Abstract
<p>In this manuscript we present the new friendly seismic tomography software based on joint inversion of active and passive seismic sources called PARTOS (Passive Active Ray TOmography Software). This code has been developed on the base of two well-known widely used tomographic algorithms (LOTOS and ATOM-3D), providing a robust set of algorithms. The dataset used to set and test the program has been provided by TOMO-ETNA experiment. TOMO-ETNA database is a large, high-quality dataset that includes active and passive seismic sources recorded during a period of 4 months in 2014. We performed a series of synthetic tests in order to estimate the resolution and robustness of the solutions. Real data inversion has been carried out using 3 different subsets: i) active data; ii) passive data; and iii) joint dataset. Active database is composed by a total of 16,950 air-gun shots during 1 month and passive database includes 452 local and regional earthquakes recorded during 4 months. This large dataset provides a high ray density within the study region. The combination of active and passive seismic data, together with the high quality of the database, permits to obtain a new tomographic approach of the region under study never done before. An additional user-guide of PARTOS software is provided in order to facilitate the implementation for new users.</p>
Highlights
TOMO-ETNA experiment aimed to enlighten the complex internal structure of Mt
We describe PARTOS, a new tomographic code developed for joint inversion of active and passive datasets
The P-phase arrivals from both active and passive data used for this seismic tomography were detected and picked using an adapted version an automatic picking algorithm named AMPA [Álvarez et al 2013; García et al 2016, in this volume]
Summary
TOMO-ETNA experiment aimed to enlighten the complex internal structure of Mt. Etna and surrounding areas by developing dense active and passive seismic surveys. The major complexity of these new joint procedures is that active and passive seismic data have different characteristics such as its source location, and it is important to take into account these differences in order to better adapt the algorithms for each case.
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