Retrieval of reflections from ambient noise recorded in the Mizil area, Romania

Panea, I.; Draganov, Deyan; Vidal, C. Almagro; Mocanu, V.

doi:10.1190/geo2013-0292.1

Cited by 31 publications

(17 citation statements)

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“…For long periods (25–100 s) earthquake coda waves contribute to the recovery of deep Earth phases (Boué et al, ; Lin & Tsai, ; Lin et al, ; Poli et al, ) and pollute the reconstruction from the ambient noise background field (Boué et al, ). For a thorough study of body waves, a large data set and careful processing are paramount (Nakata et al, ; Olivier et al, ; Panea et al, ). To the best of the authors' knowledge, only a few studies exploited body waves retrieved from noise correlation functions for tomographic purposes (Nakata et al, ; Olivier et al, ), and due to the limitations mentioned above it has not become a standard extension of ambient noise tomography.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, body waves are present in the ambient noise field (Gal et al, 2015;Gerstoft et al, 2006Gerstoft et al, , 2008Landès et al, 2010;Liu et al, 2016;Ruigrok et al, 2011;Schulte-Pelkum et al, 2004), and the excitation of secondary microseism P waves is understood well enough to be modeled (Farra et al, 2016;Gualtieri et al, 2014). Their emergence in correlation functions is also confirmed on local (Dantas et al, 2018;Draganov et al, 2013Draganov et al, , 2007Draganov et al, , 2009Nakata et al, 2015;Olivier et al, 2015;Panea et al, 2014;Roux, Sabra, Gerstoft, et al, 2005;Ruigrok et al, 2011), regional Zhan et al, 2010), and global scales (Boué et al, 2013;Haned et al, 2016;Nishida, 2013;. However, several problems are encountered with their observation and interpretation.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Seismic Noise Correlation Functions to Global Noise Sources

et al. 2018

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Surface waves in seismic noise correlation functions are routinely exploited for tomographic studies. However, the observation and proper interpretation of body waves is considerably more difficult. To understand body wave generation in particular and the emergence of noise correlation signals in general, we investigate the sensitivity of correlation functions with respect to global noise sources. Treating noise correlation functions as self‐consistent observables, without the enforcement of Green function retrieval, enables us to investigate the quantitative contribution of different noise sources in space to a signal in a specific time window. The source sensitivity kernel for a surface wave reveals the expected dominant and broad region of stationary phase and thereby confirms the robustness of noise tomography in its current state. In contrast, kernels for potential body waves are highly oscillatory and indicate significant contributions from cross terms, for example, from body wave‐surface wave interactions. While the short‐wavelength nature of the body wave kernel may in the future allow us to infer noise sources with great detail, the importance of cross terms combined with the natural heterogeneity of noise sources renders an interpretation of such an arrival as a pure body wave almost impossible. Furthermore, we show that even long‐wavelength Earth structure significantly distorts sensitivity kernels. It is therefore essential to incorporate 3‐D heterogeneities into high‐resolution imaging of ambient noise sources, especially at higher frequencies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity of Seismic Noise Correlation Functions to Global Noise Sources

et al. 2018

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“…Globally, seismic sensors are used to detect earthquakes and explosions Ringdal, 2006, 2012). On a regional scale, sensor arrays can assist in the analysis of body and surfaces waves (Harmon et al, 2008;Vidal et al, 2011;Panea et al, 2014). On a local scale, arrays are used in exploration seismology to detect microseismic activity in a reservoir or a mine (Potvin and Hudyma, 2001;Chambers et al, 2010;Boué et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Extracting low signal-to-noise ratio events with the Hough transform from sparse array data

