Abstract

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.

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.

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.

doi: https://doi.org/10.5194/egusphere-egu21-13152

Abstract

Since explosive and impulsive seismic sources such as dynamite, air guns, gas guns or even vibroseis can have a big impact on the environment, some companies have decided to record ambient seismic noise and use it to estimate the physical properties of the subsurface. Big challenges arise when the aim is extracting body waves from recorded passive signals, especially in the presence of strong surface waves. In passive seismic signals, such body waves are usually weak in comparison to surface waves that are much more prominent. To understand the characteristics of passive signals and the effect of natural source locations, three simple synthetic models were created. To extract body waves from simulated passive signals we propose and test a Radon-correlation method. This is a time-spatial correlation of amplitudes with a train of time-shifted Dirac delta functions through different hyperbolic paths. It is tested on a two-layer horizontal model, a three-layer model that includes a dipping layer (with and without lateral heterogeneity) and also on synthetic Marmousi model data sets. Synthetic tests show that the introduced method is able to reconstruct reflection events at the correct time-offset positions that are hidden in results obtained by the general cross-correlation method. Also, a depth migrated section shows a good match between imaged horizons and the true model. It is possible to generate off-end virtual gathers by applying the method to a linear array of receivers and to construct a velocity model by semblance velocity analysis of individually extracted gathers.

doi: https://doi.org/10.1093/jge/gxab004

Abstract

Seismologists are eagerly seeking new and preferably low‐cost ways to map and track changes in the complex structure of the top few kilometers of the crust. By understanding it better, they can build on what is known regarding important, practical issues. These include telling us whether imminent earthquakes and volcanic eruptions are generating telltale underground signs of hazard, about mitigation of induced seismicity such as from deep injection of wastewater, how the Earth and its atmosphere couple, and where accessible natural resources are. Passive seismic imaging usually relies on blind correlations within extended recordings of Earth’s ceaseless “hum” or coda of well‐mixed, small vibrations. In this article, we propose a complementary approach. It is seismic interferometry using opportune sources—specifically ones not stationary in time and moving in a well‐understood configuration. Its interpretation relies on an accurate understanding of how these sources radiate seismic waves, precise timing, careful placement of pairs of listening stations, and seismic phase differentiation (surface and body waves). Massive freight trains were only recently recognized as such a persistent, powerful cultural (human activity‐caused) seismic source. One train passage may generate a tremor with an energy output of a magnitude 1 earthquake and be detectable for up to 100 km from the track. We discuss the source mechanisms of train tremors and review the basic theory on sources. Finally, we present case studies of body‐ and surface‐wave retrieval as an aid to mineral exploration in Canada and to monitoring of a southern California fault zone. We believe noise recovery from this new signal source, together with dense data acquisition technologies such as nodes or distributed acoustic sensing, will deeply transform our ability to monitor activity in the shallow crust at sharpened resolution in time and space.

doi: https://doi.org/10.1785/0220200248

 Abstract

Trains are now recognized as powerful sources for seismic interferometry based on noise correlation, but the optimal use of these signals still requires a better understanding of their source mechanisms. Here, we present a simple approach for modeling train‐generated signals inspired by early work in the engineering community, assuming that seismic waves are emitted by sleepers regularly spaced along the railway and excited by passing train wheels. Our modeling reproduces well seismological observations of tremor‐like emergent signals and of their harmonic spectra. We illustrate how these spectra are modulated by wheel spacing, and how their high‐frequency content is controlled by the distribution of axle loads over the rail, which mainly depends on ground stiffness beneath the railway. This is summarized as a simple rule of thumb that predicts the frequency bands in which most of train‐radiated energy is expected, as a function of train speed and of axle distance within bogies. Furthermore, we identify two end‐member mechanisms—single stationary source versus single moving load—that explain two types of documented observations, characterized by different spectral signatures related to train speed and either wagon length or sleeper spacing. In view of using train‐generated signals for seismic applications, an important conclusion is that the frequency content of the signals is dominated by high‐frequency harmonics and not by fundamental modes of vibrations. Consequently, most train traffic worldwide is expected to generate signals with a significant high‐frequency content, in particular in the case of trains traveling at variable speeds that produce truly broadband signals. Proposing a framework for predicting train‐generated seismic wavefields over meters to kilometers distance from railways, this work paves the way for high‐resolution passive seismic imaging and monitoring at different scales with applications to near‐surface surveys (aquifers, civil engineering), natural resources exploration, and natural hazard studies (landslides, earthquakes, and volcanoes).

