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Advances in Seismic Interferometry

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 30843

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Special Issue Editors

Dr. Cornelis Weemstra

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Guest Editor

Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
Interests: seismic interferometry; seismic imaging; (probabilistic) inversion; seismic monitoring; source inversions; ambient noise; surface waves; industrial applications

Dr. Nori Nakata

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Guest Editor

Massachusetts Institute of Technology, 77 Massachusetts Ave., 54-212, Cambridge, MA 02139, USA
Interests: subsurface structural imaging and monitoring; seismic wave phenomena; seismic interferometry; ambient noise

Dr. Aurélien Mordret

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Guest Editor

ISTerre, Université Grenoble Alpes, 1381 Rue de la Piscine, 38610 Gières, France
Interests: seismic interferometry; surface-wave tomography; coda-wave interferometry monitoring; cryo-seismology; environmental seismology

Special Issue Information

Dear Colleagues,

Although initial studies date back more than fifty years, seismic interferometry (SI) has really only taken off since the early 2000s. As such, it has revolutionized seismology over the past two decades. SI takes advantage of existing (ambient) wavefield recordings through the generation of so-called virtual sources. The medium’s response to these virtual sources can be harnessed to image and/or monitor that medium. A myriad of applications has emerged that, in one way or another, benefit from this technique. Examples vary from glacial monitoring using ambient seismic noise to the monitoring of deep-ocean temperatures using acoustic noise. Industrial applications include monitoring geothermal reservoirs using multiply scattered surface waves, Marchenko redatuming in the context of seismic exploration, and CO2 storage monitoring using ambient body-wave energy.

With this call, we invite studies on all types of (system-Earth related) interferometric applications, as well as papers highlighting recent methodological advances in the field of seismic interferometry. Studies of interest may therefore involve large-N arrays, distributed acoustic sensing, machine learning, full-waveform inversion, surface-wave extraction, noise characterization, hydroacoustic monitoring, and other recent advancements in the field of seismic interferometry.

Dr. Cornelis Weemstra
Dr. Nori Nakata
Dr. Aurélien Mordret
Guest Editors

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Keywords

Seismic interferometry
Ambient noise
Coda-based monitoring
Marchenko redatuming
Noise-based inversions

Published Papers (12 papers)

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Research

18 pages, 8939 KiB

Open AccessArticle

Investigation of Time-Lapse Changes with DAS Borehole Data at the Brady Geothermal Field Using Deconvolution Interferometry

by Hilary Chang and Nori Nakata

Remote Sens. 2022, 14(1), 185; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010185 - 01 Jan 2022

Cited by 9 | Viewed by 1694

Abstract

Distributed acoustic sensing (DAS) has great potential for monitoring natural-resource reservoirs and borehole conditions. However, the large volume of data and complicated wavefield add challenges to processing and interpretation. In this study, we demonstrate that seismic interferometry based on deconvolution is a convenient tool for analyzing this complicated wavefield. We also show the limitation of this technique, in that it still requires good coupling to extract the signal of interest. We extract coherent waves from the observation of a borehole DAS system at the Brady geothermal field in Nevada. The extracted waves are cable or casing ringing that reverberate within a depth interval. These ringing phenomena are frequently observed in the vertical borehole DAS data. The deconvolution method allows us to examine the wavefield at different boundary conditions and separate the direct waves and the multiples. With these benefits, we can interpret the wavefields using a simple 1D string model and monitor its temporal changes. The velocity of this wave varies with depth, observation time, temperature, and pressure. We find the velocity is sensitive to disturbances in the borehole related to increasing operation intensity. The velocity decreases with rising temperature. The reverberation can be decomposed into distinct vibration modes in the spectrum. We find that the wave is dispersive and the fundamental mode propagates with a large velocity. This interferometry method can be useful for monitoring borehole conditions or reservoir property changes using densely-sampled DAS data. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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22 pages, 14966 KiB

Open AccessArticle

On the Potential of 3D Transdimensional Surface Wave Tomography for Geothermal Prospecting of the Reykjanes Peninsula

by Amin Rahimi Dalkhani, Xin Zhang and Cornelis Weemstra

Remote Sens. 2021, 13(23), 4929; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234929 - 04 Dec 2021

