High-frequency ambient noise surface wave tomography at the Marathon PGE-Cu deposit (Ontario, Canada)

2021 ◽  
Author(s):  
Daniela Teodor ◽  
Charles Beard ◽  
Laura Alejandra Pinzon-Rincon ◽  
Aurélien Mordret ◽  
François Lavoué ◽  
...  
Keyword(s):  
Surface Wave ◽  
High Frequency ◽  
Ambient Noise ◽  
Dispersion Curves ◽  
Wave Analysis ◽  
S Wave ◽  
Noise Sources ◽  
Velocity Models ◽  
Wave Tomography ◽  
Average Dispersion

<p>Ambient noise surface wave tomography (ANSWT) is an environmentally friendly and cost-effective technique for subsurface imaging. In this study, we used natural (low-frequency) and anthropogenic (high-frequency) noise sources to map the velocity structure of the Marathon Cu-PGE deposit (Ontario, Canada) to a depth of 1 km. The Marathon deposit is a circular (ø = 25 km) alkaline intrusion comprising gabbros at the rim and an overlying series of syenites in the centre. Cu-PGE mineralisation is hosted by gabbros close to the inward-dipping footwall of the intrusion. The country rocks are Archaean volcanic breccias that are seismically slower than the gabbros, and similar in velocity to the syenites. We used ANSWT to image the footwall contact that controls the location of the mineralisation.</p><p>An array of 1024 vertical-component receivers were deployed for 30 days to record ambient noise required for surface wave analysis. Two overlapping grids were used: a 200 m x 6040 m dense array with node spacing of 50 m, and a 2500 m x 4000 m sparse array with node spacing of 150 m.  The signal was down-sampled to 50 Hz, divided into segments of 30 minutes, cross-correlated and stacked. Surface wave analysis was conducted over the dense array and the sparse array data. We considered the fundamental mode of Rayleigh wave propagation for our frequency-wavenumber (F-K) analysis and focused on the phase velocity variation in the high-frequency ambient noise signal (up to 22 Hz). We reconstructed the shallow structure with progressively increased resolution using surface wave dispersion curves extracted from receiver arrays divided into segments of variable lengths. Several average dispersion curves were computed from individual dispersion curves belonging to different seismic lines. Each average dispersion curve was inverted to obtain S-wave velocity models using an McMC transdimensional Bayesian approach.</p><p>The tomographic images reveal a shallow high-velocity anomaly, which we interpret as being related to the gabbro intrusion that hosts the mineralization. The large-wavelength structures in the S-wave velocity models are relatively consistent with the geological structures inferred from surface mapping and drill core data. These results show that the ANSWT, focused on the high-frequency signal provided by anthropogenic noise sources, is an efficient technique for imaging “shallow" (1 km depth) geological structures in a mineral exploration context. </p>

P- and S-wave velocity models from surface wave dispersion curves data transform

2017 ◽  
Author(s):  
Valentina Socco ◽  
Farbod Khosro Anjom ◽  
Cesare Comina ◽  
Daniela Teodor
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models

scholarly journals Surface-wave analysis for building near-surface velocity models — Established approaches and new perspectives

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. 75A83-75A102 ◽  
Author(s):  
Laura Valentina Socco ◽  
Sebastiano Foti ◽  
Daniele Boiero
Keyword(s):  
Surface Wave ◽  
Rapid Evolution ◽  
Surface Velocity ◽  
Wave Analysis ◽  
S Wave ◽  
Near Surface ◽  
Scientific Debate ◽  
Velocity Models ◽  
Surface Wave Method ◽  
Lateral Variations

Today, surface-wave analysis is widely adopted for building near-surface S-wave velocity models. The surface-wave method is under continuous and rapid evolution, also thanks to the lively scientific debate among different disciplines, and interest in the technique has increased significantly during the last decade. A comprehensive review of the literature in the main scientific journals provides historical perspective, methodological issues, applications, and most-promising recent approaches. Higher modes in the inversion and retrieval of lateral variations are dealt with in great detail, and the current scientific debate on these topics is reported. A best-practices guideline is also outlined.

scholarly journals Structure of Suasselkä Postglacial Fault in northern Finland obtained by analysis of local events and ambient seismic noise

