An attractive feature seen on seismic data, also known as funny-looking thing or FLT, henceforth has a wide range of interpretations, from noise patterns to amplitude anomalies. An example of an FLT is the similar faulting patterns between a volcanic intrusion and a salt intrusion from the point of view of a machine learning (ML) algorithm. Oftentimes, seismic interpreters do not have a complete data set or geologic background to determine the genesis of the observed features. This can be particularly perplexing when trying to determine if an intrusion is volcanic or halokinetic in origin because they exhibit similar geomorphologies. We examine the differences in these features in the Gulf of Mexico, which is a well-documented salt basin, and the Taranaki Basin in New Zealand, which is igneous prone. The analysis aims to discern geologic features based on the geometries and attributes shared by seismic data and remote sensing tools. Seismic attributes and ML techniques highlight differences and similarities between the intrusions. We discuss ML techniques, such as self-organized maps (SOMs), an unsupervised ML technique, and cluster fault systems without regard to the geologic context. The attributes used in the SOM are fault probability, fault dip azimuth, fault dip magnitude, and thin-bed detector. Fault probability is performed through a combination of convolutional neural network fault prediction and a skeletonization process. Once the faults are clustered using SOM, the visualization of fault architecture due to the existing mount (either volcano or salt dome) is done considering high fault probabilities (>75%). The methodology consists of selecting the neurons from the SOM grid corresponding to the presence of faults and combining them with fault probability and a fault dip azimuth using a crossplot. The crossplot product assists in the automatic extraction of the fault planes using: (1) a voxel representation of the fault planes and (2) fault patches representing the fault planes. Moreover, the visualization technique defined demonstrates that the crossplot product yields better-defined fault planes. With the fault system characterized, we compare horizon slices using coherence, fault dip magnitude, and azimuth against remote sensing images with similar attributes. In conclusion, our methodology combines technologies to differentiate the genesis of intrusion — salt or igneous — using the fault presence and could be helpful in frontier exploration or planetary exploration.

'The present is the key to the past'- a foundational geological concept that helps us contextualize buried subsurface features in terms of current geologic analogs. As seismic interpreters, we are used to some of the more common reflection patterns. However, we also find anomalous geoforms in our seismic data that we often overlook. As a result, we often disservice ourselves when we overlook these features, potentially ignoring additional geologic context and interpreting these features. These features or funny looking things (FLT) may provide further geologic context and aid us in solving the geological model if included. Herein, in this study, we describe and analyze a crevasse splay on a continental slope marine environment. We visualized the FLT using a combination of attributes, seismic inversion and voxel based classification. We discuss possible causes that may have triggered the break of the levee on the Exmouth Basin during the Early Eocene. We show that the preexisting faults in the subsurface are the likely culprit for a levee break that created the crevasses splay rather than an abundant sediment supply. Thus, in contrast with the Led Zeppelin song about why the levee breaks, it is equally important to understand the preexisting faults when analyzing sediment supply. Also, this highlights the importance of integral stratigraphic sequence interpretation - from deep to shallow to understand geology in a full context.

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