Geological Type Recognition by Machine Learning on In-Situ Data of EPB Tunnel Boring Machines

At present, many large-scale engineering equipment can obtain massive in-situ data at runtime. In-depth data mining is conducive to the real-time understanding of equipment operation status or recognition of service environment. This paper proposes a geological type recognition system by the analysis of in-situ data recorded during TBM tunneling to address geological information acquisition during TBM construction. Owing to high dimensionality and nonlinear coupling between parameters of TBM in-situ data, the dimensionality reduction feature engineering and machine learning methods are introduced into TBM in-situ data analysis. The chi-square test is used to screen for sensitive features due to the disobedience to common distributions of TBM parameters. Considering complex relationships, ANN, SVM, KNN, and CART algorithms are used to construct a geology recognition classifier. A case study of a subway tunnel project constructed using an earth pressure balance tunnel boring machine (EPB-TBM) in China is used to verify the effectiveness of the proposed geological recognition method. The result shows that the recognition accuracy gradually increases to a stable level with the increase of input features, and the accuracy of all algorithms is higher than 97%. Seven features are considered as the best selection strategy among SVM, KNN, and ANN, while feature selection is an inherent part of the CART method which shows a good recognition performance. This work provides an intelligent path for obtaining geological information for underground excavation TBM projects and a possibility for solving the problem of engineering recognition of more complex geological conditions.

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Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data

Computational Intelligence and Neuroscience ◽

10.1155/2021/3904273 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Shanglin Liu ◽

Kaihong Yang ◽

Jie Cai ◽

Siyang Zhou ◽

Qian Zhang

Keyword(s):

Neural Network ◽

Time Series ◽

Short Term Memory ◽

Time Window ◽

Tunnel Boring Machine ◽

Heavy Load ◽

In Situ Data ◽

Geological Conditions ◽

Geological Information

A tunnel boring machine (TBM) is a type of heavy load equipment that is widely used in underground tunnel construction. The geological conditions in the tunneling process are decisive factors that directly affect the control of construction equipment. Because TBM tunneling always takes place underground, the acquisition of geological information has become a key issue in this field. This study focused on the internal relationships between the sequential nature of tunnel in situ data and the continuous interaction between equipment and geology and introduced the long short-term memory (LSTM) time series neural network method for processing in situ data. A method for predicting the geological parameters in advance based on TBM real-time state monitoring data is proposed. The proposed method was applied to a tunnel project in China, and the R2 of the prediction results for five geological parameters are all higher than 0.98. The performance of the LSTM was compared with that of an artificial neural network (ANN). The prediction accuracy of the LSTM was significantly higher compared with that of the ANN, and the generalization and robustness of LSTM are also better than those of ANN, which indicates that the proposed LSTM method could extract the sequence properties of the in situ data. The rule of equipment-geology interaction was reflected by increasing the memory structure of the model through the introduction of the “gate” concept, and the accurate prediction of geological parameters during tunneling was realized. Additionally, the influence of time window and distance of prediction on the model is discussed. The proposed method provides a new approach toward obtaining geological information during TBM construction and also provides a certain reference for the effective analysis of the in situ data with sequence properties.

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

Remote Sensing ◽

10.3390/rs13142848 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2848

Author(s):

Hao Sun ◽

Qian Xu

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Water Resource Management ◽

Large Scale ◽

Gap Filling ◽

Spatial Continuity ◽

Study Results ◽

Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

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Basic Mechanical Parameter Relations of Soft Clay in Pearl River Delta

The Open Civil Engineering Journal ◽

10.2174/1874149501408010320 ◽

2014 ◽

Vol 8 (1) ◽

pp. 320-325 ◽

Cited By ~ 1

Author(s):

Zhangming Li ◽

Na Qi ◽

Zhibin Masumi ◽

Weidi Lin

Keyword(s):

Shear Strength ◽

Pearl River Delta ◽

Large Scale ◽

Soft Clay ◽

River Delta ◽

Pearl River ◽

Cylindrical Cavity Expansion ◽

In Situ Data ◽

The Pearl River

Basic parameters relations among CPT parameters, un-drained strength and other mechanical parameters of soft clay are presented based on an elastic-plastic solution for cylindrical cavity expansion for soil investigation in energy engineering. The relation between CPT parameters and shear strength from vane test is also presented based on the result. Thus, the CPT parameters can be determined directly by elastic parameters and shear strength or vane shear parameters and vice versa. That makes it possible to save the high test costs and provide theoretical formulas to avoid some tests which are limited due to the site and/or other condition. Results are compared between the relations and in situ data at a large-scale project in the Pearl River Delta. The results showed consistency between the relation and in situ data.

