Enhancing Resolution in Local Passive Seismic Tomography: Adaptive Grid Implementation at Agung Volcano, Indonesia

Abstract

Passive seismic tomography is a widely used method for imaging subsurface structures, relying on natural seismicity sources without the need for artificial sources. Grid‑node parameterisation is a key aspect of subsurface reconstruction in passive seismic tomography. Although many data distribution‑based adaptive grid‑node methodologies and schemes have been developed, their implementation in local‑scale passive seismic tomography is still very limited. In particular, no adaptive grid scheme has yet been applied to Agung Volcano, despite the 2017 eruption involving a notably complex magma migration phase. In response to this, we implemented an adaptive grid scheme in passive seismic tomography to investigate the local structure of Agung Volcano using data from the 2017 eruption and compared the results with those obtained using a fixed grid scheme. In the computing process, we use the Python programming language, considering its well‑established and popular ecosystem. We conducted synthetic tests to examine the validity of our calculation process, and the results indicate that the calculations and inversion scheme are accurate and robust to be implemented for real data. The results of real data inversion show that the adaptive grid scheme produces significantly lower RMS residual values compared to the conventional fixed grid scheme. In addition, there is a clear increase in spatial resolution compared to the initial grid configuration, which facilitates more accurate interpretation of subsurface structures and more precise delineation of magmatic system boundaries.