Long-Wavelength Earth Model via Accelerated Full-Waveform Inversion

2021 ◽  
Author(s):  
Solvi Thrastarson ◽  
Dirk-Philip van Herwaarden ◽  
Lion Krischer ◽  
Martin van Driel ◽  
Christian Boehm ◽  
...  
Keyword(s):  
Waveform Inversion ◽  
Large Data ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Waveform Data ◽  
Long Wavelength ◽  
Computational Overhead ◽  
Full Dataset ◽  
Full Waveform ◽  
Seismic Waveform

<p>As the volume of available seismic waveform data increases, the responsibility to use the data in an effective way emerges. This requires computational efficiency as well as maximizing the exploitation of the information associated with the data.</p><p>In this contribution, we present a long-wavelength Earth model, created by using the data recorded from over a thousand earthquakes, starting from a simple one-dimensional background (PREM). The model is constructed with an accelerated full-waveform inversion (FWI) method which can seamlessly include large data volumes with a significantly reduced computational overhead. Although we present a long-wavelength model, the approach has the potential to go to much higher frequencies, while maintaining a reasonable cost.</p><p>Our approach combines two novel FWI variants. (1) The dynamic mini-batch approach which uses adaptively defined subsets of the full dataset in each iteration, detaching the direct scaling of inversion cost from the number of earthquakes included. (2) Wavefield-adapted meshes which utilize the azimuthal smoothness of the wavefield to design meshes optimized for each individual source location. Using wavefield adapted meshes can drastically reduce the cost of both forward and adjoint simulations as well as it makes the scaling of the computing cost to modelled frequencies more favourable.</p>

Full-waveform inversion of the African Plate

2021 ◽  
Author(s):  
Dirk-Philip van Herwaarden ◽  
Michael Afanasiev ◽  
Sölvi Thrastarson ◽  
Andreas Fichtner
Keyword(s):  
Waveform Inversion ◽  
Computational Cost ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Dynamic Topography ◽  
African Plate ◽  
Full Dataset ◽  
Full Waveform ◽  
Novel Approach ◽  
Complete Dataset

<p>We present a full-waveform inversion (FWI) of the African plate. Starting from the Collaborative Seismic Earth Model, we invert seismograms that are filtered to 35 s and compute gradients using the adjoint state method. This FWI uses a novel approach that we introduced earlier with the name Evolutionary FWI.</p><p>In contrast to conventional waveform inversion, our approach uses dynamically changing mini-batches (subsets of the full dataset) that approximate the gradient of the larger dataset at each iteration. This has three major advantages, (1) We exploit redundancies within the dataset, which results in a reduced computational cost for model updates, (2) The size of the complete dataset does not directly impact the computational cost of an iteration, thereby enabling us to work with larger datasets, and (3) The nature of the algorithm makes it trivial to assimilate new data, as the new data can simply be added to the complete dataset from which the mini-batches are sampled.</p><p>The aforementioned advantages enable us to extend the boundaries of what was previously possible for a given computational budget. We perform more than 80 mini-batch iterations and invert waveforms from over 400 unique earthquakes. This has the same cost as 8 iterations with all data. Our latest model clearly images tectonic features such as the Afar triple junction as well as slow zones below areas with dynamic topography, such as the Tibesti and Hoggar mountain ranges.</p>

3-D seismic full-waveform inversion of the Taurus-Zagros region in Iran and Turkey

2020 ◽  
Author(s):  
Neda Masouminia ◽  
Dirk-Philip van Herwaarden ◽  
Sölvi Thrastarson ◽  
Habib Rahimi ◽  
Lion Krischer ◽  
...  
Keyword(s):  
Upper Mantle ◽  
Waveform Inversion ◽  
Spectral Element ◽  
Earth Model ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Geophysical Research ◽  
Seismic Stations ◽  
Full Waveform ◽  
Seismic Waveform

