A linear algorithm for ambient seismic noise double beamforming without explicit crosscorrelations

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.

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

10.5194/egusphere-egu2020-6022 ◽

2020 ◽

Author(s):

Eileen Martin ◽

Nate Lindsey ◽

Biondo Biondi ◽

Jonathan Ajo-Franklin ◽

Tieyuan Zhu

Keyword(s):

Strain Rate ◽

Correlation Functions ◽

Ambient Noise ◽

Fiber Optic ◽

Noise Correlation ◽

Rate Data ◽

Near Surface ◽

Noise Interferometry ◽

Small Team ◽

Distributed Strain

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.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.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.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.

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Ambient seismic noise interferometry using rotational ground motion

10.5194/egusphere-egu21-995 ◽

2021 ◽

Author(s):

Céline Hadziioannou ◽

Paul Neumann ◽

Joachim Wassermann ◽

Heiner Igel ◽

Ulrich Schreiber ◽

...

Keyword(s):

Ground Motion ◽

Seismic Noise ◽

Ambient Noise ◽

Love Waves ◽

Ambient Seismic Noise ◽

Motion Sensors ◽

Rotational Ground Motion ◽

Noise Interferometry ◽

Ring Lasers ◽

Component Displacement

In seismology, new sensing technologies are currently emerging that can measure ground motion beyond the conventional seismic translation measurements. In particular, rotational motion sensors record an additional 3 components of ground motion and thus provide access to additional information about the seismic wavefield.&#160;So far, most studies of rotational ground motion are mainly based on recordings of earthquakes or active sources. In this study, we push the limit towards the very weak motions associated with ocean-generated ambient seismic noise. Our aim is to show the potential of using these measurements in the context of ambient noise interferometry.&#160;We use recordings from two ring lasers in Germany: the `G-Ring' at the Wettzell Geodetic Observatory, and `ROMY' at the F&#252;rstenfeldbruck Observatory near Munich, at a distance of approximately 160 km. These are the most sensitive instruments to date which offer a local, direct measurement of rotational ground motion.&#160;We demonstrate that the sensitivity of the Wettzell instrument has been sufficiently improved to detect Love waves in the primary microseismic frequency band. Both the G-Ring and ROMY ring lasers are also capable of detecting Love waves in the stronger secondary microseismic band. This latter frequency range is used to test the possibility of performing noise interferometry with rotational records.&#160;The first results of rotational noise interferometry between the two ring lasers are promising. The correlation waveform is verified by comparison with interferometry carried out with co-located seismometer data at both locations, as well as with numerical simulations.&#160;In conclusion, we show that ambient noise interferometry is in principle feasible using real rotational recordings of ocean-generated noise. This proof of concept study forms a first step towards noise interferometery of 6-component displacement data.&#160;

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Synthetic Modeling of Ambient Seismic Noise Tomography Data

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/873/1/012096 ◽

2021 ◽

Vol 873 (1) ◽

pp. 012096

Author(s):

Firman Syaifuddin ◽

Andri Dian Nugraha ◽

Zulfakriza ◽

Shindy Rosalia

Keyword(s):

Seismic Noise ◽

Ambient Noise ◽

Noise Analysis ◽

Reference Signal ◽

Synthetic Data ◽

Data Modeling ◽

The United States ◽

Ambient Seismic Noise ◽

Near Surface ◽

Tomography Method

Abstract Ambient seismic noise tomography is one of the most widely used methods in seismological studies today, especially after a comprehensive Earth noise model was published and noise analysis was performed on the IRIS Global Seismographic Network. Furthermore, the Power Spectral Density technique was introduced to identify background seismic noise in the United States. Many studies have been carried out using the ambient seismic noise tomography method which can be broadly grouped into several groups based on the objectives and research targets, such as to determine the structure of the earth’s crust and the upper mantle, to know the thickness of the sedimentary basins, to know the tectonic settings and geological structures, to know volcanic systems and geothermal systems, knowing near-surface geological features and as a monitoring effort the Ambient Noise Tomography method carried out by repeated measurements or time lapse. In this study, we investigate the characteristics of the ambient noise seismic tomography method, both its advantages and limitations of the method by utilizing synthetic data modeling using a simple geological model. Synthetic data is generated based on 1D dispersion curve forward modelling and the forward modeling of surface waves travel time for each period, which is then convoluted with the wavelets of each periods, then doing reverse correlation using a reference signal to produce synthetic recording data. We found that the estimate target depth and vertical resolution depend on the recorded data periods and the synthetic data modeling can be used as a basis in determining the acquisition design.

