Obtaining high‐quality ground penetrating radar (GPR) images in karst is difficult because materials resulting from the weathering of carbonate rocks might be electrically conductive. As a consequence, penetration depth and signal resolution might be greatly reduced due to attenuation. In addition, fractures and faults might cause a significant amount of electromagnetic wave scattering. We present a 2D data processing flow which allows improving the quality of GPR images in carbonate karst. The processing flow is composed of the following steps: obtaining a zero‐offset section by removing the direct wave, low‐frequency noise removal, geometrical spreading and exponential gain compensation, spectral balancing, Kirchhoff migration, bandpass filtering, amplitude‐volume enhancement, and topographic correction. For a 200‐MHz dataset, we present in detail each step of the processing flow, exemplifying how to parameterize every step. Spectral balancing is of key importance because it can approximately replenish the high‐frequency content lost due to propagation effects. In this step, we recommend to shift the centroid frequency as much as possible to high‐frequency values, even exceeding the nominal value of the antenna center frequency, but still looking for a band‐limited spectrum as the goal. Despite the difficulty of migrating GPR data, we show that migration (even assuming a constant velocity) might enhance the lateral continuity of the reflection events and allows identification of discontinuities such as faults and fractures. If imaged in a better way, these structures can have special importance as they are often the boundaries of dissolution features. Obtaining images based on amplitude‐volume enhancement techniques allows to better visualize karst voids and deep‐rooted discontinuities because these features are often associated with low‐amplitude zones, which are highlighted in such images. Due to this processing flow, stratigraphic, structural and dissolution features can be enhanced, allowing the interpreter to establish spatial and genetic associations among these elements to obtain a better understanding of the karst formation process.

Additional information regarding the continuity and resolution of selected seismic reflectors in reverse time migration (RTM) images can be beneficial for seismic interpretation. We have developed and evaluated new imaging conditions for RTM based on the phase coherence between the forward- and backward-propagated wavefields. These imaging conditions make use of the instantaneous phase and envelope of the analytical signals of the source and receiver wavefields, in addition to their real parts. Once the analytical wavefields are available, these imaging conditions can be calculated simultaneously with conventional conditions at little or no extra cost. The availability of these fields at each image point enables several alternative ways to define imaging conditions. We explore, in addition to pure phase crosscorrelation (PC), two approaches of amplitude-weighted PC. Our numerical experiments, imaging synthetic and field data sets, indicate that these new imaging conditions provide additional images that can highlight some weak reflectors by locally improving the resolution of RTM images. In our examples, this happens particularly in the deep portions of the seismic images. In addition, reflection events produced at discontinuities are enhanced as sharp signals, suggesting that the proposed imaging conditions can help to delineate stratigraphic and structural features that are harder to see in conventional images. These properties of the PC imaging conditions make them an interesting tool to provide additional information that can aid seismic interpretation in complex structural settings.

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SUMMARY Hypocentre location is an ill‐posed inverse problem even assuming that the velocity model is known, because different sets of hypocentre locations may satisfy the fitting criterion. We present a regularized hypocentre inversion in which the constraints of spatial proximity of the hypocentres to target planes are used. This constraint introduces the geological bias that earthquakes might occur along fault planes. Here, the target planes may be either (1) planes specified by the interpreter or (2) planes fitting groups of events. We assume also that initial estimates of hypocentres and origin times are available. Then, the initial hypocentre estimates, origin times and target planes are used as input to an inversion problem to relocate the hypocentres so that the maximum‐possible clustering of events along the given planes is attained, matching the observed traveltimes. We use L1 norm for data fitting, L2 norm for the plane proximity criterion and a polytope algorithm to minimize the functional. Results from synthetic and real data indicate that the plane proximity constraint allows for hypocentre relocation presenting a high degree of clustering along planes. The real‐data example is an intraplate earthquake sequence in NE Brazil. Our methodology defined the geometry and strike of fault segments close to known geology and focal mechanism data. In addition, the new method indicates that the fault is characterized by a splay geometry in its southern end and that more than three fault segments are necessary to explain the hypocentre distribution.