date: 2022-08-20T16:37:03Z
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pdf:docinfo:title: Neural network-based classification of rock properties and seismic vulnerability
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dc:description: BACKGROUND AND OBJECTIVES: Soil or rock types in a region are often interpreted qualitatively by visually comparing various geophysical properties such as seismic wave velocity and vulnerability, as well as gravity data. Better insight and less human-dependent interpretation of soil types can be obtained from a joint analysis of separated and independent geophysical parameters. This paper discusses the application of a neural network approach to derive rock properties and seismic vulnerability from horizontal-to-vertical seismic ratio and seismic wave velocity data recorded in Majalengka-West Java, Indonesia.
METHODS: Seismic microtremors were recorded at 54 locations and additionally multichannel analyses of surface wave experiments were performed at 18 locations because the multichannel analyses of surface wave experiment needs more effort and space. From the two methods, the values of the average shear wave velocity for the upper 30 meters, peak amplitudes and the dominant frequency between the measurement points were obtained from the interpolation of those geophysical data. Neural network was then applied to adaptively cluster and map the geophysical parameters. Four learning model clusters were developed from the three input seismic parameters: shear wave velocity, peak amplitude, and dominant frequency.
FINDINGS: Generally, the values of the horizontal to vertical spectral ratios in the west of the study area were low (less than 5) compared with those in the southeastern part. The dominant frequency values in the west were mostly low at around 0.1?3 Hertz, associated with thick sedimentary layer. The pattern of the shear wave velocity map correlates with that of the horizontal to vertical spectral ratio map as the amplification is related to the soil or rock rigidity represented by the shear wave velocity. The combination of the geophysical data showed new features which is not found on the geological map such as in the eastern part of the study area. 
CONCLUSION: The application of the neural network based clustering analysis to the geophysical data revealed four rock types which are difficult to observe visually. The four clusters classified based on the variation of the geophysical parameters show a good correlation to rock types obtained from previous geological surveys. The clustering classified safe and vulnerable regions although detailed investigation is still required for confirmation before further development. This study demonstrates that low-cost geophysical experiments combined with neural network-based clustering can provide additional information which is important for seismic hazard mitigation in densely populated areas.

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subject: BACKGROUND AND OBJECTIVES: Soil or rock types in a region are often interpreted qualitatively by visually comparing various geophysical properties such as seismic wave velocity and vulnerability, as well as gravity data. Better insight and less human-dependent interpretation of soil types can be obtained from a joint analysis of separated and independent geophysical parameters. This paper discusses the application of a neural network approach to derive rock properties and seismic vulnerability from horizontal-to-vertical seismic ratio and seismic wave velocity data recorded in Majalengka-West Java, Indonesia.
METHODS: Seismic microtremors were recorded at 54 locations and additionally multichannel analyses of surface wave experiments were performed at 18 locations because the multichannel analyses of surface wave experiment needs more effort and space. From the two methods, the values of the average shear wave velocity for the upper 30 meters, peak amplitudes and the dominant frequency between the measurement points were obtained from the interpolation of those geophysical data. Neural network was then applied to adaptively cluster and map the geophysical parameters. Four learning model clusters were developed from the three input seismic parameters: shear wave velocity, peak amplitude, and dominant frequency.
FINDINGS: Generally, the values of the horizontal to vertical spectral ratios in the west of the study area were low (less than 5) compared with those in the southeastern part. The dominant frequency values in the west were mostly low at around 0.1?3 Hertz, associated with thick sedimentary layer. The pattern of the shear wave velocity map correlates with that of the horizontal to vertical spectral ratio map as the amplification is related to the soil or rock rigidity represented by the shear wave velocity. The combination of the geophysical data showed new features which is not found on the geological map such as in the eastern part of the study area. 
CONCLUSION: The application of the neural network based clustering analysis to the geophysical data revealed four rock types which are difficult to observe visually. The four clusters classified based on the variation of the geophysical parameters show a good correlation to rock types obtained from previous geological surveys. The clustering classified safe and vulnerable regions although detailed investigation is still required for confirmation before further development. This study demonstrates that low-cost geophysical experiments combined with neural network-based clustering can provide additional information which is important for seismic hazard mitigation in densely populated areas.

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description: BACKGROUND AND OBJECTIVES: Soil or rock types in a region are often interpreted qualitatively by visually comparing various geophysical properties such as seismic wave velocity and vulnerability, as well as gravity data. Better insight and less human-dependent interpretation of soil types can be obtained from a joint analysis of separated and independent geophysical parameters. This paper discusses the application of a neural network approach to derive rock properties and seismic vulnerability from horizontal-to-vertical seismic ratio and seismic wave velocity data recorded in Majalengka-West Java, Indonesia.
METHODS: Seismic microtremors were recorded at 54 locations and additionally multichannel analyses of surface wave experiments were performed at 18 locations because the multichannel analyses of surface wave experiment needs more effort and space. From the two methods, the values of the average shear wave velocity for the upper 30 meters, peak amplitudes and the dominant frequency between the measurement points were obtained from the interpolation of those geophysical data. Neural network was then applied to adaptively cluster and map the geophysical parameters. Four learning model clusters were developed from the three input seismic parameters: shear wave velocity, peak amplitude, and dominant frequency.
FINDINGS: Generally, the values of the horizontal to vertical spectral ratios in the west of the study area were low (less than 5) compared with those in the southeastern part. The dominant frequency values in the west were mostly low at around 0.1?3 Hertz, associated with thick sedimentary layer. The pattern of the shear wave velocity map correlates with that of the horizontal to vertical spectral ratio map as the amplification is related to the soil or rock rigidity represented by the shear wave velocity. The combination of the geophysical data showed new features which is not found on the geological map such as in the eastern part of the study area. 
CONCLUSION: The application of the neural network based clustering analysis to the geophysical data revealed four rock types which are difficult to observe visually. The four clusters classified based on the variation of the geophysical parameters show a good correlation to rock types obtained from previous geological surveys. The clustering classified safe and vulnerable regions although detailed investigation is still required for confirmation before further development. This study demonstrates that low-cost geophysical experiments combined with neural network-based clustering can provide additional information which is important for seismic hazard mitigation in densely populated areas.

