## 4 Experimental Results  
### Dataset Description  
In this paper, three distinct types of TIs are selected to verify the reconstruction performance of CSNGAN-ASPP for various reservoir geological bodies: the nonstationary delta facies model, the lithofacies-fold structure model, and the heterogeneous aquifer model. These three original TIs are depicted in Figure 6.  
Among them, Figure 6a represents the nonstationary delta facies model with a size of 400 \(\times\) 200 (unit: pixel), which mainly includes fluvial (the blue area) and mudstone (the gray area) two facies types. The delta is one of the important sedimentary facies types in reservoir studies and is closely related to oil and gas reservoirs. Deltaic depositional systems are primarily found in river-lake, river-bay, and river-delta systems. Firstly, the original TIs are divided into multiple 2D sub-models of 128 \(\times\) 128 (unit: pixel) size using sliding window segmentation. To increase the complexity of the attribute distribution patterns, some of the sub-models are randomly selected for mirror symmetry or rotation. After segmentation, a total of 19,584 training samples are obtained, with 18,000 randomly chosen for the training dataset, along with the corresponding conditioning data labels. For each sub-model, 20 mudstone and 20 river facies type location points are randomly selected as conditioning well facies data within the 2D reservoir domain in the experiment. Due to limited funding and other constraints, it is challenging to obtain large amounts of data through traditional exploration methods in reality. This insufficiency in prior data can introduce noise in geological tasks. Similarly, in experiments, using sparsely distributed well data as conditioning points (each represented by a single pixel in the reservoir domain) can also result in inaccuracies due to weak constraints. If the conditional loss term focuses only on the single pixel location, there may be a problem with local facies attribute mutations from its surroundings, commonly known as \"local pixel noise\" [41]. To address this, we adopted a strategy of expanding each conditioning point from 1 \(\times\) 1 pixels to 3 \(\times\) 3 pixel blocks. This approach enhances the constraint effectiveness of conditioning data and promotes smoother transitions of facies attributes in neighboring  
Figure 6: The TIs for experimental validation: (**a**) nonstationary delta facies model; (**b**) lithofacies-fold structural model; (**c**) heterogeneous aquifer model. regions. Therefore, expanding the blocks not only alleviates local noise but also enhances the impact of the conditioning data on the generated results, leading to more consistent global patterns. The remaining 1584 samples are used as the testing dataset to verify the realism of the simulation results and the accuracy of facies attribute reconstruction at the conditioning point locations. The 3D TIs include the fold structure model shown in Figure 6b and the heterogeneous aquifer model depicted in Figure 6c, with dimensions of 180 \(\times\) 150 \(\times\) 120 and 340 \(\times\) 200 \(\times\) 80 (unit: voxel), respectively. Folds, formed through the plastic deformation of rock layers, are fundamental geological formations that are widely distributed in the Earth's crust, providing insight into the tectonics of the study area and the distribution of resources. Aquifers, the geological layers where groundwater is stored and flows, are crucial components of groundwater resources and play a significant role in water resource exploration and utilization. For the 3D model, a sliding voxel segmentation method is used to segment the original TI into 3D sub-models of 64 \(\times\) 64 \(\times\) 64 (unit: voxel). This segmentation process yields 5184 3D sub-models from the fold structure model, with 4700 randomly selected as the training dataset and 484 reserved for testing. Similarly, the heterogeneous aquifer model was split into 6272 sub-models, from which 5600 were randomly chosen for training and 672 for testing.