Instantaneous phase variation for seismic velocity monitoring from ambient noise at the exploration scale

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. Q37-Q44 ◽  
Author(s):  
Margherita Corciulo ◽  
Philippe Roux ◽  
Michel Campillo ◽  
Dominique Dubucq
Keyword(s):  
Seismic Noise ◽  
Seismic Velocity ◽  
Phase Measurement ◽  
Signal To Noise Ratio ◽  
Phase Variation ◽  
Computation Time ◽  
Real Data ◽  
Velocity Change ◽  
Data Set ◽  
Instantaneous Phase

Recent studies in geophysics have investigated the use of seismic-noise correlations to measure weak-velocity variations from seismic-noise recordings. However, classically, the existing algorithms used to monitor medium velocities need extensive efforts in terms of computation time. This implies that these techniques are not appropriate at smaller scales in an exploration context when continuous data sets on dense arrays of sensors have to be analyzed. We applied a faster technique that allows the monitoring of small velocity changes from the instantaneous phase measurement of the seismic-noise crosscorrelation functions. We performed comparisons with existing algorithms using synthetic signals. The results we have obtained for a real data set show that the statistical distribution of the velocity-change estimates provides reliable measurements, despite the low signal-to-noise ratio obtained from the noise-correlation process.

Ambient seismic noise monitoring: an online application for decision makers – example of various applications for different slopes configurations.

2020 ◽  
Author(s):  
Alexandra Royer ◽  
Mathieu Le Breton ◽  
Antoine Guillemot ◽  
Noélie Bontemps ◽  
Eric Larose ◽  
...  
Keyword(s):  
Seismic Noise ◽  
Seismic Velocity ◽  
Early Warning Systems ◽  
Soil Liquefaction ◽  
Decision Makers ◽  
Velocity Variation ◽  
Ambient Seismic Noise ◽  
Landslide Monitoring ◽  
Velocity Change ◽  
Online Application

<p>Monitoring landslides is essential to understand their dynamics and to reduce the risk of human losses by detecting precursors before failures. In general, surface observations need to be complemented by observation at depth, in the bulk of the material. A decade ago, the ambient seismic noise interferometry method was proposed to monitor changes in the seismic surface wave velocity. As seismic wave velocities are directly related to the rigidity of the material, any reduction of seismic velocity can be associated to a loss of rigidity with high probability (a route toward soil liquefaction or to high fracturation). This technique led to detect a velocity decrease several days before the failure of a clayey landslide [1], paving the way to a novel precursor signal that could serve for alert or early warning systems. Here we report at least five different landslides that have been monitored, over several years [2]. In this paper, we detail the standard experimental configuration, the basic signal processing procedure, the sensitivity and resolution of the method, together with its advantages and possible limitations. Environmental effects on the relative seismic velocity change are discussed.</p><p>In order to make the technology operational for decision makers, we built an online application with web portal displaying daily evolution of seismic velocity variation. This portal also integrates other available observations like environmental parameters (weather, precipitations) or surface observation (photogrammetry, gps, extensometers…).</p><p>[1] G. Mainsant, E. Larose, C. Brönnimann, D. Jongmans, C. Michoud, M. Jaboyedoff, <em>Ambient seismic noise monitoring of a clay landslide : toward failure prediction</em>, J. Geophys. Res. <strong>117</strong>, F01030 (2012).</p><p>[2] M. Le Breton, N. Bontemps, A. Guillemot, L. Baillet, E. Larose,<sup> </sup><em>Landslide Monitoring Using Seismic Ambient Noise In-terferometry: Challenges and Applications,</em> Earth Science Review (under review) (2020)</p>

Variable-depth streamer acquisition: Broadband data for imaging and inversion

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. WA27-WA39 ◽  
Author(s):  
Robert Soubaras ◽  
Yves Lafet
Keyword(s):  
Signal To Noise Ratio ◽  
Real Data ◽  
Variable Depth ◽  
Constant Depth ◽  
Data Set ◽  
Low Frequencies ◽  
Optimal Receiver ◽  
Amplitude Versus Offset ◽  
Elastic Inversion ◽  
And Inversion