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Effect of Advancing Direction of Working Face on Mining Stress Distribution in Deep Coal Mine

Advances in Civil Engineering ◽

10.1155/2021/7402164 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yuesong Tang ◽

Wenchao Sun ◽

Xin Zhang ◽

Pengju Liu

Keyword(s):

Stress Distribution ◽

Large Scale ◽

In Situ Stress ◽

Deep Mining ◽

Deep Coal Mine ◽

Working Face ◽

Geological Conditions ◽

Original Rock ◽

High Level

Deep mining has become the normal state of coal mining; compared with the mine with shallow buried depth, the consequent high level of in situ stress and complex distribution have brought severe threats to the stability of the stope and the surrounding rock of the roadway. In this research, taking the 121304 working face of Kouzidong Mine as the engineering background, the characteristics of mining-induced stress distribution under complex in situ stress environment in deep mining are analyzed by using on-site measurement of the original rock stress and mining stress, establishing a theoretical model centered on the middle section of the working face, and establishing large-scale numerical calculation models for different advancing directions. It was found that under deep mining conditions, the maximum stress of the original rock is 25.12 MPa, and the direction is vertical. The advanced influence range of mining stress is about 150 m, and the abutment pressure presents a three-peak distribution characteristic in front of the working face. The research results provide important theoretical guiding value for guiding the mining of coal mines with similar geological conditions.

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Freeboard, snow depth and sea-ice roughness in East Antarctica from in situ and multiple satellite data

Annals of Glaciology ◽

10.3189/172756411795931570 ◽

2011 ◽

Vol 52 (57) ◽

pp. 242-248 ◽

Cited By ~ 32

Author(s):

Thorsten Markus ◽

Robert Massom ◽

Anthony Worby ◽

Victoria Lytle ◽

Nathan Kurtz ◽

...

Keyword(s):

Sea Ice ◽

Correlation Coefficient ◽

Satellite Data ◽

Snow Depth ◽

Large Scale ◽

Mean Difference ◽

Laser Altimeter ◽

In Situ Data ◽

Ice Roughness

AbstractIn October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km × 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50–500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. the results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

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Sequence of studying engineering and geological conditions of mineral deposits from exploration to development

10.21440/0536-1028-2020-7-83-91 ◽

2020 ◽

Vol 7 (7) ◽

pp. 83-91

Author(s):

Irina V. Abaturova ◽

◽

Ivan A. Savintsev ◽

Liubov A. Storozhenko ◽

Elvina D. Nugmanova ◽

...

Keyword(s):

Large Scale ◽

Geological Environment ◽

Geological Time ◽

Protective Measures ◽

Early Stages ◽

Geological Processes ◽

The Earth ◽

Geological Conditions ◽

Geological Information ◽

Further Development

geological environment. Actively change all the components of engineering-geological conditions (EGC), formed during the long geological time: the topography, structure of rocks, hydrogeological and permafrost conditions, are formed by geological processes and, at the same time on the surface of the Earth formed a new strata of man-made structures, and often man-made deposits. The scale of technogenesis in mining today is comparable to the results of geological activity that took place over many millions of years. Therefore, even at the early stages of studying the EGC MD, it is necessary to understand the dynamics of changes in the EGC in order to provide preliminary protective measures. Purpose of work. Consideration of striking examples of the dynamics of the EGC MD (from exploration to development), in order to provide methods for managing these changes. Methodology. The article considers the stages of obtaining engineering and geological information for the period of MD operation, which will solve the problems of rational use of the subsoil and protection of the geological environment. Results. For example, the number of objects marked all the stages of learning to yoke the dynamics of their changes, which led to the formation of engineering-geological processes that adversely affect the further testing of MD. Summary. The reaction of the geological environment in the development of MD is not long in coming and is expressed in the development of large-scale engineering and geological processes, which often do not allow further development of MD and threaten people's lives. Therefore, even at the early stages of studying the EGC MD, it is necessary to understand the dynamics of changes in the EGC in order to provide preliminary protective measures.

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Use of absorption optical indices to assess seasonal variability of dissolved organic matter in amazon floodplain lakes

10.5194/bg-2019-324 ◽

2019 ◽

Author(s):

Maria Paula da Silva ◽

Lino A. Sander de Carvalho ◽

Evlyn Novo ◽

Daniel S. F. Jorge ◽

Claudio C. F. Barbosa

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Dissolved Organic Matter ◽

Seasonal Variability ◽

Large Scale ◽

River Bank ◽

In Situ Data ◽

Amazon Floodplain ◽

Significant Difference

Abstract. Given the importance of DOM in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. This study assesses the use of available absorption optical indices based on in situ data to both characterize the seasonal variability of the DOM dynamics in a highly complex environment and their viability of being used for satellite remote sensing on large scale studies. The study area comprises four lakes located at the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of coloured dissolved organic matter (CDOM) and remote sensing reflectance (Rrs) were acquired in situ. The Rrs was applied to simulate MSI visible bands and used in the proposed models. Differences between lakes were tested regarding CDOM indices. Significant difference in the average of aCDOM (440), aCDOM spectra and S275–295 were found between lakes located inside the flood forest and those near the river bank. The proposed model showed that aCDOM can be used as proxy of S275–295 during rising water with good validation results, demonstrating the potential of Sentinel/MSI imagery data in large scale studies on the dynamics of DOM.

