High-frequency full-waveform inversion with deep learning for seismic and medical ultrasound imaging

2020 ◽  
Author(s):  
Micha Feigin ◽  
Yizhaq Makovsky ◽  
Daniel Freedman ◽  
Brian W. Anthony
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging ◽  
High Frequency ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Medical Ultrasound ◽  
Medical Ultrasound Imaging ◽  
Full Waveform

Data-driven low-frequency signal recovery using deep-learning predictions in full-waveform inversion

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. A37-A43
Author(s):  
Jinwei Fang ◽  
Hui Zhou ◽  
Yunyue Elita Li ◽  
Qingchen Zhang ◽  
Lingqian Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
High Frequency ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Data Driven ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Recovery Method ◽  
Full Waveform

The lack of low-frequency signals in seismic data makes the full-waveform inversion (FWI) procedure easily fall into local minima leading to unreliable results. To reconstruct the missing low-frequency signals more accurately and effectively, we have developed a data-driven low-frequency recovery method based on deep learning from high-frequency signals. In our method, we develop the idea of using a basic data patch of seismic data to build a local data-driven mapping in low-frequency recovery. Energy balancing and data patches are used to prepare high- and low-frequency data for training a convolutional neural network (CNN) to establish the relationship between the high- and low-frequency data pairs. The trained CNN then can be used to predict low-frequency data from high-frequency data. Our CNN was trained on the Marmousi model and tested on the overthrust model, as well as field data. The synthetic experimental results reveal that the predicted low-frequency data match the true low-frequency data very well in the time and frequency domains, and the field results show the successfully extended low-frequency spectra. Furthermore, two FWI tests using the predicted data demonstrate that our approach can reliably recover the low-frequency data.

scholarly journals Ultrasound Imaging of the Brain using Full-Waveform Inversion

2020 ◽  
Author(s):  
Ulas Taskin ◽  
Kjersti Solberg Eikrem ◽  
Geir Naevdal ◽  
Morten Jakobsen ◽  
Dirk J. Verschuur ◽  
...  
Keyword(s):  
Ultrasound Imaging ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
The Brain

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

Layered low-frequency extrapolation with deep learning in full-waveform inversion

2021 ◽  
Author(s):  
Yao Liu ◽  
Baodi Liu ◽  
Jianping Huang ◽  
Jun Wang ◽  
Honglong Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Full Waveform

Deep-learning seismic full-waveform inversion for realistic structural models

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. R31-R44
Author(s):  
Bin Liu ◽  
Senlin Yang ◽  
Yuxiao Ren ◽  
Xinji Xu ◽  
Peng Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Waveform Inversion ◽  
Structural Models ◽  
Velocity Model ◽  
Dense Layer ◽  
Full Waveform Inversion ◽  
Model Inversion ◽  
Full Waveform ◽  
Inversion Methods ◽  
The Common

Velocity model inversion is one of the most important tasks in seismic exploration. Full-waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion methods, but it heavily depends on initial models and is computationally expensive. In recent years, a large number of deep-learning (DL)-based velocity model inversion methods have been proposed. One critical component in those DL-based methods is a large training set containing different velocity models. We have developed a method to construct a realistic structural model for the DL network. Our compressional-wave velocity model building method for creating dense-layer/fault/salt body models can automatically construct a large number of models without much human effort, which is very meaningful for DL networks. Moreover, to improve the inversion result on these realistic structural models, instead of only using the common-shot gather, we also extract features from the common-receiver gather as well. Through a large number of realistic structural models, reasonable data acquisition methods, and appropriate network setups, a more generalized result can be obtained through our proposed inversion framework, which has been demonstrated to be effective on the independent testing data set. The results of dense-layer models, fault models, and salt body models that we compared and analyzed demonstrate the reliability of our method and also provide practical guidelines for choosing optimal inversion strategies in realistic situations.

An analog beamformer for integrated high-frequency medical ultrasound imaging

2011 ◽  
Author(s):  
Gokce Gurun ◽  
Jaime Zahorian ◽  
Coskun Tekes ◽  
Mustafa Karaman ◽  
Paul Hasler ◽  
...  
Keyword(s):  
Ultrasound Imaging ◽  
High Frequency ◽  
Medical Ultrasound ◽  
Medical Ultrasound Imaging

scholarly journals High frequency refraction/ reflection full-waveform inversion case study from North West Shelf offshore Australia

2018 ◽  
Vol 2018 (1) ◽  
pp. 1-5
Author(s):  
David Dickinson ◽  
Fabio Mancini ◽  
Xiang Li ◽  
Kai Zhao
Keyword(s):  
High Frequency ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
North West ◽  
Full Waveform
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