To evaluate the relationship between detected pits with UMAP-based sequence clustering and the clusters identified by the K-means algorithm, we performed two statistical tests. First, we analyzed the centroids of detected objects by To ensure clarity and avoid overcrowding, only 0.001% of all time series is displayed within a limited time range from -0.4 to 0.2 s. All curves were grouped into two clusters using the K-means algorithm (see SI for the elbow plot). The curves for each cluster are displayed in orange and blue, with dotted black lines representing the average behavior of the two clusters as determined by K-means. After clustering, the time series were reconstructed into a single image, with pixels colored blue or white to indicate the two clusters identified in (b). Blue pixels correspond to the blue cluster in (b), while white pixels represent the orange cluster for enhanced visual clarity. The orange dots in (c) denote the locations of initially detected pits (at t = 0.4 s for this time series), with some pits labeled and their zoomed-in insets shown around the reconstructed image. The orange cross in these insets marks the center of pit initiation. The size of each pixel (black squares forming the inset images) is 0.48 µm. The region highlighted with a red rectangle marks the first detected pixels of the pits at the moment of their appearance.