2016 ◽  
Author(s):  
Nikita Afonin ◽  
Elena Kozlovskaya ◽  
Ilmo Kukkonen ◽  
DAFNE/FINLAND Working Group
Keyword(s):  
Seismic Noise ◽  
Dispersion Curves ◽  
Ambient Seismic Noise ◽  
Seismic Events ◽  
S Wave ◽  
Noise Sources ◽  
Velocity Models ◽  
Wave Velocities ◽  
Short Period ◽  
Single Station

Abstract. Understanding inner structure of seismogenic faults and their ability to reactivate is particularly important in investigating continental intraplate seismicity regime. In our study we address this problem using analysis of local seismic events and ambient seismic noise recorded by the temporary DAFNE array in northern Fennoscandian Shield. The main purpose of the DAFNE/FINLAND passive seismic array experiment was to characterize the present-day seismicity of the Suasselkä post-glacial fault (SPGF) that was proposed as one potential target for the DAFNE (Drilling Active Faults in Northern Europe) project. The DAFNE/FINLAND array comprised the area of about 20 to 100 km and consisted of 8 short-period and 4 broad-band 3-component autonomous seismic stations installed in the close vicinity of the fault area. The array recorded continuous seismic data during September, 2011–May, 2013. Recordings of the array have being analyzed in order to identify and locate natural earthquakes from the fault area and to discriminate them from the blasts in the Kittilä Gold Mine. As a result, we found several dozens of natural seismic events originating from the fault area, which proves that the fault is still seismically active. In order to study the inner structure of the SPGF we use cross-correlation of ambient seismic noise recorded by the array. Analysis of azimuthal distribution of noise sources demonstrated that during the time interval under consideration the distribution of noise sources is close to the uniform one. The continuous data were processed in several steps including single station data analysis, instrument response removal and time-domain stacking. The data were used to estimate empirical Green’s functions between pairs of stations in the frequency band of 0.1–1 Hz and to calculate correspondent surface wave dispersion curves. The S-wave velocity models were obtained as a result of dispersion curves inversion. The results suggest that the area of the SPGF corresponds to a narrow region of low S-wave velocities surrounded by rocks with high S-wave velocities. We interpret this low velocity region as a non-healed mechanically weak fault damage zone (FDZ) that remained after the last major earthquake that occurred after the last glaciation.

Surface wave tomography using 3D active-source seismic data

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. EN13-EN26
Author(s):  
Ilaria Barone ◽  
Emanuel Kästle ◽  
Claudio Strobbia ◽  
Giorgio Cassiani
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Wave Velocity ◽  
Computational Time ◽  
Surface Wave Tomography ◽  
S Wave ◽  
Data Set ◽  
Velocity Models ◽  
Active Source ◽  
Wave Tomography

Surface wave tomography (SWT) is a powerful and well-established technique to retrieve 3D shear-wave (S-wave) velocity models at the regional scale from earthquakes and seismic noise measurements. We have applied SWT to 3D active-source data, in which higher modes and heterogeneous spatial sampling make phase extraction challenging. First, synthetic traveltimes calculated on a dense, regular-spaced station array are used to test the performance of three different tomography algorithms (linearized inversion, Markov chain Monte Carlo [MCMC], and eikonal tomography). The tests suggest that the lowest misfit to the input model is achieved with the MCMC algorithm, at the cost of a much longer computational time. Then, real phases were extracted from a 3D exploration data set at different frequencies. This operation included an automated procedure to isolate the fundamental mode from higher order modes, phase unwrapping in two dimensions, and the estimation of the zero-offset phase. These phases are used to compute traveltimes between each source-receiver couple, which are input into the previously tested tomography algorithms. The resulting phase-velocity maps show good correspondence, highlighting the same geologic structures for all three methods. Finally, individual dispersion curves obtained by the superposition of phase-velocity maps at different frequencies are depth inverted to retrieve a 3D S-wave velocity model.

scholarly journals 3D crustal structure of the Ligurian Sea revealed by ambient noise tomography using ocean bottom seismometer data

2021 ◽  
Author(s):  
Felix Noah Wolf ◽  
Dietrich Lange ◽  
Anke Dannowski ◽  
Martin Thorwart ◽  
Wayne Crawford ◽  
...  
Keyword(s):  
Group Velocity ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Time Windows ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
S Wave ◽  
Noise Sources ◽  
Ligurian Sea ◽  
Velocity Models

