Bayesian and Neural Network Approaches to Estimate Deep Temperature Distribution for Assessing a Supercritical Geothermal System: Evaluation Using a Numerical Model     

Ishitsuka  Kazuya  Kobayashi  Yosuke  Watanabe  Norihiro  Yamaya  Yusuke  Bjarkason  Elvar  Suzuki  Anna  Mogi  Toru  Asanuma  Hiroshi  Kajiwara  Tatsuya  Sugimoto  Takeshi  Saito  Ryoichi 《Natural Resources Research》2021,30(5):3289-3314

The temperature distribution at depth is a key variable when assessing the potential of a supercritical geothermal resource as well as a conventional geothermal resource. Data-driven estimation by a machine-learning approach is a promising way to estimate temperature distributions at depth in geothermal fields. In this study, we developed two methodologies—one based on Bayesian estimation and the other on neural networks—to estimate temperature distributions in geothermal fields. These methodologies can be used to supplement existing temperature logs, by estimating temperature distributions in unexplored regions of the subsurface, based on electrical resistivity data, observed geological/mineralogical boundaries, and microseismic observations. We evaluated the accuracy and characteristics of these methodologies using a numerical model of the Kakkonda geothermal field, Japan, where a temperature above 500 °C was observed below a depth of about 3.7 km. When using geological and geophysical knowledge as prior information for the machine learning methods, the results demonstrate that the approaches can provide subsurface temperature estimates that are consistent with the temperature distribution given by the numerical model. Using a numerical model as a benchmark helps to understand the characteristics of the machine learning approaches and may help to identify ways of improving these methods.

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A meteorite crater on Earth formed on September 15, 2007: The Carancas hypervelocity impact     

G. Tancredi  J. Ishitsuka  P. H. Schultz  R. S. Harris  P. Brown  D. O. Revelle  K. Antier  A. Le Pichon  D. Rosales  E. Vidal  M. E. Varela  L. Sánchez  S. Benavente  J. Bojorquez  D. Cabezas  A. Dalmau 《Meteoritics & planetary science》2009,44(12):1967-1984

Abstract— On September 15, 2007, a bright fireball was observed and a big explosion was heard by many inhabitants near the southern shore of Lake Titicaca. In the community of Carancas (Peru), a 13.5 m crater and several fragments of a stony meteorite were found close to the site of the impact. The Carancas event is the first impact crater whose formation was directly observed by several witnesses as well as the first unambiguous seismic recording of a crater‐forming meteorite impact on Earth. We present several lines of evidence that suggest that the Carancas crater was a hypervelocity impact. An event like this should have not occurred according to the accepted picture of stony meteoroids ablating in the Earth's atmosphere, therefore it challenges our present models of entry dynamics. We discuss alternatives to explain this particular event. This emphasizes the weakness in the pervasive use of “average” parameters (such as tensile strength, fragmentation behavior and ablation behavior) in current modeling efforts. This underscores the need to examine a full range of possible values for these parameters when drawing general conclusions from models about impact processes.  相似文献