标题： 一种多条件扩散变换器用于通用地震波生成 显示英文标题

标题： A Mutil-conditional Diffusion Transformer for Versatile Seismic Wave Generation

摘要： 地震波生成为源参数反演、地下分析以及特别是训练人工智能地震学模型创建了带标签的波形数据集。 传统上，地震波生成耗时较长，而使用生成对抗网络的人工智能方法在真实性和稳定性方面常常面临挑战。 本研究提出了地震波生成器，这是一种具有变换器的多条件扩散模型。 扩散模型通过强大的去噪能力生成高质量、多样化和稳定的输出。 与其它模型相比，它们提供了更优越的理论基础，并且对生成过程有更大的控制能力。 变换器在地震波处理中表现出色，能够捕捉长距离依赖关系和时空模式，相比传统模型提高了特征提取和预测准确性。 为了评估生成波形的真实性，我们在生成的数据上训练了下游模型，并将其性能与在真实地震波形上训练的模型进行了比较。 在生成数据上训练的地震相位识别模型在真实地震波形上实现了99%的召回率和精确度。 此外，震级估计模型减少了由于训练数据不均衡带来的预测偏差。 这些发现表明，基于扩散的生成模型可以解决区域标记数据有限的挑战，并有望在未来填补观测数据的空白。 显示英文摘要

摘要： Seismic wave generation creates labeled waveform datasets for source parameter inversion, subsurface analysis, and, notably, training artificial intelligence seismology models. Traditionally, seismic wave generation has been time-consuming, and artificial intelligence methods using Generative Adversarial Networks often struggle with authenticity and stability. This study presents the Seismic Wave Generator, a multi-conditional diffusion model with transformers. Diffusion models generate high-quality, diverse, and stable outputs with robust denoising capabilities. They offer superior theoretical foundations and greater control over the generation process compared to other models. Transformers excel in seismic wave processing by capturing long-range dependencies and spatial-temporal patterns, improving feature extraction and prediction accuracy compared to traditional models. To evaluate the realism of the generated waveforms, we trained downstream models on generated data and compared their performance with models trained on real seismic waveforms. The seismic phase-picking model trained on generative data achieved 99% recall and precision on real seismic waveforms. Furthermore, the magnitude estimation model reduced prediction bias from uneven training data. These findings suggest that diffusion-based generation models can address the challenge of limited regional labeled data and hold promise for bridging gaps in observational data in the future.