【学术报告】AI赋能地球科学的优势与隐忧
Artificial Intelligence (AI) is quickly revolutionizing the geosciences field, spanning everything from seismic data analysis and reservoir evaluation to mineral prospecting and climate simulation. This lecture will explore both the opportunities and challenges presented by AI in geosciences through an objective perspective, covering its advantages, drawbacks, and limitations.
Its strengths lie in the ability to identify nuanced patterns within high-dimensional geophysical datasets, speed up interpretation procedures, boost fault and horizon identification, strengthen AVO categorization, swiftly generate geoscience laboratories (as shown in Figure 1) and educational resources, and facilitate immediate decision support. Machine learning algorithms, especially deep neural network architectures, can surpass conventional deterministic approaches when developed using extensive, meticulously curated training data, delivering enhanced efficiency and consistency. The discussion will demonstrate how physics-constrained machine learning approaches, including autoencoder-based wave equation inversion and principal component wave equation inversion (refer to Figure 2), can excel beyond certain early-stage wave equation inversion techniques.
On the downside, AI techniques can be susceptible to overfitting, inherent data biases, insufficient uncertainty quantification, and inappropriate use of black-box models lacking physical grounding. Numerous AI systems generate compelling predictions yet falter when deployed beyond their training data scope. Substandard validation protocols, data leakage issues, and skewed training datasets may result in excessively confident outputs.
The more troubling aspects address fundamental concerns: difficulties in model interpretation, ethical dilemmas, gaps in reproducibility, and the gradual loss of physical insight when these models replace rather than supplement geological analysis. Without incorporating rock physics principles, wave propagation theory, and subject-matter expertise, AI may devolve into an elaborate curve-fitting mechanism rather than genuine scientific progress. Perhaps the gravest worry for some is that critical functions such as interpretation, project and strategic planning, or code development can be performed more efficiently by large language models. Consequently, numerous professional positions will be displaced by AI, resulting in fewer qualified professionals to supervise end products.
The presentation showcases real-world examples illustrating the strengths, weaknesses, and concerns associated with AI implementation in geosciences, with particular emphasis on physics-driven machine learning, notable advancements in geoscience pedagogy, thorough validation procedures, uncertainty assessment, and seamless integration with conventional geophysical frameworks. When anchored in domain expertise, AI serves not as a substitute for geoscientists but as a formidable enhancer of their capabilities. For certain geoscience educators, AI is heralding a renaissance in preparing the forthcoming generation of geoscientists.
人工智能（AI）正在深刻变革地球科学领域，涵盖地震资料解析、储层评价、矿产勘查以及气候模拟等多个方向。本报告将以客观中立的立场——兼顾长处、短板与隐忧——深入剖析人工智能在地球科学中所蕴含的机遇与风险。
人工智能的长处体现在其能够从高维地球物理数据中辨识细微特征，提升解释流程效率，优化断层与层位的识别效果，强化AVO分类能力，迅速搭建地球科学实验平台（如图1所示）及教学资源，并支持实时决策。机器学习算法，尤其是深度神经网络，在依托大规模、高质量标注数据集训练的前提下，能够突破常规确定性方法的局限，实现更高的运算效率和结果一致性。文中将阐释融合物理约束的机器学习手段——如自编码器波动方程反演及主成分波动方程反演（参见图2）——在性能上超越部分早期波动方程反演技术的具体表现。
人工智能方法的不足之处在于，它易受过度拟合、数据偏差、不确定性评估缺失以及脱离物理约束的黑箱模型误用等问题困扰。诸多人工智能系统尽管能给出颇具说服力的预测结果，但在应用于训练数据分布之外的情境时往往会失效。不严谨的验证流程、数据泄露风险以及有偏的训练数据集，均可能引发过度自信的结论。
更为严峻的问题触及更深层次的议题：模型可解释性难题、伦理层面的担忧、结果可复现性的缺失，以及在模型取代而非辅助地质推理时所引发的物理直觉退化。若未能融入岩石物理理论、波传播原理以及领域专家经验，人工智能或将退化为复杂的曲线拟合手段，而非真正意义上的科学跃迁。尤其值得警惕的是，在解释工作、项目与战略规划、代码编写等关键环节，大语言模型展现出更高的效率。由此，大量专业岗位将面临被人工智能替代的命运，而能够最终把关的专业人员数量也将相应减少。
本次报告通过具体案例呈现了其在地球科学领域应用中的优势、劣势与隐患，重点聚焦于融合物理机制的机器学习、地球科学教学领域的显著提升、严谨的验证流程、不确定性评估方法以及与传统地球物理理论的深度结合。当人工智能扎根于领域知识时，它并非地球科学家的替代品，而是其专业能力的有力倍增器。对部分地球科学教育工作者而言，人工智能正引领着一场培育新一代地球科学人才的复兴浪潮。