Applications of autoregressive models and time–frequency analysis to the study of volcanic tremor and long-period events

Lesage, Philippe; Glangeaud, F.; Mars, Jérôme

doi:10.1016/s0377-0273(01)00298-0

Cited by 56 publications

(31 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Every sharp spectral peak is associated with a pair of conjugated poles. The corresponding frequency f and quality factor Q can be calculated from the values of the poles or from the associated coefficients a k (Lesage et al, 2002). Finally, each pair of poles can be represented by a point in the complex frequency plane (f, g), where g is the growth rate, defined as g= − f 2Q .…”

Section: Methodsmentioning

confidence: 99%

“…In order to study the features of these kinds of seismic event, volcano-seismologists have developed or adapted a set of specific methods that includes spectral and autoregressive analysis, amplitude measurement, dynamical and stochastic approaches and noise analysis (Seidl et al, 1990;Nakano et al, 1998;Lesage et al, 2002;Endo and Murray, 1991;Carniel et al, 2003;Jaquet and Carniel, 2001;Vila et al, 2006;Brenguier et al, 2008). Autoregressive (AR) modelling is one of the most interesting methods for extracting information from LP event records (Nakano et al, 1998, Lesage et al, 2002.…”

Section: Introductionmentioning

confidence: 99%

“…Autoregressive (AR) modelling is one of the most interesting methods for extracting information from LP event records (Nakano et al, 1998, Lesage et al, 2002. It is particularly well-suited to analyzing signals generated by resonance processes, as it models them as the output of an AR filter, also called an Infinite Impulse Response (IIR) filter, which can be considered a resonator.…”

Section: Introductionmentioning

confidence: 99%

“…The detection of these variations may be a useful tool for volcano monitoring. Once AR modelling has been carried out for a given LP event, the record can be filtered using the corresponding inverse filter, in order to produce an estimation of the excitation function of the resonator; a function that is related to the physical processes occurring at the source (Nakano et al, 1998;Lesage et al, 2002). This function can be inverted to obtain detailed information about the mechanism involved (Nakano et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic estimation of optimal autoregressive filters for the analysis of volcanic seismic activity

Lesage

2008

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Long-period (LP) events observed on volcanoes provide important information for volcano monitoring and for studying the physical processes in magmatic and hydrothermal systems. Of all the methods used to analyse this kind of seismicity, autoregressive (AR) modelling is particularly valuable, as it produces precise estimations of the frequencies and quality factors of the spectral peaks that are generated by resonance effects at seismic sources and, via deconvolution of the observed record, it allows the excitation function of the resonator to be determined. However, with AR modelling methods it is difficult to determine the order of the AR filter that will yield the best model of the signal. This note presents an algorithm to overcome this problem, together with some examples of applications. The approach described uses the kurtosis (fourth order cumulant) of the deconvolved signal to provide an objective criterion for selecting the filter order. This approach allows the partial automation of the AR analysis and thus provides interesting possibilities for improving volcano monitoring methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic estimation of optimal autoregressive filters for the analysis of volcanic seismic activity

Lesage

2008

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The study of TR earthquakes is considered crucial for the investigation of gas/liquid within a magma conduit (Martinelli, 1997) and also for improving eruption forecasting since, as LP earthquakes, TR events have been frequently observed prior to volcanic eruptions (Lesage et al, 2002). …”

Section: Tremors (Tr)mentioning

confidence: 99%

The Automated Identification of Volcanic Earthquakes: Concepts, Applications and Challenges

Orozco-Alzate¹,

Acosta-Muñoz²,

Londoño³

2012

Earthquake Research and Analysis - Seismology, Seismotectonic and Earthquake Geology

View full text Add to dashboard Cite

Characteristics of fluid‐induced resonances observed during microseismic monitoring

et al. 2014

View full text Add to dashboard Cite

Three groups of resonances are observed during a two‐stage hydraulic experiment recorded by 12 three‐component geophones. The injected fluid is composed of a slurry of mostly water and proppant plus some supercritical nitrogen. Resonance characteristics are estimated using an autoregressive model. Three resonance models are investigated: fluid‐filled cracks, nonlaminar fluid flow, and repetitive events in terms of anticipated resonance frequencies, quality factors, and amplitudes. The observed resonances are very stable and positively correlated with either the slurry flow or the nitrogen injection rate, which is in contradiction with the repetitive events and fluid‐filled crack models, respectively. Resonances obtained by numerical simulations of an unstable jet agree with the main characteristics of most observed resonances. Our observations suggest that variations in resonance frequencies are mainly driven by variations in fluid flow, whereas quality factors are more sensitive to the fluid composition through variations in nitrogen injection rate. This study also suggests that resonance frequencies and quality factors can provide complementary information for real‐time monitoring of fluid injection into reservoirs, for hydraulic stimulations, geothermal operations, carbon capture, and storage or fluid movement during volcano eruptions.

show abstract

Applications of autoregressive models and time–frequency analysis to the study of volcanic tremor and long-period events

Cited by 56 publications

References 42 publications

Automatic estimation of optimal autoregressive filters for the analysis of volcanic seismic activity

Automatic estimation of optimal autoregressive filters for the analysis of volcanic seismic activity

The Automated Identification of Volcanic Earthquakes: Concepts, Applications and Challenges

Characteristics of fluid‐induced resonances observed during microseismic monitoring

Contact Info

Product

Resources

About