Abstract in Spanish:
El proyecto PIXIL es un esfuerzo de colaboración científico-tecnológica transfronterizo y multidisciplinar, con el objetivo de desarrollar las herramientas más punteras para analizar el subsuelo terrestre, con especial interés en favorecer el crecimiento de la energía geotérmica en la región. El proyecto contribuirá a hacer de la zona transpirenaica un referente en tecnología de caracterización del subsuelo en los próximos dos años y su consecución espera servir de impulso a la creación de empleo y riqueza en la zona, relacionados con la generación y gestión de recursos naturales subterráneos.
PIXIL tiene un marcado perfil de investigación, donde prima la colaboración científica entre los centros y cuyos resultados son avances en las tres disciplinas que conforman el imaging geofísico moderno: Geofísica, Matemática Aplicada y Computación.
El principal objetivo de PIXIL es la creación de una red transfronteriza orientada a llegar al primer nivel mundial en I+D relacionado con la caracterización del subsuelo mediante el "imaging geofísico". La aplicación tecnológica será en el campo de la energía geotérmica, fomentándose particularmente su aplicación en la región. Esta red generará prototipos de imaging útiles para su aplicación industrial, listos para ser empleados en supercomputadores.
Objetivos específicos:
- Generar una red de intercambio de I+D transfronteriza en el campo del imaging geofísico para la caracterización del subsuelo, que permita a la región establecerse como líder mundial en el campo. Promover nuestros desarrollos a otras comunidades que usan ondas para estudiar medios complejos u ocultos.
- Generar prototipos de software libre de altas prestaciones (HPC) para problemas industriales del ámbito del imaging geofísico, con foco particular en la energía geotérmica, que sean validados por empresas del sector (algunas de las cuales son socios asociados de PIXIL) y resulten en el embrión de aplicaciones finalistas competitivas comercialmente.
- Impulsar el desarrollo y abaratamiento de la geotermia en la región, así como congregar a impulsores de dichas tecnologías y potenciales clientes públicos y privados en jornadas de diseminación. Todo esto a ambos lados de la frontera con el fin de expandir el uso de dicha tecnología y exponer casos de uso
Abstract in English:
The PIXIL project is a transnational and multidisciplinary scientific and technological cooperation. Its main goal is to develop the most advanced tools to analyze the Earth's subsurface, with a special focus on fostering the uptake of geothermal energy in the region. The project will contribute to making the trans-Pyrenean area a technology hub in subsoil characterization within two years. Its success is expected to boost the wealth and creation of jobs related to the generation and management of underground natural resources in the area.
PIXIL has a strong research profile where scientific collaboration between institutions prevails. The project outcomes will result in advances in the three disciplines that make up modern geophysical imaging: Geophysics, Applied Mathematics and Computing.
Specific objectives:
- Generate a cross-border R&D exchange network in the field of geophysical imaging for the characterization of the subsoil, allowing the region to establish itself as a world leader in the field. Promote our developments to other communities that use waves to study complex or hidden media.
- Generate free high-performance computing (HPC) software prototypes for industrial problems in the field of geophysical imaging, with a particular focus on geothermal energy. Such software will be validated by companies in the sector (some of which are associate partners of PIXIL) and will be the seed of future competitive products.
- Promote the development and cost reduction of geothermal energy in the region, as well as to gather promoters of these technologies and potential public and private clients in dissemination events. Both sides of the border will be targeted in order to expand the use of such technology and expose successful use cases in the region.
FEDER reference
SP: Fondo de Desarrollo Regional (FEDER)
EN: European Regional Development Fund (ERDF)
Link to POCTEFA: www.poctefa.eu
Link to the EU policy: ec.europa.eu/regional_policy/es/
Funding
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)
More information about PIXIL project: https://pixil-project.eu/
UB Investigadora Principal: Pilar Queralt