A seismic coherency method using spectral amplitudes

Sui, Jingkun; Zheng, Xiaodong; Li, Yandong

doi:10.1007/s11770-015-0501-5

Cited by 21 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Li and Lu (2014) combine spectral decomposition and complex coherence computation to map discontinuities at different scales. To avoid false low-coherence values in steeply dipping structures, Sui et al (2015) propose a coherence algorithm that analyses the eigenstructure of the spectral amplitudes of seismic traces. More recently, Alaudah and AlRegib (2016) propose a generalizedtensor-based coherence (GTC) attribute that derives covariance matrices from the unfolding matrices of a seismic analysis tensor along different modes corresponding to time, inline, and crossline directions, respectively.…”

Section: Fault Detectionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Section: Fault Detectionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…They then sharpen their faults using a commercial swarm intelligence algorithm. Sui et al (2015) address the multispectral coherence analysis problem by constructing a covariance matrix from the spectral magnitudes a m :…”

Section: Rgb Color Blendingmentioning

confidence: 99%

Coherence attribute applications on seismic data in various guises — Part 1

Chopra

Marfurt

2018

Interpretation

View full text Add to dashboard Cite

We have previously discussed some alternative means of modifying the frequency spectrum of the input seismic data to modify the resulting coherence image. The simplest method was to increase the high-frequency content by computing the first and second derivatives of the original seismic amplitudes. We also evaluated more sophisticated techniques, including the application of structure-oriented filtering to different spectral components before spectral balancing, thin-bed reflectivity inversion, bandwidth extension, and the amplitude volume technique. We further examine the value of coherence computed from individual spectral voice components, and alternative means of combining three or more such coherence images, providing a single volume for interpretation.

show abstract

“…Li and Lu (2014) and Li et al (2015) compute coherence from different spectral components and corender them using a red, green, and blue (RGB) color model. Sui et al (2015) add covariance matrices computed from a suite of spectral magnitude components, obtaining a coherence image superior to that of the original broadband data. Marfurt (2017) expands on this idea, adds coherence matrices computed from analytic spectral components (the spectral voices and their Hilbert transforms) along the structural dip, and obtains improved suppression of random noise and enhancement of small faults and karst collapse features.…”

Section: Introductionmentioning

confidence: 99%

Multiazimuth coherence

et al. 2017

SEG Technical Program Expanded Abstracts 2017

View full text Add to dashboard Cite

Since its introduction two decades ago, coherence has been widely used to map structural and stratigraphic discontinuities such as faults, cracks, karst collapse features, channels, stratigraphic edges, and unconformities. With the intent to map azimuthal variations of horizontal stress as well as to improve the signal-to-noise ratio of unconventional resource plays, wide-/ full-azimuth seismic data acquisition has become common. Migrating seismic traces into different azimuthal bins costs no more than migrating them into one bin. If the velocity anisotropy is not taken into account by the migration algorithm, subtle discontinuities and some major faults may exhibit lateral shifts, resulting in a smeared image after stacking. Based on these two issues, we evaluate a new way to compute the coherence for azimuthally limited data volumes. Like multispectral coherence, we modify the covariance matrix to be the sum of the covariance matrices, each of which belongs to an azimuthally limited volume, and then we use the summed covariance matrix to compute the coherent energy. We validate the effectiveness of our multiazimuth coherence by applying it to two seismic surveys acquired over the Fort Worth Basin, Texas. Not surprisingly, multiazimuth coherence exhibits less incoherent noise than coherence computed from azimuthally limited amplitude volumes. If the data have been migrated using an azimuthally variable velocity, multiazimuth coherence exhibits higher lateral resolution than that computed from the stacked data. In contrast, if the data have not been migrated using an appropriate azimuthally variable velocity model, the misalignment of each image results in a blurring of the multiazimuth coherence and the coherence computed from the stacked data. This suggests that our method may serve as a future tool for azimuthal velocity analysis.

show abstract

A seismic coherency method using spectral amplitudes

Cited by 21 publications

References 7 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

Coherence attribute applications on seismic data in various guises — Part 1

Multiazimuth coherence

Contact Info

Product

Resources

About