In an airborne laser bathymetry system, the full-waveform echo signal is usually recorded by discrete sampling. The accuracy of signal recognition and the amount of effective information that can be extracted by conventional methods are limited. To improve the validity and reliability of airborne laser bathymetry data and to extract more information to better understand the water reflection characteristics, we select the effective portion of the original waveform for further research, suppress random noise, and decompose the selected portion progressively using the half-wavelength Gaussian function with the time sequence of the received echo signals. After parameter optimization, a reasonable and effective reflection component selection mechanism is established to obtain accurate parameters for the reflected components. The processing strategy proposed in this paper reduces the problems of unreasonable decomposition and the reflected pulse peak-position shift caused by echo waveform superposition and offers good precision for waveform decomposition and peak detection. In another experiment, the regional processing result shows an obvious improvement in the shallow water area, and the bottom point cloud is as accurate as the intelligent waveform digitizer (IWD), a subsystem of airborne laser terrain mapping (ALTM). These findings confirm that the proposed method has high potential for application.

Well logs play a very important role in exploration and even exploitation of energy resources, but they usually contain kinds of noises which affect the results of the geological interpretation of them. It is common knowledge that wavelet transform does better than Fourier transform in noise removal and suppression of such non-stationary signals as logging signals. However, there are variable choices of the parameters such as the wavelet basis (mother wavelet function), the thresholding rule and the decomposition level etc. in denoising with the wavelet transform. In this paper, the wavelet denoising theory and steps are briefly introduced first, and then lots of numerical experiments on real well logs were done by the authors with different combination of the parameters and the denoising effect analyzed by comparison of the differences between the predenoising and post-denoising signals with difference value calculation and frequency spectral analysis. The experiment results show that the wavelet basis 'sym8', the soft threshold rule 'heursure' and 5-level decomposition are outstanding in the wavelet denoising of well logging data. Furthermore, we took the AC (acoustic logs) well logging data of a certain borehole in Jiyang Depression, Shandong province of North China, for a case study to check the combination of the parameters settled above. It is found that the denoised acoustic logging signal outperforms the original one in revealing the geological information of gas bearing layers. So, we believe that the wavelet transform can do an excellent job in the denoising of well logs on condition that the related parameters are set properly. Also, the authors assume that it would be of bright prospect to extract and reveal some more geological information such as stratigraphic sequences, sedimentary facies and reservoir properties etc. with reasonable denoising process of different kinds of logging data at certain scales.

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