A practical review of geostatistical processing applied to geophysical data: methods and applications

Abstract

Nowadays, geostatistics is commonly applied for numerous gridding or modelling tasks. However, it is still under used and unknown for classical geophysical applications. This paper highlights the main geostatistical methods relevant for geophysical issues, for instance to improve the quality of seismic data such as velocity cubes or interpreted horizons. These methods are then illustrated through four examples. The first example, based on a gravity survey presents how a geostatistical interpolation can be used to filter out a global trend, in order to better define real anomalies. In the second case study, dedicated to refraction surveying, geostatistical filtering is used to filter out acquisition artefacts and identify the main geological structures. The third one is an example of porosity being integrated geostatistically with a seismic acoustic impedance map. The last example deals with geostatistical time to depth conversion; the interest of performing geostatistical simulations is finally discussed.

The application of high-resolution 3D seismic data to model the distribution of mechanical and hydrogeological properties of a potential host rock for the deep storage of radioactive waste in France

“…As porosity is linked to acoustic impedance, it is relevant to use dense seismic acoustic impedance information. So a collocated cokriging of porosity integrating the seismic information is performed by using the normalized acoustic impedance as the secondary variable (Bourges et al, 2012). 3D cube makes it possible to provide 3D imaging of the connectivity of the porous bodies ).…”

Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data

“…Bedrosian et al (2007) use kriging to interpolate inverted MT-derived electrical resistivity values onto a finer mesh corresponding to that used for seismic velocity, to perform lithological classification. Bourges et al (2012) demonstrate the benefits of co-kriging on the two dataset's porosity and acoustic impedance. They found that by performing co-located co-kriging, their estimation of the key parameter, porosity, could be improved.…”

“…Basic tools of geostatistical representations at the data analysis or QC stage include histogram plots and scatter diagrams (Bourges et al 2012;Takam Takougang et al 2015). Variograms can be also created, and these allow the spatial distribution of parameters such as seismic amplitudes, velocity and porosity to be statistically quantified (Dubrule 2003).…”

Proposed methodology for measurement, survey and assessment of vertical deformation of structures

“…In addition, it is not possible to level an infinite number of points, so there will always be areas devoid of data. The software can use this information to create a regular grid linking each Z value to any coordinate of the new grid using an interpolation method with respect to the original array (Bourges et al 2012). …”