“A visual data-mining methodology for seismic facies analysis: Part 2 — Application to 3D seismic data,” Iván Dimitri Marroquín, Jean-Jules Brault, and Bruce S. Hart, GEOPHYSICS, 74, no. 1, P13–P23.

Geophysics ◽  
2009 ◽  
Vol 74 (2) ◽  
pp. Y3-Y3 ◽  
Keyword(s):  
Data Mining ◽  
Seismic Data ◽  
Facies Analysis ◽  
Seismic Facies ◽  
3D Seismic ◽  
Visual Data ◽  
Visual Data Mining ◽  
Seismic Facies Analysis ◽  
3D Seismic Data

A visual data-mining methodology for seismic facies analysis: Part 2 — Application to 3D seismic data

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. P13-P23 ◽  
Author(s):  
Iván Dimitri Marroquín ◽  
Jean-Jules Brault ◽  
Bruce S. Hart
Keyword(s):  
Data Mining ◽  
Seismic Data ◽  
Clustering Analysis ◽  
Facies Analysis ◽  
Seismic Facies ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Approach ◽  
Seismic Facies Analysis ◽  
3D Seismic Data

A visual data-mining approach to unsupervised clustering analysis can be an effective tool for visualizing and understanding patterns inherent in seismic data (i.e., seismic facies). The unsupervised clustering analysis is completely data-driven, requiring no external information (e.g., well logs) to guide the seismic-trace classification. We demonstrate the application of the visual data-mining approach to seismic facies analysis on a real 3D seismic data volume. We select two stratigraphic intervals, the first including a Devonian pinnacle reef system and the second containing a Jurassic siliciclastic channel system. Both analyses show major stratigraphic features that can be defined in horizon slices or other types of visualization. However, the visual data-mining approach creates seismic facies maps with improved visual detail, distinguishing seismic trace-shape variability in the data. We also compare the facies maps with those obtained from a commercial package for seismic facies classification. Both approaches created similar facies maps, but the visual strategy better depicts subtle stratigraphic changes in the bodies being imaged, offering insight into the nature of these features.

A visual data-mining methodology for seismic facies analysis: Part 1 — Testing and comparison with other unsupervised clustering methods

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. P1-P11 ◽  
Author(s):  
Iván Dimitri Marroquín ◽  
Jean-Jules Brault ◽  
Bruce S. Hart
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Seismic Data ◽  
Facies Analysis ◽  
Clustering Algorithms ◽  
Unsupervised Clustering ◽  
Seismic Facies ◽  
Visual Data ◽  
Visual Data Mining ◽  
Seismic Facies Analysis

Seismic facies analysis aims to identify clusters (groups) of similar seismic trace shapes, where each cluster can be considered to represent variability in lithology, rock properties, and/or fluid content of the strata being imaged. Unfortunately, it is not always clear whether the seismic data has a natural clustering structure. Cluster analysis consists of a family of approaches that have significant potential for classifying seismic trace shapes into meaningful clusters. The clustering can be performed using a supervised process (assigning a pattern to a predefined cluster) or an unsupervised process (partitioning a collection of patterns into groups without predefined clusters). We evaluate and compare different unsupervised clustering algorithms (e.g., partition, hierarchical, probabilistic, and soft competitive models) for pattern recognition based entirely on the characteristics of the seismic response. From validation results on simple data sets, we demonstrate that a self-organizing maps algorithm implemented in a visual data-mining approach outperforms all other clustering algorithms for interpreting the cluster structure. We apply this approach to 2D seismic models generated using a discrete, known number of different stratigraphic geometries. The visual strategy recovers the correct number of end-member seismic facies in the model tests, showing that it is suitable for pattern recognition in highly correlated and continuous seismic data.

Pre-stack Seismic Facies Analysis via Waveform Sparse Representation

Geophysics ◽  
2020 ◽  
pp. 1-77
Author(s):  
Kunhong Li ◽  
Zhining Liu ◽  
Bin She ◽  
Jiandong Liang ◽  
Guangmin Hu
Keyword(s):  
Spatial Structure ◽  
Dictionary Learning ◽  
Seismic Data ◽  
Facies Analysis ◽  
Intensity Variation ◽  
Seismic Facies ◽  
Distance Metric ◽  
Structure Variation ◽  
Waveform Data ◽  
Seismic Facies Analysis

Seismic facies analysis based on pre-stack data is becoming popular. Vertical elastic transitions produce the spatial structure variation of pre-stack waveforms, while lateral elastic transitions produce the amplitude intensity variation. In the stratigraphic seismic facies analysis, more attention should be paid to waveform spatial structure than amplitude intensity. Conventional classification methods based on distance metric are difficult to adapt to stratigraphic seismic facies analysis because a distance metric is a comprehensive measure of waveform structure and amplitude intensity. A dictionary learning method for pre-stack seismic facies analysis is proposed herein. The proposed method first learns several dictionaries from labeled pre-stack waveform data, and these dictionaries consist of several normalization vector bases. The pre-stack waveform spatial structure is therefore embedded in these learned dictionaries, and the amplitude intensity is eliminated by the normalization process. Afterward, these dictionaries are used to sparsely represent pre-stack seismic data. Seismic facies are classified and determined according to representation error. A source error separation method is used to improve the anti-noise performance of dictionary learning by iteratively segmenting the noise out in the training data. The results on synthetic and real seismic data show that the proposed method has a stronger tolerance to noise, and the obtained seismic facies boundary is more accurate and clearer. This demonstrates that the proposed method is an effective seismic facies analysis technique.