Abstract. The Liguro-Provençal basin was formed as a back-arc basin of the retreating Calabrian-Apennines subduction zone during the Oligocene and Miocene. The resulting rotation of the Corsica-Sardinia block is associated with rifting, shaping the Ligurian Sea. It is still debated whether oceanic or atypical oceanic crust was formed or if the crust is continental and experienced extreme thinning during the opening of the basin. We invert velocity models using an amphibious network of seismic stations, including 22 broadband Ocean Bottom Seismometers (OBS) to investigate the lithospheric structure of the Ligurian sea. The instruments were installed in the Ligurian Sea for eight months between June 2017 and February 2018 as part of the AlpArray seismic network. Because of additional noise sources in the ocean, OBS data are rarely used for ambient noise studies. However, we attentively pre-process the data, including corrections for instrument tilt and seafloor compliance. We took extra care to exclude higher modes of the ambient-noise Rayleigh waves. We calculate daily cross-correlation functions for the LOBSTER array and surrounding land stations. Additionally, we correlate short time windows that include teleseismic earthquakes that allow us to derive surface wave group velocities for longer periods than using ambient noise only. Group velocity maps are obtained by inverting Green’s functions derived from the cross-correlation of ambient noise and teleseismic events, respectively. We then used the resulting 3D group velocity information to calculate 1D depth inversions for S-wave velocities. The shear-wave velocity results show a deepening of the Moho from 12 km at the southwestern basin centre to 20–25 km at the Ligurian coast in the northeast and over 30 km at the Provençal coast. We find no hint on mantle serpentinisation and no evidence for an Alpine slab, at least down to depths of 25 km. However, we see a separation of the southwestern and northeastern Ligurian Basin that coincides with the promoted prolongation of the Alpine front.

Two-station continuous wavelet transform cross-coherence analysis for surface-wave tomography using active-source seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. EN17-EN28
Author(s):  
Tatsunori Ikeda ◽  
Takeshi Tsuji
Keyword(s):  
Wavelet Transform ◽  
Surface Wave ◽  
Phase Velocity ◽  
Seismic Data ◽  
Velocity Dispersion ◽  
Dispersion Curves ◽  
Surface Wave Tomography ◽  
S Wave ◽  
Phase Velocity Dispersion ◽  
Wave Tomography

ABSTRACT Surface-wave tomography has great potential to improve the lateral resolution of near-surface characterization compared to 2D surface-wave analysis with multichannel analysis of surface waves (MASW). Surface-wave tomography has been widely applied to obtain high-resolution maps of phase or group velocity from dispersion curves between pairs of stations in seismological studies. However, very few studies have done surface-wave tomography with active-source (exploration) seismic data, probably because extracting surface-wave dispersion curves between two stations is difficult due to the complex wave propagation in heterogeneous near-surface structures. Here, we describe a method to estimate reliable phase-velocity dispersion curves between two stations from exploration seismic data. In our approach, we compute cross coherences between pairs of stations to extract phase information, stacking the cross coherences from different shot gathers to improve the signal-to-noise ratio. To further distinguish surface-wave signals from noise in the time domain, we perform a time-frequency analysis using the continuous wavelet transform (CWT) on the stacked cross coherences. We used modeling of the wavelet transform between station pairs to extract phase-velocity dispersion curves from the stacked cross coherences. We apply this two-station CWT cross-coherence method to synthetic and field data sets. Both applications demonstrate that our method can extract stable phase-velocity dispersion curves between two stations better than two-station or multistation analysis without time-domain filtering. In phase-velocity distributions constructed by surface-wave tomography from the dispersion curves between two stations, the horizontal resolution is improved over MASW-based analyses. Improvement of the horizontal resolution is also achieved in S-wave velocity structures derived by inversion of the phase-velocity distributions. Our method is effective in estimating reliable phase-velocity dispersion curves and may contribute to constructing high-resolution S-wave velocity models located with a laterally heterogeneous structure, by subsequent surface-wave tomography and S-wave velocity inversion.

Sign in / Sign up

Export Citation Format

Share Document