Cited by 1 | Viewed by 2005

Abstract

Seismic travel time tomography using surface waves is an effective tool for three-dimensional crustal imaging. Historically, these surface waves are the result of active seismic sources or earthquakes. More recently, however, surface waves retrieved through the application of seismic interferometry have also been exploited. Conventionally, two-step inversion algorithms are employed to solve the tomographic inverse problem. That is, a first inversion results in frequency-dependent, two-dimensional maps of phase velocity, which then serve as input for a series of independent, one-dimensional frequency-to-depth inversions. As such, a set of localized depth-dependent velocity profiles are obtained at the surface points. Stitching these separate profiles together subsequently yields a three-dimensional velocity model. Relatively recently, a one-step three-dimensional non-linear tomographic algorithm has been proposed. The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps, and is referred to as transdimensional tomography. Specifically, the three-dimensional velocity field is parameterized by means of a polyhedral Voronoi tessellation. In this study, we investigate the potential of this algorithm for the purpose of recovering the three-dimensional surface-wave-velocity structure from ambient noise recorded on and around the Reykjanes Peninsula, southwest Iceland. To that end, we design a number of synthetic tests that take into account the station configuration of the Reykjanes seismic network. We find that the algorithm is able to recover the 3D velocity structure at various scales in areas where station density is high. In addition, we find that the standard deviation of the recovered velocities is low in those regions. At the same time, the velocity structure is less well recovered in parts of the peninsula sampled by fewer stations. This implies that the algorithm successfully adapts model resolution to the density of rays. It also adapts model resolution to the amount of noise in the travel times. Because the algorithm is computationally demanding, we modify the algorithm such that computational costs are reduced while sufficiently preserving non-linearity. We conclude that the algorithm can now be applied adequately to travel times extracted from station–station cross correlations by the Reykjanes seismic network. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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19 pages, 13224 KiB

Open AccessArticle

Application of Seismic Interferometry by Multidimensional Deconvolution to Earthquake Data Recorded in Malargüe, Argentina

by Faezeh Shirmohammadi, Deyan Draganov, Mohammad Reza Hatami and Cornelis Weemstra

Remote Sens. 2021, 13(23), 4818; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234818 - 27 Nov 2021

Cited by 3 | Viewed by 1681

Abstract

Seismic interferometry (SI) refers to the principle of generating new seismic responses using crosscorrelations of existing wavefield recordings. In this study, we report on the use of a specific interferometric approach, called seismic interferometry by multidimensional deconvolution (SI by MDD), for the purpose of retrieving surface-wave responses. In theory, SI by MDD suffers less from irregularities in the distribution of (passive) sources than conventional SI. Here, we confirm this advantage for the application to surface waves originating from regional earthquakes close to Central Chile. For that purpose, we use the Malargüe seismic array in Argentina. This T-shaped array consists of two perpendicular lines of stations, which makes it rather suitable for the application of SI by MDD. Comparing the responses retrieved through SI by MDD to the responses retrieved using conventional SI, we find that the application of SI by MDD results in surface-wave responses that are both more accurate and more stable than surface-wave responses that are retrieved using conventional SI. That is, our results demonstrate that SI by MDD suffers less from non-uniformly distributed earthquakes and differences in the power spectra of earthquake responses. In addition, we show that SI by MDD mitigates the effect of site amplification on the retrieved surface waves. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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25 pages, 3442 KiB

Open AccessArticle

Time-Domain Multidimensional Deconvolution: A Physically Reliable and Stable Preconditioned Implementation

by David Vargas, Ivan Vasconcelos, Matteo Ravasi and Nick Luiken

Remote Sens. 2021, 13(18), 3683; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183683 - 15 Sep 2021

Cited by 12 | Viewed by 2855

Abstract

Multidimensional deconvolution constitutes an essential operation in a variety of geophysical scenarios at different scales ranging from reservoir to crustal, as it appears in applications such as surface multiple elimination, target-oriented redatuming, and interferometric body-wave retrieval just to name a few. Depending on the use case, active, microseismic, or teleseismic signals are used to reconstruct the broadband response that would have been recorded between two observation points as if one were a virtual source. Reconstructing such a response relies on the the solution of an ill-conditioned linear inverse problem sensitive to noise and artifacts due to incomplete acquisition, limited sources, and band-limited data. Typically, this inversion is performed in the Fourier domain where the inverse problem is solved per frequency via direct or iterative solvers. While this inversion is in theory meant to remove spurious events from cross-correlation gathers and to correct amplitudes, difficulties arise in the estimation of optimal regularization parameters, which are worsened by the fact they must be estimated at each frequency independently. Here we show the benefits of formulating the problem in the time domain and introduce a number of physical constraints that naturally drive the inversion towards a reduced set of stable, meaningful solutions. By exploiting reciprocity, time causality, and frequency-wavenumber locality a set of preconditioners are included at minimal additional cost as a way to alleviate the dependency on an optimal damping parameter to stabilize the inversion. With an interferometric redatuming example, we demonstrate how our time domain implementation successfully reconstructs the overburden-free reflection response beneath a complex salt body from noise-contaminated up- and down-going transmission responses at the target level. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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18 pages, 2282 KiB

