scholarly journals Deep learning for low-frequency extrapolation from multioffset seismic data

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. R989-R1001 ◽  
Author(s):  
Oleg Ovcharenko ◽  
Vladimir Kazei ◽  
Mahesh Kalita ◽  
Daniel Peter ◽  
Tariq Alkhalifah
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Low Frequencies ◽  
Seismic Surveys ◽  
Full Waveform ◽  
Frequency Components ◽  
Marine Seismic

Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropriate signal-to-noise ratio in the low-frequency part of the spectrum. We have extrapolated low-frequency data from the respective higher frequency components of the seismic wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. We numerically simulate marine seismic surveys for random subsurface models and train a deep convolutional neural network to derive a mapping between high and low frequencies. The trained network is then tested on sections from the BP and SEAM Phase I benchmark models. Our results indicate that we are able to recover 0.25 Hz data from the 2 to 4.5 Hz frequencies. We also determine that the extrapolated data are accurate enough for FWI application.

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

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
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.

Delaying the source ghost to improve the ultralow-frequency response of a marine air-gun source

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. P45-P51
Author(s):  
Honglei Shen ◽  
Thomas Elboth ◽  
Chunhui Tao ◽  
Gang Tian ◽  
Hanchuang Wang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Ultralow Frequency ◽  
Full Waveform ◽  
Air Gun ◽  
Marine Source ◽  
Frequency Output ◽  
Marine Seismic ◽  
Marine Air

The competing effect between the fundamental bubble and its source-ghost response results in a strong attenuation of the lowest frequencies (below 7 Hz). This loss cannot be compensated easily by adjusting the source depth. Consequently, the low-frequency content in marine seismic data is not optimal, degrading the performance of low-frequency dependent processing approaches, such as full-waveform inversion. To overcome this, we have developed an additional source to counteract the ghost from the main source. In this situation, the fundamental bubble is characterized by the depth of the main source, whereas the ghost response is characterized by the summed depth of the main and additional sources. This source setup mitigates the competing effect and reduces the suppression of ultralow frequencies. Compared with a conventional horizontal source, our source design will reduce the mid- to high-frequency output, which may be beneficial in situations in which environmental constraints limit the maximum allowed output of a marine source.

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.

The value of very low frequencies and far offsets for seismic data in the Permian Basin: Case study on a new dense survey from the Central Basin Platform

2022 ◽  
Vol 41 (1) ◽  
pp. 34-39
Author(s):  
Vincent Durussel ◽  
Dongren Bai ◽  
Amin Baharvand Ahmadi ◽  
Scott Downie ◽  
Keith Millis
Keyword(s):  
Seismic Data ◽  
Seismic Waves ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Central Basin ◽  
Permian Basin ◽  
Depth Of Penetration ◽  
Low Frequencies ◽  
Frequency Components ◽  
Band Limited

The depth of penetration and multidimensional characteristics of seismic waves make them an essential tool for subsurface exploration. However, their band-limited nature can make it difficult to integrate them with other types of ground measurements. Consequently, far offsets and very low-frequency components are key factors in maximizing the information jointly inverted from all recorded data. This explains why extending seismic bandwidth and available offsets has become a major industry focus. Although this requirement generally increases the complexity of acquisition and has an impact on its cost, improvements have been clearly and widely demonstrated on marine data. Onshore seismic data have generally followed the same trend but face different challenges, making it more difficult to maximize the benefits, especially for full-waveform inversion (FWI). This paper describes a new dense survey acquired in 2020 in the Permian Basin and aims to objectively assess the quality and benefits brought by a richer low end of the spectrum and far offsets. For this purpose, we considered several aspects, from acquisition design and field data to FWI imaging and quantitative interpretation.

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

scholarly journals Time-domain full-waveform inversion of exponentially damped wavefield using the deconvolution-based objective function

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R77-R88 ◽  
Author(s):  
Yunseok Choi ◽  
Tariq Alkhalifah
Keyword(s):  
Objective Function ◽  
Frequency Band ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Low Frequencies ◽  
Full Waveform ◽  
Adjoint State ◽  
Recorded Data ◽  
Adjoint State Method

Full-waveform inversion (FWI) suffers from the cycle-skipping problem when the available frequency-band of data is not low enough. We have applied an exponential damping to the data to generate artificial low frequencies, which helps FWI to avoid cycle skipping. In this case, the least-squares misfit function does not properly deal with the exponentially damped wavefield in FWI because the amplitude of traces decays almost exponentially with increasing offset in a damped wavefield. Thus, we use a deconvolution-based objective function for FWI of the exponentially damped wavefield. The deconvolution filter includes inherently a normalization between the modeled and observed data; thus, it can address the unbalanced amplitude of a damped wavefield. We specifically normalize the modeled data with the observed data in the frequency-domain to estimate the deconvolution filter and selectively choose a frequency-band for normalization that mainly includes the artificial low frequencies. We calculate the gradient of the objective function using the adjoint-state method. The synthetic and benchmark data examples indicate that our FWI algorithm generates a convergent long-wavelength structure without low-frequency information in the recorded data.

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