Training specific deep learning models for particular tasks is common across
various domains within seismology. However, this approach encounters two
limitations: inadequate labeled data for certain tasks and limited
generalization across regions. To address these challenges, we develop
SeisCLIP, a seismology foundation model trained through contrastive learning
from multi-modal data. It consists of a transformer encoder for extracting
crucial features from time-frequency seismic spectrum and an MLP encoder for
integrating the phase and source information of the same event. These encoders
are jointly pre-trained on a vast dataset and the spectrum encoder is
subsequently fine-tuned on smaller datasets for various downstream tasks.
Notably, SeisCLIP's performance surpasses that of baseline methods in event
classification, localization, and focal mechanism analysis tasks, employing
distinct datasets from different regions. In conclusion, SeisCLIP holds
significant potential as a foundational model in the field of seismology,
paving the way for innovative directions in foundation-model-based seismology
research.

通过对多模态数据进行对比学习，开发了 SeisCLIP 基础模型，以解决在地震学中特定任务的深度学习模型所遇到的标注数据不足和区域通用性有限的问题。该模型在事件分类、定位和震源机制分析等任务中的表现优于基准方法，适用于不同地区的不同数据集，具有更大的潜力成为地震学领域的基础模型，为基于基础模型的地震学研究开辟了创新方向。

SeisCLIP：基于多模态数据预训练的地震学基础模型，用于多目标地震特征提取

SeisCLIP: A seismology foundation model pre-trained by multi-modal data  for multi-purpose seismic feature extraction

With the recent rise of neural operators, scientific machine learning offers
new solutions to quantify uncertainties associated with high-fidelity numerical
simulations. Traditional neural networks, such as Convolutional Neural Networks
(CNN) or Physics-Informed Neural Networks (PINN), are restricted to the
prediction of solutions in a predefined configuration. With neural operators,
one can learn the general solution of Partial Differential Equations, such as
the elastic wave equation, with varying parameters. There have been very few
applications of neural operators in seismology. All of them were limited to
two-dimensional settings, although the importance of three-dimensional (3D)
effects is well known.
In this work, we apply the Fourier Neural Operator (FNO) to predict ground
motion time series from a 3D geological description. We used a high-fidelity
simulation code, SEM3D, to build an extensive database of ground motions
generated by 30,000 different geologies. With this database, we show that the
FNO can produce accurate ground motion even when the underlying geology
exhibits large heterogeneities. Intensity measures at moderate and large
periods are especially well reproduced.
We present the first seismological application of Fourier Neural Operators in
3D. Thanks to the generalizability of our database, we believe that our model
can be used to assess the influence of geological features such as sedimentary
basins on ground motion, which is paramount to evaluating site effects.

本文介绍了利用神经算子和傅里叶神经算子在三维地震学中进行高保真数值模拟并模拟地面运动时系列的应用。模型可以用于评估地质特征对地面运动的影响，特别是介于中等和大范围的区间的地面运动强度。