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

Michal Chamarczuk, Michal Malinowski, Yohei Nishitsuji, Jan Thorbecke, Emilia Anna-Liisa Koivisto, Suvi Heinonen, Sanna Juurela, Milosz Mężyk, Deyan Draganov

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

Kuvaus

The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent 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 event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal 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 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h 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% 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 SI for retrieval of reflections.

Alkuperäiskielienglanti
LehtiGeophysics
Vuosikerta84
Numero3
SivutQ13-Q25
Sivumäärä13
ISSN0016-8033
DOI - pysyväislinkit
TilaJulkaistu - helmikuuta 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

Tieteenalat

  • 1171 Geotieteet

Lainaa tätä

Chamarczuk, Michal ; Malinowski, Michal ; Nishitsuji, Yohei ; Thorbecke, Jan ; Koivisto, Emilia Anna-Liisa ; Heinonen, Suvi ; Juurela, Sanna ; Mężyk, Milosz ; Draganov, Deyan. / Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider. Julkaisussa: Geophysics. 2019 ; Vuosikerta 84, Nro 3. Sivut Q13-Q25.
@article{22e4b8e1bd5c46bfb67d03441103a4a9,
title = "Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider",
abstract = "The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent 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 event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal 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 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h 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{\%} 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 SI for retrieval of reflections.",
keywords = "1171 Geosciences, SEISMIC NOISE CORRELATIONS, WAVE RECONSTRUCTION, BODY, INTERFEROMETRY, REFLECTIONS, RETRIEVAL, PITON, SVD",
author = "Michal Chamarczuk and Michal Malinowski and Yohei Nishitsuji and Jan Thorbecke and Koivisto, {Emilia Anna-Liisa} and Suvi Heinonen and Sanna Juurela and Milosz Mężyk and Deyan Draganov",
year = "2019",
month = "2",
doi = "10.1190/geo2018-0504.1",
language = "English",
volume = "84",
pages = "Q13--Q25",
journal = "Geophysics",
issn = "0016-8033",
publisher = "Society of Exploration Geophysicists",
number = "3",

}

Chamarczuk, M, Malinowski, M, Nishitsuji, Y, Thorbecke, J, Koivisto, EA-L, Heinonen, S, Juurela, S, Mężyk, M & Draganov, D 2019, 'Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider', Geophysics, Vuosikerta 84, Nro 3, Sivut Q13-Q25. https://doi.org/10.1190/geo2018-0504.1

Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider. / Chamarczuk, Michal; Malinowski, Michal; Nishitsuji, Yohei; Thorbecke, Jan; Koivisto, Emilia Anna-Liisa; Heinonen, Suvi; Juurela, Sanna; Mężyk, Milosz; Draganov, Deyan.

julkaisussa: Geophysics, Vuosikerta 84, Nro 3, 02.2019, s. Q13-Q25.

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

TY - JOUR

T1 - Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider

AU - Chamarczuk, Michal

AU - Malinowski, Michal

AU - Nishitsuji, Yohei

AU - Thorbecke, Jan

AU - Koivisto, Emilia Anna-Liisa

AU - Heinonen, Suvi

AU - Juurela, Sanna

AU - Mężyk, Milosz

AU - Draganov, Deyan

PY - 2019/2

Y1 - 2019/2

N2 - The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent 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 event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal 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 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h 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% 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 SI for retrieval of reflections.

AB - The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent 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 event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal 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 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h 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% 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 SI for retrieval of reflections.

KW - 1171 Geosciences

KW - SEISMIC NOISE CORRELATIONS

KW - WAVE RECONSTRUCTION

KW - BODY

KW - INTERFEROMETRY

KW - REFLECTIONS

KW - RETRIEVAL

KW - PITON

KW - SVD

U2 - 10.1190/geo2018-0504.1

DO - 10.1190/geo2018-0504.1

M3 - Article

VL - 84

SP - Q13-Q25

JO - Geophysics

JF - Geophysics

SN - 0016-8033

IS - 3

ER -