ResumoEste trabalho apresenta uma interpretação atualizada da estruturação interna da Bacia Sedimentar do Araripe, localizada ao sul da porção setentrional da Província Borborema, por meio do processamento e da análise de dados aerogeofísicos de magnetometria pertencentes à Companhia de Pesquisa de Recursos Minerais -Serviço Geológico do Brasil. Essa bacia é reconhecida como um conjunto de meio-grábens assimétricos, fragmentados por altos de embasamento e complexos sistemas de falhas e lineamentos E-W a NE-SW, associados ao trend estrutural do Orógeno Borborema, e NW-SE, relacionados a processos extencionais cuja evolução poderia estar associada ao rift-valley na extremidade norte do rift abortado da Bacia do Recôncavo. A partir da imagem geofísica do campo magnético anômalo foram extraídas suas derivadas de primeira ordem nas direções x, y, z, seu gradiente horizontal total, sua amplitude do sinal analítico (ASA) e inclinação do sinal analítico. A deconvolução de Euler foi aplicada ao grid ASA, o que permitiu uma aproximação da posição horizontal e estimativa da profundidade do topo das principais fontes magnéticas. A análise conjunta dos diversos mapas gerados permitiu uma interpretação regional dos domínios e lineamentos magnéticos, os quais foram associados às principais estruturas em subsuperfície que compõem o arcabouço da bacia. Os resultados demonstram que a compartimentação estrutural da Bacia Sedimentar do Araripe é muito mais complexa do que a descrita anteriormente por outros autores. O padrão estrutural da bacia apresenta variação lateral, de W para E, conforme demonstrado pela divisão dos domínios magnéticos, indicando haver variações substanciais na espessura das rochas sedimentares, com áreas mais espessas a E e mais rasas a W.Palavras-chave: Bacia do Araripe; Aeromagnetometria; Deconvolução de Euler; Arcabouço estrutural. AbstractThis paper aims an updated interpretation of the Araripe Sedimentary Basin internal structure, located in the south portion of the Borborema Province, Brazilian northeast, through aeromagnetic data interpretation, provided by the Geological Service of Brazil. This basin is recognized as a set of asymmetrical half-grabens fragmented by basement highs, complex faults system and E-W to NE-SW lineaments, those associated with the Borborema Orogen trend, and NW-SE lineaments, related to extensional processes whose evolution could be associated to the Reconcavo Basin aborted-rift system. From the geophysical map of total magnetic intensity, the first order derivatives were extracted the in the x, y, z directions; and the total horizontal derivative, the analytic signal amplitude (ASA) and the titl derivative were obtained. The Euler deconvolution was applied to the ASA grid, allowing an estimation of the depth and horizontal position of the top of the main anomalous magnetic sources. The joint analysis of the various maps made possible the interpretation of magnetic domains and lineaments, which were associated with the main subsurface geological structures that make up the s...
Here, we develop and test an artificial intelligence (AI)-based approach to monitor major Brazilian aquifers. The approach combines Gravity Recovery and Climate Experiment (GRACE) data and ground-based hydrogeological measurements from Brazil's Integrated Groundwater Monitoring Network at hundreds of wells distributed in twelve aquifers across the country. We use a model ensemble composed of four different AI models: Extreme Gradient Boost, Light Gradient Boosting Model, CatBoost and Multilayer Perceptron. The approach is further boosted with wavelet and seasonal decomposition processes applied to GRACE data. To determine the sensitivity of the AI approach to data availability, we propose four experiments combining hydrogeological measurements from different aquifers. Groundwater storage estimates from the Global Land Data Assimilation System (GLDAS) are used as the benchmark. The AI approach successfully reproduces groundwater storage estimates at all Brazilian aquifers. Results show that the proposed approach outperforms GLDAS in all experiments, with an average Nash-Sutcliff efficiency of 0.91 and an average RMSE of 0.43cm for the experiment that covers all monitored wells in Brazil. GLDAS resulted in -1.311 and 5.84cm, respectively. This study demonstrates that combining satellite data and AI can be a cost-effective alternative to monitor poorly equipped aquifers at the continental scale.
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