The project focuses on research into key issues in multi-factor, high-magnetic-field intelligent inversion.It proposes a composite intelligent inversion theoretical framework consisting of a “data sample network + physical model network,” which enables joint inversion of multi-source, multi-scale, and multi-parameter data through mechanisms such as dynamic information fusion. Ultimately, the project independently developed a gravity and magnetic inversion imaging software platform capable of large-scale industry-wide deployment, facilitating the sharing of research results andindustry-wide adoption.项目围绕多要素重磁智能反演关键问题开展研究。提出“数据样本网络+物理模型网络”的复合智能反演理论框架，通过动态信息融合机制等实现多源数据联合反演实现多源、多尺度、多参数联合反演；最终自主研发具有行业规模化推广应用能力的重磁反演成像软件平台，实现成果共享与行业推广。
Network Architecture of a Composite Intelligent Inverse Physical U-Net Model复合智能反演物理U-Net模型网络架构
A structured overview of AI for geophysics-based subsurface imaging understanding, and why subsurface data make machine learning harder than in natural or medical imaging.对面向地球物理地下成像理解的 AI 进行结构化梳理，并阐释为何地下数据使机器学习比自然图像或医学影像更具挑战性。
Decade-spanning survey of 652 publications across four subsurface interpretation directions, with citation-network analysis that reveals cross-domain knowledge flow and methodological evolution from seismic attributes to domain foundation models.建立国内外首套多要素重磁智能反演基础样本库
Systematic task-wise review of deep-learning methods across structure, geobody, facies, and property tasks, tracing each evolutionary trajectory and identifying the remaining bottlenecks for cross-survey generalization.提出"数据样本网络+物理模型网络"复合智能反演理论
CIG-Bench open-source library: pretrained baselines for fault, RGT,
channel, karst, and property tasks with cross-survey transferability, plus one-click
pip install inference APIs that load weights automatically from ModelScope, with one-line
dataset downloads.实现多源重磁数据联合反演
Long-term, community-maintained ecosystem: standardized splits and metrics, continuous model updates, and a roadmap for integrating community-contributed baselines and new tasks as the field evolves.实现样本库、模型库和软件平台开源共享
Subsurface interpretation is decomposed into four conceptually overlapping but methodologically distinct categories.地下解释被分解为概念上相互重叠、但方法学上各有侧重的四类任务。
Faults, horizons, unconformities, and relative geologic time (RGT) for constructing structural frameworks and stratigraphic models.断层、层位、不整合面与相对地质年代 (RGT)，用于构建构造格架与地层模型。
3D segmentation of salt bodies, channels, karst cavities, and igneous intrusions with relatively independent geometries.对盐体、河道、岩溶洞穴与岩浆侵入体等具有相对独立几何形态的目标进行三维分割。
Classification of seismic units by amplitude, frequency, continuity, and stratification — reflecting depositional environments.依据振幅、频率、连续性与层理对地震单元进行分类——反映沉积环境。
Inversion of impedance, velocity, porosity, density, and Vp from seismic data calibrated with sparse well logs.利用稀疏测井标定，从地震数据反演阻抗、速度、孔隙度、密度与纵波速度 (Vp)。
Click tabs or use arrows to explore the decade-long evolution of each task category.点击标签页或使用箭头，探索各任务类别跨越十年的演进历程。
One-line install. One-line dataset download. Five predictors. Weights downloaded automatically on first call. Each task is paired with an example result on real field data.一行命令安装。一行代码下载数据集。五个预测器。首次调用时自动下载权重。每个任务都配有在真实野外数据上的示例结果。
pip install cig_bench
from cig_bench.dataset import cig_structureData, cig_geobodyData # One line each — downloads the subset into a directory you choose # and returns its local path. Two subsets: Structure and Geobody. structure_dir = cig_structureData("./CIG-Bench-Dataset") # -> ./CIG-Bench-Dataset/Structure geobody_dir = cig_geobodyData("./CIG-Bench-Dataset") # -> ./CIG-Bench-Dataset/Geobody
from cig_bench.predictor.fault import FaultPredictor fault_predictor = FaultPredictor(device="cuda") prob, used = fault_predictor.predict( seis, # (tline,iline,xline) rank=4, chunk_size=64, # memory-bounded chunked inference threshold=0.5, scale_t=0.5, scale_h=0.85, scale_w=0.85, resize_back=True, # return result at the original (T, H, W) ) fault_predictor.visualize(used, prob)
Example result · Fault. Fault segmentation on four
field surveys (a–d): the input seismic (columns *-1) and the predicted faults rendered
in red over the seismic (columns *-2), shown on crossline-style cubes (a, b) and
inline sections (c, d). The model resolves subtle polygonal faults, extremely dense intersecting
networks, and deep-rooted dipping planes with associated secondary faults.
