English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/94517
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Ver citas en Google académico
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar otros formatos: Exportar EndNote (RIS)Exportar EndNote (RIS)Exportar EndNote (RIS)
Título

EMT - Empirical-mode-decomposition-based Magneto-Telluric Processing

Autor Neukirch, Mail ; García, Xavier
Fecha de publicación 25-abr-2012
EditorEuropean Geosciences Union
Citación Geophysical Research Abstracts 14: EGU2012-9457-1 (2012)
ResumenWe present a new Magneto-Telluric (MT) data processing scheme based on an emerging non linear, non stationary time series analysis tool, called the Empirical Mode Decomposition (EMD) or Hilbert-Huang Transform (HHT), to transform data into a non-stationary frequency domain and a robust principal component regression to estimate the most likely MT transfer functions from the data with the 2- confidence intervals computed by a bootstrap algorithm. Optionally, data quality can be controlled by a physical coherence and a signal power filter. MT sources are assumed to be quasi stationary and therefore a (windowed) Fourier Transform is often ap- plied to transform the time series into the frequency domain in which Transfer Functions (TF) are defined between the electromagnetic field components. This assumption can break down in the presence of noise or when the sources are non stationary, and then TF estimates can become unreliable when obtained through a stationary transform like the Fourier transform. Our TF estimation scheme naturally deals with non stationarity without introducing artifacts and, therefore, potentially can distinguish quasi-stationary sources and non-stationary noise. In contrast to previous works on using HHT for MT processing, we argue the necessity of a multivariate EMD to model the MT problem physically correctly and highlight the resulting possibility to use instantaneous parameters as independent and identically distributed variables. Furthermore, we define a homogenization between data channels of frequency discrepancies due to non stationarity and noise. The TF estimation in the frequency domain bases on a robust principal component analysis in order to find two source polarizations. These two principal components are used as predictor to regress robustly the data channels within a bootstrap algorithm to estimate the Earth¿s Transfer function with 2-¿ confidence interval supplied by the measured data.The scheme can be used with and without aid by any number of remote reference stations. The performance of this scheme will be demonstrated on MT data and compared with BIRRP, a widely used MT processing software by Alan Chave
Descripción European Geosciences Union General Assembly 22-27 April 2012, Vienna, Austria.-- 1 page
Versión del editorhttp://meetingorganizer.copernicus.org/EGU2012/oral_programme/9075
URI http://hdl.handle.net/10261/94517
E-ISSN1607-7962
Aparece en las colecciones: (UTM) Comunicaciones congresos
(ICM) Comunicaciones congresos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
EGU2012-9457-1.pdf35,62 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.