scholarly journals Denoising ambient seismic field correlation functions with convolutional autoencoders

2019 ◽  
Vol 220 (3) ◽  
pp. 1521-1535
Author(s):  
Loïc Viens ◽  
Chris Van Houtte
Keyword(s):  
Real Time ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Seismic Velocity ◽  
Temporal Evolution ◽  
Seismic Interferometry ◽  
Filter Method ◽  
Near Surface ◽  
Single Station ◽  
Field Correlation

SUMMARY Seismic interferometry is an established method for monitoring the temporal evolution of the Earth’s physical properties. We introduce a new technique to improve the precision and temporal resolution of seismic monitoring studies based on deep learning. Our method uses a convolutional denoising autoencoder, called ConvDeNoise, to denoise ambient seismic field correlation functions. The technique can be applied to traditional two-station cross-correlation functions but this study focuses on single-station cross-correlation (SC) functions. SC functions are computed by cross correlating the different components of a single seismic station and can be used to monitor the temporal evolution of the Earth’s near surface. We train and apply our algorithm to SC functions computed with a time resolution of 20 min at seismic stations in the Tokyo metropolitan area, Japan. We show that the relative seismic velocity change [dv/v(t)] computed from SC functions denoised with ConvDeNoise has less variability than that calculated from raw SC functions. Compared to other denoising methods such as the SVD-based Wiener Filter method developed by Moreau et al., the dv/v results obtained after using our algorithm have similar precision. The advantage of our technique is that once the algorithm is trained, it can be apply to denoise near-real-time SC functions. The near-real-time aspect of our denoising algorithm may be useful for operational hazard forecasting models, for example when applying seismic interferometry at an active volcano.

Crosscoherence-based interferometry for the retrieval of first arrivals and subsequent tomographic imaging of differential weathering

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. Q37-Q48 ◽  
Author(s):  
Joachim Place ◽  
Deyan Draganov ◽  
Alireza Malehmir ◽  
Christopher Juhlin ◽  
Chris Wijns
Keyword(s):  
Seismic Waves ◽  
Seismic Velocity ◽  
Signal To Noise Ratio ◽  
Seismic Profile ◽  
Seismic Interferometry ◽  
Near Surface ◽  
Tomographic Images ◽  
Traveltime Tomography ◽  
Seismic Methods ◽  
Source Signal

Exhumation of crust exposes rocks to weathering agents that weaken the rocks’ mechanical strength. Weakened rocks will have lower seismic velocity than intact rocks and can therefore be mapped using seismic methods. However, if the rocks are heavily weathered, they will attenuate controlled-source seismic waves to such a degree that the recorded wavefield would become dominated by ambient noise and/or surface waves. Therefore, we have examined the structure of differential weathering by first-break traveltime tomography over a seismic profile extending approximately 3.5 km and acquired at a mining site in Zambia using explosive sources and a source based on the swept-impact seismic technique (SIST). Seismic interferometry has been tested for the retrieval of supervirtual first arrivals masked by uncorrelated noise. However, use of crosscorrelation in the retrieval process makes the method vulnerable to changes in the source signal (explosives and SIST). Thus, we have developed a crosscoherence-based seismic-interferometry method to tackle this shortcoming. We investigate the method’s efficiency in retrieving first arrivals and, simultaneously, correctly handling variations in the source signal. Our results illustrate the superiority of the crosscoherence- over crosscorrelation-based method for retrieval of the first arrivals, especially in alleviating spurious ringyness and in terms of the signal-to-noise ratio. These benefits are observable in the greater penetration depth and the improved resolution of the tomography sections. The tomographic images indicate isolated bodies of higher velocities, which may be interpreted as fresh rocks embedded into a heavily weathered regolith, providing a conspicuous example of differential weathering. Our study advances the potential of seismic methods for providing better images of the near surface (the critical zone).

scholarly journals Characterization of seasonal glacial seismicity from a single-station on-ice record at Holtedahlfonna, Svalbard