doi: https://doi.org/10.1785/0220200133

Link to the accepted version and supplementary material archived on the PACIFIC knowledge base platform

Abstract

The extraction of body waves from passive seismic recordings has great potential for monitoring and imaging applications. The low environmental impact, low cost, and high accessibility of passive techniques makes them especially attractive as replacement or complementary techniques to active‐source exploration. There still, however, remain many challenges with body‐wave extraction, mainly the strong dependence on local seismic sources necessary to create high‐frequency body‐wave energy. Here, we present the Marathon dataset collected in September 2018, which consists of 30 days of continuous recordings from a dense surface array of 1020 single vertical‐component geophones deployed over a mineral exploration block. First, we use a cross‐correlation beamforming technique to evaluate the wavefield each minute and discover that the local highway and railroad traffic are the primary sources of high‐frequency body‐wave energy. Next, we demonstrate how selective stacking of cross‐correlation functions during periods where vehicles and trains are passing near the array reveals strong body‐wave arrivals. Based on source station geometry and the estimated geologic structure, we interpret these arrivals as virtual refractions due to their high velocity and linear moveout. Finally, we demonstrate how the apparent velocity of these arrivals along the array contains information about the local geologic structure, mainly the major dipping layer. Although vehicle sources illuminating array in a narrow azimuth may not seem ideal for passive reflection imaging, we expect this case will be commonly encountered and should serve as a good dataset for the development of new techniques in this domain.

https://doi.org/10.1785/0220200023

Abstract
We develop a new method to monitor and locate seismic velocity changes in the subsurface using seismic noise interferometry. Contrary to most ambient noise monitoring techniques, we use the ballistic Rayleigh waves computed from 30 d records on a dense nodal array located above the Groningen gas field (the Netherlands), instead of their coda waves. We infer the daily relative phase velocity dispersion changes as a function of frequency and propagation distance with a cross-wavelet transform processing. Assuming a 1-D velocity change within the medium, the induced ballistic Rayleigh wave phase shift exhibits a linear trend as a function of the propagation distance. Measuring this trend for the fundamental mode and the first overtone of the Rayleigh waves for frequencies between 0.5 and 1.1 Hz enables us to invert for shear wave daily velocity changes in the first 1.5 km of the subsurface. The observed deep velocity changes (±1.5 per cent) are difficult to interpret given the environmental factors information available. Most of the observed shallow changes seem associated with effective pressure variations. We observe a reduction of shear wave velocity (–0.2 per cent) at the time of a large rain event accompanied by a strong decrease in atmospheric pressure loading, followed by a migration at depth of the velocity decrease. Combined with P-wave velocity changes observations from a companion paper, we interpret the changes as caused by the diffusion of effective pressure variations at depth. As a new method, noise-based ballistic wave passive monitoring could be used on several dynamic (hydro-)geological targets and in particular, it could be used to estimate hydrological parameters such as the hydraulic conductivity and diffusivity.

https://doi.org/10.1093/gji/ggaa016

Abstract
Unveiling the mechanisms of earthquake and volcanic eruption preparation requires improving our ability to monitor the rock mass response to transient stress perturbations at depth. The standard passive monitoring seismic interferometry technique based on coda waves is robust but recovering accurate and properly localized P- and S-wave velocity temporal anomalies at depth is intrinsically limited by the complexity of scattered, diffracted waves. In order to mitigate this limitation, we propose a complementary, novel, passive seismic monitoring approach based on detecting weak temporal changes of velocities of ballistic waves recovered from seismic noise correlations. This new technique requires dense arrays of seismic sensors in order to circumvent the bias linked to the intrinsic high sensitivity of ballistic waves recovered from noise correlations to changes in the noise source properties. In this work we use a dense network of 417 seismometers in the Groningen area of the Netherlands, one of Europe’s largest gas fields. Over the course of 1 month our results show a 1.5 per cent apparent velocity increase of the P wave refracted at the basement of the 700-m-thick sedimentary cover. We interpret this unexpected high value of velocity increase for the refracted wave as being induced by a loading effect associated with rainfall activity and possibly canal drainage at surface. We also observe a 0.25 per cent velocity decrease for the direct P-wave travelling in the near-surface sediments and conclude that it might be partially biased by changes in time in the noise source properties even though it appears to be consistent with complementary results based on ballistic surface waves presented in a companion paper and interpreted as a pore pressure diffusion effect following a strong rainfall episode. The perspective of applying this new technique to detect continuous localized variations of seismic velocity perturbations at a few kilometres depth paves the way for improved in situ earthquake, volcano and producing reservoir monitoring.

