Deep convolutional neural network for automatic fault recognition from 3D seismic datasets

An, Yu; Jiu-lin, Guo; Ye, Qing; Childs, Conrad; Walsh, John J.; Dong, Ruihai

doi:10.1016/j.cageo.2021.104776

Cited by 54 publications

(23 citation statements)

References 29 publications

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“…Furthermore, these solutions helped to cover functionalities for waveform analysis, such as genetic algorithm, least-square fitting, auto-picking, fast Fourier transforms, location, attenuation, focal mechanisms, waveform, modeling techniques for rapid estimation of earthquake source parameters, and others. Other authors [ 51 ] used deep convolutional neural networks to convert geological project files to a data format suitable for deep learning with processing, analysis, model evaluation, and comparative results. The main disadvantage is a large amount of data for training and automatic seismic interpretation.…”

Section: Resultsmentioning

confidence: 99%

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Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review

Arrais

Urquiza-Aguiar

Tripp-Barba

2022

Sensors

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Organizations responsible for seismic and volcanic monitoring worldwide mainly gather information from instrumental networks composed of specialized sensors, data-loggers, and transmission equipment. This information must be available in seismological data centers to improve early warning diffusion. Furthermore, this information is necessary for research purposes to improve the understanding of the phenomena. However, the acquisition data systems could have some information gaps due to unstable connections with instrumental networks and repeater nodes or exceeded waiting times in data acquisition processes. In this work, we performed a systematic review around information availability issues and solutions in data acquisition systems, instrumental networks, and their interplay with transmission media for seismic and volcanic monitoring. Based on the SLR methodology proposed by Kitchenham, B., a search string strategy was considered where 1938 articles were found until December 2021. Subsequently, through selection processes, 282 articles were obtained and 51 relevant articles were extracted using filters based on the content of articles mainly referring to seismic–volcanic data acquisition, data formats, monitoring networks, and early warnings. As a result, we identified two independent partial solutions that could complement each other. One focused on extracting information in the acquisition systems corresponding to continuous data generated by the monitoring points through the development of mechanisms for identifying sequential files. The other solution focused on the detection and assessment of the alternative transmission media capabilities available in the seismic–volcanic monitoring network. Moreover, we point out the advantage of a unified solution by identifying data files/plots corresponding to information gaps. These could be recovered through alternate/backup transmission channels to the monitoring points to improve the availability of the information that contributes to real-time access to information from seismic–volcanic monitoring networks, which speeds up data recovery processes.

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Section: Resultsmentioning

confidence: 99%

“…Y. An et al [ 51 ] proposed a workflow for automatic fault recognition in seismic data using deep convolutional neural networks (DCNNs). It required conversion of geological project files to other formats.…”

Section: Methodology Validationmentioning

confidence: 99%

Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review

Arrais

Urquiza-Aguiar

Tripp-Barba

2022

Sensors

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“…ThebeFault [58]- [60], to our knowledge, is the largest opensource field fault recognition seismic dataset available. The dataset contains raw seismic data from the Thebe gas field on the North West Shelf of Australia and annotations by expert fault interpreters from the University College Dublin Fault Analysis Group.…”

Section: A Datasetsmentioning

confidence: 99%

Disentangling Noise Patterns From Seismic Images: Noise Reduction and Style Transfer

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Seismic interpretation is a fundamental approach for obtaining static and dynamic information about subsurface reservoirs, such as geological faults/salt bodies and associated fluid types and distribution. Due to the exponential growth in seismic data volume and considerable uncertainty in manual interpretation, deep learning (DL) algorithms have been introduced to assist seismic interpretation. Our investigation of the trained neural networks suggests that they underperform on seismic data with different noise characteristics. One of the main issues is that the noise patterns of seismic data are highly inconsistent due to many factors, including geological features, sampling parameters and human intervention. To address this problem, we propose a noise pattern transfer (NPT) framework to transfer or remove seismic noise style between datasets by treating noise patterns as styles of image, which can also improve the generality of automatic seismic interpretation algorithms. Extensive experiments on three synthetic datasets and two field seismic datasets demonstrate the promising performance of our proposed NPT approach. Pairs of clean and stylised seismic data are generated by extending the use of the neural style transfer algorithm beyond the artistic domain. We then demonstrate how our method achieves superior noise pattern transferability between datasets and denoising performance on field datasets. Associated improvements in accuracy and generalisation of the neural network-based fault recognition tasks successfully demonstrate the practicality of our NPT approach. The source code is made publicly available online at https://github.com/Magnomic/nptcode.

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“…In geophysics, current fault detection methods can be broadly categorized into two classes: traditional methods [17][18][19], and machine-learning-based algorithms [1][2][3][4][5][20][21][22][23]. In general, traditional methods work by detecting the local discontinuity in seismic images on the basis of some of the seismic features, such as semblance, variance, etc.…”

Section: Seismic Fault Detectionmentioning

confidence: 99%

Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification

2021

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This paper empirically evaluates two kinds of features, which are extracted, respectively, with traditional statistical methods and convolutional neural networks (CNNs), in order to improve the performance of seismic patch image classification. In the latter case, feature vectors, named “CNN-features”, were extracted from one trained CNN model, and were then used to learn existing classifiers, such as support vector machines. In this case, to learn the CNN model, a technique of transfer learning using synthetic seismic patch data in the source domain, and real-world patch data in the target domain, was applied. The experimental results show that CNN-features lead to some improvements in the classification performance. By analyzing the data complexity measures, the CNN-features are found to have the strongest discriminant capabilities. Furthermore, the transfer learning technique alleviates the problems of long processing times and the lack of learning data.

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Deep convolutional neural network for automatic fault recognition from 3D seismic datasets

Cited by 54 publications

References 29 publications

Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review

Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review

Disentangling Noise Patterns From Seismic Images: Noise Reduction and Style Transfer

Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification

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