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Self-Supervised Temporal Pattern Mining for coastal climate resilience planning in carbon-negative infrastructure

Self-Supervised Temporal Pattern Mining for coastal climate resilience planning in carbon-negative infrastructure Introduction: A Storm, a Dataset, and an Unexpected Pattern Two winters ago, I spent three weeks embedded with a small coastal engineering team evaluating a proposed carbon-negative seawall — a structure built from mineralized CO₂ aggregates designed to sequester carbon while buffering storm surge. The project had terabytes of sensor data: tide gauges, wave buoys, satellite altimetry, salinity probes, and even acoustic sediment transport monitors. And yet, when the team sat down to plan for the next 30 years of climate scenarios, they were still relying on hand-picked historical storm events and expert intuition. I remember sitting in that room, staring at a 40-year time series of surge residuals, and asking a deceptively simple question: what if we let the data tell us which temporal patterns actually matter, instead of assuming we already know? That question launched a months-long exploration that pulled me deep into self-supervised learning, temporal pattern mining, and the surprisingly rich intersection of climate resilience and carbon-negative infrastructure. This article is the story of what I learned — the architectures, the experiments, the failures, and the patterns that emerged when I stopped supervising the model and let it discover structure on its own.

用于碳负基础设施海岸气候韧性规划的自监督时间模式挖掘。引言：一场风暴、一个数据集和一个意想不到的模式。两年前的冬天，我花了三周时间与一个小型的海岸工程团队合作，评估一座拟建的碳负海堤——这是一种由矿化二氧化碳骨料建造的结构，旨在封存碳的同时缓冲风暴潮。该项目拥有数太字节的传感器数据：验潮仪、波浪浮标、卫星测高仪、盐度探头，甚至还有声学泥沙输移监测仪。然而，当团队坐下来规划未来30年的气候情景时，他们仍然依赖于人工挑选的历史风暴事件和专家直觉。我记得坐在那个房间里，盯着40年的风暴潮残差时间序列，问了一个看似简单的问题：如果我们让数据告诉我们哪些时间模式真正重要，而不是假设我们已经知道了，会怎样？这个问题开启了长达数月的探索，让我深入研究了自监督学习、时间模式挖掘，以及气候韧性与碳负基础设施之间令人惊讶的丰富交集。本文讲述了我所学到的东西——当我停止监督模型并让它自行发现结构时，所出现的架构、实验、失败和模式。

Why Carbon-Negative Infrastructure Changes the Problem Before diving into the ML, it’s worth grounding the domain. Carbon-negative infrastructure (CNI) refers to built systems that actively remove CO₂ from the atmosphere — think enhanced weathering seawalls, biochar-amended breakwaters, or algae-integrated coastal defenses. These systems have a dual mandate: Resilience: Protect coastlines against sea-level rise, storm surge, and erosion. Sequestration: Permanently store carbon in mineral or biological form. The tension is real. A seawall optimized purely for wave attenuation might use carbon-intensive concrete. A sequestration-first design might be structurally fragile under extreme events. And critically, the temporal dynamics matter enormously — carbonation reactions happen on decadal timescales, while storm damage happens in hours. While exploring this dual-objective problem, I realized that traditional supervised approaches were fundamentally mismatched. We don’t have labeled data for “the storm that will destroy this structure in 2055.” We have unlabeled multivariate time series, and we need to mine patterns that generalize across climate scenarios.