https://doi.org/10.1093/gji/ggz440

Abstract
Monitoring temporal changes of volcanic interiors is important to understand magma, fluid pressurization and transport leading to eruptions. Noise-based passive seismic monitoring using coda wave interferometry is a powerful tool to detect and monitor very slight changes in the mechanical properties of volcanic edifices. However, the complexity of coda waves limits our ability to properly image localized changes in seismic properties within volcanic edifices. In this work, we apply a novel passive ballistic wave seismic monitoring approach to examine the active Piton de la Fournaise volcano (La Réunion island). Using noise correlations between two distant dense seismic arrays, we find a 2.4 per cent velocity increase and −0.6 per cent velocity decrease of Rayleigh waves at frequency bands of 0.5–1 and 1–3 Hz, respectively. We also observe a −2.2 per cent velocity decrease of refracted P waves at 550 m depth at the 6–12 Hz band. We interpret the polarity differences of seismic velocity changes at different frequency bands and for different wave types as being due to strain change complexity at depth associated with subtle pressurization of the shallow magma reservoir. Our results show that velocity changes measured using ballistic waves provide complementary information to interpret temporal changes of the seismic properties within volcanic edifices.

https://doi.org/10.1093/gji/ggz466

Abstract
There are important economic, environmental and societal reasons for monitoring production from oil, gas and geothermal fields. Unfortunately, standard microseismic monitoring is often not useful due to low levels of microseismicity. We propose to use body and surface waves reconstructed from ambient seismic noise for such monitoring. In this work, we use seismic data recorded from a dense sensor array at the Groningen gas field in northern Holland and show how direct P-waves can be extracted from the ambient noise cross correlations and then used to monitor seismic velocity variations over time. This approach has advantages over the use of coda wave interferometry due to the ability to localise such changes in the subsurface. We show how both direct and refracted (head) P-waves as well as Rayleigh surface waves can be used for such field monitoring, with changes of ∼1% being resolved. Both fundamental and first overtone Rayleigh waves are used to localise such changes, which correspond nicely to known geology to within 100 m.

https://doi.org/10.1190/segam2019-3216217.1

Abstract
Laboratory experiments report that detectable seismic velocity changes should occur in the vicinity of fault zones prior to earthquakes. However, operating permanent active seismic sources to monitor natural faults at seismogenic depth is found to be nearly impossible to achieve. We show that seismic noise generated by vehicle traffic, and especially heavy freight trains, can be turned into a powerful repetitive seismic source to continuously probe the Earth’s crust at a few kilometers depth. Results of an exploratory seismic experiment in Southern California demonstrate that correlations of train‐generated seismic signals allow daily reconstruction of direct P body waves probing the San Jacinto Fault down to 4‐km depth. This new approach may facilitate monitoring most of the San Andreas Fault system using the railway and highway network of California.

https://doi.org/10.1029/2019GL083438 and available as PDF here

Abstract

Seismologists are eagerly seeking new and preferably low‐cost ways to map and track changes in the complex structure of the top few kilometers of the crust. By understanding it better, they can build on what is known regarding important, practical issues. These include telling us whether imminent earthquakes and volcanic eruptions are generating telltale underground signs of hazard, about mitigation of induced seismicity such as from deep injection of wastewater, how the Earth and its atmosphere couple, and where accessible natural resources are. Passive seismic imaging usually relies on blind correlations within extended recordings of Earth’s ceaseless “hum” or coda of well‐mixed, small vibrations. In this article, we propose a complementary approach. It is seismic interferometry using opportune sources—specifically ones not stationary in time and moving in a well‐understood configuration. Its interpretation relies on an accurate understanding of how these sources radiate seismic waves, precise timing, careful placement of pairs of listening stations, and seismic phase differentiation (surface and body waves). Massive freight trains were only recently recognized as such a persistent, powerful cultural (human activity‐caused) seismic source. One train passage may generate a tremor with an energy output of a magnitude 1 earthquake and be detectable for up to 100 km from the track. We discuss the source mechanisms of train tremors and review the basic theory on sources. Finally, we present case studies of body‐ and surface‐wave retrieval as an aid to mineral exploration in Canada and to monitoring of a southern California fault zone. We believe noise recovery from this new signal source, together with dense data acquisition technologies such as nodes or distributed acoustic sensing, will deeply transform our ability to monitor activity in the shallow crust at sharpened resolution in time and space.