Open AccessArticle

A Linear Inversion Approach to Measuring the Composition and Directionality of the Seismic Noise Field

by Patrick M. Meyers, Tanner Prestegard, Vuk Mandic, Victor C. Tsai, Daniel C. Bowden, Andrew Matas, Gary Pavlis and Ross Caton

Remote Sens. 2021, 13(16), 3097; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163097 - 05 Aug 2021

Cited by 2 | Viewed by 1779

Abstract

We develop a linear inversion technique for measuring the modal composition and directionality of ambient seismic noise. The technique draws from similar techniques used in astrophysics and gravitational-wave physics, and relies on measuring cross-correlations between different seismometer channels in a seismometer array. We characterize the sensitivity and the angular resolution of this technique using a series of simulations and real-world tests. We then apply the technique to data acquired by the three-dimensional seismometer array at the Homestake mine in Lead, SD, to estimate the composition and directionality of the seismic noise at microseism frequencies. We show that, at times of low-microseism amplitudes, noise is dominated by body waves (P and S), while at high-microseism times, the noise is dominated by surface Rayleigh waves. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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20 pages, 8716 KiB

Open AccessArticle

In-Reservoir Waveform Retrieval for Monitoring at Groningen—Seismic Interferometry with Active and Passive Deep Borehole Data

by Muhammad F. Akbar, Ivan Vasconcelos, Hanneke Paulssen and Wen Zhou

Remote Sens. 2021, 13(15), 2928; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152928 - 26 Jul 2021

Viewed by 1748

Abstract

The Groningen gas field in the Netherlands is an ideal test bed for in-situ reservoir monitoring techniques because of the availability of both active and passive in-reservoir seismic data. In this study, we use deconvolution interferometry to estimate the reflection and transmission response using active and passive borehole data within the reservoir at ∼3-km depth and separate up- and downgoing P- and S-wave fields by f-k filtering. We validate the results using synthetic data of a 1D elastic model built from sonic logs recorded in the well. The estimated full-waveform reflection response for a virtual source at the top geophone is consistent with the synthetic response. For the virtual source at the bottom geophone, the reflection response appears to be phase delayed, though its arrivals are consistent with the local subsurface geology. Similarly, the first-order estimated local transmission response successfully approximates that of the P-wave velocity in the reservoir. The study shows that reliable subsurface information can be obtained from borehole interferometry without detailed knowledge of the medium parameters. In addition, the method could be used for passive reservoir monitoring to detect velocity, attenuation, and/or interface time-lapse variations. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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17 pages, 10448 KiB

Open AccessArticle

Rigidity Strengthening of Landslide Materials Measured by Seismic Interferometry

by Keng-Hao Kang, Wei-An Chao, Che-Ming Yang, Ming-Chien Chung, Yu-Ting Kuo, Chih-Hsiang Yeh, Hsin-Chang Liu, Chun-Hung Lin, Chih-Pin Lin, Jyh-Jong Liao, Jui-Ming Chang, Yin-Jeh Ngui, Chien-Hsin Chen and Tung-Lin Tai

Remote Sens. 2021, 13(14), 2834; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142834 - 19 Jul 2021