为什么碳负基础设施改变了问题？在深入研究机器学习之前，有必要先了解该领域。碳负基础设施（CNI）是指能够主动从大气中去除二氧化碳的建筑系统——例如增强风化海堤、生物炭改良防波堤或藻类集成海岸防御系统。这些系统具有双重使命：韧性：保护海岸线免受海平面上升、风暴潮和侵蚀的影响；封存：以矿物或生物形式永久储存碳。这种张力是真实存在的。纯粹为减弱波浪而优化的海堤可能会使用高碳混凝土。而优先考虑封存的设计在极端事件下可能结构脆弱。至关重要的是，时间动态非常重要——碳化反应发生在十年尺度上，而风暴破坏发生在几小时内。在探索这一双重目标问题时，我意识到传统的监督学习方法在根本上是不匹配的。我们没有关于“2055年摧毁该结构的风暴”的标记数据。我们拥有的是未标记的多变量时间序列，我们需要挖掘能够在不同气候情景下泛化的模式。

The Case for Self-Supervised Temporal Learning In my research of temporal representation learning, I kept returning to a core insight: the structure of a time series is itself a supervisory signal. If a model can predict the next timestep, reconstruct a masked segment, or distinguish a real subsequence from a shuffled one, it has learned something meaningful about the underlying dynamics — without any human labels. This is the essence of self-supervised learning (SSL) applied to time series. Three families of pretext tasks dominate: Masked reconstruction (à la BERT for time series): hide a segment, predict it from context. Contrastive learning (à la SimCLR): pull augmented views of the same window together, push different windows apart. Predictive coding: forecast future values from past context. For coastal climate data, I found that contrastive learning with domain-aware augmentations worked best, because it forces the encoder to become invariant to nuisance factors (sensor drift, calibration noise) while preserving physically meaningful structure.

自监督时间学习的理由。在我的时间表征学习研究中，我不断回到一个核心洞察：时间序列的结构本身就是一个监督信号。如果一个模型能够预测下一个时间步、重建一个被掩码的片段，或者将真实的子序列与打乱的子序列区分开来，那么它就已经在没有任何人工标签的情况下，学到了关于底层动态的有意义的东西。这就是应用于时间序列的自监督学习（SSL）的本质。三种主要的预训练任务占据主导地位：掩码重建（类似于时间序列的BERT）：隐藏一个片段，根据上下文预测它；对比学习（类似于SimCLR）：将同一窗口的增强视图拉近，将不同窗口推远；预测编码：根据过去的上下文预测未来的值。对于海岸气候数据，我发现结合领域感知增强的对比学习效果最好，因为它迫使编码器对干扰因素（传感器漂移、校准噪声）保持不变，同时保留具有物理意义的结构。

Building the Encoder: A Minimal Implementation Let me share a stripped-down version of the temporal encoder I built. It’s a 1D convolutional backbone with a transformer head — nothing exotic, but the augmentations are where the domain knowledge lives. [Code block omitted for brevity] The in_channels=6 here reflects my actual feature set: water level, wave height, wave period, wind speed, atmospheric pressure, and — importantly — a carbonation progress proxy derived from embedded pH sensors in the CNI material.

构建编码器：最小化实现。让我分享一下我构建的时间编码器的精简版本。它是一个带有Transformer头的一维卷积骨干网络——没有什么特别的，但领域知识体现在增强方法中。[代码块省略] 这里的in_channels=6反映了我的实际特征集：水位、波高、波周期、风速、大气压，以及——重要的是——从CNI材料中嵌入的pH传感器导出的碳化进度代理指标。

Domain-Aware Augmentations: Where the Learning Happens Here’s the finding that genuinely surprised me during experimentation. Standard time-series augmentations (jittering, scaling, cropping) produced embeddings that were too invariant — the model couldn’t distinguish a calm tide from a storm surge because both got normalized away. The fix was to design augmentations that respect physical constraints: [Code block omitted for brevity] That last line — enforcing monotonicity on the carbonation channel — was a small change with an outs

领域感知增强：学习发生的地方。这是我在实验过程中真正让我感到惊讶的发现。标准的时间序列增强（抖动、缩放、裁剪）产生的嵌入过于不变——模型无法区分平静的潮汐和风暴潮，因为两者都被归一化了。解决方法是设计符合物理约束的增强方法：[代码块省略] 最后一行——在碳化通道上强制执行单调性——是一个微小的改变，却产生了巨大的影响。