Curvelet-based seismoelectric data processing

Abstract

International audienceSeismoelectromagnetic conversions are induced through electrokinetic phenomena occurring when passing seismic waves induce relative fluid to solid displacements. Information on porous material properties provided by the type I coseismic electric field accompanying surface and body waves is limited to the vicinity of the electrical receivers, as opposed to the type II electromagnetic interface response (IR) which can help detect porous contrasts and/or fluid changes within the subsurface 'at depth'. For general field geometries, the problem is that type II disturbances exhibit lower amplitudes than type I fields and are difficult to access directly. Hence, separating both types of waves is a critical step when processing seismoelectric data. Synthetic seismograms and electrograms, generated with a full-waveform seismoelectric forward modelling code written by Garambois & Dietrich, enabled us to study the behaviour of the IR's characteristic dipolar amplitude pattern recovered through filtering. The definitive version is available at www.blackwell-synergy.com To better preserve the IR amplitudes, we have developed a new filtering strategy based on the Fast Discrete Curvelet Transform. Seismic or seismoelectric wave fronts can be optimall described using this multiscale decomposition over multidirectional anisotropic needle-shape structures. We have built a mask in the curvelet domain zeroing out seismoelectric samples corresponding to non-zero samples from the accelerogram, thus taking advantage of the relationship between seismic and seismoelectric waves for type I fields. This mask consists of a threshold function combined with a Gaussian distribution promoting horizontal (i.e. zero- slowness) directions. When applied to synthetic data, this filter enabled to successfully extract the IR although less altering its dipolar radiation pattern than the conventional dip-based techniques. This strategy was successfully applied to a seismoelectric data set acquired in sedimentary deposits, and permitted to isolate an IR generated at the water table

Zeta potential of porous rocks in contact with mixtures of monovalent and divalent electrolytes

“…Haartsen and Pride ; Garambois and Dietrich ; Warden et al . ; Zyserman, Monachesi and Jouniaux , ) and seismoelectric well logging (e.g. Hu, Guan and Harris ; Guan and Hu ; Zyserman et al .…”

Seismoelectromagnetic homogeneous space Green’s functions

“…Several numerical models have been developed for 2D media (Han and Wang, 2001; Haines and Pride, 2006;Zyserman et al, 2010;Guan et al, 2013;Kroeger et al, 2014). Recently, dedicated data processing techniques were developed (Haines et al, 2007;Warden et al, 2012). Inversion and laboratory experiments with successful inversion results have also been developed (Mahardika et al, 2012;Haas et al, 2013).…”

Influence of water pressure dynamics and fluid flow on the streaming‐potential response for unsaturated conditions

“…; Mauri, Williams‐Jones, and Saracco, ; Warden et al . ), as well as by using particle swarm optimization (Fernandez‐Martinez, Garcia‐Gonzalo, and Naudet, ). These models require a good understanding of the streaming potential coefficient (Ahmad, ; Bogoslovsky and Ogilvy, ; Jouniaux, Lallemant, and Pozzi, ; Pozzi and Jouniaux, ; Guichet, Jouniaux, and Catel, ) and the electrical resistivity (Bekri et al .…”

“…These observations are interpreted regarding the electrokinetic coupling, which also leads to seismoelectric conversion effects (Gao and Hu, 2010;Zyserman, Gauzellino, and Santos, 2010;Strahser et al 2011;Warden et al 2013;Zhu and Toksöz, 2013;Kröger, Yaramanci, and Kemna, 2014). Self-potential observations have been interpreted by direct modelling (Kilty, 1984;Cull, 1985;Jouniaux et al 1999;Pain et al 2005;Sheffer and Oldenburg, 2007) in the wavelet domain to identify the location and intensity of the source of groundwater flows (Moreau et al 1997;Gibert and Pessel, 2001;Sailhac, Darnet, and Marquis, 2004;Saracco et al 2007;Mauri, Williams-Jones, and Saracco, 2010;Warden et al 2012), as well as by using particle swarm optimization (Fernandez-Martinez, Garcia-Gonzalo, and Naudet, 2010). These models require a good understanding of the streaming potential coefficient (Ahmad, 1964;Bogoslovsky and Ogilvy, 1972;Jouniaux, Lallemant, and Pozzi, 1994;Pozzi and Jouniaux, 1994;Guichet, Jouniaux, and Catel, 2006) and the electrical resistivity (Bekri et al 2003;Jouniaux, Zamora, and Reuschlé, 2006).…”