Near-Surface Characterization Using Simultaneous Joint Inversion of Refracted and Surface Waves – A Case Study

Speziali, M.; Re, Simone; Clementi, M.; Herrera, Luis Viertel; Quadi, A. Al; Mosa, M. Mahmood

doi:10.3997/2214-4609.20140994

Cited by 6 publications

(1 citation statement)

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“…SJI can also invert for various wave propagators out of the same elastic source. This is the case shown in Speziali, ( 2014), where a successful solution is reached by combining the information content of refractions and ultra-shallow surface waves.…”

Section: Data Examplesmentioning

confidence: 95%

Quantitative Integration of Measurements in Near Surface Characterization

Mantovani¹

2015

Proceedings

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A valid support to seismic in shallow anomaly detection is offered by additional geophysical measurements such as potential fields or electromagnetic induction soundings. In fact, gravity and electromagnetics (EM) measure complementary properties and, even though they do not sense any quantity directly related to P-velocity, they can be successfully used to improve the statics solution. Since the different geophysical domains are sensitive -to a large extent -to the same geometry structure of subsurface rock formations, there exists a structural link between the properties measured by the various methods, which are supposed to sense consistent anomaly shapes. A high confidence solution for the near surface is therefore derived through a Simultaneous Joint Inversion (SJI) process (De Stefano and Colombo, 2007). Within this process, seismic and non-seismic data are inverted simultaneously together with the structural (or petrophysical) link established between the corresponding physical properties (velocity, density, resistivity, etc.). The solution explains seismic as well as plenty of logging information other than the simple sonic curve. Moreover, EM and potential field data are also explained by the output model simultaneously with seismic and borehole data. Thus, each of the measurements complements the others, as each is affected by different and uncorrelated noise.

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Section: Data Examplesmentioning

confidence: 95%