2019 ◽  
Vol 60 (79) ◽  
pp. 23-36 ◽  
Author(s):  
Andreas Köhler ◽  
Valerie Maupin ◽  
Christopher Nuth ◽  
Ward van Pelt
Keyword(s):  
Seismic Velocity ◽  
Temporal Distribution ◽  
Velocity Model ◽  
Ice Dynamics ◽  
Near Surface ◽  
Waveform Modeling ◽  
Single Station ◽  
Glacier Runoff ◽  
Fast Flow ◽  
Seismic Background

ABSTRACTGlacial seismicity provides important insights into glacier dynamic processes. We study the temporal distribution of cryogenic seismic signals (icequakes) at Holtedahlfonna, Svalbard, between April and August 2016 using a single three-component sensor. We investigate sources of observed icequakes using polarization analysis and waveform modeling. Processes responsible for five icequake categories are suggested, incorporating observations of previous studies into our interpretation. We infer that the most dominant icequake type is generated by surface crevasse opening through hydrofracturing. Secondly, bursts of high-frequency signals are presumably caused by repeated near-surface crevassing due to high strain rates during glacier fast-flow episodes. Furthermore, signals related to resonance in water-filled cracks, fracturing or settling events in dry firn or snow before the melt season, and processes at the glacier bed are observed. Amplitude of seismic background noise is clearly related to glacier runoff. We process ambient seismic noise to invert horizontal-to-vertical spectral ratios for a sub-surface seismic velocity model used to model icequake signals. Our study shows that a single seismic sensor provides useful information about seasonal ice dynamics in case deployment of a network is not feasible.

Near-real-time near-surface 3D seismic velocity and uncertainty models by wavefield gradiometry and neural network inversion of ambient seismic noise

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. KS13-KS27 ◽  
Author(s):  
Ruikun Cao ◽  
Stephanie Earp ◽  
Sjoerd A. L. de Ridder ◽  
Andrew Curtis ◽  
Erica Galetti
Keyword(s):  
Wave Equation ◽  
Real Time ◽  
Seismic Wave ◽  
Noise Level ◽  
Seismic Velocity ◽  
3D Seismic ◽  
Near Surface ◽  
Wave Velocities ◽  
Seismic Wave Velocities ◽  
And Inversion

With the advent of large and dense seismic arrays, novel, cheap, and fast imaging and inversion methods are needed to exploit the information captured by stations in close proximity to each other and produce results in near real time. We have developed a sequence of fast seismic acquisition for dispersion curve extraction and inversion for 3D seismic models, based on wavefield gradiometry, wave equation inversion, and machine-learning technology. The seismic array method that we use is Helmholtz wave equation inversion using measured wavefield gradients, and the dispersion curve inversions are based on a mixture of density neural networks (NNs). For our approach, we assume that a single surface wave mode dominates the data. We derive a nonlinear relationship among the unknown true seismic wave velocities, the measured seismic wave velocities, the interstation spacing, and the noise level in the signal. First with synthetic and then with the field data, we find that this relationship can be solved for unknown true seismic wave velocities using fixed point iterations. To estimate the noise level in the data, we need to assume that the effect of noise varies weakly with the frequency and we need to be able to calibrate the retrieved average dispersion curves with an alternate method (e.g., frequency wavenumber analysis). The method is otherwise self-contained and produces phase velocity estimates with tens of minutes of noise recordings. We use NNs, specifically a mixture density network, to approximate the nonlinear mapping between dispersion curves and their underlying 1D velocity profiles. The networks turn the retrieved dispersion model into a 3D seismic velocity model in a matter of seconds. This opens the prospect of near-real-time near-surface seismic velocity estimation using dense (and potentially rolling) arrays and only ambient seismic energy.

scholarly journals Toward Using Seismic Interferometry to Quantify Landscape Mechanical Variations after Earthquakes

