Deep learning is arguably one of the most important innovations in artificial intelligence in recent times. It allows for computational solutions to problems that are not easily characterized by a mathematical model or deterministic algorithm. It also allows for automated solutions to problems that are inherently subjective. Both of these criteria are endemic in the earth sciences, so innovative solutions to these challenges should be welcomed. We demonstrate a recent refinement to a deep-learning fault identification process that improves the continuity and compactness of predicted fault planes in areas where faults intersect. Historically, predictions from both deep learning and traditional algorithmic approaches were characterized by “blurry” clouds of intermediate probability values that extended well beyond the fault plane. To remediate this blurring problem and enhance confidence of inferences, we demonstrate a preprocessing technique in the image domain by using generative adversarial networks (GANs) that sharpen the seismic image prior to training and prediction. This sharpening solution consists of two neural networks. A feature-extraction network is used for extracting both local and global features from an unrelated, high-quality “donor” seismic survey. Then, the data set of interest is sent through a donor reconstruction network where a generator architecture creates plausible-looking images at a denser sampling rate with high perceptual quality. In the study presented here, we create our sharpening network using a modern, high-fidelity, deepwater 3D survey with well-imaged faults as the donor. The resulting generator architecture is then applied to our data set of interest — an altogether separate deepwater data set in the Gulf of Mexico. Similar in intent to a 5D interpolation, the GANs-based supersampled data contain three times the inline and crossline trace density, and the sampling interval is upsampled by a factor of three. This approach aims to preserve spatial and temporal frequency content of the parent data while providing a denser data set for deep-learning applications. The supersampled data is then deployed into our deep-learning training regimen to enhance the performance of our fault detection network. By introducing a preprocessing sharpening step, the predicted faults are less blurry, more compact, and more amenable to programmatic attempts to segment them into discrete features.

Noise attenuation for ordinary images using machine learning technology has achieved great success in the computer vision field. However, directly applying these models to seismic data would not be effective since the evaluation criteria from the geophysical domain require a high-quality visualized image and the ability to maintain original seismic signals from the contaminated wavelets. This paper introduces an approach equipped with a specially designed deep learning model that can effectively attenuate swell noise with different intensities and characteristics from shot gathers with a relatively simple workflow applicable to marine seismic data sets. Three significant benefits are introduced from the proposed deep learning model. First, our deep learning model doesn't need to consume a pure swell-noise model. Instead, a contaminated swell-noise model derived from field data sets (which may contain other noises or primary signals) can be used for training. Second, inspired by the conventional algorithm for coherent noise attenuation, our neural network model is designed to learn and detect the swell noise rather than inferring the attenuated seismic data. Third, several comparisons (signal-to-noise ratio, mean squared error, and intensities of residual swell noises) indicate that the deep learning approach has the capability to remove swell noise without harming the primary signals. The proposed deep learning-based approach can be considered as an alternative approach that combines and takes advantage of both the conventional and data-driven method to better serve swell-noise attenuation. The comparable results also indicate that the deep learning method has strong potential to solve other coherent noise-attenuation tasks for seismic data.

People attend to the same event or object by using a global or local processing style across different environments. Different physical environmental conditions, such as orderliness and disorderliness, activate different psychological states and produce different kinds of outcomes. However, previous work has rarely examined whether individuals exposed to different orderly or disorderly environments attend to the “global” or the “local” differently. Thus, in the current study, we conducted three behavioral experiments to directly examine the impact of disorder versus order cues on people's types of perceptual and conceptual processing (global vs. local). We asked participants to perform a typical Kimchi–Palmer figures task or a categorization task: with pre‐primed disorderly or orderly physical environmental pictures (Experiment 1), with basic visual pictures (Experiment 2), and imagining a real environment (Experiment 3). The results revealed that in any of the above operations, orderly experience led to global perceptual processing, whereas disorderly experience led to local perceptual processing. This difference in processing style was not influenced by the participants’ daily habits or their preference for the need for structure. However, this difference in perceptual processing style did not spill over to the conceptual processing style. These findings provide direct evidence of the effects of disorderliness versus orderliness on global versus local perceptual and conceptual processing and imply that environmental orderliness or disorderliness may functionally affect cognitive processing (i.e., how we see and think about events and objects). Thus, the findings creatively bridge several lines of research and shed light on a basic cognitive mechanism responsible for perceptions of order/disorder.

No abstract

No abstract