Partial automation of the seismic to well tie with deep learning and Bayesian optimization

Tschannen, Valentin; Ghanim, Ammar; Ettrich, N.

doi:10.1016/j.cageo.2022.105120

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Their model avoids the shortcoming that the conventional methods, which are affected by manual intervention and require substantial data preprocessing, rely on. Tschannen et al (2022) accelerated the compilation speed in wavelet extraction through deep learning, which restrains the iterative adjustment parameters and potential noise. Similarly, some researchers in surface wave exploration have applied deep learning to extract dispersion curves (Alyousuf et al, 2018;Zhang et al, 2020;Dai et al, 2021) and invert velocity structures (Aleardi and Stucchi, 2021;Fu et al, 2021;Luo et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Rapid construction of Rayleigh wave dispersion curve based on deep learning

2023

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Introduction: The dispersion curve of the Rayleigh-wave phase velocity (VR) is widely utilized to determine site shear-wave velocity (Vs) structures from a distance of a few metres to hundreds of metres, even on a ten-kilometre crustal scale. However, the traditional theoretical-analytical methods for calculating VRs of a wide frequency range are time-consuming because numerous extensive matrix multiplications, transfer matrix iterations and the root searching of the secular dispersion equation are involved. It is very difficult to model site structures with many layers and apply them to a population-based inversion algorithm for which many populations of multilayers forward modelling and many generations of iterations are essential.Method: In this study, we propose a deep learning method for constructing the VR dispersion curve in a horizontally layered site with great efficiency. A deep neural network (DNN) based on the fully connected dense neural network is designed and trained to directly learn the relationships between Vs structures and dispersion curves. First, the training and validation sets are generated randomly according to a truncated Gaussian distribution, in which the mean and variance of the Vs models are statistically analysed from different regions’ empirical relationships between soil Vs and its depth. To be the supervised dataset, the corresponding VRs are calculated by the generalized reflection-transmission (R/T) coefficient method. Then, the Bayesian optimization (BO) is designed and trained to seek the optimal architecture of the deep neural network, such as the number of neurons and hidden layers and their combinations. Once the network is trained, the dispersion curve of VR can be constructed instantaneously without building and solving the secular equation.Results and Discussion: The results show that the DNN-BO achieves a coefficient of determination (R2) and MAE for the training and validation sets of 0.98 and 8.30 and 0.97 and 8.94, respectively, which suggests that the rapid method has satisfactory generalizability and stability. The DNN-BO method accelerates the dispersion curve calculation by at least 400 times, and there is almost no increase in computation expense with an increase in soil layers.

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Section: Introductionmentioning

confidence: 99%

Rapid construction of Rayleigh wave dispersion curve based on deep learning

2023

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“…At the same time, the geophysics community has been actively adopting deep-learning techniques to boost and automate numerous seismic interpretation tasks including fault picking [9,10], salt delineation [11,12], well-to-seismic tie [13,14], horizon tracking [15,16], multiple removal [17,18], etc. Nonetheless, to the best of our knowledge, there has not been yet any work exploring the application of diffusion models to seismic data and thus, studying their potential advantages to already established deep-learning approaches in this domain.…”

Section: Introductionmentioning

confidence: 99%

Deep Diffusion Models for Seismic Processing

Durall¹,

Ghanim²,

Fernández³

et al. 2022

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Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In the past years, there has been a remarkable increase of machine-learning-based solutions that have addressed the aforementioned issues. In particular, deep-learning practitioners have usually relied on heavily fine-tuned, customized discriminative algorithms. Although, these methods can provide solid results, they seem to lack semantic understanding of the provided data. Motivated by this limitation, in this work, we employ a generative solution, as it can explicitly model complex data distributions and hence, yield to a better decision-making process. In particular, we introduce diffusion models for three seismic applications: demultiple, denoising and interpolation. To that end, we run experiments on synthetic and on real data, and we compare the diffusion performance with standardized algorithms. We believe that our pioneer study not only demonstrates the capability of diffusion models, but also opens the door to future research to integrate generative models in seismic workflows.

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