Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS...Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations.We evaluated the performance of the CNN model in terms of its vertical and spatial distribution,as well as seasonal variation of OSSS estimation.Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS.However,the estimation accuracy of the CNN model varies with depth,with the most challenging depth being approximately 70 m,corresponding to the halocline layer.Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes.The results show that the CNN model effectively captures the seasonal variability of salinity,demonstrating its high performance in salinity estimation using sea surface data.Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers,while sea surface height anomaly plays a more significant role in deeper layers.These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.展开更多
Characterizing the subsurface structure is an important parameter for the improvement of seismic hazard assessment.Due to the tectonic complexity of the earth,some deep fractures do not reach the earth's surface a...Characterizing the subsurface structure is an important parameter for the improvement of seismic hazard assessment.Due to the tectonic complexity of the earth,some deep fractures do not reach the earth's surface and are not detectable with visual analysis.Therefore,the lack of knowledge of faults and fractures can result in disasters,especially in urban planning.Many geophysical methods can be used to estimate subsurface structure characterization.However,a more reliable method is required to assess seismic hazards and reduce potential damage in metropolitan areas without destroying buildings and structures.This paper aims to identify hidden faults and structures using shear wave velocity sections.To do this,surface wave dispersion curve was extracted from the vertical component of microtremor array recording using the spatial autocorrelation(SPAC)method in two profiles and 13 array stations(perpendicular to the altitudes)to obtain shear wave velocity structure(Vs)in the west of Mashhad,northeast of Iran.The results of shear wave velocity profiles(Vs)indicate sudden changes in the thickness of sediments.This can be related to the displacement of a normal fault in this area causing the bottom rock to fall and an increase in the alluvial thickness in the central part of the plain.The velocity in the floor rock is 2000 meters per second in this area.According to the surface outcrops and water wells data,its material is slate and Phyllite metamorphic rocks that are exposed in the adjacent heights.Besides,the seismic profile results were well consistent with electrical resistance data and well logs indicating that the tool array method is flexible,non-invasive,relatively fast,and effective for urban areas with satisfactory accuracy.展开更多
The study area is located at the south of the eastern desert of Egypt between latitudes 24<span style="white-space:nowrap;"><span style="white-space:nowrap;">°</span><...The study area is located at the south of the eastern desert of Egypt between latitudes 24<span style="white-space:nowrap;"><span style="white-space:nowrap;">°</span></span>N to 25<span style="white-space:nowrap;"><span style="white-space:nowrap;">°</span></span>N and longitudes 33<span style="white-space:nowrap;"><span style="white-space:nowrap;">°</span></span>E to 33<span style="white-space:nowrap;"><span style="white-space:nowrap;">°</span></span>50'E covering an area of about 9407 km<sup>2</sup>. The study area is mainly covered with sediments whose age extends from the upper Cretaceous to the Quaternary, in addition to the presence of some basement rocks such as younger granites, metasediments and metagabbro. The research aims essentially to determine the thickness of the sedimentary basin by determining the depth to the top of basement and delineating the subsurface geological structures which affected this sedimentary basin. The Euler depth map exhibited that the north parts of the area have shallow depth values from 1000 m to 2000 m. The southern parts also show a shallow to moderate depths ranging from 1000 m to 2400 m. The deepest parts are located at the middle and at the western parts and are ranging in value from 3000 m to more than 4000 m. The horizontal derivative and tilt derivative techniques proved that the most effective trends all over the study area are NW-SE and NE-SW directions as mentioned in geologic lineaments map. The basement tectonic map shows clearly all the faults affected the area. It shows that there are many high blocks trending mainly in NW-SE and NE-SW directions. All high blocks surround a large sedimentary basin reaches depth of about more than 4000 m. All the results produced from 2D-modeling illustrate that the sedimentary basinal area (G2) is the deeper basin all over the area and it is controlled by some faults and fractures. 3D inversion was used and resulted in that the area of study have many high blocks at shallow to moderate depths which surrounding a large sedimentary basinal area with very deep depth values. All the techniques which applied in this research led to that the largest sedimentary basin is located at the center of the study area with NW-SE trend and depth value of about 4000 m.展开更多
