Dumpsite Seabed Characterization of the San Pedro Basin, CA, From Autonomous Vehicle Observations

A wide‐area seabed survey was conducted using autonomous underwater vehicles (AUVs) in a known dumpsite off the coast of Southern California that has been found to have widespread sediment contamination from the chemical dichlorodiphenyltrichloroethane (DDT). The AUVs were equipped with synthetic Aperture Sonar (SAS), an electronic still camera (ESC), and a laser line scanner to characterize fine‐scale spatial variations of the surficial sediments to gain insight into their mobility. Surveys covered of the known dumpsites to assess sediment texture and distribution patterns. In parallel, a remotely operated vehicle (ROV) performed targeted optical surveys in areas identified by the AUV. The ROV collected 300 h of high‐definition video footage, providing more detailed seafloor observations that were not captured with the ESC. This study employs machine learning and seafloor roughness characterization techniques on SAS data that are informed by the other modalities of seabed sensing. The physics‐informed techniques are applied for seafloor texture classification and provide new insights for interpreting SAS imagery and the seabed in the vicinity of these dumpsites that are of emerging environmental concern.