Detailed seismic velocity of the incoming subducting sediments in the 2004 great Sumatra earthquake rupture zone from full waveform inversion of long offset seismic data

Investigations of the deep lithosphere aiming at the reconstruction of the geological models remain one of the key sources of the knowledge about the processes shaping the outer shell of our planet. Among different methods, the active seismic Ocean-Bottom Seismometer (OBS) experiments conducted in wide-angle configuration are routinely employed to better understand these processes. Indeed, long-offset seismic data, combined with computationally efficient travetime tomographic methods, have a great potential to constrain the macro-scale subsurface velocity models at large depths.&#160;On the other hand, decades of development of acquisition systems, more and more efficient algorithms and high-performance computing resources make it now feasible to move beyond the regional raytracing-based traveltime tomography. In particular, the waveform inversion methods, such as Full-Waveform Inversion (FWI), are able to exhaustively exploit the rich information collected along the long-offset diving and refraction wavepaths, additionally enriched with the wide-angle reflection arrivals. So far however, only a few attempts have been conducted in the academic community to combine wide-angle seismic data with FWI for high-resolution crustal-scale velocity model reconstruction. This is partially due to the non-convexity of FWI misfit function, which increases with the complexity of the geological setting reflected by the seismograms.&#160;In its classical form FWI is a nonlinear least-squares problem, which is solved through the local optimization techniques. This imposes the strong constraint on the accuracy of the starting FWI model. To avoid cycle-skipping problem the initial model must predict synthetic data within the maximum error of half-period time-shift with respect to the observed data. The criterion is difficult to fulfil when facing the crustal-scale FWI, because the long-offset acquisition translates to the long time of wavefront propagation and therefore accumulation of the traveltime error along the wavepath simulated in the initial model. This in turns increases the possibility of the cycle-skipping taking into account large number of propagated wavelengths.Searching to mitigate this difficulty, here we investigate FWI with a Graph-Space Optimal Transport (GSOT) misfit function. Comparing to the classical least-squares norm, GSOT is convex with respect to the patterns in the waveform which can be shifted in time for more than half-period. Therefore, with proper data selection strategy GSOT misfit-function has potential to reduce the risk of cycle-skipping. We demonstrate the robustness of this novel approach using 2D wide-angle OBS data-set generated in a GO_3D_OBS synthetic model of subduction zone (30 km x 175 km). We show that using GSOT cost-function combined with the multiscale FWI strategy, we reconstruct in details the highly complex geological structure starting from a simple 1D velocity model. We believe that further developments of OT-based misfit functions can significantly reduce the constraints on the starting model accuracy and reduce the overall risk of cycle-skipping during FWI of wide-angle OBS data.

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A study of full waveform inversion of seismic velocity structure under a rugged seafloor by using joint OBN and VSP synthetic seismic data

Marine Geophysical Research ◽

10.1007/s11001-019-09382-w ◽

2019 ◽

Vol 40 (3) ◽

pp. 419-431

Author(s):

Jianlong Yuan ◽

Jiashun Yu ◽

Xiaobo Fu ◽

Chao Han ◽

Weizu Liu ◽

...

Keyword(s):

Seismic Data ◽

Velocity Structure ◽

Seismic Velocity ◽

Waveform Inversion ◽

Full Waveform Inversion ◽

Seismic Velocity Structure ◽

Full Waveform

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Full Waveform Inversion (FWI) for glaciological seismic data –Improving the seismic characterisation of glacier firn

10.5194/egusphere-egu21-916 ◽

2021 ◽

Author(s):

Emma Pearce ◽

Adam Booth ◽

Sebastian Rost ◽

Paul Sava ◽

Alex Brisbourne ◽

...

Keyword(s):

