Preliminary Results from Phase One of the WA Array Passive Seismic Project, Western Australia

Abstract

The first phase of the WA-Array project, initiated by the Government of Western Australia in late 2022. This project marks a ground-breaking seismic imaging initiative with the deployment of 165 seismometers strategically across the state at 40-km intervals. Over a 10-years period, this project will span the entire state, covering over 2.5 million square kilometres, evolving into one of the world’s largest passive-source seismic programs. With a primary focus on mapping the crustal and lithospheric mantle structure, the project also significantly contributes to mineral exploration, energy studies, seismic risk assessments and green energy land use planning. As a temporary seismic network, the project encountered distinctive challenges in site selection and deployment of diverse instrumentation, requiring strategic compromises between time, budget, feasibility, and project requirements in terms of network geometry and data quality. This paper explores the challenges encountered during Phase One (2022-2023) of the WA-Array project, particularly emphasizing station deployment and data quality control and management. Rigorous validation processes were employed to ensure the reliability and consistency of seismic recordings across the entire array. Due to logistical issues, the deployment consisted of a wide range of instruments which required that the data was comparable across all stations. In order to present a coherent data set which can be used for many studies, all instrument pairs used in the network underwent comprehensive testing on metadata and earthquake waveforms recording quality check through collocation on a slab. In line with industry standards, we also conducted noise level analysis across diverse frequency ranges, estimating Power Spectral Density (PSD) at each station. Comparisons with standard New Low and High Noise Models (NLNM and NLHM) not only validated data robustness but also facilitated the detection of operational issues and monitoring temporal variations in noise levels. This paper presents preliminary data quality results, including insights gained from a comprehensive calibration test, the current status of initial modelling, updates to the model, and adaptive strategies employed to address these challenges.