Automatic Approach for Fast Processing and Data Analysis of Seismic Ahead-Prospecting Method: A Case Study in Yunnan, China

The seismic ahead-prospecting method is useful to detect anomalous zones in front of the tunnel face. However, most existing seismic detection method is designed for drilling and blasting tunnel. The detection method should be improved to satisfy the rapid tunneling of Tunnel Boring Machines (TBMs). This study focuses on reducing the time spent on seismic data processing and result analysis. Therefore, to reduce the data processing time, an automatic initial model establishment method based on surrounding rock grade is proposed. To reduce the time spent on result analysis and avoid subjective judgment, a modified k-means++ method is adopted to interpret the detecting results and extracting anomalous zones. The efficacy of the developed method is demonstrated by field tests. The fractured zones such as cavity collapse and fissure are successfully predicted and identified

Effect of surface contamination on electrochemical corrosion behavior of ultrasonic shot peened TA2 alloy

Ultrasonic shot peening (USSP) is a technique to tune the performance of metallic materials by generating gradient nanocrystalline structure (GNS) as well as residual compressive stress. However, surface contamination can also be introduced during USSP. The nature of this surface contamination layer and how it influences the corrosion behavior of alloy require further investigation. In this work, we found that a Fe-rich oxide layer with a thickness of around 50 nm, mainly in an amorphous state, was introduced on the topmost surface layer of TA2 alloy during USSP treatment. Electrochemical corrosion results indicate that this Fe-rich oxide significantly decreases the corrosion resistance of TA2 alloy. By polishing off this surface contamination layer, the corrosion resistance of the peened sample becomes superior compared to the untreated counterpart, indicating that the introduction of the Fe-rich oxide layer conceals the beneficial effect of GNS caused by USSP. Additionally, the corrosion resistance of TA2 alloy after USSP treatment exhibits a downward trend with the increasing repeated use of steel shots. The corrosion mechanism and potential use of this surface contamination layer are also briefly discussed

Microstructure, thermal stability and corrosion behavior of ultrasonic shot peened AA7034

Microstructure, thermal stability at room temperature and corrosion behavior of ultrasonic shot peened AA7034 were investigated in this work. Microstructure modification induced by ultrasonic shot peening was obtained by using OM, SEM, EBSD and XRD, and it was found that a ∼200 μm thick gradient surface layer with nano-scale grains on the topmost layer was induced on the peened sample. XRD peaks of the pre-existing aging-induced precipitates, i.e., η′ and η phases, disappeared after peening and did not show up during 52 months-long natural aging treatment, showing good thermal stability at room temperature. After peening, a significant improvement in intergranular corrosion resistance is evident. Electrochemical characterizations like open circuit potential and electrochemical impedance spectroscopy were conducted on various depths of the peened sample and the results indicated that uniform corrosion rate was largely accelerated by the presence of foreign Fe-rich contamination, implying that modifications should be made to reduce/avoid the foreign contamination. Additionally, mechanisms of corrosion improvement and good thermal stability of the nanocrystalline surface layer of AA7304 are also discussed

Seismic ahead-prospecting based on deep learning of retrieving seismic wavefield

Unknown geology ahead of the tunnel boring machine (TBM) brings a large safety risk for tunnel construction. Seismic ahead-prospecting using TBM drilling noise as a source can achieve near-real-time detection, meeting the requirements of TBM rapid drilling. Seismic wavefield retrieval is the key data processing step for the efficient utilization of TBM drilling noise. The traditional solution is based on cross-correlation to extract reflected waves, but the reference waves remain in the result, disturbing the imaging and interpretation of the adverse geology. To solve this problem, the deep learning method was introduced in wavefield retrieval to improve the accuracy of geological prospecting. We trained a deep neural network (DNN) with its strong nonlinear mapping capability to transform seismic data from TBM drilling noise to data from the active source. The issue lies in its features for this specific tunnel task, including the decay of the seismic signal with time and the incomplete spatial correspondence. Thus, we improved a classical DNN with the time constraint as an additional input, and an additional pre-decoder to enlarge the receptive field. Additionally, a loss function weighted by the ground truth and time constraint is improved to achieve an accurate retrieval of the effective signal, considering the little effective information in tunnel data. Finally, the workflow of the proposed method was given, and a dataset designed with reference to the field case was employed to train the network. The proposed method accurately retrieved the reflection signal with higher dominant frequencies, which helped improve the accuracy of imaging. Numerical simulations and imaging on typical geological models show that the proposed method can suppress reference waves and get more accurate results with fewer artifacts. The proposed method has been applied in the Gaoligongshan Tunnel and imaged two abnormal zones, providing meaningful geological information for TBM drilling and tunnel construction

Harmonizing Aerosol Carbon Measurements between Two Conventional Thermal/Optical Analysis Methods

Although total carbon (TC) can be consistently quantified by various aerosol carbon measurement methods, the demarcation of TC into organic carbon (OC) and elemental carbon (EC) has long been inconsistent. The NIOSH and IMPROVE protocols are most widely used for thermal/optical analysis (TOA), but current knowledge rests in the description that the NIOSH protocol usually gives lower EC values than does the IMPROVE protocol. This study seeks to explore the possibility of quantitatively linking the difference between the two TOA protocols. Residential coal-burning samples that had been collected and analyzed following the NIOSH protocol in previous studies were directly reanalyzed following the IMPROVE protocol for this study. A comparison of each pair of NIOSH and IMPROVE EC values reveals the dynamic relation between the two protocols, which can be expressed as a regression equation, y = (1-x)/(1 + 4.86x(2)) (R-2 = 0.96), where the independent x is the EC/TC ratio (R-EC/TC) for the IMPROVE protocol, and the dependent y is the difference between IMPROVE and NIOSH R-EC/TC relative to IMPROVE R-EC/TC. This regression equation may be the first effort in formulating the relationship between the two TOA protocols, and it is very helpful in harmonizing inconsistent TOA measurements, for example, source characterization, ambient monitoring, and atmospheric modeling.Although total carbon (TC) can be consistently quantified by various aerosol carbon measurement methods, the demarcation of TC into organic carbon (OC) and elemental carbon (EC) has long been inconsistent. The NIOSH and IMPROVE protocols are most widely used for thermal/optical analysis (TOA), but current knowledge rests in the description that the NIOSH protocol usually gives lower EC values than does the IMPROVE protocol. This study seeks to explore the possibility of quantitatively linking the difference between the two TOA protocols. Residential coal-burning samples that had been collected and analyzed following the NIOSH protocol in previous studies were directly reanalyzed following the IMPROVE protocol for this study. A comparison of each pair of NIOSH and IMPROVE EC values reveals the dynamic relation between the two protocols, which can be expressed as a regression equation, y = (1-x)/(1 + 4.86x(2)) (R-2 = 0.96), where the independent x is the EC/TC ratio (R-EC/TC) for the IMPROVE protocol, and the dependent y is the difference between IMPROVE and NIOSH R-EC/TC relative to IMPROVE R-EC/TC. This regression equation may be the first effort in formulating the relationship between the two TOA protocols, and it is very helpful in harmonizing inconsistent TOA measurements, for example, source characterization, ambient monitoring, and atmospheric modeling