<p><span>We present an interpretation of a 3-D velocity model resulting from a regional analysis of earthquake waveforms. This model contains 3-D structure of the crust and upper mantle beneath the Arabian-Eurasian collision zone in eastern Turkey and Iran. We use full-waveform inversion (FWI) of three-component recordings from permanent networks. FWI can exploit all parts of a seismogram, including body and multi-mode surface waves in a broad range of frequencies. This allows us to constrain seismic structure of both the crust and the upper mantle.</span></p><p><span>In our method we simulate 3-D visco-elastic wavefields using a spectral-element method (Fichtner <em>et al,</em>2018). Our numerical mesh honors topography of the surface. We compare observed and synthetic waveforms using time-frequency phase misfits. Using adjoint techniques, we then compute sensitivity kernels with respect to the model parameters, which are V<sub>SV</sub>, V<sub>SH</sub>, V<sub>PV</sub>, and V<sub>PH</sub>. Finally, the kernels enable the iterative solution of the nonlinear inverse problem with the help of the L-BFGS algorithm and without a need for crustal corrections.</span></p><p><span>For this study we obtained seismic waveform data of 59 earthquakes within the magnitude range of Mw 4.5 to 6.3 that occurred in the region between 2012 and 2016. These events were recorded by 398 broadband seismic stations belonging to the two national Iranian networks and freely available seismic stations of the Turkish Network, made available by IRIS.</span></p><p> <span>Starting from the first generation of the Collaborative Seismic Earth Model (Afanasiev <em>et al</em>.2019), we first constrained longer-wavelength structure. To this end, we considered 3-component recordings from a subset of 37 events in the period range from 50 to 80 s. This band was successively broadened by reducing the shorter period from 50 s to 40 s, and finally to 20 s. For each period band, the number and the length of measurement windows are increased; the number of events is also increased to 59 to use the complete dataset. After 46 iterations our model can explain recordings of events, which were not used in the inversion. The results provide to discuss about high-velocity anomaly beneath the Zagros and the shallow low velocities beneath Central Iran using cross-sections to investigate lateral variation of seismic velocity in the lithosphere.</span></p><p><span>REFERENCES</span></p><p><span>Afanasiev, M., Boehm, C., van Driel, M., Krischer, L., Rietmann, M., May, D. A., Knepley, M. G., Fichtner, A., 2019. Modular and flexible spectral-element waveform modelling in two and three dimensions. Geophysical Journal International 216, 1675-1692, doi: 10.1093/gji/ggy469.</span></p><p><span>Fichtner, A., van Herwaarden, D.-P., Afanasiev, M., Simute, S., Krischer, L., Cubuk-Sabuncu, Y., Taymaz, T., Colli, L., Saygin, E., Villasenor, A., Trampert, J., Cupillard, P., Bunge, H.-P., Igel, H., 2018. The Collaborative Seismic Earth Model: Generation I. Geophysical Research Letters 45, doi: 10.1029/2018GL077338.</span></p>

Application of multiparameter full-waveform inversion in Central Luconia Basin, Sarawak

2016 ◽  
Vol 4 (4) ◽  
pp. SU17-SU24 ◽  
Author(s):  
Vanessa Goh ◽  
Kjetil Halleland ◽  
René-Édouard Plessix ◽  
Alexandre Stopin
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Thin Layers ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Full Waveform ◽  
Velocity Variations ◽  
Central Luconia

Reducing velocity inaccuracy in complex settings is of paramount importance for limiting structural uncertainties, therefore helping the geologic interpretation and reservoir characterization. Shallow velocity variations due, for instance, to gas accumulations or carbonate reefs, are a common issue offshore Malaysia. These velocity variations are difficult to image through standard reflection-based velocity model building. We have applied full-waveform inversion (FWI) to better characterize the upper part of the earth model for a shallow-water field, located in the Central Luconia Basin offshore Sarawak. We have inverted a narrow-azimuth data set with a maximum inline offset of 4.4 km. Thanks to dedicated broadband preprocessing of the data set, we could enhance the signal-to-noise ratio in the 2.5–10 Hz frequency band. We then applied a multiparameter FWI to estimate the background normal moveout velocity and the [Formula: see text]-parameter. Full-waveform inversion together with broadband data processing has helped to better define the faults and resolve the thin layers in the shallow clastic section. The improvements in the velocity model brought by FWI lead to an improved image of the structural closure and flanks. Moreover, the increased velocity resolution helps in distinguishing between two different geologic interpretations.

Accelerating Subsurface Data Processing and Interpretation with Cloud-Based Full Waveform Inversion Systems

2021 ◽  
Author(s):  
Sirivan Chaleunxay ◽  
Nikhil Shah
Keyword(s):  
Service Sector ◽  
Model Building ◽  
Job Scheduling ◽  
Waveform Inversion ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Spare Capacity ◽  
Velocity Models ◽  
Full Waveform ◽  
Job Scheduler

