning the volcano “on” and “off”.
  • Trends and non-homogeneous processes.
  • How can we account for that in the time series analysis?
clustering of eruptions since 1524
Clustering of eruptions since 1524?

4

3

VEI

2

1

1900

2000

1500

1600

1700

1800

Year