Groundwater monitoring for the Maltese Islands from ambient seismic noise correlations&#160;
Authors: Laudi Luca , Agius Matthew R. , Galea Pauline , D'Amico Sebastiano , Schimmel Martin , Lecocq Thomas 
Published: March 2022

&lt;p&gt;Malta, a small island nation in the centre of the Mediterranean, is deemed as the European country facing the highest stress on its water resources. Malta has a semi-arid climate with approximately 550 mm of annual rainfall over an area of &amp;#8764;315 km&lt;sup&gt;2&lt;/sup&gt; and a very high population density. Consequently, 80% of the water used in the Maltese agricultural sector is directly abstracted from groundwater resources via boreholes or underground galleries. To improve the groundwater monitoring in Malta, which currently depends on a network of &lt;em&gt;in situ&lt;/em&gt; borehole readings, we analyse ambient seismic noise data recorded on the Malta Seismic Network (MSN) as part of the project SIGMA (Seismic Imaging of Groundwater for Maltese Aquifers). We investigate temporal changes in seismic velocity as an indication of the variability of water in underground rocks. Water-saturated rocks have an increased pore pressure, which, in turn, leads to the opening of cracks in the rock that reduces the contact area between different grains of rock leading to a decrease in seismic velocity.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We compiled the seismic data from each station of the MSN (Galea et al., 2021, SRL) and the FASTMIT experiment (Bozionelos et al., 2019, Xjenza) consisting of a combination of eight broadband and six short-period, three-component seismic stations for the years 2017-2020. The data was pre-processed by demeaning, tapering and merging into a 1-day long trace, which were then band-pass filtered and decimated or downsampled. Power Spectral Density (PSD) charts for the data show that most microseisms energy has a period range of 1-10s. We therefore test different filtering bands encompassing this frequency range. We perform auto and cross-correlation of noise data from 78 station pairs. We perform stacking for 1, 5 and 10 days for smoother cross-correlation functions. We then compute the time delays using the Moving-Window Cross-Spectral analysis (Clarke et al., 2011, JGI). Finally, the change in velocity (&lt;em&gt;dv/v&lt;/em&gt;) is determined from the calculated time delays. The algorithm was run via the software package MSNoise (Lecocq et al., 2014, SRL).&lt;/p&gt;&lt;p&gt;We find that the changes in the &lt;em&gt;dv/v&lt;/em&gt; time series (~&amp;#177;0.01%) have seasonal patterns, where a negative &lt;em&gt;dv/v&lt;/em&gt; in the winter period and a positive &lt;em&gt;dv/v&lt;/em&gt; in summer is observed. We compare the auto and cross-correlations with the time series of groundwater measurements from nearby boreholes (ranging from 0.25-3.3m above mean sea level) to investigate the correlation between them. We also take into consideration the NW-SE geology of the island, distinguished by an impermeable layer between the geological strata (Blue Clay) in the north. We find that long paths traversing across different geological layers show weak correlations. We present the tests performed and the results showing the extended spatial coverage for groundwater monitoring in conjunction with the borehole data for the Maltese Islands.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Project SIGMA is financed by the Energy and Water Agency under the National Strategy for Research and Innovation in Energy and Water (2021-2030).&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;