Seismic Data ◽

Seismic Velocity ◽

Waveform Inversion ◽

Snow Accumulation ◽

Small Error ◽

Full Waveform Inversion ◽

Melt Temperature ◽

Climate Conditions ◽

Velocity Models ◽

Full Waveform

Full Waveform Inversion (FWI) is a well-established seismic imaging technique used in the exploration industry to acquire high resolution, high precision velocity models of the subsurface from seismic data. Although FWI is computationally expensive and requires customized data acquisition, the technique has the potential to improve subsurface glaciological imaging.Firn is formed as an intermediate material (of density ~400 &#8211; 810 kg m-3) as snow is compressed into ice (~810 &#8211; 917 kg m-3). Variations in surface conditions and periods of surface melting commonly lead to the presence of discrete layers and lenses of refrozen (&#8216;infiltration&#8217;) ice within the firn column; layers that can be from millimetres to several tens of metres thick. Therefore, firn characteristics provide a tool for reconstructing climate conditions relating to the amount of snow accumulation, melt, temperature conditions and subsequent snow preservation. Given the complexity of these relationships, it has not been possible to develop a theoretical model that predicts accurately variations in firn properties or density with depth. Consequently, seismic techniques, which are logistically less demanding than extracting firn cores, are typically used to reconstruct these physical properties of the firn column.Firn seismic velocity is often derived from seismic data using the Herglotz-Wiechert (HW) inversion. A velocity trend would be expected to increase from ~400 m s-1 in snow through to ~3,800 m s-1 in ice. Thus, the presence of infiltration ice within the firn column results in anomalously high velocity intervals at shallow depths. HW inversion can be limited by the accuracy of first-break picking (specifically in the near offset, where a small error in the travel time pick gives the greatest variability to the HW velocity output), and it cannot recover the velocity inversion below a refrozen ice layer without elastodynamic redatumming. Importantly, FWI has the capacity to mitigate issues such as these, and thereby potentially offers a new standard for glaciological seismic modelling.Using seismic datasets obtained from Pine Island Glacier, Antarctica, and synthetic data that simulate firn columns that include substantial thicknesses of infiltration ice (&#8216;ice slabs&#8217;, up to 100 m thick and from 5-80 m deep), we show how FWI improves on current seismic techniques in terms of identifying the velocity variations associated with both included ice layers and the firn underlying them. We present a best practice methodology for the use of FWI with glaciological data, including (i) the extraction of a source wavelet from the data for the use with modelling, (ii) the steps needed to ensure a consistent waveform, (iii) the appropriate offset-to-depth ratio, and (iv) the requirement of a constraint for the uppermost part of the velocity model. Finally, we evaluate the robustness of the FWI approach by comparing it with well-established HW methods for building velocity models.

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Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope

Pure and Applied Geophysics ◽

10.1007/s00024-021-02727-w ◽

2021 ◽

Author(s):

Ehsan Jamali Hondori ◽

Chen Guo ◽

Hitoshi Mikada ◽

Jin-Oh Park

Keyword(s):

Seismic Data ◽

Initial Velocity ◽

Waveform Inversion ◽

Velocity Model ◽

Control Measures ◽

Japan Trench ◽

Full Waveform Inversion ◽

Full Waveform ◽

Time Migration ◽

Shallow Sediments

AbstractFull-waveform inversion (FWI) of limited-offset marine seismic data is a challenging task due to the lack of refracted energy and diving waves from the shallow sediments, which are fundamentally required to update the long-wavelength background velocity model in a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to ensure that the observed and simulated waveforms kinematically fit within an error of less than half a wavelength to protect the FWI iterative local optimization scheme from cycle skipping. We use a migration-based velocity analysis (MVA) method, including a combination of the layer-stripping approach and iterations of Kirchhoff prestack depth migration (KPSDM), to build an accurate initial velocity model for the FWI application on 2D seismic data with a maximum offset of 5.8 km. The data are acquired in the Japan Trench subduction zone, and we focus on the area where the shallow sediments overlying a highly reflective basement on top of the Cretaceous erosional unconformity are severely faulted and deformed. Despite the limited offsets available in the seismic data, our carefully designed workflow for data preconditioning, initial model building, and waveform inversion provides a velocity model that could improve the depth images down to almost 3.5 km. We present several quality control measures to assess the reliability of the resulting FWI model, including ray path illuminations, sensitivity kernels, reverse time migration (RTM) images, and KPSDM common image gathers. A direct comparison between the FWI and MVA velocity profiles reveals a sharp boundary at the Cretaceous basement interface, a feature that could not be observed in the MVA velocity model. The normal faults caused by the basal erosion of the upper plate in the study area reach the seafloor with evident subsidence of the shallow strata, implying that the faults are active.

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Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

Journal of Geophysics and Engineering ◽

10.1093/jge/gxz062 ◽

2019 ◽

Vol 16 (6) ◽

pp. 1017-1031 ◽

Cited By ~ 5

Author(s):

Yong Hu ◽

Liguo Han ◽

Rushan Wu ◽

Yongzhong Xu

Keyword(s):

Frequency Domain ◽

Seismic Data ◽

Waveform Inversion ◽

Low Frequency ◽

Full Waveform Inversion ◽

Local Correlation ◽

Time Frequency ◽

Model Dependence ◽

Full Waveform ◽

Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

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