Coherent noise suppression via a self-supervised blind-trace deep learning scheme

Liu, Sixiu; Birnie, Claire; Alkhalifah, Tariq

doi:10.48550/arxiv.2206.00301

Cited by 4 publications

(7 citation statements)

References 30 publications

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“…Whilst N2V's successor, StructN2V, can suppress coherent noise, it requires a consistent noise pattern for which a specific noise mask is built, e.g., masking the noise along a specific direction. This has shown great promise for trace-wise noise suppression, such as dead sensors (Liu et al, 2022a) or blended data (Luiken et al, 2022), however it is not practical for the suppression of the general seismic noise field which is continuously evolving. In this work, we have proposed to initially train a network on simplistic synthetic datasets and then fine-tune the model in a selfsupervised manner on the noisy field data.…”

Section: Discussionmentioning

confidence: 99%

“…Building on this, Liu et al (2022a,b) proposed to extend the blind-spot property to become a blind-trace, adapting the previously proposed selfsupervised denoiser for the suppression of trace-wise noise. Similarly, both Luiken et al (2022) and Wang et al (2022) proposed blind-trace networks implemented at the architecture level, as opposed to the likes of Krull et al (2019); Birnie et al (2021); Liu et al (2022a) which were implemented as processing steps. Both Liu et al (2022a) and Luiken et al (2022) illustrated successful suppression of tracewise noise, specifically poorly coupled receivers in common shot gathers and blending noise in common channel gathers, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, both Luiken et al (2022) and Wang et al (2022) proposed blind-trace networks implemented at the architecture level, as opposed to the likes of Krull et al (2019); Birnie et al (2021); Liu et al (2022a) which were implemented as processing steps. Both Liu et al (2022a) and Luiken et al (2022) illustrated successful suppression of tracewise noise, specifically poorly coupled receivers in common shot gathers and blending noise in common channel gathers, respectively. Similar to Birnie et al (2021), both these studies highlighted the potential of self-supervised learning to outperform conventional methods, when applied optimally.…”

Section: Introductionmentioning

confidence: 99%

“…Earlier implementations of selfsupervised, blind-spot networks have all focussed on suppressing a single component of the noise field as opposed to tackling the noise field as a whole. In order to tackle the continually evolving noise field throughout a field seismic recording, it is not possible to use a rigid scheme involving a strict noise mask-such as the trace-wise noise suppression of Liu et al (2022a) and Luiken et al (2022)-instead a more flexible solution is needed.…”

Section: Introductionmentioning

confidence: 99%

“…The second (b) noise type shown is trace-wise, arising from a handful of poorlycoupled receivers. In this instance, the noise is coherent across the trace so the blind-spot is extended to a blind-trace, as proposed by Liu et al (2022a). The next noise type (c) is the arrival of a seismic wave from a distant event, causing a linear move-out pattern.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Birnie

Alkhalifah

2022

Front. Earth Sci.

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Seismic Data Denoising Using a Self-Supervised Deep Learning Network

Wang

Chen

et al. 2023

Math Geosci

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Diffraction denoising using self‐supervised learning

Markovic

Malehmir

2023

Geophysical Prospecting

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Diffraction wavefield contains valuable information on subsurface composition through velocity extraction and sometimes anisotropy estimation. It can also be used for the delineation of geological features, such as faults, fractures and mineral deposits. Diffraction recognition is, therefore, crucial for improved interpretation of seismic data. To date, many workflows for diffraction denoising, including deep‐learning applications, have been provided, however, with a major focus on sedimentary settings or for ground‐penetrating radar data. In this study, we have developed a workflow for a self‐supervised learning technique, an autoencoder, for diffraction denoising on synthetic seismic, ground‐penetrating radar and hardrock seismic datasets. The autoencoder provides promising results especially for the ground‐penetrating radar data. Depending on the target of the studies, diffraction signals can be tackled using the autoencoder both as the signal and/or noise when, for example, a reflection is a target. The real hardrock seismic data required additional pre‐ and post‐autoencoder image processing steps to improve automatic delineation of the diffraction. Here, we also coupled the autoencoder with Hough transform and pixel edge detection filters. Along inlines and crosslines, diffraction signals have sometimes a similar character as the reflection and may spatially be correlated making the denoising workflow unsuccessful. Coupled with additional image processing steps, we successfully isolated diffraction that is generated from a known volcanogenic massive sulphide deposit. These encouraging results suggest that the self‐supervised learning techniques such as the autoencoder can be used also for seismic mineral exploration purposes and are worthy to be implemented as additional tools for data processing and target detections.

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Coherent noise suppression via a self-supervised blind-trace deep learning scheme

Cited by 4 publications

References 30 publications

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

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

Seismic Data Denoising Using a Self-Supervised Deep Learning Network

Diffraction denoising using self‐supervised learning

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