Surface and deep subsurface geological structural trends,stratigraphic features,and reservoir characteristics play important roles in assessment of hydrocarbon potential.Here,an approach that integrates digital elevat...Surface and deep subsurface geological structural trends,stratigraphic features,and reservoir characteristics play important roles in assessment of hydrocarbon potential.Here,an approach that integrates digital elevation modelling,seismic interpretation,seismic attributes,three-dimensional(3D)geological structural modeling predicated on seismic data interpretation,and petrophysical analysis is presented to visualize and analyze reservoir structural trends and determine residual hydrocarbon potential.The digital elevation model is utilized to provide verifiable predictions of the Dhulian surface structure.Seismic interpretation of synthetic seismograms use two-way time and depth contour models to perform a representative 3D reservoir geological structure evaluation.Based on Petrel structural modeling efficiency,reservoir development indexes,such as the true 3D structural trends,slope,geometry type,depth,and possibility of hydrocarbon prospects,were calculated for the Eocene limestone Chorgali,upper Paleocene limestone Lockhart,early Permian arkosic sandstone Warcha,and Precambrian Salt Range formations.Trace envelope,instantaneous frequency,and average energy attribute analyses were utilized to resolve the spatial predictions of the subsurface structure,formation extrusion,and reflector continuity.We evaluated the average porosity,permeability,net to gross ratio,water saturation,and hydrocarbon saturation of early Eocene limestone and upper Paleocene limestone based on the qualitative interpretation of well log data.In summary,this integrated study validates 3D stratigraphic structural trends and fault networks,facilitates the residual hydrocarbon potential estimates,and reveals that the Dhulian area has a NE to SW(fold axis)thrust-bounded salt cored anticline structure,which substantiates the presence of tectonic compression.The thrust faults have fold axes trending from ENE to WSW,and the petrophysical analysis shows that the mapped reservoir is of good quality and has essential hydrocarbon potential,which can be exploited economically.展开更多
基金Supported by the National Key Research and Development Program of China(No.2022YFF0801400)the National Natural Science Foundation of China(No.42176010)the Natural Science Foundation of Shandong Province,China(No.ZR2021MD022)。
文摘Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations.We evaluated the performance of the CNN model in terms of its vertical and spatial distribution,as well as seasonal variation of OSSS estimation.Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS.However,the estimation accuracy of the CNN model varies with depth,with the most challenging depth being approximately 70 m,corresponding to the halocline layer.Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes.The results show that the CNN model effectively captures the seasonal variability of salinity,demonstrating its high performance in salinity estimation using sea surface data.Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers,while sea surface height anomaly plays a more significant role in deeper layers.These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.
文摘Characterizing the subsurface structure is an important parameter for the improvement of seismic hazard assessment.Due to the tectonic complexity of the earth,some deep fractures do not reach the earth's surface and are not detectable with visual analysis.Therefore,the lack of knowledge of faults and fractures can result in disasters,especially in urban planning.Many geophysical methods can be used to estimate subsurface structure characterization.However,a more reliable method is required to assess seismic hazards and reduce potential damage in metropolitan areas without destroying buildings and structures.This paper aims to identify hidden faults and structures using shear wave velocity sections.To do this,surface wave dispersion curve was extracted from the vertical component of microtremor array recording using the spatial autocorrelation(SPAC)method in two profiles and 13 array stations(perpendicular to the altitudes)to obtain shear wave velocity structure(Vs)in the west of Mashhad,northeast of Iran.The results of shear wave velocity profiles(Vs)indicate sudden changes in the thickness of sediments.This can be related to the displacement of a normal fault in this area causing the bottom rock to fall and an increase in the alluvial thickness in the central part of the plain.The velocity in the floor rock is 2000 meters per second in this area.According to the surface outcrops and water wells data,its material is slate and Phyllite metamorphic rocks that are exposed in the adjacent heights.Besides,the seismic profile results were well consistent with electrical resistance data and well logs indicating that the tool array method is flexible,non-invasive,relatively fast,and effective for urban areas with satisfactory accuracy.
