Suppressing noise while preserving signal for surface microseismic monitoring: The case for the patch design

Auger, E.; Schisselé-Rebel, Estelle; Jia, Jeff

doi:10.1190/segam2013-1396.1

Cited by 17 publications

(4 citation statements)

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“…In order to focus on basic differences between the methods and to avoid artefacts imposed by a particular Figure 1 Randomly distributed surface recording array with P-wave amplitudes normalized to the maximum recorded P-wave amplitude. The positive and negative amplitudes reflect a strike-slip source mechanism placed beneath the centre of the array at a depth of 1,600 m. monitoring geometry, e.g., a star pattern or regular grid pattern (Auger, Schisselé-Rebel, and Jia 2013), the receiver array had an optimal geometry to reduce any artefacts resulting from such choices. In this study, the receiver array consisted of 673 stations randomly placed over a 3.8 by 3.8 km square area ( Fig.…”

Section: Synthetic Data Generationmentioning

confidence: 99%

Comparison of migration‐based location and detection methods for microseismic events

Trojanowski

Eisner

2016

Geophysical Prospecting

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Microseismic monitoring in the oil and gas industry commonly uses migration‐based methods to locate very weak microseismic events. The objective of this study is to compare the most popular migration‐based methods on a synthetic dataset that simulates a strike‐slip source mechanism event with a low signal‐to‐noise ratio recorded by surface receivers (vertical components). The results show the significance of accounting for the known source mechanism in the event detection and location procedures. For detection and location without such a correction, the ability to detect weak events is reduced. We show both numerically and theoretically that neglecting the source mechanism by using only absolute values of the amplitudes reduces noise suppression during stacking and, consequently, limits the possibility to retrieve weak microseismic events. On the other hand, even a simple correction to the data polarization used with otherwise ineffective methods can significantly improve detections and locations. A simple stacking of the data with a polarization correction provided clear event detection and location, but even better results were obtained for those data combined with methods that are based on semblance and cross‐correlation.

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Section: Synthetic Data Generationmentioning

confidence: 99%

Comparison of migration‐based location and detection methods for microseismic events

Trojanowski

Eisner

2016

Geophysical Prospecting

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“…Noise is particularly troublesome for microseismic monitoring where the signal often arises from a low magnitude event, and is therefore, hidden below the noise (Maxwell, 2014), which can result in errors and artefacts in detection and imaging procedures (Bardainne et al, 2009). As such, large efforts are made to reduce the noise from data collection (e.g., Maxwell, 2010;Auger et al, 2013;Schilke et al, 2014) to data processing (e.g., Eisner et al, 2008;Mousavi and Langston, 2016;Birnie et al, 2017), and to ensure monitoring algorithms are adequately tested under realistic noise conditions (Birnie et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Transfer learning for self-supervised, blind-spot seismic denoising

Birnie

Alkhalifah

2022

Front. Earth Sci.

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Noise is ever present in seismic data and arises from numerous sources and is continually evolving, both spatially and temporally. The use of supervised deep learning procedures for denoising of seismic datasets often results in poor performance: this is due to the lack of noise-free field data to act as training targets and the large difference in characteristics between synthetic and field datasets. Self-supervised, blind-spot networks typically overcome these limitation by training directly on the raw, noisy data. However, such networks often rely on a random noise assumption, and their denoising capabilities quickly decrease in the presence of even minimally-correlated noise. Extending from blind-spots to blind-masks has been shown to efficiently suppress coherent noise along a specific direction, but it cannot adapt to the ever-changing properties of noise. To preempt the network’s ability to predict the signal and reduce its opportunity to learn the noise properties, we propose an initial, supervised training of the network on a frugally-generated synthetic dataset prior to fine-tuning in a self-supervised manner on the field dataset of interest. Considering the change in peak signal-to-noise ratio, as well as the volume of noise reduced and signal leakage observed, using a semi-synthetic example we illustrate the clear benefit in initialising the self-supervised network with the weights from a supervised base-training. This is further supported by a test on a field dataset where the fine-tuned network strikes the best balance between signal preservation and noise reduction. Finally, the use of the unrealistic, frugally-generated synthetic dataset for the supervised base-training includes a number of benefits: minimal prior geological knowledge is required, substantially reduced computational cost for the dataset generation, and a reduced requirement of re-training the network should recording conditions change, to name a few. Such benefits result in a robust denoising procedure suited for long term, passive seismic monitoring.

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“…Thus, a dense 2D array is essential for removing back-scattered noise. (Regone & Rethford, 1990;Regone, 1997;Regone, 1998;Petrochilos & Drew, 2014;Auger et al, 2013;Schissele-Rebel & Meunier, 2013). The ARCO Button Patch did not use a dense 2D array (Barr, 2013;Biondi, 2006).…”

Section: Introductionmentioning

confidence: 99%

Using 2D Ring Arrays to Remove Back-scattered Surface Noise from Land Seismic Data

Stork¹,

Flentge²,

Dingus³

et al. 2016

78th EAGE Conference and Exhibition 2016

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Surface noise often dominates signal in land seismic acquisition by a large amount, perhaps 2x to 20x, which limits the usefulness of land seismic data, especially converted waves and possibly attributes. Much of this noise is also back-scattered. We propose an approach of reducing back-scattered surface noise by designing field acquisition to better sample the noise in X & Y so that it can be more easily removed in processing. The key concept is that deliberate 2D arrays that sample the noise can reduce noise with an effectiveness that scales by an array element count of N or greater while the approach of stacking randomness using large fold reduces noise by √. The difference is large when N is about 9 or greater. The challenges in making this new approach work are designing the 2D arrays, careful inverse filtering of the array data, handling a wide frequency range, and addressing irregular illumination in processing.

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Suppressing noise while preserving signal for surface microseismic monitoring: The case for the patch design

Cited by 17 publications

References 0 publications

Comparison of migration‐based location and detection methods for microseismic events

Comparison of migration‐based location and detection methods for microseismic events

Transfer learning for self-supervised, blind-spot seismic denoising

Using 2D Ring Arrays to Remove Back-scattered Surface Noise from Land Seismic Data

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