What changes when we use ambient noise recorded by fiber optics?

Author(s):  
Eileen Martin ◽  
Nate Lindsey ◽  
Biondo Biondi ◽  
Jonathan Ajo-Franklin ◽  
Tieyuan Zhu

<p>Ambient noise seismology has greatly reduced the cost of acquiring data for seismic monitoring and imaging by reducing the need for active sources. For applications requiring time-lapse imaging or continuous monitoring, we desire sensor arrays that require little effort, money, and power to maintain over long periods of time. Distributed Acoustic Sensing repurposes a standard fiber optic cable as a series of single-component strain rate sensors with spacing at the scale of meters over distances of kilometers. With a single location providing the power source and recording all data, along with the ability to use existing underground fiber optic networks, a small team is now able to easily establish a monitoring network and acquire massive amounts of strain rate data continuously.</p><p>This talk will explore two conceptual changes when using DAS data for ambient noise interferometry: greatly increased data volumes, and the difference between velocity and distributed strain-rate data. These two challenges will be illustrated in the context of experiments with applications in near-surface Vs imaging with applications in earthquake hazard analysis, permafrost thaw monitoring, and urban geohazard and hydrology monitoring.</p><p>On the issue of data volumes: Orders of magnitude more sensors and high sample rates (often in the kilohertz range) quickly result in data quantities that exceed the limits of computational infrastructure and algorithms available to many seismologists, potentially at the petabyte/year scale for modern acquisition instruments. New algorithms focused on reduced data movement are improving our ability to analyze more data with existing resources. This talk will include a brief overview of some recent algorithmic improvements for both ambient noise interferometry for imaging, and interferometry-based event detection.</p><p>On the issue of changing from velocity to distributed strain rate data: Because strain rate is a tensor quantity and velocities are a vector quantity, the sensitivity of DAS to seismic sources at different orientations is quite different from typical seismometers. This difference can be clear both in polarity and amplitude of the signal, and is particularly significant in shear and Love wave recordings. We will describe simple models to describe expected changes in how seismometers and DAS record the same noises, and the corresponding changes expected in noise correlation functions. These sensitivity differences are more pronounced in ambient noise correlation functions than they are in raw signal recordings, effectively emphasizing a different distribution of ambient noise sources. Modeling these sensitivities helps determine which sensor orientations are reliable for use in ambient noise interferometry imaging.</p>

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. F1-F8
Author(s):  
Eileen R. Martin

Geoscientists and engineers are increasingly using denser arrays for continuous seismic monitoring, and they often turn to ambient seismic noise interferometry for low-cost near-surface imaging. Although ambient noise interferometry greatly reduces acquisition costs, the computational cost of pair-wise comparisons between all sensors can be prohibitively slow or expensive for applications in engineering and environmental geophysics. Double beamforming of noise correlation functions is a powerful technique to extract body waves from ambient noise, but it is typically performed via pair-wise comparisons between all sensors in two dense array patches (scaling as the product of the number of sensors in one patch with the number of sensors in the other patch). By rearranging the operations involved in the double beamforming transform, I have developed a new algorithm that scales as the sum of the number of sensors in two array patches. Compared to traditional double beamforming of noise correlation functions, the new method is more scalable, easily parallelized, and it does not require raw data to be exchanged between dense array patches.


2013 ◽  
Vol 118 (12) ◽  
pp. 6134-6145 ◽  
Author(s):  
Jesse F. Lawrence ◽  
Marine Denolle ◽  
Kevin J. Seats ◽  
Germán A. Prieto

1971 ◽  
Vol 49 (1A) ◽  
pp. 139-139
Author(s):  
J. M. Thorleifson ◽  
R. J. Jordan

2011 ◽  
Vol 188 (2) ◽  
pp. 513-523 ◽  
Author(s):  
Kevin J. Seats ◽  
Jesse F. Lawrence ◽  
German A. Prieto

2020 ◽  
Author(s):  
Korbinian Sager ◽  
Christian Boehm ◽  
Victor Tsai

<p>Noise correlation functions are shaped by both noise sources and Earth structure. The extraction of information is thus inevitably affected by source-structure trade-offs. Resorting to the principle of Green’s function retrieval deceptively renders the distribution of ambient noise sources unimportant and existing trade-offs are typically ignored. In our approach, we consider correlation functions as self-consistent observables. We account for arbitrary noise source distributions in both space and frequency, and for the complete seismic wave propagation physics in 3-D heterogeneous and attenuating media. We are therefore not only able to minimize the detrimental effect of a wrong (homogeneous) source distribution on 3D Earth structure by including it as an inversion parameter, but also to quantify underlying trade-offs.</p><p>The forward problem of modeling correlation functions and the computation of sensitivity kernels for noise sources and Earth structure are implemented based on the spectral-element solver Salvus. We extend the framework with the evaluation of second derivatives in terms of Hessian-vector products. In the context of probabilistic inverse problems, the inverse Hessian matrix in the vicinity of an optimal model with vanishing first derivatives and under the assumption of Gaussian statistics can be interpreted as an approximation of the posterior covariance matrix. The Hessian matrix therefore contains all the information on resolution and trade-offs that we are trying to retrieve. We investigate the geometry of trade-offs and the effect of the measurement type. In addition, since we only invert for sources at the surface of the Earth, we study how potential scatterers at depth are mapped into the inferred source distribution.</p><p>A profound understanding of the physics behind correlation functions and the quantification of trade-offs is essential for full waveform ambient noise inversion that aims to exploit waveform details for the benefit of improved resolution compared to traditional ambient noise tomography.</p>


2017 ◽  
Vol 211 (1) ◽  
pp. 418-426 ◽  
Author(s):  
L. Moreau ◽  
L. Stehly ◽  
P. Boué ◽  
Y. Lu ◽  
E. Larose ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cheng-Cheng Zhang ◽  
Bin Shi ◽  
Song Zhang ◽  
Kai Gu ◽  
Su-Ping Liu ◽  
...  

AbstractVertical deformation profiles of subterranean geological formations are conventionally measured by borehole extensometry. Distributed strain sensing (DSS) paired with fiber-optic cables installed in the ground opens up possibilities for acquiring high-resolution static and quasistatic strain profiles of deforming strata, but it is currently limited by reduced data quality due to complicated patterns of interaction between the buried cables and their surroundings, especially in upper soil layers under low confining pressures. Extending recent DSS studies, we present an improved approach using microanchored fiber-optic cables—designed to optimize ground-to-cable coupling at the near surface—for strain determination along entire lengths of vertical boreholes. We proposed a novel criterion for soil–cable coupling evaluation based on the geotechnical bearing capacity theory. We applied this enhanced methodology to monitor groundwater-related vertical motions in both laboratory and field experiments. Corroborating extensometer recordings, acquired simultaneously, validated fiber optically determined displacements, suggesting microanchored DSS as an improved means for detecting and monitoring shallow subsurface strain profiles.


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