Cited by 2 | Viewed by 2543

Abstract

Landslides have caused extensive infrastructure damage and caused human fatalities for centuries. Intense precipitation and large earthquakes are considered to be two major landslide triggers, particularly in the case of catastrophic landslides. The most widely accepted mechanistic explanation for landslides is the effective-stress dependent shear strength reduction due to increases in pore water pressure. The Chashan landslide site, selected for the present study, has been intensively studied from geological, geophysical, geodetic, geotechnical, hydrological, and seismological perspectives. Our seismic monitoring of daily relative velocity changes (dv/v) indicated that landslide material decreases coincided with the first half of the rainy period and increased during the latter half of the rainy period. The geodetic surveys before and after the rainy period identified vertical subsidence without horizontal movement. The results from the multidisciplinary investigation enabled us to draw a conceptual model of the landslide recovery process induced by water loading. Where all sliding materials were stable (safety factor > 1.0), unconsolidated landslide colluvium and impermeable sliding surfaces trapped the seepage water to form a water tank, provided that compact forces were acting on the materials below the sliding boundary. The vertical force of compaction facilitates an increase in the cohesion and strength of landslide materials, thereby increasing the landslide materials’ stability. We demonstrated that the recovery process periodically occurs only under the combined conditions of prolonged and intense precipitation and the related stability conditions. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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18 pages, 10854 KiB

Open AccessArticle

Seismic Ambient Noise Imaging of a Quasi-Amagmatic Ultra-Slow Spreading Ridge

by Mohamadhasan Mohamadian Sarvandani, Emanuel Kästle, Lapo Boschi, Sylvie Leroy and Mathilde Cannat

Remote Sens. 2021, 13(14), 2811; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142811 - 17 Jul 2021

Cited by 1 | Viewed by 2332

Abstract

Passive seismic interferometry has become very popular in recent years in exploration geophysics. However, it has not been widely applied in marine exploration. The purpose of this study is to investigate the internal structure of a quasi-amagmatic portion of the Southwest Indian Ridge by interferometry and to examine the performance and reliability of interferometry in marine explorations. To reach this goal, continuous vertical component recordings from 43 ocean bottom seismometers were analyzed. The recorded signals from 200 station pairs were cross-correlated in the frequency domain. The Bessel function method was applied to extract phase–velocity dispersion curves from the zero crossings of the cross-correlations. An average of all the dispersion curves was estimated in a period band 1–10 s and inverted through a conditional neighborhood algorithm which led to the final 1D S-wave velocity model of the crust and upper mantle. The obtained S-wave velocity model is in good agreement with previous geological and geophysical studies in the region and also in similar areas. We find an average crustal thickness of 7 km with a shallow layer of low shear velocities and high Vp/Vs ratio. We infer that the uppermost 2 km are highly porous and may be strongly serpentinized. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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37 pages, 22140 KiB

Open AccessArticle

Seismic Interferometry from Correlated Noise Sources

by Daniella Ayala-Garcia, Andrew Curtis and Michal Branicki

Remote Sens. 2021, 13(14), 2703; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142703 - 09 Jul 2021

Cited by 11 | Viewed by 3187

Abstract

It is a well-established principle that cross-correlating seismic observations at different receiver locations can yield estimates of band-limited inter-receiver Green’s functions. This principle, known as Green’s function retrieval or seismic interferometry, is a powerful technique that can transform noise into signals which enable remote interrogation and imaging of the Earth’s subsurface. In practice it is often necessary and even desirable to rely on noise already present in the environment. Theory that underpins many applications of ambient noise interferometry assumes that the sources of noise are uncorrelated in time. However, many real-world noise sources such as trains, highway traffic and ocean waves are inherently correlated in space and time, in direct contradiction to the these theoretical foundations. Applying standard interferometric techniques to recordings from correlated energy sources makes the Green’s function liable to estimation errors that so far have not been fully accounted for theoretically nor in practice. We show that these errors are significant for common noise sources, always perturbing or entirely obscuring the phase one wishes to retrieve. Our analysis explains why stacking may reduce the phase errors, but also shows that in commonly encountered circumstances stacking will not remediate the problem. This analytical insight allowed us to develop a novel workflow that significantly mitigates effects arising from the use of correlated noise sources. Our methodology can be used in conjunction with already existing approaches, and improves results from both correlated and uncorrelated ambient noise. Hence, we expect it to be widely applicable in ambient noise studies. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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20 pages, 5311 KiB

Open AccessArticle

Physics-Based Relationship for Pore Pressure and Vertical Stress Monitoring Using Seismic Velocity Variations

by Eldert Fokker, Elmer Ruigrok, Rhys Hawkins and Jeannot Trampert

Remote Sens. 2021, 13(14), 2684; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142684 - 07 Jul 2021