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The MLT distribution and detailed structure of ring current: MMS observations

10.5194/egusphere-egu21-3730 ◽

2021 ◽

Author(s):

Xin Tan ◽

Malcolm Dunlop ◽

Xiangcheng Dong ◽

Yanyan Yang ◽

Christopher Russell

Keyword(s):

Current Density ◽

Large Scale ◽

Ring Current ◽

Current System ◽

Radial Profile ◽

Three Dimensional ◽

Current Density Distribution ◽

Magnetic Storms ◽

In Situ Data

The ring current is an important part of the large-scale magnetosphere-ionosphere current system; mainly concentrated in the equatorial plane, between 2-7 RE, and strongly ordered between &#177; 30 &#176; latitude. The morphology of ring current directly affects the geomagnetic field at low to middle latitudes. Rapid changes in ring current densities can occur during magnetic storms/sub-storms. Traditionally, the Dst index is used to characterize the intensity of magnetic storms and to reflect the variation of ring current intensity, but this index does not reflect the MLT distribution of ring current. In fact, the ring current has significant variations with MLT, depending on geomagnetic activity, due to the influence of multiple factors; such as, the partial ring current, region 1/region 2 field-aligned currents, the magnetopause current and sub-storm cycle (magnetotail current). The form of the ring current has been inferred from the three-dimensional distribution of ion differential fluxes from neutral atom imaging; however, this technique can not directly obtain the current density distribution (as can be obtained using multi-spacecraft in situ data). Previous in situ estimates of current density have used: Cluster, THEMIS and other spacecraft groups to study the distribution of the ring current for limited ranges of either radial profile, or MLT and MLAT variations. Here, we report on an extension to these studies using FGM data from MMS obtained during the period September 1, 2015 to December 31, 2016, when the MMS orbit and configuration provided good coverage. We employ the curlometer method to calculate the current density, statistically, to analysis the MLT distribution according to different geomagnetic conditions. Our results show the clear asymmetry of the ring current and its different characteristics under different geomagnetic conditions.

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Augmenting satellite-derived soil moisture with multiple data streams using machine learning

10.5194/egusphere-egu2020-7428 ◽

2020 ◽

Author(s):

Rene Orth ◽

Sungmin Oh

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Data Streams ◽

Large Scale ◽

Extreme Weather Events ◽

Soil Moisture Data ◽

Wide Range ◽

Hydrological Hazards ◽

Target Data

Soil moisture plays a key role in land-atmosphere interactions through its influence on the energy and water cycles. Furthermore, its spatiotemporal variations can affect the development and persistence of extreme weather events. Consequently, soil moisture information is required for a wide range of research and applications, such as agricultural monitoring, flood and drought prediction, climate projection, and carbon-cycle modeling. Despite its scientific and societal importance, observations of soil moisture are sparse, in particular across time and at large spatial scales. Only models and satellite retrievals can provide global soil moisture information. While the ability of land surface models to represent the complex land-atmosphere interplay is still limited, satellite-based soil moisture data are a valuable alternative. However, these products suffer from a scaling based on models, and can only capture the top few centimeters of the soil.&#160;In this study, we aim to augment satellite-based soil moisture data using machine learning. For this purpose we integrate satellite soil moisture with multiple hydro-meteorological data streams to derive global gridded soil moisture using Long Short-Term Memory (LSTM) neural networks. These networks are trained using in-situ soil moisture measurements as target data. With the resulting self-learned relationships, the LSTMs can produce in-situ-like soil moisture globally. We further analyze the implications of using point-scale target data to infer large scale information. The new dataset is derived separately for the surface and the deeper soil, thereby extending beyond the range covered by the satellite-based products. The integration of many data streams and multiple soil moisture observations through a powerful synergistic technique offers the potential to yield high accuracy. This is tested through rigorous cross-validation of the derived dataset. Finally, the planned datasets will permit consistent long-term, large-scale analysis to enhance our understanding of the hydrology-biosphere-climate interplay, to better constrain models and to support hydrological hazards monitoring and climate projections.

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