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Retrieval of reflectivity images from ambient seismic noise correlations using machine learning as a noise-panel classification tool

10.5194/egusphere-egu2020-1680 ◽

2020 ◽

Author(s):

Boris Boullenger ◽

Merijn de Bakker ◽

Arie Verdel ◽

Stefan Carpentier

Keyword(s):

Machine Learning ◽

Seismic Noise ◽

Body Wave ◽

The Body ◽

Body Waves ◽

Ambient Seismic Noise ◽

Depth Range ◽

Classification Models ◽

Coherent Noise ◽

Noise Interferometry

The theory of ambient seismic noise interferometry offers techniques to retrieve estimates of inter-receiver responses from continuously recorded ambient seismic noise. This is usually achieved by correlating and stacking successive noise panels over sufficiently long periods of time. If the noise panels contain significant body-wave energy, the stacked correlations expected to result in retrieved estimates of the body-wave responses, including reflections. Such application combined with a dense surface seismic array is promising for imaging the subsurface structures at lower cost and lower environmental impact as compared to with controlled seismic sources. Subsequently, this technique can be an alternative to active-source surveys in a range of challenging scenarios and locations, and can also be used to perform time-lapse subsurface characterization.In this study, we apply seismic body-wave noise interferometry to 30-days of continuous records from a surface line of 31 receivers spaced by 25 meters in the South of the Netherlands with the aim to image subsurface reflectors, at depths from a few hundreds of meters to a few kilometers. As a first step, we compute stacked auto-correlations and compare the retrieved zero-offset section with a co-located stacked section from a past active reflection survey on the site.Yet, the retrieval of reflectivity estimates relies on the identification and collection of a sufficient number of noise panels with recorded body waves that have travelled from the subsurface towards the array. Even in the case of favorable body-wave noise conditions, the panels are most often contaminated with stronger anthropogenic coherent seismic noise, mainly in the form of surface waves, which in turn prevents the stacked correlations to reveal reflectivity. Because of the limited effect of frequency filtering, the application of seismic body-wave noise interferometry requires in fact extensive effort to identify noise panels without prominent coherent noise from the surface activity. Typically, this leads to disregard a significant amount of actually useful data.For this reason, we designed, trained and tested a deep convolutional neural network to perform this classification task more efficiently and facilitate the repetition of the retrieval method over long periods of time. We tested several supervised learning schemes to classify the panels, where two classes are defined, according to the presence or absence of prominent coherent noise. The retained classification models achieved close to 90% of prediction accuracy on the test set.We used the trained classification models to correlate and stack panels which were predicted in the class with coherent noise absent. The resulting stacked correlations exhibit potential reflectors in a larger depth range than previously achieved. The results show the benefits of using machine learning to collect efficiently a maximum amount of favorable noise panels and a way forward to the upscaling of seismic body-wave noise interferometry for reflectivity imaging.

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H/V spectral ratios at the InSight landing site using ambient noise and Marsquake records

10.5194/egusphere-egu21-9243 ◽

2021 ◽

Author(s):

Sebastián Carrasco ◽

Brigitte Knapmeyer-Endrun ◽

Ludovic Margerin ◽

Cédric Schmelzbach ◽

John Clinton ◽

...

Keyword(s):

Seismic Noise ◽

Ambient Noise ◽

Seismic Energy ◽

Landing Site ◽

Body Waves ◽

Ambient Seismic Noise ◽

Seismic Events ◽

Spectral Ratios ◽

Low Velocity ◽

Velocity Layer

The InSight mission landed on Mars on November 26th, 2018 and its seismometer, the Seismic Experiment for Interior Structure (SEIS), has recorded continuous Martian seismic data since February 2019, consisting of mainly ambient seismic noise but also hundreds of seismic events.We used the SEIS data to study the horizontal-to-vertical spectral ratios from both the ambient seismic noise (nHV) and the seismic events (eHV), for frequencies above 0.6 Hz, in order to get further constraints on the first tens of meters at the Insight landing site. The nHV curve was obtained by using data segments of 50 s over more than 400 Sols. The preferred nHV curve is observed during the northern spring and summer at low wind levels and it is a mostly flat curve with a prominent trough around ~2.4 Hz. Outside of these time periods, the nHV curve is contaminated with artificial peaks likely related to lander modes. On the other hand, the eHV curve was created using 336 seismic events with quality either A, B or C, as defined by the Marsquake Service. For each seismic event, we computed the signal-to-noise ratio (SNR) at each frequency and only frequencies with SNR>3 were used to obtain the final eHV curve. In addition to the 2.4 Hz trough, the final eHV curve shows a strong peak around 8 Hz, which is not observed from the ambient noise data possibly due to a lack of seismic energy in this frequency band able to excite it.A preliminary inversion of the eHV curve, considering the fundamental mode of the Rayleigh wave only, shows that the 2.4 Hz trough and the 8 Hz peak can be explained by a shear-wave velocity model increasing from the surface to a depth of 5-8 m (likely the boundary between the regolith and coarse ejecta), in good agreement with previous analysis based on compliance observations, hammering measurements and satellite images. At this depth, a discontinuity leading to a higher velocity layer is observed, which is followed by a deeper low-velocity layer about 20 m thick. The modeling assuming body waves only or a full diffuse seismic wavefield is currently under investigation.

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