Noise-based ballistic wave passive seismic monitoring. Part 1: body waves
Florent BrenguierRoméo CourbisAurélien MordretXander CampmanPierre BouéMałgorzata ChmielTomoya TakanoThomas LecocqWim van der VeenSophie PostifD. Hollis
31
Citation
38
Reference
10
Related Paper
Citation Trend
Abstract:
SUMMARY 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.Keywords:
Seismic Noise
Seismometer
Passive seismic
Microseism
Rayleigh Wave
Ambient noise level
Dispersive body waves
Coda
Abstract It is now established that the primary microseism, the secondary microseisms, and the hum are the three main components of seismic noise in the frequency band from about 0.003 Hz to 1.0 Hz. Monthly averages of seismic noise are dominated by these signals in seismic noise. There are, however, some temporary additional signals in the same frequency band, such as signals from tropical cyclones (hurricanes and typhoons) in the ocean and on land, stormquakes, weather bombs, tornadoes, and wind-related atmospheric pressure loading. We review these effects, lasting only from a few hours to a week but are significant signals. We also attempt to classify all seismic noise. We point out that there are two broad types of seismic noise, the propagating seismic waves and the quasi-static deformations. The latter type is observed only for surface pressure changes at close distances. It has been known since about 1970 but has not been emphasized in recent literature. Recent data based on co-located pressure and seismic instruments clearly show its existence. Because the number of phenomena in the first type is large, we propose to classify all seismic noise into three categories: (1) propagating seismic waves from ocean sources, (2) propagating seismic waves from on-land sources, and (3) quasi-static deformation at ocean bottom and on land. The microseisms and the hum are in the first category although there are differences in the detailed processes of their excitation mechanisms. We will also classify temporary signals by these categories.
Microseism
Seismic Noise
Rayleigh Wave
Passive seismic
Shadow zone
Ambient noise level
Vertical seismic profile
Dispersive body waves
Seismic interferometry
Cite
Citations (3)
SUMMARY 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.
Seismic Noise
Seismometer
Passive seismic
Microseism
Rayleigh Wave
Ambient noise level
Dispersive body waves
Coda
Cite
Citations (31)
Summary Near-surface structures and conditions have a large impact on the quality of land seismic data and the final subsurface image. However, the mechanism that causes the variations in this data quality is complex. This potentially indicates that the understanding of near surface could be useful to improve the quality of active seismic data in terms of not only seismic processing but also seismic acquisition. This study discusses the relationship between source performance of active seismic acquisition, which represents the magnitude of energy for seismic waves with early arrivals propagated from sources to receivers, and near-surface structures of S-wave velocity reconstructed by ambient noise. We used ambient noise on two seismic datasets in this study. While we investigated this relationship using an active 2D seismic dataset, we also utilized another passive seismic dataset at relatively sparse locations to estimate source performance with limited cost before active seismic acquisition. We provide a case study about onshore Japan to examine this relationship. This investigation compares the S-wave velocity profile estimated by applying seismic interferometry and multi-channel analysis of surface waves to the ambient noise in active/passive seismic data with the source performance analyzed active seismic data and borehole data.
Passive seismic
Seismic Noise
Seismic to simulation
Ambient noise level
Dispersive body waves
Vertical seismic profile
Seismic interferometry
Synthetic seismogram
Cite
Citations (0)
A critical consideration in the design of next generation gravitational wave detectors is the understanding of the seismic environment that can introduce coherent and incoherent noise of seismic origin at different frequencies. We present detailed low-frequency ambient seismic noise characterization (0.1--10~Hz) at the Gingin site in Western Australia. Unlike the microseism band (0.06--1~Hz) for which the power shows strong correlations with nearby buoy measurements in the Indian Ocean, the seismic spectrum above 1~Hz is a complex superposition of wind induced seismic noise and anthropogenic seismic noise which can be characterized using beamforming to distinguish between the effects of coherent and incoherent wind induced seismic noise combined with temporal variations in the spatio-spectral properties of seismic noise. This also helps characterizing the anthropogenic seismic noise. We show that wind induced seismic noise can either increase or decrease the coherency of background seismic noise for wind speeds above 6~m/s due to the interaction of wind with various surface objects. In comparison to the seismic noise at the Virgo site, the secondary microseism (0.2~Hz) noise level is higher in Gingin, but the seismic noise level between 1 and 10~Hz is lower due to the sparse population and absence of nearby road traffic.