Conventional marine acquisition uses a streamer towed at a constant depth. The resulting receiver ghost notch gives the maximum recoverable frequency. To push this limit, the streamer must be towed at a quite shallow depth, but this compromises the low frequencies. Variable-depth streamer (VDS) acquisition is an acquisition technique aimed at achieving the best possible signal-to-noise ratio at low frequencies by towing the streamer very deeply, but by using a depth profile varying with offset in order not to limit the high-frequency bandwidth by notches as in conventional constant-depth streamer acquisition. The idea is to use notch diversity, each receiver having a different notch, so that the final result, combining different receivers, will have no notches. The key step to process VDS acquisitions is the receiver deghosting. We found that the optimal receiver deghosting, instead of being a preprocessing step, should be done postimaging, by using a dual-input, migration and mirror migration, and a new joint deconvolution algorithm that produces a 3D real amplitude deghosted output. This method can be applied poststack, the inputs being the migration and mirror migration images and the output being the deghosted image. Using a multichannel joint deconvolution, the inputs are the migrated and mirror migrated image gathers and the outputs are the prestack deghosted image gathers. This method preserves the amplitude-versus-offset behavior, as the deghosted output can be seen on synthetic examples to be equal to a reference computed by migrating the data modeled without any reflecting water surface. A real data set was used to illustrate this method, and another one was used to check the possibility of performing prestack elastic inversion on the deghosted gathers.

2.5D multifocusing imaging of crooked-line seismic surveys

Geophysics ◽  
2021 ◽  
pp. 1-67
Author(s):  
Hossein Jodeiri Akbari Fam ◽  
Mostafa Naghizadeh ◽  
Oz Yilmaz
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Line Geometry ◽  
Data Set ◽  
Seismic Surveys ◽  
Seismic Image ◽  
New Formulation ◽  
Optimization Search

Two-dimensional seismic surveys often are conducted along crooked line traverses due to the inaccessibility of rugged terrains, logistical and environmental restrictions, and budget limitations. The crookedness of line traverses, irregular topography, and complex subsurface geology with steeply dipping and curved interfaces could adversely affect the signal-to-noise ratio of the data. The crooked-line geometry violates the assumption of a straight-line survey that is a basic principle behind the 2D multifocusing (MF) method and leads to crossline spread of midpoints. Additionally, the crooked-line geometry can give rise to potential pitfalls and artifacts, thus, leads to difficulties in imaging and velocity-depth model estimation. We develop a novel multifocusing algorithm for crooked-line seismic data and revise the traveltime equation accordingly to achieve better signal alignment before stacking. Specifically, we present a 2.5D multifocusing reflection traveltime equation, which explicitly takes into account the midpoint dispersion and cross-dip effects. The new formulation corrects for normal, inline, and crossline dip moveouts simultaneously, which is significantly more accurate than removing these effects sequentially. Applying NMO, DMO, and CDMO separately tends to result in significant errors, especially for large offsets. The 2.5D multifocusing method can perform automatically with a coherence-based global optimization search on data. We investigated the accuracy of the new formulation by testing it on different synthetic models and a real seismic data set. Applying the proposed approach to the real data led to a high-resolution seismic image with a significant quality improvement compared to the conventional method. Numerical tests show that the new formula can accurately focus the primary reflections at their correct location, remove anomalous dip-dependent velocities, and extract true dips from seismic data for structural interpretation. The proposed method efficiently projects and extracts valuable 3D structural information when applied to crooked-line seismic surveys.

Broadband receiver response from dual‐streamer data and applications in deep reflection seismology

Geophysics ◽  
1996 ◽  
Vol 61 (1) ◽  
pp. 232-243 ◽  
Author(s):  
Satish C. Singh ◽  
R. W. Hobbs ◽  
D. B. Snyder
Keyword(s):  
Signal To Noise Ratio ◽  
Spectral Response ◽  
Synthetic Data ◽  
Real Data ◽  
Reflection Seismology ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Streamer Data ◽  
Lower Crustal

A method to process dual‐streamer data with under and over configuration is presented. The method combines the results of dephase‐sum and dephase‐subtraction methods. In the dephase methods, the response of one streamer is time shifted so that the primary arrivals on both streamers are aligned, and these responses are then summed or subtracted. The method provides a broad spectral response from dual‐streamer data and increases the signal‐to‐noise ratio by a factor of 1.5. Testing was done on synthetic data and then applied to a real data set collected by the British Institutions Reflection Profiling Syndicate (BIRPS). Its application to a deep seismic reflection data set from the British Isles shows that the reflections from the lower crust contain frequencies up to 80 Hz, suggesting that some of the lower crustal reflectors may have sharp boundaries and could be 20–30 m thick.

