Abstract A dense single-node 3D seismic survey has been carried out around the Scrovegni Chapel in Padua (Italy), in order to give new insights about the archaeological setting of the area. The survey made use of nearly 1500 vertical nodes deployed over two rectangular grids. 38 shot positions were fired all around the two receiver patches. The fundamental mode Rayleigh wave signal is here analysed: traveltimes are directly inferred from the signal phases, and phase velocity maps are obtained using Eikonal tomography. Also surface wave amplitudes are used, to produce autospectrum gradient maps. The joint analysis of phase velocity and autospectrum gradient allowed the identification of several buried features, among which possible remains of radial walls of the adjacent Roman amphitheater, structures belonging to a medieval convent, and the root area of an eradicated tree. Finally, depth inversion of 1D dispersion curves allowed the reconstruction of a quasi-3D shear-wave velocity model.
For decades, bad practices in municipal and industrial waste management have had negative environmental impacts, generating high health risks for people and the environment. The use of badly designed, not engineered, and not well-operated landfills has, around the world, produced a large number of potentially contaminated sites, for which there are urgent needs to assess the actual risk and to proceed, in case, with reclamation activities. One of these sites, an abandoned waste disposal site located near a Site of Community Importance on the central-eastern coast of Sardinia (Italy), is the subject of the case history described in this work. As a part of a multi-method geophysical characterisation, a frequency-domain electromagnetic (FDEM) mapping survey was carried out with the specific aim of detecting the presence of buried materials (waste) and of delineating the lateral extent of the landfill by identifying the electrical conductivity anomalies produced, for the most part, by the conductive waste fill. Using an EM31 device in the vertical-dipole configuration, at a height of 0.9 m above the ground, both quadrature and in-phase electromagnetic responses were collected over a 7-hectare area with elevation varying between 6 m and 2.8 m above sea level. After removing the measurements identified as data coming from any recognisable surface man-made features within the survey area or near its perimeter, the filtered quadrature response (expressed as apparent conductivity) ranged from 5.5 mS/m to about 188.6 mS/m. All values are beyond the low induction number (LIN) condition and valid for the classical EM31 mapping, thus requiring advanced data processing. To obtain undistorted, meaningful, and interpretable high-resolution maps, measured data have been processed to correct the bias, introduced by the nonlinearity of the device, as a function of height above ground and the topography. The comparative analysis of the apparent conductivity map, obtained by the properly processed EM31 data and some aerial photos that clearly documented the site history, has allowed unequivocal delineation of the landfill extent, in good agreement with the results obtained with other geophysical methods (not described in this paper) and with the ground truthing data provided by three boreholes, which were core-drilled at the end of the study at three locations selected on the basis of the apparent conductivity map.
This work presents the results of an advanced geophysical characterization of a contaminated site, where a correct understanding of the dynamics in the unsaturated zone is fundamental to evaluate the effective management of the remediation strategies. Large-scale surface electrical resistivity tomography (ERT) was used to perform a preliminary assessment of the structure in a thick unsaturated zone and to detect the presence of a thin layer of clay supporting an overlying thin perched aquifer. Discontinuities in this clay layer have an enormous impact on the infiltration processes of both water and solutes, including contaminants. In the case here presented, the technical strategy is to interrupt the continuity of the clay layer upstream of the investigated site in order to prevent most of the subsurface water flow from reaching the contaminated area. Therefore, a deep trench was dug upstream of the site and, in order to evaluate the effectiveness of this approach in facilitating water infiltration into the underlying aquifer, a forced infiltration experiment was carried out and monitored using ERT and ground-penetrating radar (GPR) measurements in a cross-hole time-lapse configuration. The results of the forced infiltration experiment are presented here, with a particular emphasis on the contribution of hydro-geophysical methods to the general understanding of the subsurface water dynamics at this complex site.
Abstract Lateral velocity variations in the near-surface reflect the presence of buried geological or anthropic structures, and their identification is of interest for many fields of application. Surface wave tomography (SWT) is a powerful technique for detecting both smooth and sharp lateral velocity variations at very different scales. A surface-wave inversion scheme derived from SWT is here applied to a 2-D active seismic dataset to characterize the shape of an urban waste deposit in an old landfill, located 15 km South of Vienna (Austria). First, the tomography-derived inverse problem for the 2-D case is defined: under the assumption of straight rays at the surface connecting sources and receivers, the forward problem for one frequency reduces to a linear relationship between observed phase differences at adjacent receivers and wavenumbers (from which phase velocities are straightforwardly derived). A norm damping regularization constraint is applied to ensure a smooth solution in space: the choice of the damping parameter is made through a minimization process, by which only phase variations of the order of the average wavelength are modelled. The inverse problem is solved for each frequency with a weighted least-squares approach, to take into account the data error variances. An independent multi-offset phase analysis (MOPA) is performed using the same dataset, for comparison: pseudo-sections from the tomography-derived linear inversion and MOPA are very consistent, with the former giving a more continuous result both in space and frequency and less artefacts. Local dispersion curves are finally depth inverted and a quasi-2-D shear wave velocity section is retrieved: we identify a well-defined low velocity zone and interpret it as the urban waste deposit body. Results are consistent with both electrical and electromagnetic measurements acquired on the same line.