Seismic Noise
Microseism
Ambient noise level
Passive seismic
Dispersive body waves
Cite
Citations (0)
Abstract The increased use of ambient seismic noise for seismic imaging requires better understanding of the ambient seismic noise wavefield and its source locations and mechanisms. Although the source regions and mechanisms of Rayleigh waves have been studied extensively, characterization of Love wave source processes are sparse or absent. We present here the first systematic comparison of ambient seismic noise source directions within the primary (~10–20 s period) and secondary (~5–10 s period) microseism bands for both Rayleigh and Love waves in the Southern Hemisphere using vertical‐ and horizontal‐component ambient seismic noise recordings from a dense temporary network of 68 broadband seismometers in New Zealand. Our analysis indicates that Rayleigh and Love waves within the primary microseism band appear to be mostly generated in different areas, whereas in the secondary microseism band they arrive from similar backazimuths. Furthermore, the source areas of surface waves within the secondary microseism band correlate well with modeled deep‐water and near‐coastal source regions.
Microseism
Seismic Noise
Seismometer
Rayleigh Wave
Ambient noise level
Cite
Citations (55)
Rayleigh wave phase velocity models for gravitational wave detectors using an array of nodal sensors
Array studies of ambient seismic noise have gained much importance in recent years for the purpose of classifying noise sources corresponding to different frequency bands. Stehly et al. (2006), Snieder et al. (2009), Wapenaar et al. (2010), have also demonstrated useful applications of using ambient noise recordings for surface wave tomography. Seismic motions generated by natural and artificial sources propagate through the subsurface both in the form of body and shear waves. But the major contribution to the seismic noise field is in the form of Rayleigh and Love waves (Haubrich et al., 1963), especially at shallow depths. In the context of gravitational wave detectors, such displacement of the subsurface couples with the suspended elements of the detector through gravitational forces of attraction and is referred to as Newtonian noise. In order to subtract this noise, it is necessary to understand the sources of seismic noise near the detectors and the propagation characteristics. Hence, an optimal seismic array was designed and a passive seismic survey was carried out at the Advanced Virgo gravitational wave detector in Italy. Easily deployable 5 Hz vertical component wireless geophones were used for continuous acquisition of the seismic noise.
Seismic Noise
Rayleigh Wave
Geophone
Passive seismic
Seismic array
Vertical seismic profile
Dispersive body waves
Seismogram
Ambient noise level
Cite
Citations (3)
A critical consideration in the design of next generation gravitational wave detectors is the understanding of the seismic environment that can introduce coherent and incoherent noise of seismic origin at different frequencies. We present detailed low-frequency ambient seismic noise characterization (0.1--10~Hz) at the Gingin site in Western Australia. Unlike the microseism band (0.06--1~Hz) for which the power shows strong correlations with nearby buoy measurements in the Indian Ocean, the seismic spectrum above 1~Hz is a complex superposition of wind induced seismic noise and anthropogenic seismic noise which can be characterized using beamforming to distinguish between the effects of coherent and incoherent wind induced seismic noise combined with temporal variations in the spatio-spectral properties of seismic noise. This also helps characterizing the anthropogenic seismic noise. We show that wind induced seismic noise can either increase or decrease the coherency of background seismic noise for wind speeds above 6~m/s due to the interaction of wind with various surface objects. In comparison to the seismic noise at the Virgo site, the secondary microseism (0.2~Hz) noise level is higher in Gingin, but the seismic noise level between 1 and 10~Hz is lower due to the sparse population and absence of nearby road traffic.
