# Seismic Attribute-Guided Deep Learning for Rockfall Signal Recognition: A New Paradigm for Microseismic Data Analysis

> This project provides a complete deep learning pipeline that uses a two-stage training strategy of seismic waveform attribute pre-training and rockfall event fine-tuning to intelligently identify rockfall signals from three-component microseismic data.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-12T05:56:04.000Z
- 最近活动: 2026-05-12T06:10:07.145Z
- 热度: 139.8
- 关键词: 微震监测, 深度学习, 落石识别, 地震波形, 迁移学习, 地质灾害, 开源
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-yuxi-chenc-microseismic
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-yuxi-chenc-microseismic
- Markdown 来源: floors_fallback

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## [Introduction] Seismic Attribute-Guided Deep Learning for Rockfall Signal Recognition: A New Paradigm

This project proposes a deep learning pipeline integrating geophysical domain knowledge, using a two-stage strategy of seismic attribute pre-training + rockfall event fine-tuning to intelligently identify rockfall signals from three-component microseismic data. The project open-sources code, datasets, and pre-trained models, solving the problems of low efficiency in traditional manual recognition and parameter sensitivity of classic algorithms, and providing a new paradigm for geological disaster monitoring.

## Research Background: Challenges in Microseismic Monitoring and Limitations of Traditional Methods

Microseismic monitoring is a key method for studying the failure process of rocky slopes, but continuous waveform data is large in volume, and rockfall signals are easily submerged by noise/interference. Traditional methods rely on manual recognition (low efficiency, subjective) or algorithms like STA/LTA (parameter-sensitive, difficult to distinguish rockfall from other events). The core challenge is to accurately and efficiently extract rockfall signals from massive data.

## Technical Solution: Two-Stage Training Strategy and Model Architecture

The core innovation is the seismic attribute-guided two-stage training:
1. Pre-training phase: Calculate seismic attributes such as SSD and raw kurtosis from three-component waveforms, allowing the model to learn the physical characteristics of seismic waves;
2. Fine-tuning phase: Use labeled data of rockfall/non-rockfall for transfer learning to achieve fast convergence. The model integrates GCAM visualization technology to enhance interpretability, and the training process is automatically orchestrated by scripts.

## Data Processing: Three-Component Waveforms and Seismic Attribute Feature Engineering

Process three-component (E/N/Z direction) waveform data, with samples as fixed-length windows, using point-by-point annotation to locate rockfall event periods. Features are built by `feature_set.py` and `physical_features.py`, supporting attributes like SSD and raw kurtosis, and users can extend new calculation methods.

## Practical Application and Open Science: Real Data Validation and Reproducibility Support

The prediction module supports batch processing of SAC format data and outputs CSV results. Real data comes from the Séchilienne landslide in France (including an automatic download script) and the Illgraben landslide in Switzerland. The project open-sources code, releases organized datasets and pre-trained weights on Zenodo, and has a clear workflow to facilitate users in adapting to their own data.

## Significance and Outlook: Promotion Value of Integrating Domain Knowledge with Deep Learning

The project not only provides practical tools but also demonstrates the methodology of integrating domain knowledge into deep learning, which can be extended to tasks such as earthquake detection and volcanic microseism. It provides a complete reference implementation for geological disaster monitoring practitioners and researchers, accelerating the application of deep learning in the geophysics field. Project address: https://github.com/yuxi-chenc/microseismic