The AI-Ready Downhole Microseismic Benchmark Database (AMBER)

AMBER contains labelled waveforms from microseismic events recorded on deep downhole sensor arrays, generated to stimulate research and development in the use of AI tools for downhole microseismic processing tasks. Raw SEGY waveforms can be converted into Seisbench-compatible datasets (waveforms.hdf5 + metadata.csv) using the extraction pipeline extract.py. This repository also provides an event-centric PyTorch dataset with configurable downhole-specific augmentations for training deep-learning models on multi-station, multi-event waveforms. AMBER has been compiled from 10 datasets (or sub-datasets): - Cotton Valley Stage B - Aneth CCS - Clearfield mw4 monitoring well - Clearfield mw6 monitoring well - MSEEL stage 3H - MSEEL stage 5H - FORGE geothermal 2019 - FORGE geothermal 2022 - Preston New Road PNR-1 - Preston New Road PNR-2AMBER 数据集收录了深部井下传感器阵列记录的微地震事件标注波形，旨在推动面向井下微地震处理任务的人工智能工具研发。 可通过提取脚本extract.py将原始SEGY波形转换为兼容Seisbench的数据集（waveforms.hdf5 + metadata.csv）。 本代码仓库还提供了一个以事件为中心的PyTorch数据集，支持可配置的井下专属数据增强方案，可用于基于多台站、多事件波形开展深度学习模型训练。 AMBER 数据集由10个数据集（或子数据集）整合而来： - Cotton Valley Stage B - Aneth CCS - Clearfield mw4 监测井 - Clearfield mw6 监测井 - MSEEL stage 3H - MSEEL stage 5H - FORGE 地热2019 - FORGE 地热2022 - Preston New Road PNR-1 - Preston New Road PNR-2