English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Seismic interpretation is now serving as a fundamental tool for depictingsubsurface geology and assisting activities in various domains, such asenvironmental engineering and petroleum exploration. However, most of theexisting interpretation techniques are designed for interpreting a certainseismic pattern (e.g., faults and salt domes) in a given seismic dataset at onetime; correspondingly, the rest patterns would be ignored. Interpreting all theimportant seismic patterns becomes feasible with the aid of multipleclassification techniques. When implementing them into the seismic domain,however, the major drawback is the low efficiency particularly for a largedataset, since the classification need to be repeated at every seismic sample.To resolve such limitation, this study first present a seismic patterninterpretation dataset (SpiDat), which tentatively categorizes 12commonly-observed seismic patterns based on their signal intensity and lateralgeometry, including these of important geologic implications such as faults,salt domes, gas chimneys, and depositional sequences. Then we propose a seismicpattern interpretation network (SpiNet) based on the state-of-the-artdeconvolutional neural network, which is capable of automatically recognizingand annotating the 12 defined seismic patterns in real time. The impacts of theproposed SpiNet come in two folds. First, applying the SpiNet to a seismic cubeallows interpreters to quickly identify the important seismic patterns as inputto advanced interpretation and modeling. Second, the SpiNet paves thefoundation for deriving more task-oriented seismic interpretation networks,such as fault detection. It is concluded that the proposed SpiNet holds greatpotentials for assisting the major seismic interpretation challenges andadvancing it further towards cognitive seismic data analysis.

Developing a seismic pattern interpretation network (SpiNet) for automated seismic interpretation