Machine-learning Based Automated Fault Detection in Seismic Traces

Zhang, C.; Frogner, Charlie; Araya‐Polo, Mauricio; Hohl, Detlef

doi:10.3997/2214-4609.20141500

Cited by 63 publications

(26 citation statements)

References 5 publications

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“…To alleviate such a limitation, in recent years, researchers have proposed conducting interpretation tasks on prestack seismic data, which are the input of time or depth migration and have less noise than migrated ones. Zhang et al (2014) introduce kernel-regularized least-squares fitting to identify and localize faults in prestacked seismic data at the initial stage of velocity model building. Similarly, Lin et al (2017) employ the Nyström method for dimensionality reduction and apply the kernel ridge regression model to extract faults from prestack seismic measurements.…”

Section: The Leading Edgementioning

confidence: 99%

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

et al. 2018

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As a process that identifies geologic structures of interest such as faults, salt domes, or elements of petroleum systems in general, seismic structural interpretation depends heavily on the domain knowledge and experience of interpreters as well as visual cues of geologic structures, such as texture and geometry. With the dramatic increase in size of seismic data acquired for hydrocarbon exploration, structural interpretation has become more time consuming and labor intensive. By treating seismic data as images rather than signal traces, researchers have been able to utilize advanced image-processing and machine-learning techniques to assist interpretation directly. In this paper, we mainly focus on the interpretation of two important geologic structures, faults and salt domes, and summarize interpretation workflows based on typical or advanced image-processing and machine-learning algorithms. In recent years, increasing computational power and the massive amount of available data have led to the rise of deep learning. Deep-learning models that simulate the human brain's biological neural networks can achieve state-of-the-art accuracy and even exceed human-level performance on numerous applications. The convolutional neural network — a form of deep-learning model that is effective in analyzing visual imagery — has been applied in fault and salt dome interpretation. At the end of this review, we provide insight and discussion on the future of structural interpretation.

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Section: The Leading Edgementioning

confidence: 99%

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

et al. 2018

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“…Meanwhile, especially in the last few years, encouraged by the growth of the so called big data and by the increased computational power, there has been an increasing renewed interest in machine learning techniques. Recently these methodologies have been increasingly explored, for different applications, also by the geophysical community (Smith and Treitel, 2010;Zhang et al, 2014;Zhao et al, 2015;Kobrunov and Priezzhev, 2016).…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to facies classification using well logs

Bestagini

Lipari

Tubaro

2017

SEG Technical Program Expanded Abstracts 2017

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In this work we describe a machine learning pipeline for facies classification based on wireline logging measurements. The algorithm has been designed to work even with a relatively small training set and amount of features. The method is based on a gradient boosting classifier which demonstrated to be effective in such a circumstance. A key aspect of the algorithm is feature augmentation, which resulted in a significant boost in accuracy. The algorithm has been tested also through participation to the SEG machine learning contest.

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“…To avoid this limitation, another type of learning method has been recently proposed and developed, i.e., learning from pre-stack seismic data directly. In the work of Araya-Polo et al (2017) and Zhang et al (2014), supervised learning methods are directly applied to the pre-stack seismic data to look for patterns which indicates the geologic features. Specifically, in Araya-Polo et al (2017) deep neural network was utilized to seismic data sets to obtain geologic faults.…”

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confidence: 99%

Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm

Lin

Wang

Thiagarajan

et al. 2018

Geophysical Journal International

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Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based on pre-stack seismic measurements. Our detection method employs an efficient and accurate machine-learning detection approach to extract useful subsurface geologic features automatically. Specifically, our method is based on kernel ridge regression model. The conventional kernel ridge regression can be computationally prohibited because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nyström method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate detection. We provide thorough computational cost analysis to show efficiency of our new geological feature detection methods. We further validate the performance of our new arXiv:1710.04329v1 [cs.LG] 11 Oct 2017 2 Lin et al.subsurface geologic feature detection method using synthetic surface seismic data for 2D acoustic and elastic velocity models. Our numerical examples demonstrate that our new detection method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we show that our method yields a speed-up ratio on the order of ∼ 10 2 to ∼ 10 3 in a multi-core computational environment.

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Machine-learning Based Automated Fault Detection in Seismic Traces

Cited by 63 publications

References 5 publications

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

A machine learning approach to facies classification using well logs

Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm

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