A filter to improve seismic discontinuity data for fault interpretation

SEG Technical Program Expanded Abstracts 2018

2018

Seismic interpretation is now serving as a fundamental tool for depicting subsurface geology and assisting activities in various domains, such as environmental engineering and petroleum exploration. In the past decades, a number of computer-aided tools have been developed for speeding the interpretation process and improving the interpretation accuracy. However, most of the existing interpretation techniques are designed for interpreting a certain seismic feature (e.g., faults and salt domes) in a seismic section or volume at a time; correspondingly, the rest features would be ignored. Full-feature interpretation becomes feasible with the aid of multiple classification techniques. When implemented into the seismic domain, however, the major drawback is the low efficiency particularly for a large dataset, since the classification need to be repeated at every seismic sample. To resolve such limitation, this study proposes implementing the deconvolutional neural network (DCNN) for the purpose of real-time seismic interpretation, so that all the important features in a seismic image can be identified and interpreted both accurately and simultaneously. The performance of the new DCNN tool is verified through application of segmenting the F3 seismic dataset into nine major features, including salt domes, strong reflections, steep dips, etc. Good match is observed between the results and the original seismic signals, indicating not only the capability of the proposed DCNN network in seismic image analysis but also its great potentials for realtime seismic feature interpretation of an entire volume.

Section: Real-time Seismic Interpretation Via Dcnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-time seismic-image interpretation via deconvolutional neural network

Wang

SEG Technical Program Expanded Abstracts 2018

2018

“…For further enhancing the accuracy of seismic discontinuity analysis, additional efforts are then needed to suppress these non‐faulting features prior to reliable fault interpretation (Ashbridge et al . ; Barnes ). For example, Pedersen et al .…”

Section: Introductionmentioning

confidence: 99%

“…Al‐BinHassan and Marfurt () applied the 2D Hough transform for extracting the fault lines on a time section, and later Wang and AlRegib () extended it to 3D space for fault surface detection from a semblance volume. Barnes () performed eigenvector analysis to a coherence volume and designed a discontinuity filter for imaging the steeply dipping discontinuities. Lavialle et al .…”

Section: Introductionmentioning

confidence: 99%

Semi‐automatic fault/fracture interpretation based on seismic geometry analysis

Geophysical Prospecting

2019

A B S T R A C TFault and fracture interpretation is a fundamental but essential tool for subsurface structure mapping and modelling from 3D seismic data. The existing methods for semi-automatic/automatic fault picking are primarily based on seismic discontinuity analysis that evaluates the lateral changes in seismic waveform and/or amplitude, which is limited by its low resolution on subtle faults and fractures without apparent vertical displacements in seismic images. This study presents an innovative workflow for computer-aided fault/fracture interpretation based on seismic geometry analysis. First, the seismic curvature and flexure attributes are estimated for highlighting both the major faults and the subtle fractures in a seismic volume. Then, fault probability is estimated from the curvature and flexure volumes for differentiation between the potential faults and non-faulting features in the geometric attributes. Finally, the seeded fault picking is implemented for interpreting the target faults and fractures guided by the knowledge of interpreters to avoid misinterpretation and artefacts in the presence of faulting complexities as well as coherent seismic noises. Applications to two 3D seismic volumes from the Netherlands North Sea and the offshore New Zealand demonstrate the added values of the proposed method in imaging and picking the subtle faults and fractures that are often overlooked in the conventional seismic discontinuity analysis and the following fault-interpretation procedures.

“…However, it is limited by the interpretation efficiency especially for a large seismic dataset with complicated deformation history (e.g., folding and faulting). Correspondingly, the computer-aided fault interpretation becomes the research focus with the progress in computer graphics and image processing since 2000, and various methods/algorithms have been developed for refining the edge-detection attributes and interpreting fault surfaces (e.g., Pedersen et al, 2002;Barnes, 2006;Admasu et al, 2006;Hale, 2013;Zhang et al, 2014;Machado et al, Patch-level MLP classification 2016). For example, Pedersen et al (2002) introduced the concept of ant colony optimization from computer science and developed an ant-tracking algorithm for sharpening the lineaments in a variance volume.…”

Section: Introductionmentioning

confidence: 99%

Patch-level MLP classification for improved fault detection

Shafiq

SEG Technical Program Expanded Abstracts 2018

2018

Fault detection and interpretation has been one of the routine tools used for subsurface structure mapping and reservoir characterization from three-dimensional (3D) seismic data. With the recent developments in machine learning and big data analysis, this study proposes an innovative method for efficient seismic fault detection based on semi-supervised classification of multiple attribute patches through the popular multi-layer perceptron (MLP) technique. Such method consists with five components: (a) attribute selection, (b) training sample labelling, (c) attribute patch retrieval, (d) MLP model training, and (e) volumetric processing. Compared to the traditional fault-detection schemes, the proposed one is superior in three aspects. First, the MLP classifier is capable of integrating as many attributes as specified by seismic interpreters, so that the seismic features are mapped and differentiated in a highdimensional attribute domain. Second, the artificial intelligence makes it possible for optimizing the contributions from all input attributes to achieve best detection, so that the negative effects from using a less useful or "wrong" attribute are minimized. Third, the use of attribute patches incorporates local seismic patterns into training an optimal classifier, so that the random noises and/or artifacts of distinct patterns are efficiently excluded from the detection. The added values of the proposed method are verified through applications to the 3D seismic dataset over the Great South Basin (GSB) in New Zealand, where the subsurface structure is dominated by polygonal faults of varying sizes and orientations. The results demonstrate not only good match between the detected lineaments and the original seismic faults, but also great potential of the new workflow for assisting the existing fault interpretation tools, e.g. seeded picking and automatic extraction, to facilitate structural framework modeling in the exploration areas rich of faults and fractures.