PyAWD可生成高分辨率地震波模拟数据，解决真实数据稀疏问题。
PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation
- 基于物理建模生成二维/三维非均匀介质中的声波传播数据
- 支持精细控制波速、力源、时空分辨率等参数，生成大规模数据集
- 适合缺乏实测数据的地震定位与机器学习研究
地震数据因布设地震仪成本高、部署难，常呈现稀疏且分布不均的问题，限制了机器学习在地震分析中的应用。尽管已有模拟方法，但尚无工具能生成包含地面运动模拟测量的大规模数据集。为此，我们提出PyAWD——一个用于生成高分辨率合成数据的Python库，可模拟二维和三维非均匀介质中时空声波传播。通过精细控制波速、外力、空间与时间离散化及介质组成等参数，PyAWD能生成符合机器学习需求的复杂地震波数据集。我们以震源定位任务为例，展示其在构建复杂、精确地震问题中的适用性，尤其适用于真实数据稀疏或缺失时的先进机器学习方法研究。同时，该工具还可用于震源定位中的数据预算问题研究。
原文摘要 · Abstract (English)
Seismic data is often sparse and unevenly distributed due to the high costs and logistical challenges associated with deploying physical seismometers, limiting the application of Machine Learning (ML) in earthquake analysis. While simulation methods exist, no tool allows the generation of large datasets containing simulated measurements of the ground motion. To address this gap, we introduce PyAWD, a Python library designed to generate high-resolution synthetic datasets simulating spatio-temporal acoustic wave propagation in both two-dimensional and three-dimensional heterogeneous media. By allowing fine control over parameters such as the wave speed, external forces, spatial and temporal discretization, and media composition, PyAWD enables the creation of ML-scale datasets that capture the complexity of seismic wave behavior. We illustrate the library's potential with an epicenter retrieval task, showcasing its suitability for designing complex, accurate seismic problems that require advanced ML approaches in the absence or lack of dense real-world data. We also show the usefulness of our tool to tackle the problem of data budgeting in the framework of epicenter retrieval.
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