在四个野外工区 (a–d) 上的断层分割：输入地震数据（*-1 列）与以红色叠加在地震上的预测断层（*-2 列），分别以联络测线式数据体
(a, b) 与主测线剖面 (c, d) 展示。模型能够刻画细微的多边形断层、极其密集的交叉断层网络，以及深部根植的倾斜断面及其伴生的次级断层。
from cig_bench.predictor.rgt import RGTPredictor rgt_predictor = RGTPredictor(device="cuda") rgt_vol, used = rgt_predictor.predict(seis) # (tline,iline,xline) horizons = rgt_predictor.extract_horizons(rgt_vol, n_horizons=100) rgt_predictor.visualize(used, rgt_vol, horizons) # visualize() also auto-traces horizons when none are passed: # rgt_predictor.visualize(used, rgt_vol)
Example result · RGT. Relative-geologic-time
estimation on four field surveys (a–d): input seismic (*-1), the regressed RGT volume
(*-2), and horizons extracted from the RGT volume and overlaid on the seismic
(*-3). Predictions stay smooth and stratigraphically consistent across slope bodies,
unconformities, densely faulted zones, and multi-package stratigraphy.
在四个野外工区 (a–d) 上的相对地质年代估计：输入地震数据（*-1）、回归得到的 RGT 体（*-2），以及从 RGT
体中提取并叠加到地震上的层位（*-3）。在斜坡体、不整合面、密集断裂带与多套地层组合中，预测结果均保持平滑且地层一致。
from cig_bench.predictor.channel import ChannelPredictor channel_predictor = ChannelPredictor(device="cuda") scores, used = channel_predictor.predict( seis, # (tline,iline,xline) scales=[0.5, 0.75, 1.0, 1.25, 1.5], # custom scale set accumulate="sum", ) mask = channel_predictor.postprocess(scores, threshold=0.75, min_size=50000) channel_predictor.visualize(used, scores, mask) # The karst predictor is used identically — only the checkpoint changes: from cig_bench.predictor.karst import KarstPredictor karst_predictor = KarstPredictor(device="cuda") scores, used = karst_predictor.predict(seis) # (tline,iline,xline) mask = karst_predictor.postprocess(scores, threshold=0.75)
Example result · Channel / Karst. Geobody
segmentation on three field examples (rows a–c): input seismic (*-1), the predicted
probability overlaid on the seismic (*-2), and the extracted 3D geobody surface
(*-3) after thresholding and connected-component cleanup. (a, b) channel systems and
(c) karst-cave systems. 在三个野外示例（a–c
行）上的地质体分割：输入地震数据（*-1）、叠加在地震上的预测概率（*-2），以及经阈值化与连通域清理后提取的三维地质体表面（*-3）。(a,
b) 为河道体系，(c) 为岩溶洞穴体系。
import numpy as np from cig_bench.predictor.property import PropertyPredictor prop_predictor = PropertyPredictor(device="cuda") vp_vol, used, wells = prop_predictor.predict( seis, vp_log, # (tline,iline,xline) infer_shape=(640, 512, 512), ) prop_predictor.visualize(used, vp_vol, wells)
Example result · Property. Property modeling under the promptable conditional paradigm with seismic + sparse well-log inputs. (a) input seismic; (b–f) dense 3D property volumes predicted from the seismic conditioned on sparse well logs (the thin vertical strips are the conditioning wells) — e.g. acoustic impedance, gamma-ray, lithology, sonic, and Vp. Predictions stay coherent with the seismic reflection patterns despite spatially sparse well constraints. 在可提示的条件建模范式下，以地震 + 稀疏测井为输入的属性建模。(a) 输入地震；(b–f) 以稀疏测井为条件、从地震预测得到的稠密三维属性体（细的垂直条带为作为条件的井）——例如声波阻抗、伽马、岩性、声波时差与纵波速度 (Vp)。尽管井约束在空间上稀疏，预测结果仍与地震反射模式保持一致。
Weights download automatically on first use from ModelScope.
Datasets download with one line: from cig_bench.dataset import cig_structureData, cig_geobodyData.权重在首次使用时自动从 ModelScope 下载。
数据集一行代码下载：from cig_bench.dataset import cig_structureData, cig_geobodyData。
If you find this survey or benchmark useful, please cite our work:如果本综述或基准对您有帮助，请引用我们的工作：
@article{dou2026cigbench,
title = {CIG-Bench: A Comprehensive Survey and Benchmark for AI-Driven Subsurface Imaging Understanding},
author = {Yimin Dou and Xinming Wu and Hui Gao and Mingliang Liu and
Tao Zhao and Zhi Zhong and Haibin Di and Min Jun Park and
Robert G. Clapp and Zhixiang Guo and Long Han and Sergey Fomel},
journal = {arXiv preprint arXiv:2606.09094},
year = {2026},
eprint = {2606.09094},
archivePrefix = {arXiv},
doi = {10.48550/arXiv.2606.09094},
url = {https://arxiv.org/abs/2606.09094}
}