Abstract Understanding the earth's subsurface is critical to the needs of the exploration and production (E&P) industry for minimizing risk and maximizing recovery. Until recently, the industry's service sector has not made many advances in data-driven automated earth model building from raw exploration seismic data. But thankfully, that has now changed. The industry's leading technique to gain an unprecedented increase in resolution and accuracy when establishing a view of the interior of the earth is known as the Full Waveform Inversion (FWI). Advanced formulations of FWI are capable of automating subsurface model building using only raw unprocessed data. Cloud-based FWI is helping to accelerate this journey by encompassing the most sophisticated waveform inversion techniques with the largest compute facility on the planet. This combines to give verifiable accuracy, more automation and more efficiency. In this paper, we describe the transformation of enabling cloud-based FWI to natively take advantage of the public cloud platform's main strength in terms of flexibility and on-demand scalability. We start from lift-and-shift of a legacy MPI-based application designed to be run by a traditional on-prem job scheduler. Our specific goals are to (1) utilize a heterogeneous set of compute hardware throughout the lifecycle of a production FWI run without having to provision them for the entire duration, (2) take advantage of cost-efficient spare-capacity compute instances without uptime guarantees, and (3) maintain a single codebase that can be run both on on-prem HPC systems and on the cloud. To achieve these goals meant transitioning the job-scheduling and "embarrassingly parallel" aspects of the communication code away from using MPI, and onto various cloud-based orchestration systems, as well as finding cloud-based solutions that worked and scaled well for the broadcast/reduction operation. Placing these systems behind a customized TCP-based stub for MPI library calls allows us to run the code as-is in an on-prem HPC environment, while on the cloud we can asynchronously provision and suspend worker instances (potentially with very different hardware configurations) as needed without the burden of maintaining a static MPI world communicator. With this dynamic cloud-native architecture, we 1) utilize advanced formulations of FWI capable of automating subsurface model building using only raw unprocessed data, 2) extract velocity models from the full recorded wavefield (refractions, reflections and multiples), and 3) introduce explicit sensitivity to reflection moveout, invisible to conventional FWI, for macro-model updates below the diving wave zone. This makes it viable to go back to older legacy datasets acquired in complex environments and unlock considerable value where FWI until now has been impossible to apply successfully from a poor starting model.

scholarly journals Wavefield compression for adjoint methods in full-waveform inversion

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. R385-R397 ◽  
Author(s):  
Christian Boehm ◽  
Mauricio Hanzich ◽  
Josep de la Puente ◽  
Andreas Fichtner
Keyword(s):  
Finite Difference Methods ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Adjoint Methods ◽  
Polynomial Degree ◽  
Computational Overhead ◽  
Full Waveform ◽  
Sensitivity Kernel ◽  
Gradient Based ◽  
A Minor

Adjoint methods are a key ingredient of gradient-based full-waveform inversion schemes. While being conceptually elegant, they face the challenge of massive memory requirements caused by the opposite time directions of forward and adjoint simulations and the necessity to access both wavefields simultaneously for the computation of the sensitivity kernel. To overcome this bottleneck, we have developed lossy compression techniques that significantly reduce the memory requirements with only a small computational overhead. Our approach is tailored to adjoint methods and uses the fact that the computation of a sufficiently accurate sensitivity kernel does not require the fully resolved forward wavefield. The collection of methods comprises reinterpolation with a coarse temporal grid as well as adaptively chosen polynomial degree and floating-point precision to represent spatial snapshots of the forward wavefield on hierarchical grids. Furthermore, the first arrivals of adjoint waves are used to identify “shadow zones” that do not contribute to the sensitivity kernel. Numerical experiments show the high potential of this approach achieving an effective compression factor of three orders of magnitude with only a minor reduction in the rate of convergence. Moreover, it is computationally cheap and straightforward to integrate in finite-element wave propagation codes with possible extensions to finite-difference methods.

The impact of acquisition geometry on full-waveform inversion updates

2021 ◽  
Vol 40 (5) ◽  
pp. 335-341
Author(s):  
Denes Vigh ◽  
Xin Cheng ◽  
Kun Jiao ◽  
Wei Kang ◽  
Nolan Brand
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Depth Image ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Full Waveform ◽  
Data Content ◽  
The Impact

Full-waveform inversion (FWI) is a high-resolution model-building technique that uses the entire recorded seismic data content to build the earth model. Conventional FWI usually utilizes diving and refracted waves to update the low-wavenumber components of the velocity model. However, updates are often depth limited due to the limited offset range of the acquisition design. To extend conventional FWI beyond the limits imposed by using only transmitted energy, we must utilize the full acquired wavefield. Analyzing FWI kernels for a given geology and acquisition geometry can provide information on how to optimize the acquisition so that FWI is able to update the velocity model for targets as deep as basement level. Recent long-offset ocean-bottom node acquisition helped FWI succeed, but we would also like to be able to utilize the shorter-offset data from wide-azimuth data acquisitions to improve imaging of these data sets by developing the velocity field with FWI. FWI models are heading toward higher and higher wavenumbers, which allows us to extract pseudoreflectivity directly from the developed velocity model built with the acoustic full wavefield. This is an extremely early start to obtaining a depth image that one would usually produce in much later processing stages.

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