Analysis of ballistic waves in seismic noise monitoring of water table variations in a water field site: added value from numerical modelling to data understanding

2019 ◽  
Vol 219 (3) ◽  
pp. 1636-1647 ◽  
Author(s):  
S Garambois ◽  
C Voisin ◽  
M A Romero Guzman ◽  
D Brito ◽  
B Guillier ◽  
...  
Keyword(s):  
Numerical Modelling ◽  
Surface Waves ◽  
Water Table ◽  
Seismic Noise ◽  
Seismic Velocity ◽  
Signal To Noise Ratio ◽  
Numerical Approach ◽  
P Wave ◽  
Added Value ◽  
Observation Window

SUMMARY Passive seismic interferometry allows to track continuously the weak seismic velocity changes in any medium by correlating the ambient seismic noise between two points to reconstruct the Green’s function. The ballistic surface waves of the reconstructed Green’s functions are used to monitor the changes of water table induced by a controlled experiment in the Crépieux-Charmy (France) exploitation field. Viscoelastic numerical modelling of the monitoring experiment reproduces quite satisfactorily the sensitivity of the surface waves to the water table previously observed with seismic noise data. This numerical approach points out that this sensitivity is controlled by mode mixing of Rayleigh waves. It also made it possible to identify the refracted P wave and to extract its anticorrelated sensitivity to water table variations. Depending on the offset between receivers, it was observed numerically that the interferences between the different waves (with different velocities) composing the seismic wavefield slightly affect the quantitative sensitivity to water table changes. This suggests the use of an optimal spatial and temporal observation window for which wave interference is minor and does not blur the quantitative response to water table variations. We were thus able to determine the relationship between velocity and water table variations for all waves involved. From numerical computations, we identify a weak signal-to-noise ratio phase in the noise correlograms, with a anticorrelated sensitivity to the water table: the reconstructed refracted waves.

Seismic noise monitoring of the water table in a deep-seated, slow-moving landslide

2016 ◽  
Vol 4 (3) ◽  
pp. SJ67-SJ76 ◽  
Author(s):  
Christophe Voisin ◽  
Stéphane Garambois ◽  
Chris Massey ◽  
Romain Brossier
Keyword(s):  
Water Table ◽  
Frequency Band ◽  
Seismic Noise ◽  
Seismic Velocity ◽  
Slip Surface ◽  
Ground Surface ◽  
Velocity Change ◽  
Fluid Saturation ◽  
Limited Frequency ◽  
Velocity Changes

Daily correlations of ambient seismic noise on a large landslide at Utiku, New Zealand, reveal seismic velocity changes up to [Formula: see text] that follow a summer/winter cycle consistent with the pore-water pressures monitored at the basal slip surface in the landslide. The annual pattern of velocity changes is borne by a limited frequency band (6–8 Hz typically) that suggests a localized change in the medium. The Rayleigh waves that form the seismic signal within this frequency band have a maximum sensitivity at a depth of 2–3 m below the ground surface, consistent with the water table level. Fluid saturation changes in the landslide modeled using the Biot-Gassmann theory explain the limited frequency band and the amplitude of the seismic velocity change. This set of arguments suggests that seismic noise correlations are sensitive to water table oscillations through saturation changes and could be used as a nondestructive hydrologic monitoring tool.

Automatic nonhyperbolic velocity analysis by polynomial chaos expansion

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. U79-U88 ◽  
Author(s):  
Mostafa Abbasi ◽  
Ali Gholami
Keyword(s):  
Seismic Data ◽  
Polynomial Chaos ◽  
Seismic Velocity ◽  
Real Data ◽  
Velocity Analysis ◽  
Data Set ◽  
New Approach ◽  
Transverse Isotropic ◽  
Two Parameters ◽  
Vti Media

Seismic velocity analysis is one of the most crucial and, at the same time, the most laborious tasks during seismic data processing. This becomes even more difficult and time-consuming when nonhyperbolicity has to be considered in the velocity analysis. Nonhyperbolic velocity analysis provides very useful information during the processing and interpretation of seismic data. The most common approach for considering anisotropy during velocity analysis is to describe the moveout based on a nonhyperbolic equation. The nonhyperbolic moveout equation in vertically transverse isotropic (VTI) media is defined by two parameters: normal moveout (NMO) velocity [Formula: see text] and anellipticity [Formula: see text] (or horizontal velocity [Formula: see text]). We have developed a new approach based on polynomial chaos (PC) expansion for automating nonhyperbolic velocity analysis of common-midpoint (CMP) data in VTI media. For this purpose, we use the PC expansion to approximate the nonhyperbolic semblance function with a very fast-to-simulate function in terms of [Formula: see text] and [Formula: see text]. Then, using particle swarm optimization, we stochastically look for the optimum NMO and horizontal velocities that provide the maximum semblance. In contrary to common approaches for nonhyperbolic velocity analysis in which the two parameters are estimated iteratively in an alternating fashion, we find [Formula: see text] and [Formula: see text] simultaneously. This approach is tested on various data including a simple convolutional model, an anisotropic benchmark model, and a real data set. In all cases, the new method provided acceptable results. Reflections in the CMP corrected using the optimum velocities are properly flattened, and almost no residual moveout is observed.

