时域激发极化的数据驱动建模
Data-driven modeling of time-domain induced polarization
AI总结：
提出基于VAE的无监督数据驱动时域IP建模方法，证明单一标量参数足以编码大规模IP数据，并开源预训练模型。
摘要：
我们提出了一种利用变分自编码器（VAE）对时域激发极化（IP）现象进行数据驱动建模的新方法。VAE是一种贝叶斯神经网络，旨在学习潜在统计分布，从而将庞大数据集编码为低维表示。我们在加拿大、美国和哈萨克斯坦的多个地区收集了1,600,319条IP衰减曲线，并将它们汇编用于训练深度VAE。所提出的深度学习方法严格无监督且由数据驱动：它不需要对IP数据进行人工处理或真实标签标注。此外，我们的VAE方法避免了使用经验性的Cole-Cole和Debye分解模型、简单幂律模型或其他复杂机制模型进行IP参数化时的陷阱。我们展示了VAE在建模和处理IP数据方面的四种应用：（1）具有代表性的合成数据生成；（2）无监督贝叶斯去噪与数据不确定性估计；（3）信噪比的定量评估；（4）自动异常值检测。我们还解释了IP数据集的潜在表示，并揭示了其第一维与IP衰减的平均极化率之间存在强相关性。最后，我们通过改变VAE潜在空间维度进行了实验，证明单个实值标量参数包含了足够的信息来编码我们庞大的IP数据集。这一新发现表明，使用由多个自由参数控制的数学模型对时域IP数据进行建模存在歧义，而仅对平均极化率进行建模是合理的。我们模型的预训练实现——可直接应用于任何地理位置的新IP数据——已作为开源Python代码提供给应用地球物理界。
英文摘要：
We present a novel approach for data-driven modeling of the time-domain induced polarization (IP) phenomenon using variational autoencoders (VAE). VAEs are Bayesian neural networks that aim to learn a latent statistical distribution to encode extensive data sets as lower dimension representations. We collected 1 600 319 IP decay curves in various regions of Canada, the United States and Kazakhstan, and compiled them to train a deep VAE. The proposed deep learning approach is strictly unsupervised and data-driven: it does not require manual processing or ground truth labeling of IP data. Moreover, our VAE approach avoids the pitfalls of IP parametrization with the empirical Cole-Cole and Debye decomposition models, simple power-law models, or other sophisticated mechanistic models. We demonstrate four applications of VAEs to model and process IP data: (1) representative synthetic data generation, (2) unsupervised Bayesian denoising and data uncertainty estimation, (3) quantitative evaluation of the signal-to-noise ratio, and (4) automated outlier detection. We also interpret the IP compilation's latent representation and reveal a strong correlation between its first dimension and the average chargeability of IP decays. Finally, we experiment with varying VAE latent space dimensions and demonstrate that a single real-valued scalar parameter contains sufficient information to encode our extensive IP data compilation. This new finding suggests that modeling time-domain IP data using mathematical models governed by more than one free parameter is ambiguous, whereas modeling only the average chargeability is justified. A pre-trained implementation of our model -- readily applicable to new IP data from any geolocation -- is available as open-source Python code for the applied geophysics community.
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