Cited by 7 | Viewed by 3123

Abstract

Previous studies examining the relationship between the groundwater table and seismic velocities have been guided by empirical relationships only. Here, we develop a physics-based model relating fluctuations in groundwater table and pore pressure with seismic velocity variations through changes in effective stress. This model justifies the use of seismic velocity variations for monitoring of the pore pressure. Using a subset of the Groningen seismic network, near-surface velocity changes are estimated over a four-year period, using passive image interferometry. The same velocity changes are predicted by applying the newly derived theory to pressure-head recordings. It is demonstrated that the theory provides a close match of the observed seismic velocity changes. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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21 pages, 8721 KiB

Open AccessArticle

Impacts of Reservoir Water Level Fluctuation on Measuring Seasonal Seismic Travel Time Changes in the Binchuan Basin, Yunnan, China

by Chunyu Liu, Hongfeng Yang, Baoshan Wang and Jun Yang

Remote Sens. 2021, 13(12), 2421; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122421 - 21 Jun 2021

Cited by 8 | Viewed by 2347

Abstract

An airgun source in a water reservoir has been developed in the past decade as a green active source that had been proven effective to derive short-term subsurface structural changes. However, seasonal water level fluctuation in the reservoir affects the airgun signal, and thus whether the airgun signals can be used to derive robust seasonal variation in subsurface structure remains unclear. We use the airgun data observed in the Binchuan basin to estimate the seasonal variation of seismic travel time and compare the results with those derived from ambient noise data in the same frequency band. Our main observation is that seasonal change

δ t / t

from airgun is negatively correlated to the variation of dominant frequency and water table fluctuation in the reservoir. One possible explanation is that water table fluctuation in the reservoir affects the dominant frequency of the airgun signal and causes significant phase shift. We also compute the travel time changes in P-wave from the empirical Green’s function after deconvolving the waveforms from a reference station that is 50 m from the airgun source. The dominant frequency after deconvolution still shows seasonal variation and correlates inversely to the travel time changes, suggesting that deconvolution cannot completely eliminate the source effect on travel time changes. We also use ambient noise cross-correlation to retrieve coda waves and then derive travel time changes in monthly stacked cross-correlations relative to a yearly average cross-correlation. We observe that seismic travel time increases to its local maximum in the end of August. The travel time changes lag behind the precipitation for about one month. We apply a poroelastic physical model to explain seismic travel time changes and find that a combined effect from precipitation and evaporation might induce the seasonal changes as shown in the ambient noise data. However, the pattern of travel time changes from the airgun differs from that from ambient noise, reflecting the strong effects of airgun source property changes. Therefore, we should be cautious to derive long-term subsurface structural variation from the airgun source and put more attention on stabilizing the dominant frequency of each excitation in the future experiments. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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18 pages, 7439 KiB

Open AccessArticle

Automatic Image-Based Event Detection for Large-N Seismic Arrays Using a Convolutional Neural Network

by Miłosz Mężyk, Michał Chamarczuk and Michał Malinowski

Remote Sens. 2021, 13(3), 389; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030389 - 23 Jan 2021

Cited by 7 | Viewed by 3164

Abstract

Passive seismic experiments have been proposed as a cost-effective and non-invasive alternative to controlled-source seismology, allowing body–wave reflections based on seismic interferometry principles to be retrieved. However, from the huge volume of the recorded ambient noise, only selected time periods (noise panels) are contributing constructively to the retrieval of reflections. We address the issue of automatic scanning of ambient noise data recorded by a large-N array in search of body–wave energy (body–wave events) utilizing a convolutional neural network (CNN). It consists of computing first both amplitude and frequency attribute values at each receiver station for all divided portions of the recorded signal (noise panels). The created 2-D attribute maps are then converted to images and used to extract spatial and temporal patterns associated with the body–wave energy present in the data to build binary CNN-based classifiers. The ensemble of two multi-headed CNN models trained separately on the frequency and amplitude attribute maps demonstrates better generalization ability than each of its participating networks. We also compare the prediction performance of our deep learning (DL) framework with a conventional machine learning (ML) algorithm called XGBoost. The DL-based solution applied to 240 h of ambient seismic noise data recorded by the Kylylahti array in Finland demonstrates high detection accuracy and the superiority over the ML-based one. The ensemble of CNN-based models managed to find almost three times more verified body–wave events in the full unlabelled dataset than it was provided at the training stage. Moreover, the high-level abstraction features extracted at the deeper convolution layers can be used to perform unsupervised clustering of the classified panels with respect to their visual characteristics. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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