3D seismic volume visualization and interpretation: An integrated workflow with case studies

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. W1-W12 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Case Studies ◽  
Seismic Data ◽  
Volume Visualization ◽  
Seismic Facies ◽  
Data Selection ◽  
3D Seismic ◽  
Well Bore ◽  
Stratigraphic Architecture ◽  
Facies Characterization ◽  
3D Seismic Data

One of the major problems in subsurface seismic exploration is the uncertainty (nonuniqueness) in geologic interpretation because of the complexity of subsurface geology and the limited dimension of the data available. Case studies from worldwide exploration projects indicate that an integrated, three-dimensional (3D) seismic volume visualization and interpretation workflow contributes to resolving the problem by mining and exposing critical geologic information from within seismic data volumes. Following 3D seismic data acquisition and processing, the interpretation workflow consists of four integrated phases from data selection and conditioning, to structure and facies characterization, to prospect evaluation and generation, to well-bore planning. In the data selection and conditioning phase, the most favored and frequently used data are the full-angle, limited-angle, and limited-azimuth stack amplitude with significant structure and facies enhancements. Signal-to-noise ratio, color scheme, dynamic range, bit resolution, and visual contrast all affect thevisibility of features of interest. In the structure and facies characterization phase, vertical slicing along arbitrary traverses demonstrates structure styles, stratigraphic architecture, and reservoir geometry in the cross-sectional view. Time/depth slicing defines lateral and vertical variability in the structural trend and areal extent in the map view. Stratal slicing and fault slicing map chronostratigraphic seismic facies and cross-stratal, along-fault seismic signature. Volume flattening and structure restoration aid in unraveling paleostructural framework and stratigraphic architecture and their growth histories. In the prospect evaluation and generation phase, a combination of volume trimming, co-rendering, transparency, attribute analysis, and attribute-body detection is instrumental in delineating volumetric extent and evaluating spatial connectivity of critical seismic features. Finally, in the well-bore planning phase, informed decision-making relies on the integration of all the information and knowledge interrogated from 3D seismic data. Most importantly, interpreters’ geologic insight and play concept are crucial to optimal well-bore planning with high geologic potential and low economic risk.

A comparison of classification techniques for seismic facies recognition

2015 ◽  
Vol 3 (4) ◽  
pp. SAE29-SAE58 ◽  
Author(s):  
Tao Zhao ◽  
Vikram Jayaram ◽  
Atish Roy ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Support Vector ◽  
3D Seismic ◽  
Generative Topographic Mapping ◽  
Learning Methods ◽  
Facies Classification ◽  
Data Volume ◽  
3D Seismic Data

During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. To address this problem, several seismic facies classification algorithms including [Formula: see text]-means, self-organizing maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks have been successfully used to extract features of geologic interest from multiple volumes. Although well documented in the literature, the terminology and complexity of these algorithms may bewilder the average seismic interpreter, and few papers have applied these competing methods to the same data volume. We have reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised learning methods also highlighted features that might otherwise be overlooked.

Imposing interpretational constraints on a seismic interpretation CNN

Geophysics ◽  
2021 ◽  
pp. 1-36
Author(s):  
Haibin Di ◽  
Cen Li ◽  
Stewart Smith ◽  
Zhun Li ◽  
Aria Abubakar
Keyword(s):  
Seismic Data ◽  
Recent Progress ◽  
Facies Analysis ◽  
Three Dimensional ◽  
Seismic Interpretation ◽  
3D Seismic ◽  
Geologic Time ◽  
Seismic Amplitude ◽  
Subsurface Geology ◽  
3D Seismic Data

With the expanding size of three-dimensional (3D) seismic data, manual seismic interpretation becomes time consuming and labor intensive. For automating this process, the recent progress in machine learning, particularly the convolutional neural networks (CNNs), has been introduced into the seismic community and successfully implemented for interpreting seismic structural and stratigraphic features. In principle, such automation aims at mimicking the intelligence of experienced seismic interpreters to annotate subsurface geology both accurately and efficiently. However, most of the implementations and applications are relatively simple in their CNN architectures, which primary rely on the seismic amplitude but undesirably fail to fully use the pre-known geologic knowledge and/or solid interpretational rules of an experienced interpreter who works on the same task. A general applicable framework is proposed for integrating a seismic interpretation CNN with such commonly-used knowledge and rules as constraints. Three example use cases, including relative geologic time-guided facies analysis, layer-customized fault detection, and fault-oriented stratigraphy mapping, are provided for both illustrating how one or more constraints can be technically imposed and demonstrating what added values such a constrained CNN can bring. It is concluded that the imposition of interpretational constraints is capable of improving CNN-assisted seismic interpretation and better assisting the tasks of subsurface mapping and modeling.

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