et al. 2018

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Low-frequency acoustic, i.e., infrasound, waves are measured by sparse arrays of microbarometers. Recorded data are processed by automatic detection algorithms based on array-processing techniques such as time-domain beam forming and f-k analysis. These algorithms use a signal-to-noise ratio (S/N) value as a detection criterion. In the case of high background noise or in the presence of multiple coinciding signals, the event's S/N decreases and can be missed by automatic processing. In seismology, detecting low-S/N events with geophone arrays is a well-known problem. Whether it is in global earthquake monitoring or reservoir microseismic activity characterization, detecting low-S/N events is needed to better understand the sources or the medium of propagation. We use an image-processing technique as a postprocessing step in the automatic detection of low S/N events. In particular, we consider the use of the Hough transform (HT) technique to detect straight lines in beam-forming results, i.e., a back azimuth (BA) time series. The presence of such lines, due to similar BA values, can be indicative of a low-S/N event. A statistical framework is developed for the HT parameterization, which includes defining a threshold value for detection as well as evaluating the false alarm rate. The method is tested on synthetic data and five years of recorded infrasound from glaciers. It is shown that the automatic detection capability is increased by detecting low-S/N events while keeping a low false-alarm rate.

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“…The latter aim to extract portions of the recorded wavefield characterized by common metrics. For ambient-noise processing methods some effective solutions are beamforming (Rost and Thomas, 2002), illumination diagnosis (Almagro Panea et al, 2014), coherency filtering (Nakata et al, 2015), selective stacking (Nakata et al 2015;Olivier et al, 2016), and filtering based on singular-value decomposition (SVD, Melo et al, 2013;Moreau et al, 2017). In terms of detection methods, some interesting examples include source-scanning algorithms (Kao et al, 2004), template matching (Shelly et al, 2007), the STA/LTA technique (Allen, 1978), the fuzzy-logic method (Cercone, 1993), and a recently developed local-similarity approach (Li et al, 2018) allowing detection of very weak events recorded with large-N arrays.…”

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confidence: 99%

Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider

et al. 2019

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Automatic 3D illumination-diagnosis method for large-N arrays: robust data scanner and machine-learning feature provider Chamarczuk, M.; Malinowski, M.; Nishitsuji, Yohei; Thorbecke, Jan Willem; Koivisto, E.; Heinonen, S.; Juurela, S.; Mężyk, M.; Draganov, Deyan ABSTRACTThe main issues related to passive-source reflection imaging with seismic interferometry are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omni-present surface-wave sources.Additionally, long recordings provide large data volumes that require robust and efficient processing methods. We address these problems by developing a two-step wavefield evaluation and detection method (TWEED) of body waves in recorded ambient noise. TWEED evaluates the spatio-temporal characteristics of noise recordings by simultaneous analysis of adjacent receiver lines. We test our method on synthetic data representing transient ambient-noise sources at the surface and in the deeper subsurface. We discriminate between basic types of seismic events by using three adjacent receiver lines. Subsequently, we apply TWEED to 600 hours of ambient noise acquired with ~1000-receiver array deployed over an active underground mine in Eastern Finland. We demonstrate detection of body-wave events related to mine blasts and other routine mining activities using a representative one-hour noise panel.Using TWEED, we successfully detect 1093 body-wave events in the full data set. To increase the computational efficiency, we use slowness parameters derived from the first step of TWEED as input to a support vector machine (SVM) algorithm. Using this approach, we detect 94 percent of the TWEED-evaluated body-wave events indicating the possibility to limit the illumination analysis to only one step and therefore increase the time efficiency at the price of lower detection rate. However, TWEED on a small volume of the recorded data followed by SVM on the rest of the data could be efficiently used for a quick and robust (real-time) scanning for body-wave energy in large data volumes for subsequent application of seismic interferometry for retrieval of reflections.

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Retrieval of reflections from ambient noise recorded in the Mizil area, Romania

Cited by 31 publications

References 53 publications

Sensitivity of Seismic Noise Correlation Functions to Global Noise Sources

Sensitivity of Seismic Noise Correlation Functions to Global Noise Sources

Extracting low signal-to-noise ratio events with the Hough transform from sparse array data

Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider

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