A new Method for Fault-Scarp Detection Using Linear Discriminant Analysis (LDA) in High-Resolution Bathymetry Data From the Alarcón Rise and Pescadero Basin, Gulf of California.

The mapping of faults and fractures is a problem of high relevance in Earth Sciences. However, their identification in digital elevation models is a time-consuming task given the resulting networks' fractal nature. The effort is especially challenging in submarine environments, given their inaccessibility and difficulty in collecting direct observations. Here, we propose a semi-automated method for detecting faults in high-resolution gridded bathymetry data (~1 m horizontal and ~0.2 m vertical) of the Pescadero Basin in the southern Gulf of California, which were collected by MBARI's D. Allan B autonomous underwater vehicle. This problem is well suited to be explored by machine learning and deep-learning methods. The method learns from a model trained to recognize fault-line scarps based on key morphological attributes in the neighboring Alarcón Rise. We use the product of the mass diffusion coefficient with time, scarp height, and root-mean-square error as training attributes. The method consists of projecting the attributes from a three-dimensional space to a one-dimensional space in which normal probability density functions are generated to classify faults. The LDA implementation results in various cross-sectional profiles along the Pescadero Basin show that the proposed method can detect fault-line scarps of different sizes and degradation stages. Moreover, the method is robust to moderate amounts of noise (i.e., random topography and data collection artifacts) and correctly handles different fault dip angles. Experiments show that both isolated and linkage fault configurations are detected and tracked reliably.