doi: https://doi.org/10.1785/0220200248

 Abstract

Trains are now recognized as powerful sources for seismic interferometry based on noise correlation, but the optimal use of these signals still requires a better understanding of their source mechanisms. Here, we present a simple approach for modeling train‐generated signals inspired by early work in the engineering community, assuming that seismic waves are emitted by sleepers regularly spaced along the railway and excited by passing train wheels. Our modeling reproduces well seismological observations of tremor‐like emergent signals and of their harmonic spectra. We illustrate how these spectra are modulated by wheel spacing, and how their high‐frequency content is controlled by the distribution of axle loads over the rail, which mainly depends on ground stiffness beneath the railway. This is summarized as a simple rule of thumb that predicts the frequency bands in which most of train‐radiated energy is expected, as a function of train speed and of axle distance within bogies. Furthermore, we identify two end‐member mechanisms—single stationary source versus single moving load—that explain two types of documented observations, characterized by different spectral signatures related to train speed and either wagon length or sleeper spacing. In view of using train‐generated signals for seismic applications, an important conclusion is that the frequency content of the signals is dominated by high‐frequency harmonics and not by fundamental modes of vibrations. Consequently, most train traffic worldwide is expected to generate signals with a significant high‐frequency content, in particular in the case of trains traveling at variable speeds that produce truly broadband signals. Proposing a framework for predicting train‐generated seismic wavefields over meters to kilometers distance from railways, this work paves the way for high‐resolution passive seismic imaging and monitoring at different scales with applications to near‐surface surveys (aquifers, civil engineering), natural resources exploration, and natural hazard studies (landslides, earthquakes, and volcanoes).

doi: https://doi.org/10.1785/0220200133

Link to the accepted version and supplementary material archived on the PACIFIC knowledge base platform

Abstract

The extraction of body waves from passive seismic recordings has great potential for monitoring and imaging applications. The low environmental impact, low cost, and high accessibility of passive techniques makes them especially attractive as replacement or complementary techniques to active‐source exploration. There still, however, remain many challenges with body‐wave extraction, mainly the strong dependence on local seismic sources necessary to create high‐frequency body‐wave energy. Here, we present the Marathon dataset collected in September 2018, which consists of 30 days of continuous recordings from a dense surface array of 1020 single vertical‐component geophones deployed over a mineral exploration block. First, we use a cross‐correlation beamforming technique to evaluate the wavefield each minute and discover that the local highway and railroad traffic are the primary sources of high‐frequency body‐wave energy. Next, we demonstrate how selective stacking of cross‐correlation functions during periods where vehicles and trains are passing near the array reveals strong body‐wave arrivals. Based on source station geometry and the estimated geologic structure, we interpret these arrivals as virtual refractions due to their high velocity and linear moveout. Finally, we demonstrate how the apparent velocity of these arrivals along the array contains information about the local geologic structure, mainly the major dipping layer. Although vehicle sources illuminating array in a narrow azimuth may not seem ideal for passive reflection imaging, we expect this case will be commonly encountered and should serve as a good dataset for the development of new techniques in this domain.

https://doi.org/10.1785/0220200023

Abstract
We develop a new method to monitor and locate seismic velocity changes in the subsurface using seismic noise interferometry. Contrary to most ambient noise monitoring techniques, we use the ballistic Rayleigh waves computed from 30 d records on a dense nodal array located above the Groningen gas field (the Netherlands), instead of their coda waves. We infer the daily relative phase velocity dispersion changes as a function of frequency and propagation distance with a cross-wavelet transform processing. Assuming a 1-D velocity change within the medium, the induced ballistic Rayleigh wave phase shift exhibits a linear trend as a function of the propagation distance. Measuring this trend for the fundamental mode and the first overtone of the Rayleigh waves for frequencies between 0.5 and 1.1 Hz enables us to invert for shear wave daily velocity changes in the first 1.5 km of the subsurface. The observed deep velocity changes (±1.5 per cent) are difficult to interpret given the environmental factors information available. Most of the observed shallow changes seem associated with effective pressure variations. We observe a reduction of shear wave velocity (–0.2 per cent) at the time of a large rain event accompanied by a strong decrease in atmospheric pressure loading, followed by a migration at depth of the velocity decrease. Combined with P-wave velocity changes observations from a companion paper, we interpret the changes as caused by the diffusion of effective pressure variations at depth. As a new method, noise-based ballistic wave passive monitoring could be used on several dynamic (hydro-)geological targets and in particular, it could be used to estimate hydrological parameters such as the hydraulic conductivity and diffusivity.

