Work

Enhancing seismic signal quality assessment with CNNs: A focus on effective data augmentation techniques

Year: 2025

Type: article

Abstract: The rapid expansion of seismic data from global networks demands reliable, scalable, and automated methods for evaluating signal quality, an essential step in ensuring accurate seismic analyses. In this work, we present a convolutional neural network (CNN) framework designed for fully automatic seismic signal quality assessment, utilizing data from the Engineering Strong-Motion (ESM) Database. We explore three custom CNN architectures–VGG-Variant, ResNet-Variant, and InceptionNet-Variant–and conduct a systematic examination of data augmentation techniques to enhance model generalization. Using the VGG-Variant as a baseline, we identify time stretching, time shifting, and horizontal flipping as particularly effective augmentations, while rotation, Gaussian noise addition, and frequency masking degrade performance. Applying the most beneficial techniques to all three architectures, the ResNet-Variant achieves state-of-the-art results, with a 99.61 % accuracy and an AUC of 0.9999. These findings highlight the critical influence of domain-tailored data augmentation on CNN-based seismic signal classification. By enabling automated, high-fidelity assessments, our approach reduces the burden of manual inspection and augments decision-making capabilities in seismology and related Earth science disciplines.

Source: Geodata and AI.

DOI: 10.1016/j.geoai.2025.100042

Authors: Ahmed Shalabi, Federica Di Michele, Giovanni Lanzano, Bruno Rubino, Pierangelo Marcati, Lucia Luzi

Institutions: University of L'Aquila, Istituto Nazionale di Geofisica e Vulcanologia, INGV Sezione di Milano, Gran Sasso Science Institute

Language: English

Topic: Seismology and Earthquake Studies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,000