2021 ◽  
Author(s):  
Odin Marc ◽  
Christoph Sens-Schönfelder ◽  
Luc Illien ◽  
Patrick Meunier ◽  
Manuel Hobiger ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Seismic Velocity ◽  
Landslide Hazard ◽  
Seismic Velocities ◽  
Surface Strength ◽  
Near Surface ◽  
Single Station ◽  
Seismic Velocity Changes ◽  
Velocity Changes ◽  
Large Earthquakes

ABSTRACT In mountainous terrain, large earthquakes often cause widespread coseismic landsliding as well as hydrological and hydrogeological disturbances. A subsequent transient phase with high landslide rates has also been reported for several earthquakes. Separately, subsurface seismic velocities are frequently observed to drop coseismically and subsequently recover. Consistent with various laboratory work, we hypothesize that the seismic-velocity changes track coseismic damage and progressive recovery of landscape substrate, which modulate landslide hazard and hydrogeological processes, on timescales of months to years. To test this, we analyze the near-surface seismic-velocity variations, obtained with single-station high-frequency (0.5–4 Hz) passive image interferometry, in the epicentral zones of four shallow earthquakes, for which constraints on landslide susceptibility through time exist. In the case of the 1999 Chi-Chi earthquake, detailed landslide mapping allows us to accurately constrain an exponential recovery of landslide susceptibility with a relaxation timescale of about 1 yr, similar to the pattern of recovery of seismic velocities. The 2004 Niigata, 2008 Iwate, and 2015 Gorkha earthquakes have less-resolved constraints on landsliding, but, assuming an exponential recovery, we also find matching relaxation timescales, from ∼0.1 to ∼0.6  yr, for the landslide and seismic recoveries. These observations support our hypothesis and suggest that systematic monitoring of seismic velocities after large earthquakes may help constrain and manage the evolution of landslide hazard in epicentral areas. To achieve this goal, we end by discussing several ways to improve the link between seismic velocity and landscape mechanical properties, specifically by better constraining time-dependent near-surface strength and hydrogeological changes. Hillslopes displaying coseismic surface fissuring and displacement may be an important target for future geotechnical analysis and coupled to passive geophysical investigations.

scholarly journals Drill bit noise imaging without pilot trace, a near-surface interferometry example

Solid Earth ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 1015-1023 ◽  
Author(s):  
Mehdi Asgharzadeh ◽  
Ashley Grant ◽  
Andrej Bona ◽  
Milovan Urosevic
Keyword(s):  
Cross Correlation ◽  
Acoustic Energy ◽  
Seismic Interferometry ◽  
Drill Bit ◽  
Interferometric Imaging ◽  
Near Surface ◽  
Pilot Signal ◽  
Shallow Subsurface ◽  
Drill Bits ◽  
Noise Data

Abstract. Acoustic energy emitted by drill bits can be recorded by geophones on the surface and processed for an image of the subsurface using seismic interferometry methods. Pilot sensors record bit signals on the drill rig and play an important role in processing geophone traces for the image. When pilot traces are not available, traces of the nearest geophone to the rig may be used in deconvolution and cross-correlation of data, but extra signal processing efforts are required to reduce the effect of source signature on cross-correlation results. In this study, we use the seismic interferometry method to image the shallow subsurface beneath a 2-D geophone array by converting geophones to virtual sources. As there is no pilot signal available for this survey, we use the nearest geophone trace for pilot cross-correlation and pilot deconvolution. We modify the spectrum of pilot cross-correlation and deconvolution results so that the effect of source function on virtual data is minimized. We then migrate the virtual shots and compare the results of interferometric imaging with the available image from 3-D (active source) survey and assess the efficiency of our approach. We show that drill bit noise data can be used to generate a reasonably accurate image of the subsurface even in the absence of pilot recordings, but the results should be checked for the appearance of virtual multiples and depth inconsistencies that are caused by errors in the migration velocity.

FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

2019 ◽  
Author(s):  
Carmen Guguta ◽  
Jan M.M. Smits ◽  
Rene de Gelder
Keyword(s):  
Genetic Algorithm ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Simultaneous Optimization ◽  
Powder Diffraction Data ◽  
Structure Solution ◽  
Matching Technique

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br>

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