Seismic phase picking is fundamental for microseismic monitoring and subsurface imaging. Manual processing is impractical for real-time applications and large sensor arrays, motivating the use of deep learning-based pickers trained on extensive earthquake catalogs. On a broader scale, these models are generally tuned to perform optimally in high signal-to-noise and long-duration networks and often fail to perform satisfactorily when applied to campaign-based microseismic datasets, which are characterized by low signal-to-noise ratios, sparse geometries, and limited labeled data. In this study, we present a microseismic adaptation of a network-wide earthquake phase picker, Phase Neural Operator (PhaseNO), using transfer learning and parameter-efficient fine-tuning. Starting from a model pre-trained on more than 57,000 three-component earthquake and noise records, we fine-tune it using only 200 labeled and noisy microseismic recordings from hydraulic fracturing settings. We present a parameter-efficient adaptation of PhaseNO that fine-tunes a small fraction of its parameters (only 3.6%) while retaining its global spatiotemporal representations learned from a large dataset of earthquake recordings. We then evaluate our adapted model on three independent microseismic datasets and compare its performance against the original pre-trained PhaseNO, a STA/LTA-based workflow, and two state-of-the-art deep learning models, PhaseNet and EQTransformer. We demonstrate that our adapted model significantly outperforms the original PhaseNO in F1 and accuracy metrics, achieving up to 30% absolute improvements in all test sets and consistently performing better than STA/LTA and state-of-the-art models. With our adaptation being based on a small calibration set, our proposed workflow is a practical and efficient tool to deploy network-wide models in data-limited microseismic applications.

翻译：微地震相拾取是微地震监测和地下成像的基础。针对实时应用和大规模传感器阵列，人工处理方法难以实用，这促使研究者利用基于深度学习的拾取器，并借助大规模地震目录进行训练。从宏观层面看，这些模型通常针对高信噪比、长持续时间网络进行了优化，但在应用于以低信噪比、稀疏几何结构和有限标注数据为特征的流动式微地震数据集时，往往难以达到满意效果。本研究提出了一种基于迁移学习和参数高效微调的网络级地震相拾取器——相位神经算子（PhaseNO）的微地震适应方法。从预先在超过57,000条三分量地震和噪声记录上训练的模型出发，我们仅使用200条来自水力压裂场景的含噪声标注微地震记录进行微调。我们提出了一种参数高效的PhaseNO适应方案，仅微调其3.6%的参数，同时保留模型从大规模地震记录数据集中学习到的全局时空表征。随后，我们在三个独立微地震数据集上评估了适应后模型，并将其性能与原始预训练PhaseNO、基于STA/LTA的处理流程以及两种前沿深度学习模型PhaseNet和EQTransformer进行对比。结果表明，适应后模型在F1分数和准确率指标上显著优于原始PhaseNO，在所有测试集上均实现高达30%的绝对提升，且持续优于STA/LTA及前沿模型。由于适应过程仅依赖小规模校准数据集，本方案为在数据受限的微地震应用中部署网络级模型提供了一种实用高效的工具体系。