文摘The study area is located at the south of the eastern desert of Egypt between latitudes 24<span style="white-space:nowrap;"><span style="white-space:nowrap;">°</span></span>N to 25<span style="white-space:nowrap;"><span style="white-space:nowrap;">°</span></span>N and longitudes 33<span style="white-space:nowrap;"><span style="white-space:nowrap;">°</span></span>E to 33<span style="white-space:nowrap;"><span style="white-space:nowrap;">°</span></span>50'E covering an area of about 9407 km<sup>2</sup>. The study area is mainly covered with sediments whose age extends from the upper Cretaceous to the Quaternary, in addition to the presence of some basement rocks such as younger granites, metasediments and metagabbro. The research aims essentially to determine the thickness of the sedimentary basin by determining the depth to the top of basement and delineating the subsurface geological structures which affected this sedimentary basin. The Euler depth map exhibited that the north parts of the area have shallow depth values from 1000 m to 2000 m. The southern parts also show a shallow to moderate depths ranging from 1000 m to 2400 m. The deepest parts are located at the middle and at the western parts and are ranging in value from 3000 m to more than 4000 m. The horizontal derivative and tilt derivative techniques proved that the most effective trends all over the study area are NW-SE and NE-SW directions as mentioned in geologic lineaments map. The basement tectonic map shows clearly all the faults affected the area. It shows that there are many high blocks trending mainly in NW-SE and NE-SW directions. All high blocks surround a large sedimentary basin reaches depth of about more than 4000 m. All the results produced from 2D-modeling illustrate that the sedimentary basinal area (G2) is the deeper basin all over the area and it is controlled by some faults and fractures. 3D inversion was used and resulted in that the area of study have many high blocks at shallow to moderate depths which surrounding a large sedimentary basinal area with very deep depth values. All the techniques which applied in this research led to that the largest sedimentary basin is located at the center of the study area with NW-SE trend and depth value of about 4000 m.
基金supported by grants from the National Natural Science Foundation of China(Grant Nos.42072326,41772348)the National Key Research and Development Program of China(Nos.2019YFC1805905,2017YFC0601503).
文摘Surface and deep subsurface geological structural trends,stratigraphic features,and reservoir characteristics play important roles in assessment of hydrocarbon potential.Here,an approach that integrates digital elevation modelling,seismic interpretation,seismic attributes,three-dimensional(3D)geological structural modeling predicated on seismic data interpretation,and petrophysical analysis is presented to visualize and analyze reservoir structural trends and determine residual hydrocarbon potential.The digital elevation model is utilized to provide verifiable predictions of the Dhulian surface structure.Seismic interpretation of synthetic seismograms use two-way time and depth contour models to perform a representative 3D reservoir geological structure evaluation.Based on Petrel structural modeling efficiency,reservoir development indexes,such as the true 3D structural trends,slope,geometry type,depth,and possibility of hydrocarbon prospects,were calculated for the Eocene limestone Chorgali,upper Paleocene limestone Lockhart,early Permian arkosic sandstone Warcha,and Precambrian Salt Range formations.Trace envelope,instantaneous frequency,and average energy attribute analyses were utilized to resolve the spatial predictions of the subsurface structure,formation extrusion,and reflector continuity.We evaluated the average porosity,permeability,net to gross ratio,water saturation,and hydrocarbon saturation of early Eocene limestone and upper Paleocene limestone based on the qualitative interpretation of well log data.In summary,this integrated study validates 3D stratigraphic structural trends and fault networks,facilitates the residual hydrocarbon potential estimates,and reveals that the Dhulian area has a NE to SW(fold axis)thrust-bounded salt cored anticline structure,which substantiates the presence of tectonic compression.The thrust faults have fold axes trending from ENE to WSW,and the petrophysical analysis shows that the mapped reservoir is of good quality and has essential hydrocarbon potential,which can be exploited economically.
