## 2 Methodology and Data
### High Precision and Wide Range XCH4 Fusion Dataset Construction
to a 7 km resolution. Subsequently, using the Lasso regression algorithm introduced in Section 2.7, we divided each 7 km resolution high-precision XCH\({}_{4}\) grid into 49 independent subgrid units, generating a denser grid allocation framework. We then redistributed the XCH\({}_{4}\) within each 7 km grid to its corresponding subgrids, resulting in a high-precision, high-resolution XCH\({}_{4}\) gridded dataset for the oilfield areas. The final 1 km resolution XCH\({}_{4}\) grid fused both top-down and bottom-up methane observation and accounting approaches, integrating the bottom-up GFEI inventory and the top-down GF-5 satellite observations within the high-precision XCH\({}_{4}\) grid generated by the LRF model. Figure 8 illustrates a case study of our fusion algorithm applied to an oilfield region in Dongying. Figure 8a displays the high-precision, low-resolution XCH\({}_{4}\) dataset for a portion of the study area derived from the LRF model, while Figure 8b shows the 1 km resolution XCH\({}_{4}\) dataset for the same area. From Figure 8a,b, it is evident that, although some high-concentration methane distribution areas can be identified in the 7 km resolution XCH\({}_{4}\) image, more precise attribution is challenging at this resolution. This is particularly true for concentration enhancements caused by pipeline leaks or strong point source emissions, which cannot be finely attributed at the 7 km resolution. Figure 8c,d provide magnified views of the XCH\({}_{4}\) grid after fusing the high-resolution GF-5 data in two specific areas of the Shengli Oilfield, and their corresponding satellite maps are shown in Figure 8e,f, respectively. At the 1 km resolution, it becomes much easier to spatially attribute methane column concentration enhancements. For example, in Figure 8c, at longitude 118.41 and latitude 37.88, the 1 km resolution grid clearly resolves the strong enhancement region seen in the 7 km resolution grid of Figure 8a, along with five weaker enhancements that correspond to the background enhancements within the black box in Figure 8a. In Figure 8d, a significant methane plume is identified at longitude 118.41 and latitude 37.83. According to imagery from Google Earth, this coordinate corresponds to a major methane emission source in the oilfield, where methane column concentration enhancement reached 312.3 ppb. In contrast, the corresponding 7 km grid in Figure 8a shows an enhancement of 181.5 ppb (the average concentration of all grids within the rectangle was taken as the background methane column concentration for this region). Our calculations indicate that 93% of the enhancement in this 7 km grid is due to the plume generated by this major emission source, consistent with accounting metrics provided by the oilfield company. However, due to the confidentiality of the data, we cannot provide detailed metrics in this paper. In our previous work, leveraging collaboration with the company, we conducted field verification in the region and observed a broad area of slight

Figure 8: High-resolution and high-precision XCH\({}_{4}\) dataset for oilfield regions. (**a**) shows the low-resolution data of a region after the fusion of the LRF model, (**b**) shows the high-resolution data of the same region, (**c**,**d**) are zoomed presentations of the two high-emission plant areas in (**b**), and (**e**,**f**) are satellite maps corresponding to the areas presented in (**c**) and (**d**), respectively. enhancement northeast of the emission source, corresponding to the pipeline transport area and oil tank storage area of the industrial complex. In order to illustrate the correctness of the results of this study, another case study of an oil field area in Dongying was chosen for presentation, as shown in Figure 9. Again, Figure 9a shows the high-precision, low-resolution XCH\({}_{4}\) dataset generated by the LRF model for part of the study area, while Figure 9b shows the 1-km-resolution XCH\({}_{4}\) dataset for the same area.