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title: Neural network-based classification of rock properties and seismic vulnerability
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dc:title: Neural network-based classification of rock properties and seismic vulnerability
modified: 2022-08-20T16:37:03Z
cp:subject: BACKGROUND AND OBJECTIVES: Soil or rock types in a region are often interpreted qualitatively by visually comparing various geophysical properties such as seismic wave velocity and vulnerability, as well as gravity data. Better insight and less human-dependent interpretation of soil types can be obtained from a joint analysis of separated and independent geophysical parameters. This paper discusses the application of a neural network approach to derive rock properties and seismic vulnerability from horizontal-to-vertical seismic ratio and seismic wave velocity data recorded in Majalengka-West Java, Indonesia.
METHODS: Seismic microtremors were recorded at 54 locations and additionally multichannel analyses of surface wave experiments were performed at 18 locations because the multichannel analyses of surface wave experiment needs more effort and space. From the two methods, the values of the average shear wave velocity for the upper 30 meters, peak amplitudes and the dominant frequency between the measurement points were obtained from the interpolation of those geophysical data. Neural network was then applied to adaptively cluster and map the geophysical parameters. Four learning model clusters were developed from the three input seismic parameters: shear wave velocity, peak amplitude, and dominant frequency.
FINDINGS: Generally, the values of the horizontal to vertical spectral ratios in the west of the study area were low (less than 5) compared with those in the southeastern part. The dominant frequency values in the west were mostly low at around 0.1?3 Hertz, associated with thick sedimentary layer. The pattern of the shear wave velocity map correlates with that of the horizontal to vertical spectral ratio map as the amplification is related to the soil or rock rigidity represented by the shear wave velocity. The combination of the geophysical data showed new features which is not found on the geological map such as in the eastern part of the study area. 
CONCLUSION: The application of the neural network based clustering analysis to the geophysical data revealed four rock types which are difficult to observe visually. The four clusters classified based on the variation of the geophysical parameters show a good correlation to rock types obtained from previous geological surveys. The clustering classified safe and vulnerable regions although detailed investigation is still required for confirmation before further development. This study demonstrates that low-cost geophysical experiments combined with neural network-based clustering can provide additional information which is important for seismic hazard mitigation in densely populated areas.

pdf:docinfo:subject: BACKGROUND AND OBJECTIVES: Soil or rock types in a region are often interpreted qualitatively by visually comparing various geophysical properties such as seismic wave velocity and vulnerability, as well as gravity data. Better insight and less human-dependent interpretation of soil types can be obtained from a joint analysis of separated and independent geophysical parameters. This paper discusses the application of a neural network approach to derive rock properties and seismic vulnerability from horizontal-to-vertical seismic ratio and seismic wave velocity data recorded in Majalengka-West Java, Indonesia.
METHODS: Seismic microtremors were recorded at 54 locations and additionally multichannel analyses of surface wave experiments were performed at 18 locations because the multichannel analyses of surface wave experiment needs more effort and space. From the two methods, the values of the average shear wave velocity for the upper 30 meters, peak amplitudes and the dominant frequency between the measurement points were obtained from the interpolation of those geophysical data. Neural network was then applied to adaptively cluster and map the geophysical parameters. Four learning model clusters were developed from the three input seismic parameters: shear wave velocity, peak amplitude, and dominant frequency.
FINDINGS: Generally, the values of the horizontal to vertical spectral ratios in the west of the study area were low (less than 5) compared with those in the southeastern part. The dominant frequency values in the west were mostly low at around 0.1?3 Hertz, associated with thick sedimentary layer. The pattern of the shear wave velocity map correlates with that of the horizontal to vertical spectral ratio map as the amplification is related to the soil or rock rigidity represented by the shear wave velocity. The combination of the geophysical data showed new features which is not found on the geological map such as in the eastern part of the study area. 
CONCLUSION: The application of the neural network based clustering analysis to the geophysical data revealed four rock types which are difficult to observe visually. The four clusters classified based on the variation of the geophysical parameters show a good correlation to rock types obtained from previous geological surveys. The clustering classified safe and vulnerable regions although detailed investigation is still required for confirmation before further development. This study demonstrates that low-cost geophysical experiments combined with neural network-based clustering can provide additional information which is important for seismic hazard mitigation in densely populated areas.

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