Wyniki wyszukiwania - BazTech

1

Study on groundwater table distribution of loess tableland in the South Jingyang tableland, Northwest China

Wu Jiaqi, Dai Fuchu, Liu Pan, Huang Zhiquan, Wu Daixuan

Acta Geophysica

|

2024

|

Vol. 72, no. 1

257--272

EN

The South Jingyang tableland, located in Shaanxi Province, China, is composed of loess deposits. Agricultural irrigation in this area has caused the continuous rise of the groundwater table, which triggered a series of loess landslides. Although extensive research has been conducted on the effect of rising groundwater tables on induced loess landslides, less is known about the groundwater table distribution. This study jointly investigated the distribution of the groundwater table in the South Jingyang tableland using boreholes, electrical resistivity tomography (ERT), and laboratory tests. A qualitative analysis between the apparent resistivity and the water content was estimated at different depths, using the borehole data and ERT inversion data. A quantitative relationship between the resistivity and the water content was established in the laboratory tests. A combination of the qualitative analysis of the inversion results and the quantitative relationship was used to determine the distribution of the groundwater table. The groundwater table is present at the depth where the water content of the loess is 30% (liquid limit of loess) and the resistivity is 28.35 Q m. The groundwater table elevation in the southern study area is higher than that of the northern study area and decreases in a stepwise gradient from south to north. This study not only identified the distribution of the groundwater table in the South Jingyang tableland, but also makes up for the limitation and inaccuracy in determining groundwater tables by relying solely on the borehole information or inversion results of ERT. The ascertained distribution characteristics of the groundwater table provides a basis for the analysis of the formation mechanism of landslides.

2

Processing for near-source potential resistivity based on the parallel electrical method

Hu Zean, Wu Rongxin, Cao Jianfu, Wang Xiaoyu

Acta Geophysica

|

2022

|

Vol. 70, no 6

2705--2714

EN

The parallel electrical method (PEM), which enhances the field-work efficiency by offering flexible data acquisition and processing a large amount of geoelectric field data, is mainly used to extract the high-density resistivity data. However, the method is unable to process nearly 75% of the data and hampered by noise interference. We proposed a novel method to calculate the apparent resistivity based on the near-source potential in the PEM system using the following algorithms: (1) selecting the measurement electrode nearest to the power source as the reference and keeping the AM interval as an invariant electrode distance, (2) calculating the potential difference between the measurement electrode and the first near-source electrode, (3) stepwise calculating the potential differences between other measurement electrodes and their corresponding near-source electrodes, and (4) calculating all the apparent resistivities at different positions. We further verified the effectiveness of near-source potential method to calculate resistivity based on the theoretical calculation and identified that it has higher calculation accuracy and stability. Compared to the maximum Pole-dipole deviation of 59.4%, the maximum deviation of the near-source potential resistivity is only 2.2%. The field experimental results showed that the near-source potential resistivity method performs well in the stratified geoelectric model and effectively improves the longitudinal resolution and the signal-to-noise ratio of deep apparent resistivity of the parallel direct-current resistivity method.

3

Effective denoising of magnetotelluric (MT) data using a combined wavelet method

Ling Zhenbao, Wang Peiyuan, Wan Yunxia, Li Tonglin

Acta Geophysica

|

2019

|

Vol. 67, no. 3

813--824

EN

Noise interference, especially from human noise, seriously affects the quality of magnetotelluric (MT) data. Strong human noise distorts the apparent resistivity curve, known as the near-source effect, causing poor reliability of MT data inversion. Based on analyzing the frequency characteristics of human noise resulting from the surrounding environment, a new waveletbased denoising method is proposed for both synthetic and real MT data in this paper. The new technique combines multiresolution analysis with a wavelet threshold algorithm based on Bayes estimation and has a remarkable effect on denoising at all band frequencies. The multi-resolution analysis method was employed to reduce long-period noise, and a wavelet threshold algorithm was used to eliminate strong high-frequency noise. In this research, the improved algorithm was assessed via simulated experiments and field measurements with regard to the reduction in human noises. This study demonstrates that the new denoising technique can increase the signal-to-noise ratio by at least 112% and provides an extensive analysis method for mineral resource exploration.

4

Sztuczne sieci neuronowe dla uzupełnienia danych w geofizyce otworowej - wybrane przykłady

Jarzyna J., Opyrchał A., Mozgowoj D.

Geologia / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

|

2007

|

T. 33, z. 4/1

81-102

PL

Wykorzystano sztuczne sieci neuronowe do odtwarzania profilowań geofizyki otworowej. Na podstawie dostępnych profilowań geofizyki wiertniczej odtworzono czas interwałowy rejestrowany przy profilowaniu akustycznym, gęstość objętościową będącą wynikiem profilowania gamma-gamma oraz oporność pozorną. Wybrano perceptrony i wsteczną propagację błędu jako metodę nauczania. Najbardziej skuteczne przy odtwarzaniu parametrów okazały się perceptrony z kilkoma neuronami na wejściu i przynajmniej kilkoma neuronami w warstwie ukrytej. Miarą poprawności wyników dostarczanych przez sieci były podstawowe statystyki obliczane dla odtworzonych parametrów w porównaniu z wynikami pomiarów lub estymacji. Najtrudniejsze do opracowania okazały się interwały, w których skały miały skomplikowaną litologię i zróżnicowaną przynależność stratygraficzną. Wyniki wykorzystano dla potrzeb interpretacji sejsmicznej, grawimetrycznej i magnetotellurycznej.

EN

Artificial Neural Networks were used for reconstruction of well logs. Transit interval time recorded during acoustic log, bulk density as a result of gamma-gamma log and apparent resistivity were reconstructed on the basis of available logs. Perceptrons were chosen and the back propagation method was applied as the most effective training algorithm. Perceptrons with a few input neurons and at least a few neurons in a hidden layer turned out in reconstructing parameters. Basic statistics calculated for the reconstructed parameters compared to the measured or estimated ones acted as measures of correctness of solutions delivered by the networks. Intervals containing rocks of complex lithology and diversified stratigraphy turned out to be the most difficult to interpret. Results were applied in seismics, gravimetric and magnetotelluric interpretations.