Self-training and learning the waveform features of microseismic data using an adaptive dictionary

Wang, Hang; Zhang, Quan; Zhang, Guoyin; Fang, Jinwei; Chen, Yangkang

doi:10.1190/geo2019-0213.1

Cited by 30 publications

(8 citation statements)

References 30 publications

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“…Successful extraction of hydrocarbons in shale reservoirs relies on methods like hydraulic fracturing to increase the permeability to further enhance the flow of reservoir fluids. However, fracturing may induce large‐scale fractures, causing oil and gas leakage and even earthquake disasters (Wang et al ., 2020). Therefore, while increasing oil and gas production, attention should be given to the real‐time monitoring of hydraulic fracturing to mitigate hazards and economic losses (Belayouni and Noble, 2015).…”

Section: Introductionmentioning

confidence: 99%

High‐resolution and robust microseismic grouped imaging and grouping strategy analysis

Huang

Chen

Saad

et al. 2022

Geophysical Prospecting

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As an advanced real-time monitoring technique, microseismic source-location imaging provides valuable information during hydraulic fracturing, for example, the development of fracture networks and the effective reservoir reconstruction volume. However, microseismic data always suffer from weak induced energy and susceptibility to noise interference. In the case of a low signal-to-noise ratio, it is extremely challenging to perform robust microseismic imaging. Here, we first introduce several state-of-theart imaging conditions and two hybrid imaging conditions, which are followed by a detailed analysis of the impact of different grouping strategies. Then, we briefly analyse the sensitivity of different imaging conditions to noise using a one-dimensional signal. Next, several benchmark models, including two-dimensional Marmousi-II and three-dimensional SEG Advanced Modeling, are used as numerical examples for testing the passive-source imaging algorithms. Finally, three-dimensional real microseismic data are used to further investigate the impact of the grouping strategy on the imaging. The numerical examples and field data demonstrate the effectiveness of the proposed grouping strategy for the grouped imaging conditions.

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Section: Introductionmentioning

confidence: 99%

High‐resolution and robust microseismic grouped imaging and grouping strategy analysis

Huang

Chen

Saad

et al. 2022

Geophysical Prospecting

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“…The patching technique is used to enhance the performance of the machine learning approaches (Wang et al ., 2020). The patching technique allows machine learning to learn the spatial coherency of the input data by dividing the two‐dimensional (2D) data into several 2D overlapped windows with certain window size and shift samples (Chen et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The extracted 2D overlapped patches are used as an input for the machine learning approach (Chen et al ., 2019). However, the 2D patches can be converted to a one‐dimensional (1D) vector in case the machine learning approach deals with 1D data (Wang et al ., 2020). The final output can be reconstructed from the predicted output patches using the reverse patching operation, while the overlapped samples between each neighbour windows are averaged (Chen et al ., 2019; Wang et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

“…However, the 2D patches can be converted to a one‐dimensional (1D) vector in case the machine learning approach deals with 1D data (Wang et al ., 2020). The final output can be reconstructed from the predicted output patches using the reverse patching operation, while the overlapped samples between each neighbour windows are averaged (Chen et al ., 2019; Wang et al ., 2020). This approach has been used to enhance the S/N of seismological datasets by an unsupervised machine learning approach (Chen et al ., 2019) as well as to extract the waveform features of the microseismic data using an adaptive dictionary (Wang et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

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A fully unsupervised and highly generalized deep learning approach for random noise suppression

Saad

Chen

2021

Geophysical Prospecting

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In this study, we proposed a deep learning algorithm (PATCHUNET) to suppress random noise and preserve the coherent seismic signal. The input data are divided into several patches, and each patch is encoded to extract the meaningful features. Following this, the extracted features are decompressed to retrieve the seismic signal. Skip connections are used between the encoder and decoder parts, allowing the proposed algorithm to extract high‐order features without losing important information. Besides, dropout layers are used as regularization layers. The dropout layers preserve the most meaningful features belonging to the seismic signal and discard the remaining features. The proposed algorithm is an unsupervised approach that does not require prior information about the clean signal. The input patches are divided into 80% for training and 20% for testing. However, it is interesting to find that the proposed algorithm can be trained with only 30% of the input patches with an effective denoising performance. Four synthetic and four field examples are used to evaluate the proposed algorithm performance, and compared to the f−x deconvolution and the f−x singular spectrum analysis. The results indicate the ability of the proposed algorithm in attenuating the random noise and preserving the seismic signal effectively despite the existence of a large amount of random noise, for example, when the input signal‐to‐noise ratio is as low as −14.2 dB.

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“…The denoising process for microseismic data is mainly divided into two basic strategies according to the type of data: multi-channel denoising and single-channel denoising [5]. For the multi-channel strategy, microseismic data are denoised by the spatial distribution information of the geophone.…”

Section: Introductionmentioning

confidence: 99%

Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

et al. 2021

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Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.

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Self-training and learning the waveform features of microseismic data using an adaptive dictionary

Cited by 30 publications

References 30 publications

High‐resolution and robust microseismic grouped imaging and grouping strategy analysis

High‐resolution and robust microseismic grouped imaging and grouping strategy analysis

A fully unsupervised and highly generalized deep learning approach for random noise suppression

Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

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