Reconstruction of seismic data is an important but challenging task in seismic data processing. Different machine-learning-based algorithms have been developed to solve this ill-posed problem and ...

A group of scientists using a unique method to study Swiss glaciers uncovered an encouraging discovery that could lead to ...

We explore propagation of seismic interpretation by deep learning in stacked 2D sections. We show the application of state-of-the-art image classification algorithms on seismic data. These algorithms ...

Speedata, a Tel Aviv-based startup developing an analytics processing unit (APU) designed to accelerate big data analytic and AI workloads, has raised a $44 million Series B funding round ...

The open source demo for seismic data process AI agent using MCP and LLMs, built in the Hackathon hosted at the EAGE Annual 2025 in Toulouse, France. ... Processing report generation: By using LLMs, ...

Initially, we conducted denoising and amplitude-preserving processing on the prestack seismic data. The processed data were then stacked by angle, resulting in seismic profiles for azimuth angles of ...

Edge analytics, supported by Big Data frameworks, enables processing at the source. Companies can train AI models on-device with federated learning, keeping sensitive data decentralized and secure.

Mechanical Systems and Signal Processing, ... C. and Huang, C. (2020) Non-contact Measurement of Inter-Story Drift in Three-Layer RC Structure under Seismic Vibration Using Digital Image ...

The sparsity-regularized linear inverse problem has been widely used in many fields, such as remote sensing imaging, image processing and analysis, seismic deconvolution, compressed sensing, medical ...