Seismic Stratum Segmentation Using an Encoder–Decoder Convolutional Neural Network

Wang, Detao; Chen, Guoxiong

doi:10.1007/s11004-020-09916-8

Cited by 15 publications

(5 citation statements)

References 27 publications

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“…This type of method is mainly based on the auto-encoder framework, the encoder extracts the low-dimensional features of the fault, and the decoder restores the features extracted by the encoder to the original dimension [14]- [15]. Representative methods are U-shaped network (U-Net) [16]- [17]. In 2022, Gao Kai et al used U-Net to obtain excellent fault identification results for the problems of artificially picking faults or low seismic attribute detection in complex seismic profiles [18].…”

Section: B a Semantic Segmentation-based Fault Identification Methodsmentioning

confidence: 99%

Seismic Fault Identification Based on Multi-Scale Dense Convolution and Improved Long Short-Term Memory Network

Guo,

Xiong,

Zhao

et al. 2023

IEEE Access

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Aiming at the local, global and temporal morphological characteristics of faults in seismic profiles, this paper proposes the MCD-ABiLSTM method for fault identification. The method uses multichannel, convolution kernels of different sizes and convolution of different depths to extract multi-scale seismic profile features, and makes full use of the extracted features to enhance the model's sensitivity to small faults. By combining Bi-directional Long Short-Term Memory (BiLSTM) with multi-scale dense convolution (MCD), the spatial and temporal characteristics of seismic signals are extracted successively, so that the seismic attribute features can be better represented in space and time. In order to solve the problem of extreme imbalance between label data and background data, an improved Weighted Cross Entropy is adopted as the loss function of MCD-ABiLSTM, which alleviates the imbalance between label data and background data. Then, the label data is expanded to establish a fault label data set suitable for deep learning, which further alleviates the problem of imbalance between fault label data and background data. The comparison results show that compared with FCN, U-net, U-net++ and Deeplap V3, the method proposed in this paper improves the precision by 6.27%, 3.65%, 2.94% and 4%.

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Section: B a Semantic Segmentation-based Fault Identification Methodsmentioning

confidence: 99%

Seismic Fault Identification Based on Multi-Scale Dense Convolution and Improved Long Short-Term Memory Network

Guo,

Xiong,

Zhao

et al. 2023

IEEE Access

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“…One popular network architecture for segmentation tasks is U-Net [8], which has been successfully applied in various domains, including cell segmentation. In the field of seismic data segmentation, researchers have extensively utilized U-Net to achieve remarkable results [2], [9], [10]. To address the challenge of limited labeled seismic data, Ferreira et al [11] incorporated generative adversarial networks (GANs) to generate synthetic seismic images.…”

Section: Introductionmentioning

confidence: 99%

“…To address this issue, one effective approach is to select representative samples from the dataset. Uniform sampling at equal intervals is a commonly used method in seismic data segmentation [10], [18]- [20]. However, it's important to consider that seismic data often possesses distinct structural patterns that should be taken into account during sample selection.…”

Section: Introductionmentioning

confidence: 99%

Seismic Stratigraphic Interpretation Based on Unsupervised Validation and Spectral Clustering Sampling

Luo,

Gu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the continuous development of computer technology and significant improvements in computing power, deep learning has found increasing applications in seismic stratigraphy interpretation, showcasing notable advancements over traditional methods. However, due to the unique characteristics of seismic data, labeling such data has become extremely challenging and time-consuming, necessitating the involvement of professional geologists. Consequently, few-shot learning has garnered considerable attention for seismic image segmentation.Nevertheless, two key challenges remain in few-shot learning: selecting more representative samples and validating the model during training. As the availability of labeled samples decreases, we are left with inadequate data for the validation set. In this paper, instead of solely focusing on enhancing the network structure, we propose the utilization of Spectral Clustering Sampling (SCS) methods for training data selection. Additionally, we introduce a metric called Sum of Different (SD), which can be computed without the need for labeled data, to replace the conventional validation set loss employed in traditional validation approaches. Notably, By employing SCS methods for training data selection and introducing the SD metric to replace traditional validation set loss in F3 dataset, we have achieved remarkable outcomes.

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“…These solutions range from addressing fundamental questions of Earth science to technical bottlenecks in engineering, such as the exploration of mineral and oil/gas resources. As a practical application, data-driven techniques, such as machine learning and deep learning, have played crucial roles in improving data processing methods and approaches to interpreting data in the field of remote sensing (Zhu et al 2017), applied geophysics (Wang and Chen 2021;Yu and Ma 2021), and mineral prospectivity modeling (Chen et al 2022c;Cheng and Agterberg 1999). In addition to transforming industries, data-driven science is also beginning to play an important role in advancing scientific discoveries of complex Earth systems, such as earthquake forecasting (Mousavi and Beroza 2022), global climate change (Reichstein et al 2019), planetary interior structure (Wilding et al 2022), the evolution of mass, life, and climate in the early Earth (e.g., Chen et al 2022b;Chiaradia 2014;Fan et al 2020;Hazen 2014;Keller and Schoene 2012;Puetz et al 2018), and the search for extraterrestrial life (Ma et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Special Issue: Data-Driven Discovery in Geosciences: Opportunities and Challenges

Chen

Cheng

Puetz³

2023

Math Geosci

Self Cite

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With the rapid expansion in big data and artificial intelligence (AI), Earth sciences are undergoing unprecedented advances in data processing and interpretation techniques, as well as in facilitating data-driven discoveries of complex Earth systems. This special collection explores scientific research related to data-driven discoveries in geosciences and provides a timely presentation of progress in developments and/or applications of AI and big data approaches to multiple aspects of geosciences. These include geohazards monitoring, mineral resource exploration, and environmental assessments. We hope this collection will inspire researchers and will transform the work undertaken in the field of data-driven Earth science. While many challenges remain, including the formidable tasks of transforming the deluge of geoscience data into useable information and furthering knowledge via cutting-edge AI techniques, we envision that data-driven discovery will revolutionize conventional methods of observation, analysis, modeling, and prediction in geosciences, and will further advance scientific understanding of our complex Earth system. KeywordsData-driven discovery • Big data • Artificial intelligence • Geosciences B Guoxiong Chen

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Seismic Stratum Segmentation Using an Encoder–Decoder Convolutional Neural Network

Cited by 15 publications

References 27 publications

Seismic Fault Identification Based on Multi-Scale Dense Convolution and Improved Long Short-Term Memory Network

Seismic Fault Identification Based on Multi-Scale Dense Convolution and Improved Long Short-Term Memory Network

Seismic Stratigraphic Interpretation Based on Unsupervised Validation and Spectral Clustering Sampling

Special Issue: Data-Driven Discovery in Geosciences: Opportunities and Challenges

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