SUMMARY Multioffset phase analysis (MOPA) is a fairly recent technique for evaluating seismic surface wave dispersion and estimating the presence of lateral variations. The main limitation of MOPA is that it is based on the assumption of one predominant mode, usually the fundamental mode, in the wave propagation. However, MOPA can be extended (at least) to the two-mode case: this new technique will be called multimode MOPA (MMMOPA). The method employs both amplitude and phase spectral information. The analysis is performed for each frequency independently. The presence of two modes causes the amplitude to have an oscillating behaviour as a function of offset (beats): the spatial period of the oscillating amplitude is identified, amplitude maxima and minima are extracted, and the local wavenumber is computed via linear regression. The resulting multimodal dispersion curve is consequently derived. Model uncertainties can be estimated by propagating the experimental phase and amplitude error variances through the different steps of the analysis all the way to the final phase velocities. An algorithm running the process in an automatic way has been implemented and tested on both synthetic and real data, with success. This is the base for future developments that, in the MOPA framework, can take into account rapid lateral velocity variations within the same acquisition window and estimate the modal absorption, for the estimation of the damping ratio, even in the presence of multimode surface wave propagation.
<p>Surface Wave Tomography (SWT) is a well-established technique in global seismology: signals from strong earthquakes or seismic ambient noise are used to retrieve 3D shear-wave velocity models, both at regional and global scale. This study aims at applying the same methodology to controlled source data, with specific focus on 3D acquisition geometries for seismic exploration. For a specific frequency, travel times between all source-receiver couples are derived from phase differences. However, higher modes and heterogeneous spatial sampling make phase extraction challenging. The processing workflow includes different steps as (1) filtering in f-k domain to isolate the fundamental mode from higher order modes, (2) phase unwrapping in two spatial dimensions, (3) zero-offset phase estimation and (4) travel times computation. Surface wave tomography is then applied to retrieve a 2D phase velocity map. This procedure is repeated for different frequencies. Finally, individual dispersion curves obtained by the superposition of phase velocity maps at different frequencies are depth inverted to retrieve a 3D shear wave velocity model.</p>
Seismic interferometry using ambient seismic noise is a powerful technique to constrain shear-wave velocities at different scales. Microseismic monitoring is essential to ensure the safety of industrial operations, including hydrocarbon extraction, gas storage and geothermal production. Microseismic monitoring involves recording seismic vibrations continuously, in order to identify and locate local earthquakes. However, most of the recorded seismic signals is ambient noise, that could be used to infer the shear-wave velocities in the area, thus allowing a more accurate location of the seismic events. This study aims at applying seismic interferometry to ambient noise recorded by two small microsesimic monitoring networks in Switzerland, deployed around geothermal wells. The processing workflow for each station pair includes different steps as (1) cross-correlation of the raw seismic records, (2) analysis of the zero-crossings of the cross-spectra, (3) picking of the dispersion curve and (4) depth inversion. Due to the sparse nature of the seismic networks, surface wave tomography was not applied. Considerations on the topography effects, on the lateral variability of velocities and on the possible resonance effects due to the valley geometry will be done.
Summary We performed surface-wave analysis of passive and active dense 3D data, acquired around the Scrovegni Chapel (Italy) for archaeological prospection. First, virtual source cross-correlation gathers were retrieved from ambient noise records. Active gathers and virtual source gathers were then processed separately, but using the same processing scheme, including traveltime extraction and surface-wave tomography. Phase velocity maps obtained from passive and active data were compared for the same frequency. A strategy for future joint (active and passive) analysis is finally suggested.
This collection includes all raw shot gathers collected around the Scrovegni Chapel the 29th October 2020 (data are in SEGD format). It also includes all Matlab codes used to perform the 3D surface wave analysis described in the annexed paper. This dataset is the result of the collaboration between STRYDE, that provided the seismic nodes and the technical support, and the University of Padova, that performed the data analysis.
'%3e%3cg id='b'%3e%3cpath id='a' stroke='%23000' stroke-width='2' d='M-6-25H6m-12 3H6m-12 3H6'/%3e%3cuse xlink:href='%23a' y='44'/%3e%3c/g%3e%3cpath stroke='%23fff' d='M0 17v10'/%3e%3ccircle r='12' fill='%23cd2e3a'/%3e%3cpath fill='%230047a0' d='M0-12A6 6 0 0 0 0 0a6 6 0 0 1 0 12 12 12 0 0 1 0-24z'/%3e%3c/g%3e%3cg transform='rotate(-123.69)'%3e%3cuse xlink:href='%23b'/%3e%3cpath stroke='%23fff' d='M0-23.5v3M0 17v3.5m0 3v3'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
