Figure 2 shows the example for area partition method. A 2D post-stack seismic image from a historic Gulf of Mexico dataset (Claerbout, 2006) is shown in Figure 2a. As shown by the fault mask (Figure 2b), the image contains 6 faults. We extract the left and right traces of the 6 faults, and use local similarity scan to estimate their fault slips. Then based on the 6 faults, we divide the image into 7 small parts and pad all the small parts horizontally to make each of them a rectangle. The new combined image is shown in Figure 2c. It has a larger horizontal dimension and contains 6 vertical boundaries, which are the locations where we would serially correct the results of predictive painting in the small padded areas. Figure 2d shows the dip estimated from the new image, and the dip around the boundaries has been set to be zero. With this dip, we perform predictive painting and simultaneously correct the painting result at each boundary. The predictive painting result only corresponding to the original image is recorded, which is shown in Figure 2e, where we can observe abrupt value changes alongside the faults. The picked horizons as indicated by the yellow lines in Figure 2f follow the true horizons accurately.