scholarly journals Performance analysis of the Karhunen–Loève Transform for artificial and astrophysical transmissions: denoizing and detection

2020 ◽  
Vol 494 (1) ◽  
pp. 69-83
Author(s):  
Matteo Trudu ◽  
Maura Pilia ◽  
Gregory Hellbourg ◽  
Pierpaolo Pari ◽  
Nicolò Antonietti ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Signal To Noise Ratio ◽  
Computation Time ◽  
Real Data ◽  
Detection Techniques ◽  
Standard Signal ◽  
Novel Method ◽  
Study Case ◽  
Karhunen Loeve Transform ◽  
Level Reduction

ABSTRACT In this work, we propose a new method of computing the Karhunen–Loève Transform (KLT) applied to complex voltage data for the detection and noise level reduction in astronomical signals. We compared this method with the standard KLT techniques based on the Toeplitz correlation matrix and we conducted a performance analysis for the detection and extraction of astrophysical and artificial signals via Monte Carlo (MC) simulations. We applied our novel method to a real data study-case: the Voyager 1 telemetry signal. We evaluated the KLT performance in an astrophysical context: our technique provides a remarkable improvement in computation time and MC simulations show significant reconstruction results for signal-to-noise ratio (SNR) down to −10 dB and comparable results with standard signal detection techniques. The application to artificial signals, such as the Voyager 1 data, shows a notable gain in SNR after the KLT.

scholarly journals Determination of a stress-dependent rock-physics model using anisotropic time-lapse tomographic inversion

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. C141-C152
Author(s):  
Nicolas Mastio ◽  
Pierre Thore ◽  
Marianne Conin ◽  
Guillaume Caumon
Keyword(s):  
Rock Physics ◽  
Real Data ◽  
Time Lapse ◽  
P Wave ◽  
Velocity Change ◽  
Data Set ◽  
Tomographic Inversion ◽  
P Wave Velocity ◽  
Cap Rock ◽  
Velocity Changes

In the petroleum industry, time-lapse (4D) studies are commonly used for reservoir monitoring, but they are also useful to perform risk assessment for potential overburden deformations (e.g., well shearing, cap-rock integrity). Although complex anisotropic velocity changes are predicted in the overburden by geomechanical studies, conventional time-lapse inversion workflows only deal with vertical velocity changes. To retrieve the geomechanically induced anisotropy, we have adopted a reflection traveltime tomography method coupled with a time-shift estimation algorithm of prestack data of the baseline and monitor simultaneously. For the 2D approach, we parameterize the anisotropy using five coefficients, enough to cover any type of anisotropy. Before applying the workflow to a real data set, we first study a synthetic data set based on the real data set and include velocity variations between baseline and monitor found in the literature (vertical P-wave velocity decrease in the cap rock and isotropic P-wave velocity change in the reservoir). On the synthetics, we measure the angular ray coverage necessary to retrieve the target anisotropy and observe that the retrieved anisotropies depend on the offset range. Based on a synthetic experiment, we believe that the acquisition of the real case study is suitable for performing tomographic inversion. The anisotropic velocity changes obtained on three inlines separated by 375 m are consistent and show a strong positive anomaly in the cap rock along the 45° direction (the [Formula: see text] parameter in Thomsen notation), whereas the vertical velocity change is surprisingly almost negligible. We adopt a rock-physics explanation compatible with these observations and geologic considerations: a reactivation of water-filled subvertical cracks.

scholarly journals Maximal Instance Algorithm for Fast Mining of Spatial Co-Location Patterns

2021 ◽  
Vol 13 (5) ◽  
pp. 960
Author(s):  
Guoqing Zhou ◽  
Qi Li ◽  
Guangming Deng
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Large Fraction ◽  
Synthetic Data ◽  
Computation Time ◽  
Real Data ◽  
Spatial Data Mining ◽  
Fast Method ◽  
Data Set ◽  
Location Patterns

The explosive growth of spatial data and the widespread use of spatial databases emphasize the need for spatial data mining. The subsets of features frequently located together in a geographic space are called spatial co-location patterns. It is difficult to discover co-location patterns because of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to generating row instances and candidate co-location patterns. This paper makes three main contributions for mining co-location patterns. First, the definition of maximal instances is given and a row instance (RI)-tree is constructed to find maximal instances from a spatial data set. Second, a fast method for generating all row instances and candidate co-locations is proposed and the feasibility of this method is proved. Third, a maximal instance algorithm with no join operations for mining co-location patterns is proposed. Finally, experimental evaluations using synthetic data sets and a real data set show that maximal instance algorithm is feasible and has better performance.

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