Microseismic Event Estimation Based on an Efficient Wavefield Inversion

Song, Chao; Alkhalifah, Tariq

doi:10.1109/jstars.2019.2946903

Cited by 19 publications

(12 citation statements)

References 29 publications

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“…Even under this condition of s = 0, we can still use equation 8 to initially reconstruct the scattered wavefield δu. This is equivalent to microseismic source estimation, because s i corresponds to secondary sources in the subsurface (Song and Alkhalifah, 2019). Following the Born approximation, the wave equation connects δm, δu, and u 0 as follows (Zhang and Duan, 2012):…”

Section: Inversion Of the Perturbation Modelmentioning

confidence: 99%

A reflection-based efficient wavefield inversion

Song

Alkhalifah

2021

GEOPHYSICS

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Full-waveform inversion (FWI) is popularly used to obtain a high-resolution subsurface velocity model. However, it requires either a good initial velocity model or low-frequency data to mitigate the cycle-skipping issue. Reflection-waveform inversion (RWI) uses a migration/demigration process to retrieve a background model that can be used as a good initial velocity in FWI. The drawback of the conventional RWI is that it requires the use of a least-squares migration, which is often computationally expensive, and is still prone to cycle skipping at far offsets. To improve the computational efficiency and overcome the cycle skipping in the original RWI, we incorporate it into a recently introduced method called efficient wavefield inversion (EWI) by inverting for the Born scattered wavefield instead of the wavefield itself. In this case, we use perturbation-related secondary sources in the modified source function. Unlike conventional RWI, the perturbations are calculated naturally as part of the calculation of the scattered wavefield in an efficient way. As the sources in the reflection-based EWI (REWI) are located in the subsurface, we are able to update the background model along the reflection wave path. In the background velocity inversion, we calculate the background perturbation by a deconvolution process at each frequency. After obtaining the REWI inverted velocity model, a sequential FWI or EWI is needed to obtain a high-resolution model. We demonstrate the validity of the proposed approach using synthetic data generated from a section of the Sigsbee2A model. To further demonstrate the effectiveness of the proposed approach, we test it on an ocean bottom cable (OBC) dataset from the North Sea. We find that the proposed methodology leads to improved velocity models as evidenced by flatter angle gathers.

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Section: Inversion Of the Perturbation Modelmentioning

confidence: 99%

A reflection-based efficient wavefield inversion

Song

Alkhalifah

2021

GEOPHYSICS

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“…Even under this condition, we can still use equation 10 to initially reconstruct the scattered wavefield δ u. This is equivalent to the microseismic source estimation, because s i here acts as secondary sources in the subsurface (Song and Alkhalifah, 2019). Following the Born approximation (Zhang and Duan, 2012):…”

Section: Perturbation Model Calculationmentioning

confidence: 99%

A reflection-based efficient wavefield inversion to retrieve a good initial background velocity

Song¹,

Alkhalifah²

2020

SEG Technical Program Expanded Abstracts 2020

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Full-waveform inversion (FWI) requires either a good initial velocity model or low-frequency data to mitigate the cycleskipping issue. Reflection-waveform inversion (RWI) uses a migration/demigration process to retrieve a background model that can be used as a good initial velocity in FWI. The drawback of the conventional RWI is that it requires the use of a least-squares migration, which is often computationally expensive. Efficient wavefield inversion (EWI) is a recently developed method from wavefield reconstruction inversion (WRI). EWI uses a modified source function to introduce the multiscattering components in the reconstructed wavefield. However, when the initial velocity is far from the true one, the wavefield can not be accurately reconstructed. We incorporate the RWI formulation into EWI by inverting for the Born scattered wavefield instead of the wavefield itself. In this case, we use the perturbation related to secondary sources as a modified source function. As the sources in the reflection-based EWI (REWI) are located in the subsurface, we are able to update the background model along the reflection wave path. We calculate the background model perturbation by a deconvolution process in each frequency like what is done in the original EWI. We demonstrate the validity of the proposed approach using synthetic data generated for the Marmousi model.

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“…The processing of microseismic monitoring data has confronted with difficulty in balancing accuracy and efficiency for a long time [1][2][3]. With the development of microseismic monitoring technology, hydraulic fracturing microseismic has become increasingly important in shale gas exploration and development [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

Guo

2021

International Journal of Geophysics

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In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms.

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Microseismic Event Estimation Based on an Efficient Wavefield Inversion

Cited by 19 publications

References 29 publications

A reflection-based efficient wavefield inversion

A reflection-based efficient wavefield inversion

A reflection-based efficient wavefield inversion to retrieve a good initial background velocity

First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

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