Geosciences

The embayed beaches of northern Spain’s cliffed, wave-dominated coast contain mixtures of siliciclastic sands, supplied mainly by rivers and coastal erosion, and carbonate bio-clastic sands derived from coastal organisms. The proportion of carbonate material, expressed as a percentage of CaCO₃, is a reliable indicator of the provenance and transport patterns of sediments. This parameter frequently yields more precise information than grain-size analysis. It is well established that the carbonate content typically diminishes in the direction of sediment transport from the original source. At a regional scale, sediment transport along the Cantabrian coast and much of Galicia is predominantly west-to-east, driven by prevailing northwest and west-northwest wave conditions. While large rivers supply abundant siliciclastic sediments, estuaries, rias, mudflats, and marshes contribute nutrients that enhance marine productivity and carbonate production. In the coastal regions of Galicia, the phenomenon of coastal upwelling has been shown to increase biological productivity, thereby fostering the development of carbonate-rich sediments. The skeletal assemblage is classified as bimol-bivalve dominated. Consequently, elevated carbonate percentages are indicative of regions influenced by nutrient-rich upwelling and productive coastal ecosystems. Across approximately 750 km of the Galician and Cantabrian coastline, variations in bioclastic carbonate content help define coastal compartments, identify sediment sources, and infer longshore transport pathways. In the context of the continental shelf, the interplay of opposing currents generated by northern swells, in conjunction with the influence of the rias, exerts a pronounced regulatory effect on the dispersal of sand and the dynamics of the coastline.

Conventional adaptive subtraction methods for multiple attenuation rely solely on predicted multiples, resulting in poor removal at near offsets. To address the issue, we develop an adaptive subtraction based on joint matching filtering (ASMF) algorithm. It is developed under the premise that the common midpoint (CMP) gathers consist of primaries, multiples and random noise. The algorithm assumes that accurate primaries can be obtained from the far-offset traces of the CMP gathers. Another assumption is that the near-offset and far-offset data in the gathers differ mainly in terms of fluids, amplitude variation, frequency attenuation, and signal-to-noise ratio (SNR). The method further assumes that corresponding primary events remain approximately aligned after normal moveout (NMO) correction and that angle-dependent reflectivity variations are limited and do not involve polarity reversals. After conventional separation, selected far-offset traces of the preliminary primary estimate are stacked to estimate a zero-phase wavelet for reflection coefficient inversion. The resulting reflection coefficient is convolved with trace-dependent wavelets to obtain the predicted primaries. Based on the predicted primaries and multiples, the matching filtering operators for both components are jointly calculated under the framework of morphological component analysis (MCA). Incorporating the predicted primaries into joint fitting can help improve the separation of primaries and multiples. Numerical experiments and field data tests demonstrate that, compared with the Radon transform and conventional adaptive subtraction, the proposed algorithm can effectively improve the accuracy of multiple attenuation.

Conventional geostatistical methods of spatial interpolation (e.g., inverse distance weighting or kriging) are based on spatial distance and become inaccurate in regions far from sample wells or in the case of geological discontinuities. It looks more natural to estimate geological properties using similarity, not by distance, but by geological conditions, which, in turn, can be estimated using seismic surveys. In this research, we use machine learning to derive seismic attributes that describe the local geological environment and apply interpolation in the resulting attribute space to propagate geological properties from the sample wells to the whole reservoir model. In this representation, geologically similar regions become close in the attribute space even when they are separated in the initial spatial 3D domain. Using an open synthetic dataset, we demonstrate that this approach improves propagation of various geological properties compared to the conventional spatial method and remains effective even with a small number of sample wells. The main advantage of this approach is that, while preserving the simplicity and transparency of geostatistical methods, it enables the use of seismic data without preliminary interpretation to obtain geologically consistent and quick estimates of geological properties in the 3D domain far from sample wells.