Plate subduction zones cause earthquakes and build mountain ranges due to plate collisions, which generate complex structures and induce the down-slope transport of large masses (i.e., slumps). Extensive 3D seismic data have been collected in plate subduction zones and can be used to investigate the slumps associated with earthquakes and hydrocarbon accumulations (i.e., hydrate and gas reservoirs). The development of artificial intelligence has provided new techniques (i.e., association rule learning, decision tree learning, and neural networks) for big-data analysis, such as interpreting large seismic data volumes. Here, we use a convolutional neural network (CNN) to automatically detect complex geological structures, such as slump units. We tested our method on the 3D seismic data acquired in the Nankai subduction zone. After manually defining slump units in several seismic profiles within the 3D data volume, we fed the information to the CNN, which accurately identified the spatial distribution of slump units. The CNN model was trained using real 3D seismic data until it achieved 90 % classification accuracy for slump units in the same region as the model was trained. We further applied our CNN model trained by the Nankai data to the 3D seismic data at another plate convergent margin (Sanriku-Oki in northeast Japan) and succeeded in identifying slump units in the Sanriku-Oki seismic volume. In addition to slump identification, our CNN predicted faults better than well-known methods based on seismic attributes. The slump units identified via CNN are distributed at fewer normal fault zone in the Nankai data. The high accuracy of these automatic interpretations shows that this approach can be applied to other forearc basins to investigate geological structures (e.g., slump and faults) at high spatial resolution.
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