3-D cross-gradient joint inversion of seismic refraction and DC resistivity data

Abstract

2017) '3-D cross-gradient joint inversion of seismic refraction and DC resistivity data.', Journal of applied geophysics., 141 . pp. 54-67.Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTWe present a 3-D cross-gradient joint inversion algorithm for seismic refraction and DC resistivity data. The structural similarity between seismic slowness and resistivity models is enforced by a cross-gradient term in the objective function that also includes misfit and regularization terms. A limited memory quasi-Newton approach is used to perform the optimization of the objective function. To validate the proposed methodology and its implementation, tests were performed on a typical archaeological geophysical synthetic model. The results show that the inversion model and physical parameters estimated by our joint inversion method are more consistent with the true model than those from single inversion algorithm. Moreover, our approach appears to be more robust in conditions of noise. Finally, the 3-D cross-gradient joint inversion algorithm was applied to the field data from Lin_an ancient city site in Hangzhou of China. The 3-D cross-gradient joint inversion models are consistent with the archaeological excavation results of the ancient city wall remains. However, by single inversion, seismic slowness model does not show the anomaly of city wall remains and resistivity model does not fit well with the archaeological excavation results. Through these comparisons, we conclude that the proposed algorithm can be used to jointly invert 3-D seismic refraction and DC resistivity data to reduce the uncertainty brought by single inversion scheme.

Comparison of Different Coupling Methods for Joint Inversion of Geophysical Data: A Case Study for the Namibian Continental Margin

“…In this study, we refer to all different ways to conduct this integration as “joint inversion,” where the resulting Earth model is required to explain several data sets or models at once. The constraints can either be applied by integrating an additional geophysical data set in the inversion procedure (Günther & Rücker, 2006; Heincke et al., 2017; Moorkamp, 2017; Shi et al., 2017) or by integrating a physical Earth model that was derived independently from another geophysical data set (Bedrosian, 2007; Kalscheuer et al., 2015; Mandolesi & Jones, 2014; Zhou et al., 2015), which is sometimes called cooperative inversion. Joint inversion can either be applied to different geophysical data sets depending on the same physical parameters like electrical resistivity (Candansayar & Tezkan, 2008) or seismic velocities (Parolai et al., 2005), or on a combination of data sets that measure different physical parameters (Günther & Rücker, 2006; Heincke et al., 2017; Moorkamp, 2017; Shi et al., 2017).…”

Investigate the layout and age of a large‐scale mausoleum in Hangzhou, China using combined geophysical technologies and archaeological documents

“…Geophysical techniques are widely used to investigate the subsoil for the detection of features of archaeological interest (Deiana, Leucci, & Martorana, 2018; Gaffney, 2008; Liu, Shi, Wang, & Yu, 2018; Shi et al, 2015; Shi, Hobbs, Max, Tian, & Jiang, 2017). Tombs are common archaeological detection objects and geophysical methods are usually used to investigate their location (Atya et al, 2005; Oh, Abdallatif, & Suh, 2008; Piscitelli et al, 2007; Rabbel et al, 2015) and to detect their internal structure (Booth, Szpakowska, Pischikova, & Griffin, 2015; Kamei, Marukawa, Kudo, Nishimura, & Nakai, 2000; Nuzzo, Leucci, & Negri, 2009; Pipan, Baradello, Forte, & Finetti, 2001; Rodrigues, Porsani, Santos, DeBlasis, & Giannini, 2009; Sarris et al, 2007).…”

Weighted cross-gradient function for joint inversion with the application to regional 3-D gravity and magnetic anomalies

“…Electromagnetic and seismic reflection data were inverted by Hu et al (2009), where the cross-gradient function was used to correlate electric conductivity and slowness. More recently, Shi et al (2017) correlated conductivity and slowness using a cross-gradient term in the inversion of ERT and seismic refraction data. The crossgradient function has also been extended to time-lapse joint inversion for cross-hole ERT and GPR traveltime data by Doetsch & Binley (2010) and later by Karaoulis et al (2012) for cross-hole ERT and seismic data.…”