scholarly journals High-resolution seismic tomography of Long Beach, CA using machine learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Michael J. Bianco ◽  
Peter Gerstoft ◽  
Kim B. Olsen ◽  
Fan-Chi Lin
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Travel Time ◽  
Phase Speed ◽  
Small Scale ◽  
Long Beach ◽  
Travel Time Tomography ◽  
Tomography Method ◽  
Dense Sampling ◽  
Geophysical Features

Abstract We use a machine learning-based tomography method to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a “large-N” array with 5204 geophones (~13.5 million travel times). This method, called locally sparse travel time tomography (LST) uses unsupervised machine learning to exploit the dense sampling obtained by ambient noise processing on large arrays. Dense sampling permits the LST method to learn directly from the data a dictionary of local, or small-scale, geophysical features. The features are the small scale patterns of Earth structure most relevant to the given tomographic imaging scenario. Using LST, we obtain a high-resolution 1 Hz Rayleigh wave phase speed map of Long Beach. Among the geophysical features shown in the map, the important Silverado aquifer is well isolated relative to previous surface wave tomography studies. Our results show promise for LST in obtaining detailed geophysical structure in travel time tomography studies.

High‐Resolution Lithospheric Velocity Structure of Continental China by Double‐Difference Seismic Travel‐Time Tomography

2018 ◽  
Vol 90 (1) ◽  
pp. 229-241 ◽  
Author(s):  
Hailiang Xin ◽  
Haijiang Zhang ◽  
Min Kang ◽  
Rizheng He ◽  
Lei Gao ◽  
...  
Keyword(s):  
High Resolution ◽  
Travel Time ◽  
Velocity Structure ◽  
Double Difference ◽  
Travel Time Tomography

scholarly journals Small-scale thermal upwellings under the northern East African Rift from S travel time tomography

2016 ◽  
Vol 121 (10) ◽  
pp. 7395-7408 ◽  
Author(s):  
Chiara Civiero ◽  
Saskia Goes ◽  
James O. S. Hammond ◽  
Stewart Fishwick ◽  
Abdulhakim Ahmed ◽  
...  
Keyword(s):  
Travel Time ◽  
East African Rift ◽  
Small Scale ◽  
East African ◽  
Travel Time Tomography ◽  
African Rift

scholarly journals High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment

2018 ◽  
Vol 15 (6) ◽  
pp. 1663-1682 ◽  
Author(s):  
Matthias B. Siewert
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Random Forest ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Support Vector ◽  
Small Scale ◽  
Permafrost Degradation ◽  
Discontinuous Permafrost

Abstract. Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1 m (1×1 m). A high-resolution (1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to be 8.3 ± 8.0 kg C m−2 and the SOC stored in the top meter (0–100 cm) to be 7.7 ± 6.2 kg C m−2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape's SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2, 10, 30, 100, 250 and 1000 m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions  >  30 m. This is associated with the occurrence of many small-scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales, the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.

scholarly journals High-resolution digital mapping of soil organic carbon in permafrost terrain using machine-learning: A case study in a sub-Arctic peatland environment

2017 ◽  
Author(s):  
Matthias B. Siewert
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Random Forest ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Support Vector ◽  
Small Scale ◽  
Permafrost Degradation ◽  
Discontinuous Permafrost

Abstract. Soil organic carbon (SOC) stored in northern peatlands and permafrost affected soils are key components in the global carbon cycle. I quantify SOC stocks in a sub-arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest approach performed best and was used to predict SOC for several depth increments at a spatial resolution of 2 ×2 m. A high-resolution (1 × 1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to 7.9 ± 8.0 kg C m−2 and the SOC stored in the top meter (0–100 cm) to 7.0 ± 6.3 kg C m−2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape SOC storage. A surprising large number of small scale wetland areas are mapped forming very local hot-spots of SOC storage. The results show that robust SOC predictions are possible with the available methods and very high-resolution remote sensing data. Strong environmental gradients associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scale the main factor limiting robust, high-resolution SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Absiko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic in wetland areas with permafrost related landforms. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.

scholarly journals 3-D seismic travel-time tomography validation of a detailed subsurface model: a case study of the Záncara river basin (Cuenca, Spain)

Solid Earth ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 177-192
Author(s):  
David Marti ◽  
Ignacio Marzan ◽  
Jana Sachsenhausen ◽  
Joaquina Alvarez-Marrón ◽  
Mario Ruiz ◽  
...  
Keyword(s):  
High Resolution ◽  
Travel Time ◽  
River Basin ◽  
Seismic Velocity ◽  
Underground Structure ◽  
High Capacity ◽  
Seismic Source ◽  
P Wave ◽  
Shallow Subsurface ◽  
Travel Time Tomography

Abstract. A high-resolution seismic tomography survey was acquired to obtain a full 3-D P-wave seismic velocity image in the Záncara river basin (eastern Spain). The study area consists of lutites and gypsum from a Neogene sedimentary sequence. A regular and dense grid of 676 shots and 1200 receivers was used to image a 500 m×500 m area of the shallow subsurface. A 240-channel system and a seismic source, consisting of an accelerated weight drop, were used in the acquisition. Half a million travel-time picks were inverted to provide the 3-D seismic velocity distribution up to 120 m depth. The project also targeted the geometry of the underground structure with emphasis on defining the lithological contacts but also the presence of cavities and fault or fractures. An extensive drilling campaign provided uniquely tight constraints on the lithology; these included core samples and wireline geophysical measurements. The analysis of the well log data enabled the accurate definition of the lithological boundaries and provided an estimate of the seismic velocity ranges associated with each lithology. The final joint interpreted image reveals a wedge-shaped structure consisting of four different lithological units. This study features the necessary key elements to test the travel time tomographic inversion approach for the high-resolution characterization of the shallow subsurface. In this methodological validation test, travel-time tomography demonstrated to be a powerful tool with a relatively high capacity for imaging in detail the lithological contrasts of evaporitic sequences located at very shallow depths, when integrated with additional geological and geophysical data.

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