面向地震到测井表示学习的物理跨模态掩码自编码
Physical Cross-Modal Masked Autoencoding for Seismic-to-Well Representation Learning
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中文总结 AI 辅助
提出物理跨模态掩码自编码器（CM-MAE），联合建模地震体和测井数据，利用旋转位置嵌入与稀疏混合专家层，在极端模态不对称下实现有效表示学习，显著提升测井重建性能。
中文摘要 AI 辅助
从科学测量中学习通常需要对齐具有不同空间支撑和分辨率的不同模态。地下表征是一个极端的稠密-稀疏问题，其中三维地震数据提供体积但间接的测量，而测井数据在稀疏的井位处提供高分辨率的一维测量。我们提出了一种基于物理的跨模态掩码自编码器（CM-MAE），用于地震到测井的表示学习。该模型联合将地震体和测井深度片段进行分词，使用四轴旋转位置嵌入将两种模态嵌入到连续的物理坐标中，并通过跨模态解码器重建掩码目标。稀疏混合专家层提供模态特定的能力，而密集注意力允许模态间的信息交换。预训练使用了覆盖美国海上和陆上盆地约178,000平方公里的23个地震体，以及约92,000口井。匹配掩码消融实验显示出强烈的不对称信息流。地震上下文将保留测井重建性能提高了9.73%，而测井上下文将地震重建性能提高了0.65%。我们将仅地震的伪测井预测与独立的解释员绘制的盐体掩码进行比较，以确定它们是否包含地质信号。在美国海上调查中，由压缩慢度导出的盐分数达到了0.910的AUROC。在陆上盆地中，预测的压缩慢度在未见过的调查中保留了地层尺度的结构和部分相对组织，尽管存在校准漂移。CM-MAE在极端模态不对称下学习了有用的地震到测井表示，尽管绝对伪测井校准仍然依赖于调查区域。
英文摘要
Learning from scientific measurements often requires aligning modalities with different spatial support and resolution. Subsurface characterization is an extreme dense-sparse problem in which 3D seismic provides volumetric but indirect measurements and well logs provide high-resolution 1D measurements at sparse borehole locations. We introduce a physically grounded cross-modal masked autoencoder (CM-MAE) for seismic-to-well representation learning. The model jointly tokenizes seismic volumes and well-log depth patches, embeds both modalities in continuous physical coordinates using four-axis rotary position embeddings, and reconstructs masked targets with a cross-modal decoder. Sparse Mixture-of-Experts layers provide modality-specific capacity while dense attention allows information exchange between modalities. Pretraining uses 23 seismic volumes covering approximately 178,000 square km across U.S. offshore and onshore basins, together with approximately 92,000 wells. Matched-mask ablations show strongly asymmetric information flow. Seismic context improves held-out well-log reconstruction by 9.73%, while well-log context improves seismic reconstruction by 0.65%. We evaluate seismic-only pseudo-log predictions against independent interpreter-drawn salt-geobody masks to determine whether they contain geologic signal. Across offshore U.S. surveys, compressional-slowness-derived salt scores reach an AUROC of 0.910. In onshore basins, predicted compressional slowness preserves formation-scale structure and partial relative organization in an unseen survey despite calibration drift. CM-MAE learns useful seismic-to-well representations under extreme modality asymmetry, although absolute pseudo-log calibration remains survey-dependent.
