We have studied using traveltimes of P- and S-waves and initial seismic-pulse rise-time measurements from natural microearthquakes to derive 3D P-wave velocity VP information (mostly structural) as well as P- and S-wave velocity VP/VS and P-wave quality factor QP information (mostly lithologic) in a known hydrocarbon field in southern Albania. During a 12-month monitoring period, 1860 microearthquakes were located at a 50-station seismic network and were used to obtain the above parameters. The data set included earthquakes with magnitudes ranging from –0.1 to 3.0 R (Richter scale) and focal depths typically occurring between 2 and 10 km. Kohonen neural networks were implemented to facilitate the lithological classification of the passive seismic tomography (PST) results. The obtained results, which agreed with data from nearby wells, helped delineate the structure of the reservoir. Two subregions of the investigated area, one corresponding to an oil field and one to a gas field, were correlated with the PST results. This experiment showed that PST is a powerful new geophysical technique for exploring regions that present seismic penetration problems, difficult topographies, and complicated geologies, such as thrust-belt regions. The method is economical and environmentally friendly, and it can be used to investigate very large regions for the optimal design of planned 2D or 3D conventional geophysical surveys.

Small-magnitude seismic events, either natural or induced microearthquakes, have increasingly been used in exploration seismology with applications ranging from hydrocarbon and geothermal reservoir exploration to high-resolution passive seismic tomography surveys. We developed an automated methodology for processing and analyzing continuously recorded, single-channel seismic data. This method comprised a chi-squared-based statistical test for microseismic event detection and denoising filtering in the Stransform domain based on the Otsu thresholding method. An automatic P-phase picker based on higher order statistics criteria was used. The method was used with data from a surface seismic station. The performance of the method was tested and evaluated on synthetic and real data from a microseismic network used in a high-resolution PST survey and revealed a high level of consistency.

Algorithms that deal with the automatic S-onset time identification problem are a topic of ongoing research. Modern dense seismic networks used for earthquake location, seismic tomography investigations, source studies, early warning, etc., demand accurate automatic S-wave picking. Most of the techniques that have been proposed up to now are mainly based on the polarization features of the seismic waves. We propose a new time domain method for the automatic determination of the S-phase arrival onsets, and present its implementation on local earthquake data. Eigenvalue analysis takes place over small time intervals, and the maximum eigenvalue which is obtained on each step is retained for further processing. In this way, a time series of maximum eigenvalues is formed, which serves as a characteristic function. We obtain a first S-phase arrival time estimation by applying the kurtosis criterion on the derived characteristic function. Furthermore, a multiwindow approach combined with an energy-based weighting scheme is also applied, to reduce the algorithm's dependence on the moving window's length and provide a weighted S-phase onset. Automatic picks were compared against manual reference picks, resulting in mean residual time of 0.051 s. Moreover, the proposed technique was subjected to a noise robustness test and sustained a good performance. The mean residual time remained lower than 0.1 s, for noise levels between −1 up to 8 dB. The proposed method is easy to implement, because it is almost parameter free and demands low computational resources.

As the global need for mineral resources is constantly rising and the exploitable concentrations of these resources tend to become increasingly complex to explore and exploit, the mining industry is in a constant quest for innovative and cost‐effective exploration solutions. In this context, and in the framework of the Smart Exploration action, an integrated passive seismic survey was launched in the Gerolekas bauxite mining site in Central Greece. A passive seismic network, consisting of 129 three‐component short‐period stations was installed and operated continuously for 4 months. The acquired data permitted detection of approximately 1000 microearthquakes of very small magnitude (duration magnitude ranging between –1.5 and 2.0), located within or at a very close distance from the study area. We use this microseismicity as input for the application of passive seismic interferometry for reflection retrieval, using the body waves (P‐ and S‐wave coda) of the located microearthquakes. We retrieve by autocorrelation zero‐offset virtual reflection responses, per component, below each of the recording stations. We process the acquired results using reflection processing techniques to obtain zero‐offset time and depth sections, both for P‐ and for S‐waves. In the context of the present work, we evaluate one of the acquired depth sections, using an existing seismic line passing through the Gerolekas passive seismic network, and we perform forward modelling to assess the quality and value of the acquired results. We confirm that passive seismic reflected‐wave interferometry could constitute a cost‐effective and environmentally friendly innovative exploration alternative, especially in cases of difficult exploration settings.

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