A high-resolution passive seismic investigation was performed in a 150 km 2 area around the Rio-Antirio Strait in central Greece using natural microearthquakes recorded during three months by a dense, temporary seismic network consisting of 70 three-component surface stations. This work was part of the investigation for a planned underwater rail tunnel, and it gives us the opportunity to investigate the potential of this methodology. First, 150 well-located earthquake events were selected to compute a minimum ͑1D͒ velocity model for the region. Next, the 1D model served as the initial model for nonlinear inversion for a 3D P-and S-velocity crustal structure by iteratively solving the coupled hypocenter-velocity problem using a least-squares method. The retrieved V p and V p /V s images were used as an input to Kohonen self-organizing maps ͑SOMs͒ to identify, systematically and objectively, the prominent lithologies in the region. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. This analysis revealed the existence of five major clusters, one of which may be related to the existence of an evaporite body not shown in the conventional seismic tomography velocity volumes. The survey results provide, for the first time, a 3D model of the subsurface in and around the Rio-Antirio Strait. It is the first time that passive seismic tomography is used together with SOM methodologies at this scale, thus revealing the method's potential.

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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.