Seismic Noise
Microseism
Ambient noise level
Passive seismic
Dispersive body waves
Seismic array
Vertical seismic profile
Cite
Citations (0)
The increased use of ambient seismic noise for seismic imaging requires better understanding of the ambient seismic noise wavefield and its source locations and mechanisms. Although the source regions and mechanisms of Rayleigh waves have been studied extensively, characterization of Love wave source processes are sparse or absent. We present here the first systematic comparison of ambient seismic noise source directions within the primary (~10-20 s period) and secondary (~5-10 s period) microseism bands for both Rayleigh and Love waves in the Southern Hemisphere using vertical- and horizontal-component ambient seismic noise recordings from a dense temporary network of 68 broadband seismometers in New Zealand. Our analysis indicates that Rayleigh and Love waves within the primary microseism band appear to be mostly generated in different areas, whereas in the secondary microseism band they arrive from similar backazimuths. Furthermore, the source areas of surface waves within the secondary microseism band correlate well with modeled deep-water and near-coastal source regions. Key Points Rayleigh and Love wave source regions of the secondary microseism are co-located Rayleigh and Love wave source regions of the primary microseism differ strongly Observed and modeled source directions for the secondary microseism agree well ©2012. American Geophysical Union. All Rights Reserved.
Microseism
Rayleigh Wave
Seismic Noise
Seismometer
Ambient noise level
Love wave
Cite
Citations (0)
In urban subsurface exploration, seismic surveys are mostly conducted along roads where seismic vibrators can be extensively used to generate strong seismic energy due to economic and environmental constraints. Generally, Rayleigh waves also are excited by the compressional wave profiling process. Shear-wave (S-wave) velocities can be inferred using Rayleigh waves to complement near-surface characterization. Most vibrators cannot excite seismic energy at lower frequencies (<5 Hz) to map greater depths during surface-wave analysis in areas with low S-wave velocities, but low-frequency surface waves ([Formula: see text]) can be extracted from traffic-induced noise, which can be easily obtained at marginal additional cost. We have implemented synthetic tests to evaluate the velocity deviation caused by offline sources, finding a reasonably small relative bias of surface-wave dispersion curves due to vehicle sources on roads. Using a 2D reflection survey and traffic-induced noise from the central North China Plain, we apply seismic interferometry to a series of 10.0 s segments of passive data. Then, each segment is selectively stacked on the acausal-to-causal ratio of the mean signal-to-noise ratio to generate virtual shot gathers with better dispersion energy images. We next use the dispersion curves derived by combining controlled source surveying with vehicle noise to retrieve the shallow S-wave velocity structure. A maximum exploration depth of 90 m is achieved, and the inverted S-wave profile and interval S-wave velocity model obtained from reflection processing appear consistent. The data set demonstrates that using surface waves derived from seismic reflection surveying and traffic-induced noise provides an efficient supplementary technique for delineating shallow structures in areas featuring thick Quaternary overburden. Additionally, the field test indicates that traffic noise can be created using vehicles or vibrators to capture surface waves within a reliable frequency band of 2–25 Hz if no vehicles are moving along the survey line.
Rayleigh Wave
Seismic Noise
Passive seismic
Microseism
Dispersive body waves
Seismic interferometry
Reflection
Love wave
Vertical seismic profile
Cite
Citations (18)
The increased use of ambient seismic noise for seismic imaging requires better understanding of the ambient seismic noise wavefield and its source locations and mechanisms. Although the source regions and mechanisms of Rayleigh waves have been studied extensively, characterization of Love wave source processes are sparse or absent. We present here the first systematic comparison of ambient seismic noise source directions within the primary (~10-20 s period) and secondary (~5-10 s period) microseism bands for both Rayleigh and Love waves in the Southern Hemisphere using vertical- and horizontal-component ambient seismic noise recordings from a dense temporary network of 68 broadband seismometers in New Zealand. Our analysis indicates that Rayleigh and Love waves within the primary microseism band appear to be mostly generated in different areas, whereas in the secondary microseism band they arrive from similar backazimuths. Furthermore, the source areas of surface waves within the secondary microseism band correlate well with modeled deep-water and near-coastal source regions. Key Points Rayleigh and Love wave source regions of the secondary microseism are co-located Rayleigh and Love wave source regions of the primary microseism differ strongly Observed and modeled source directions for the secondary microseism agree well ©2012. American Geophysical Union. All Rights Reserved.
Microseism
Rayleigh Wave
Seismic Noise
Seismometer
Ambient noise level
Love wave
Cite
Citations (0)