Application of advanced seismic techniques for archaeological investigations

J. Liu

Research output: ThesisDissertation (TU Delft)

43 Downloads (Pure)

Abstract

At different places in the world, the local climate conditions have helped the preservation of archaeological sites to a very high degree. This has helped us understand better our history. This situation, however, is quickly changing due to the climate change we are now facing. The condition at an increasing number of ancient sites around the world is now deteriorating due to the warming climate. Obtaining high-resolution images of the subsurface of the archaeological sites without excavation can help us make better strategies for conserving these sites. Such possibilities are provided by the application of geophysical exploration methods. Among all available geophysical approaches, high-resolution reflection seismic using transverse (S-) waves is one of the few options that can provide detailed information regarding the subsurface structure beneath archaeological sites for depths up to several meters. However, most unexcavated sites are covered by soil. Near-surface seismic data acquired in such soil-covered sites are dominated by source-generated, dispersive surface waves, and sometimes surface waves caused by other anthropogenic sources, e.g., traffic and human activities in the vicinity of the seismic line. Both of these strong events can camouflage the very shallow reflections. The conventional techniques for suppression of surface waves, e.g., muting or spatial filtering, are ineffective or even detrimental to the target reflections, especially at near offsets. This is especially challenging in surveys where the available source-receiver offset range is often quite limited, and the velocity and frequency content of the surface waves largely overlap with those of the target S-wave reflections. In chapter 2, we aim to develop a data-driven way to suppress surface-wave noise and thus reveal the very shallow reflections. We make use of seismic interferometry (SI) to retrieve both source-coherent and source-incoherent surface-wave parts of the data. The retrieved surface waves are then adaptively subtracted (AS) from the recorded data, thereby exposing the hidden reflections. We apply our schemes to both synthetic and field seismic data. We show that artifacts caused by stacking surface-wave noise are greatly reduced and that reflectors, especially at very shallow depth, can be much better imaged and interpreted. The dominance of surface waves also make it impossible to identify weak diffraction signals, which is the seismic response of buried objects of small size. The diffraction events can be used to detect and locate the distribution of shallow objects. Revealing the hidden diffraction signals from under the dominant surface waves and using them for locating objects constitute another goal of this thesis. In chapters 3 and 4, we introduce an interferometric workflow for imaging subsurface objects using masked diffractions. This workflow includes three main steps. We first reveal masked diffractions by suppression of the dominant surface waves through a combination of SI and nonstationary AS. The revealed weak diffraction signal is then enhanced by cross coherence-based super virtual interferometry (SVI). Finally, we produce a diffraction image by a multipath summation approach, which can be used to interpret the locations of subsurface diffractors. We apply our method to field data acquired at an archaeological site using two different active sources. Two shallow anomalies were detected in our sections, whose locations agree well with burial burnt stones. These burnt stones have also been detected in an independent magnetic survey and in corings. The limitation of our workflow is that it can only be applied with desired resolution to S-wave data when seismic sources and receivers polarized in the cross-line direction.
Original languageEnglish
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Ghose, R., Supervisor
  • Draganov, D.S., Supervisor
Award date23 Jun 2022
Print ISBNs978-94-6423-855-6
DOIs
Publication statusPublished - 2022

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