Adavale Basin 2D Reprocessed Seismic Data Package 2023

The Australian Government’s Data Driven Discoveries program has reprocessed 57 selected multi-era legacy seismic lines, covering approximately 2,356 km across the Adavale Basin, south-central Queensland. Reprocessing of legacy seismic data from the Adavale Basin aims to create a modern, consistent and integrated seismic dataset that provides new insights into the geological structure of the basin and deepens our understanding of the basin’s minerals, energy, underground storage and groundwater potential.The reprocessed lines were chosen to tie into 5 wells that were previously sampled for chemostratigraphic analysis through the Data Driven Discoveries program (Riley et al., 2023, eCat 147773), including Allendale 1, Boree 1, Gilmore 1, Quilberry 1 and Stafford 1. The Adavale Basin 2D Reprocessed Seismic Data Package also complements new deep crustal seismic data being acquired in the Adavale Basin by the program.The reprocessing workflow prioritised enhancing the image quality of the selected legacy seismic lines, reducing noise, and fine-tuning frequency content for specific target depths. Techniques employed included creating a 3D static model, applying noise attenuation methods, surface-consistent deconvolution, and constructing an accurate velocity model to optimise pre-stack time and depth migration. Both stacks and gather data are provided in SEG-Y format, along with navigation data, velocity, and statics. Processed gather data for this survey are available on request from [e-mail] - Quote eCat# 149018

澳大利亚政府数据驱动发现（Data Driven Discoveries）项目已完成57条精选多时代遗留地震测线的重新处理工作，这些测线覆盖昆士兰州中南部阿达瓦莱盆地（Adavale Basin）约2356千米。本次对阿达瓦莱盆地遗留地震数据的重处理，旨在构建一套现代化、统一规范且集成融合的地震数据集，为揭示该盆地地质构造提供全新视角，并深化对该盆地矿产、能源、地下储层及地下水潜力的认知。本次重处理的测线均选定用于衔接5口此前通过该项目开展化学地层分析采样的钻井，包括Allendale 1、Boree 1、Gilmore 1、Quilberry 1及Stafford 1（Riley等人，2023年，eCat编号147773）。阿达瓦莱盆地二维重处理地震数据包还可与该项目正在阿达瓦莱盆地采集的全新深部地壳地震数据形成互补。本次重处理流程优先针对所选遗留地震测线提升成像质量、压制噪声，并针对特定目标深度优化频率特征。所采用的技术手段包括构建三维静态模型、应用噪声衰减方法、开展地表一致性反褶积，以及构建精准速度模型以优化叠前时间偏移与深度偏移。数据集以SEG-Y格式提供了叠加数据与道集数据，同时配套导航数据、速度模型及静校正量。本次调查的处理道集数据可通过邮件向[e-mail]申请获取，申请时请引用eCat编号149018。