Detailed geologic mapping (639 field stations in ~700 km2) and a ~50 km-long gravity survey (142 stations) in the western Azuero Peninsula revealed two faulted and folded slivers of oceanic crust attached to the trailing edge of the Caribbean Large Igneous Plateau (CLIP). Our new data, along with published geochronology, allowed us to reconstruct the Cretaceous forearc configuration of the trailing edge of the CLIP prior to seamount collision, ophiolite accretion, and whole-margin deformation. The ophiolite in western Azuero is composed of two tectonic slivers arranged in south-verging, imbricated thrust faults that stack a ~73 Ma pillow, flow, and picritic basalt and black chert, together with a ~ 89–93 Ma and older basalt flows and capping red chert sequences. Accretion of these slivers to form a supra-subduction zone ophiolite resulted from the middle Eocene collision and accretion of Galapagos seamounts against the trailing edge of the CLIP. Accreted seamounts are arranged in a north-verging antiformal stack duplex, and below the thrust sheets. Change in kinematics after fission of the Cocos-Nazca Plate during early Miocene times prompted the propagation of the Azuero-Sona fault zone flower structure, favouring the preservation of these slivers of oceanic crust.@en

The ultimate goal of survey design is to find the acquisition parameters that enable acquiring high-quality data suitable for optimal imaging, while fulfilling budget, health, safety, and environmental constraints. We develop a target-oriented acquisition design algorithm based on full-wavefield migration. The algorithm optimizes a receiver density function that indicates the number of receivers per unit area required for obtaining the best possible image quality. The method makes use of available seismic data to create a reference model that is included in our objective function. To make the design target oriented, the objective function is multiplied with a mask that gives more weight to the target areas of interest. The results of the 2D and 3D implementations indicate an optimized receiver density function with higher values at the zones where more data are needed for improving image quality. The corresponding receiver geometries have more receivers placed in these areas. We validate the results by computing the images of the target zone using uniform and optimized geometries. The use of the latter indicates an improvement in the image quality at the target zone. In addition, we compute the number of receivers required for achieving a certain signal-to-noise ratio after imaging based on the optimized receiver density function.@en

Seismic data are traditionally acquired based on spatial sampling requirements, noise properties and budgetary constraints. However, designing a survey without taking into account the complexity of the subsurface may result in an image without the expected quality. Also, the subsequent preprocessing and processing steps may exploit or misuse the acquired data. The design should therefore incorporate the complexity of the subsurface and the (pre)processing steps that will be followed. We propose an analysis method that evaluates if the proposed combination of survey design, preprocessing and processing for a specific subsurface model fulfils a pre-defined quality criterion. With our method, we estimate a set of point-spread functions that correspond to the chosen combination, and we analyse their resolution and illumination-detection properties in the spatial and wavenumber domains, respectively. The estimated point-spread functions include the scattering and propagation effects generated by the subsurface, including internal multiples. We show that in some cases, the use of internal multiples in imaging can improve amplitude and resolution compared with the use of primaries only. The proposed analysis method is also used to evaluate the effect of blending noise when blended acquisition is carried out.@en

The ultimate goal in survey design is to obtain the acquisition parameters that enable acquiring the most affordable data that fulfills certain image quality requirements. We propose a method that allows optimization of the receiver geometry for a fixed source distribution. The former is parameterized with a receiver density function that determines the number of receivers per unit area. We optimize this receiverdensity function through an iterative gradient descent scheme that minimizes the difference between the image obtained with the current acquisition geometry and a reference image. The reference image is obtained from prior subsurface information that is assumed to be available. We tested the method with different subsurface models. The results show that the acquisition geometry is optimized according to the complexity of each subsurface model. The receivers are moved towards the areas where more data is needed for obtaining better imaging. @en

Seismic survey design deals with determining the acquisition parameters that lead to the best possible imaging and characterization of the subsurface. The design of the survey is constrained by health, safety and environmental considerations and the available budget, seeking for a balance between quality and cost. Because seismic exploration is a widely used geophysical method for revealing underground resources, information about the subsurface is available in many areas. Therefore, it can potentially be used for purposes supplementary to exploration such as the monitoring of producing fields and fluids injection. However, the available budget for these purposes is usually lower than for exploration, and it becomes a priority to maximize the benefits derived from a potentially cheaper acquisition. In this thesis, we propose new methods for the analysis and design of seismic surveys that are based on previous knowledge from existing subsurface models and that aimto maximize image quality with the lowest acquisition efforts.@en

Seismic data is traditionally acquired based on spatial sampling requirements, noise properties, and budgetary constraints. However, designing a survey without taking into account the complexity of the subsurface may result in an image without the expected quality. Also, the subsequent preprocessing and processing steps, may exploit or misuse the acquired data. The design should therefore incorporate the complexity of the subsurface and the (pre)processing steps that will be followed. We propose an analysis method that evaluates if the proposed combination of survey design, preprocessing and processing for a specific subsurface model fulfills a pre-defined quality criterion. With our method we estimate a set of point-spread functions that correspond to the chosen combination and we analyse their resolution and illumination-detection properties in the image and wavenumber domains, respectively. The estimated point-spread functions include the scattering and propagation effects generated by the subsurface, including internal multiples. We show that in some cases, the use of internal multiples in imaging can improve resolution and illumination-detection compared to the use of primaries only.@en

Seismic data is traditionally acquired based on spatial sampling requirements, noise properties, and budgetary constraints. However, designing a survey without taking into account the complexity of the subsurface may result in an image without the expected quality. Also, the subsequent preprocessing and processing steps, may exploit or misuse the acquired data. The design should therefore incorporate the complexity of the subsurface and the (pre)processing steps that will be followed. We propose an analysis method that evaluates if the proposed combination of survey design, preprocessing and processing for a specific subsurface model fulfills a pre-defined quality criterion. With our method we estimate a set of point-spread functions that correspond to the chosen combination and we analyse their resolution and illumination-detection properties in the image and wavenumber domains, respectively. The estimated point-spread functions include the scattering and propagation effects generated by the subsurface, including internal multiples. We show that in some cases, the use of internal multiples in imaging can improve resolution and illumination-detection compared to the use of primaries only.@en

The ultimate goal in survey design is to obtain the acquisition parameters that allow to acquire the most affordable data that fulfills certain image quality requirements. We propose a method that allows to optimize the receiver geometry for a fixed source distribution, e.g., think of ocean bottom nodes. The former is parameterized with a receiver density function that determines the number of receivers per unit area. We optimize this receiver density function through an iterative gradient descent scheme that minimizes the difference between the image obtained with the current acquisition geometry and a reference image. The reference image is obtained from prior subsurface information that is assumed to be available. We tested the method with different subsurface models. The results show that the acquisition geometry is optimized according to the complexity of each subsurface model. The receivers are moved towards the areas where more data is needed for obtaining better imaging. Currently, we work on extending our method for the source distribution.@en

The ultimate goal in survey design is to obtain the acquisition parameters that allow to acquire the most affordable data that fulfills certain image quality requirements. We propose a method that allows to optimize the receiver geometry for a fixed source distribution, e.g., think of ocean bottom nodes. The former is parameterized with a receiver density function that determines the number of receivers per unit area. We optimize this receiver density function through an iterative gradient descent scheme that minimizes the difference between the image obtained with the current acquisition geometry and a reference image. The reference image is obtained from prior subsurface information that is assumed to be available. We tested the method with different subsurface models. The results show that the acquisition geometry is optimized according to the complexity of each subsurface model. The receivers are moved towards the areas where more data is needed for obtaining better imaging. Currently, we work on extending our method for the source distribution.@en