In this paper, a W-band 3D-printed bandpass filter is proposed. The use of higher-order TE10n modes in groove gap waveguide (GGW) technology is evaluated in order to alleviate the manufacturing requirements. In addition to the use of higher-order modes, the coupling between them is analyzed in detail to improve the overall fabrication robustness of the component. This allows the implementation of narrow-band filters operating at millimeter-wave frequency bands (or above), which usually demand complex manufacturing techniques to provide the high accuracy required for this kind of devices. In order to show the applicability of the proposed method, a narrow-band 5th-order Chebyshev bandpass filter centered at 94 GHz, which can be easily fabricated by state-of-the-art stereolithographic (SLA) 3D-printing techniques followed by silver coating, is shown. Excellent measured performance has been obtained.
Successful use of an artificial neural network is shown to predict lateral variations of seismic velocity, density, thickness, and gamma rays associated with sand dune reservoirs identified on a previously interpreted seismic horizon. The work is presented in two main sections. Section one is a feasibility analysis based on synthetic data. A known geologic model is used, performed by pseudowells, in which lateral variations in seismic velocity, density, and gamma ray values are related to the dunes. The synthetic seismic model and the attributes derived are used as training input in the neural network. Section two is a real case example where the methodology is applied to a real seismic data set. Results indicate that using a set of data and attributes restricted to a time interval corresponding to a previously interpreted seismic horizon is more efficient than using a whole data cube, involving a very large volume of data.
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