Title: A computer vision-based approach to enhance seismic catalogues Show Chinese title

Title: 基于计算机视觉的方法增强地震目录

Abstract: In recent years, AI and deep learning earthquake detectors, combined with an increasing number of dense seismic networks deployed worldwide, have further contributed to the creation of massive seismic catalogs, significantly lowering their magnitude of completeness. However, these automated catalogs are typically released without systematic quality control, and may contain spurious detections, mislocations, or inconsistent magnitudes. In challenging scenarios, such as microseismic monitoring applications, where weak and closely spaced events often overlap in time, pick-based detection and location approaches often fail to reliably associate phases. This leads to missed detections or degraded location accuracy producing seismic catalogues polluted with false or mislocated events. To address this limitation, we present a computer vision-based workflow that integrates waveform-based seismic location methods with deep learning image classification to discriminate real seismic events from noise directly from coherence matrices. These matrices, computed via waveform stacking, exhibit distinct patterns for real events (single, focused maxima) versus noise (blurred, incoherent patterns) hence the problem of cleaning seismic catalogues can be solved as a binary image classification problem. In addition, the robustness of waveform-based location methods allows to obtain an increased resolution in the location of seismic events. Another advantage of this approach is that the training of neural networks can be based entirely on synthetic data. This synthetic-based training removes the need for large labeled datasets, enabling rapid deployment in newly instrumented areas. We validate our workflow using the publicly available COSEISMIQ dataset from the Hengill geothermal area, in Iceland. Show Chinese abstract

Abstract: 近年来，人工智能和深度学习地震探测器，结合全球部署的密集地震网络数量不断增加，进一步促进了大规模地震目录的创建，显著降低了其完整性震级。 然而，这些自动目录通常在没有系统质量控制的情况下发布，可能包含虚假检测、错误定位或不一致的震级。 在具有挑战性的场景中，例如微震监测应用，其中弱信号且紧密间隔的事件经常在时间上重叠，基于拾取的检测和定位方法往往无法可靠地关联相位。 这导致漏检或定位精度下降，产生包含虚假或错误定位事件的地震目录。 为解决这一限制，我们提出了一种基于计算机视觉的工作流程，将基于波形的地震定位方法与深度学习图像分类相结合，直接从相干矩阵中区分真实的地震事件和噪声。 这些矩阵通过波形叠加计算得出，真实事件（单个、聚焦的最大值）与噪声（模糊、不相干的模式）表现出不同的模式，因此清洁地震目录的问题可以作为二元图像分类问题来解决。 此外，基于波形的定位方法的鲁棒性使得地震事件的定位分辨率得到提高。 这种方法的另一个优势是神经网络的训练可以完全基于合成数据。 基于合成数据的训练消除了对大型标记数据集的需求，使新仪器区域的快速部署成为可能。 我们使用冰岛Hengill地热区公开可用的COSEISMIQ数据集验证了我们的工作流程。