https://doi.org/10.1093/gji/ggaa016

Abstract
Unveiling the mechanisms of earthquake and volcanic eruption preparation requires improving our ability to monitor the rock mass response to transient stress perturbations at depth. The standard passive monitoring seismic interferometry technique based on coda waves is robust but recovering accurate and properly localized P- and S-wave velocity temporal anomalies at depth is intrinsically limited by the complexity of scattered, diffracted waves. In order to mitigate this limitation, we propose a complementary, novel, passive seismic monitoring approach based on detecting weak temporal changes of velocities of ballistic waves recovered from seismic noise correlations. This new technique requires dense arrays of seismic sensors in order to circumvent the bias linked to the intrinsic high sensitivity of ballistic waves recovered from noise correlations to changes in the noise source properties. In this work we use a dense network of 417 seismometers in the Groningen area of the Netherlands, one of Europe’s largest gas fields. Over the course of 1 month our results show a 1.5 per cent apparent velocity increase of the P wave refracted at the basement of the 700-m-thick sedimentary cover. We interpret this unexpected high value of velocity increase for the refracted wave as being induced by a loading effect associated with rainfall activity and possibly canal drainage at surface. We also observe a 0.25 per cent velocity decrease for the direct P-wave travelling in the near-surface sediments and conclude that it might be partially biased by changes in time in the noise source properties even though it appears to be consistent with complementary results based on ballistic surface waves presented in a companion paper and interpreted as a pore pressure diffusion effect following a strong rainfall episode. The perspective of applying this new technique to detect continuous localized variations of seismic velocity perturbations at a few kilometres depth paves the way for improved in situ earthquake, volcano and producing reservoir monitoring.

https://doi.org/10.1093/gji/ggz440

Abstract
Monitoring temporal changes of volcanic interiors is important to understand magma, fluid pressurization and transport leading to eruptions. Noise-based passive seismic monitoring using coda wave interferometry is a powerful tool to detect and monitor very slight changes in the mechanical properties of volcanic edifices. However, the complexity of coda waves limits our ability to properly image localized changes in seismic properties within volcanic edifices. In this work, we apply a novel passive ballistic wave seismic monitoring approach to examine the active Piton de la Fournaise volcano (La Réunion island). Using noise correlations between two distant dense seismic arrays, we find a 2.4 per cent velocity increase and −0.6 per cent velocity decrease of Rayleigh waves at frequency bands of 0.5–1 and 1–3 Hz, respectively. We also observe a −2.2 per cent velocity decrease of refracted P waves at 550 m depth at the 6–12 Hz band. We interpret the polarity differences of seismic velocity changes at different frequency bands and for different wave types as being due to strain change complexity at depth associated with subtle pressurization of the shallow magma reservoir. Our results show that velocity changes measured using ballistic waves provide complementary information to interpret temporal changes of the seismic properties within volcanic edifices.

https://doi.org/10.1093/gji/ggz466

Abstract
There are important economic, environmental and societal reasons for monitoring production from oil, gas and geothermal fields. Unfortunately, standard microseismic monitoring is often not useful due to low levels of microseismicity. We propose to use body and surface waves reconstructed from ambient seismic noise for such monitoring. In this work, we use seismic data recorded from a dense sensor array at the Groningen gas field in northern Holland and show how direct P-waves can be extracted from the ambient noise cross correlations and then used to monitor seismic velocity variations over time. This approach has advantages over the use of coda wave interferometry due to the ability to localise such changes in the subsurface. We show how both direct and refracted (head) P-waves as well as Rayleigh surface waves can be used for such field monitoring, with changes of ∼1% being resolved. Both fundamental and first overtone Rayleigh waves are used to localise such changes, which correspond nicely to known geology to within 100 m.

https://doi.org/10.1190/segam2019-3216217.1

Abstract
Laboratory experiments report that detectable seismic velocity changes should occur in the vicinity of fault zones prior to earthquakes. However, operating permanent active seismic sources to monitor natural faults at seismogenic depth is found to be nearly impossible to achieve. We show that seismic noise generated by vehicle traffic, and especially heavy freight trains, can be turned into a powerful repetitive seismic source to continuously probe the Earth’s crust at a few kilometers depth. Results of an exploratory seismic experiment in Southern California demonstrate that correlations of train‐generated seismic signals allow daily reconstruction of direct P body waves probing the San Jacinto Fault down to 4‐km depth. This new approach may facilitate monitoring most of the San Andreas Fault system using the railway and highway network of California.

https://doi.org/10.1029/2019GL083438 and available as PDF here