With the increase of the global population and the improvement of people's living standards, the output of garbage generated by human activities is also increasing day by day. Choosing an appropriate ...garbage disposal site is one of the key links for the harmless disposal of garbage. However, due to the uncertainty and complexity of socio-economic development and the limited cognitive ability of decision-makers, how to rationally select the garbage disposal site has become a challenging task. This study drew a new multi-attribute decision-making method based on interval q-rung orthopair fuzzy weighted power Muirhead mean (Iq-ROFPWMM) operator to evaluate site selection scheme of garbage disposal plant, and support for garbage disposal site selection. In this method, firstly, power average and Muirhead mean operators are integrated and introduced into the interval q-rung orthopair fuzzy environment to construct an Iq-ROFPWMM operator. Meanwhile, some properties of idempotence, boundedness and monotonicity for the Iq-ROFPWMM operator are analyzed. Then, a multi-attribute decision-making method using Iq-ROFPWMM operator is established. After that, a practical case on the evaluation of garbage disposal site selection scheme is given to verify the effectiveness of the proposed method. Further, parameter analysis and comparative analysis are applied to demonstrate the superiority of our method. The results show that this method boasts wider space for evaluation information representation, more flexible adaptation to the environment evaluation, and stronger robustness of the evaluation results. Finally, some conclusions of this study are drawn and the development direction is revealed.
•Developed an interval q-rung orthopair fuzzy weighted power Muirhead mean operator.•Presented a new MADM algorithm based on the proposed operator.•Established a novel evaluation model of garbage disposal selection scheme.
Reliable estimate of the anelastic attenuation factor-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> from seismic records is highly desirable for improving seismic ...resolution. However, the conventional equivalent-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> or horizontal interval-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> estimation ignores that <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-distribution should hold the same ability for the subsurface structure characterization as seismic data. To pursue an accurate <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-model, we propose a technique for joint reflectivity and structural interval-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> estimation by using nonstationary sparse inversion. We designed a structural interval-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> model by dividing the seismic data into several structural layers with the interpreted horizon(s). Attenuations in each layer are close to each other and can be described by an equivalent-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> or gradient-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>. Based on the attenuation theory, the nonstationary sparse inversion is solved iteratively, where, at each iteration, the equivalent-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> of only one layer is optimized by searching for the corresponding optimum inverted reflectivity, leading to a structural interval-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> model. The main advantages of our method are its objectivity and accuracy because of the integration of the prior structural information from interpreted horizons into joint reflectivity-estimation and <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-estimation. The test of synthetic and field data clearly illustrates that the proposed method enables high-precision structural interval-<inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> estimation and sufficiently compensates for <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-related attenuation.
In order to obtain stable interval
Q
factor, by analyzing the spectrum of monitoring wavelet and down-going wavelet of zero-offset VSP data and referring the spectrum expression of Ricker wavelet, we ...propose a new expression of source wavelet spectrum. Basing on the new expression, we present improved amplitude spectral fitting and spectral ratio methods for interval
Q
inversion based on zero-offset VSP data, and the sequence for processing the zero-offset VSP data. Subsequently, we apply the proposed methods to real zero-offset VSP data, and carry out prestack inverse
Q
filtering to zero-offset VSP data and surface seismic data for amplitude compensation with the estimated
Q
value.
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